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Research Article

The Anatomy of American Football: Evidence from 7 Years of NFL Game Data

* E-mail: [email protected]

Affiliation School of Information Sciences, University of Pittsburgh, Pittsburgh, PA, United States of America

Affiliation Department of Computer Science and Engineering, University of California Riverside, Riverside, CA, United States of America

• Konstantinos Pelechrinis, 
• Evangelos Papalexakis

• Published: December 22, 2016
• https://doi.org/10.1371/journal.pone.0168716
• Reader Comments

How much does a fumble affect the probability of winning an American football game? How balanced should your offense be in order to increase the probability of winning by 10%? These are questions for which the coaching staff of National Football League teams have a clear qualitative answer. Turnovers are costly; turn the ball over several times and you will certainly lose. Nevertheless, what does “several” mean? How “certain” is certainly? In this study, we collected play-by-play data from the past 7 NFL seasons, i.e., 2009–2015, and we build a descriptive model for the probability of winning a game. Despite the fact that our model incorporates simple box score statistics, such as total offensive yards, number of turnovers etc., its overall cross-validation accuracy is 84%. Furthermore, we combine this descriptive model with a statistical bootstrap module to build FPM (short for Football Prediction Matchup) for predicting future match-ups. The contribution of FPM is pertinent to its simplicity and transparency, which however does not sacrifice the system’s performance. In particular, our evaluations indicate that our prediction engine performs on par with the current state-of-the-art systems (e.g., ESPN’s FPI and Microsoft’s Cortana). The latter are typically proprietary but based on their components described publicly they are significantly more complicated than FPM . Moreover, their proprietary nature does not allow for a head-to-head comparison in terms of the core elements of the systems but it should be evident that the features incorporated in FPM are able to capture a large percentage of the observed variance in NFL games.

Citation: Pelechrinis K, Papalexakis E (2016) The Anatomy of American Football: Evidence from 7 Years of NFL Game Data. PLoS ONE 11(12): e0168716. https://doi.org/10.1371/journal.pone.0168716

Editor: Kimmo Eriksson, Mälardalen University, SWEDEN

Received: July 23, 2016; Accepted: November 23, 2016; Published: December 22, 2016

Copyright: © 2016 Pelechrinis, Papalexakis. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are available within the manuscript and deposited in Github: https://github.com/kpelechrinis/footballonomics .

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1 Introduction

While American football is viewed mainly as a physical game—and it surely is—at the same time it is probably one of the most strategic sports games, a fact that makes it appealing even to an international crowd [ 1 ]. This has led to people analyzing the game with the use of data analytics methods and game theory. For instance, after the controversial last play call of Super Bowl XLIX the Economist [ 2 ] argued by utilizing appropriate data and game theory that this play was rational and not that bad after all.

The ability to analyze and collect large volumes of data has put forward a quantification-based approach in modeling and analyzing the success in various sports during the last few years. For example, pertinent to American football, Clark et al. [ 3 ] analyzed the factors that affect the success of a field goal kick and contrary to popular belief they did not identify any situational factor (e.g., regular vs post season, home vs away etc.) as being significant. In another direction Pfitzner et al. [ 4 ] and Warner [ 5 ] studied models and systems for determining a successful betting strategy for NFL games, while the authors in [ 6 ] show that the much-discussed off-field misconduct of NFL players does not affect a team’s performance. Furthermore, the spatial information collected from the RFID sensors on NFL players has been used to evaluate quarterbacks’ decision making ability [ 7 ], while efforts to assess the impact of individual offensive linemen on passing have been presented by Alamar and Weinstein-Gould [ 8 ]. Similarly, Correia et al. [ 9 ] analyzed the passing behavior of rugby players—the most similar sport to that of American football. They found that the time required to close the gap between the first attacker and the defense explained 64% of the variance found in pass duration and this can further yield information about future pass possibilities. Nevertheless, despite the availability of play data for American football and the proliferation of the sports analytics literature as well as the literature surrounding the NFL, there are only few—publicly open—studies that have focused on predicting a game’s outcome. Furthermore, some of the existing models make strong theoretical assumptions that are hard to verify (e.g., the team strength factors obeying to a first-order autoregressive process [ 10 ]). Close with our work, Cohea and Payton developed a logistic regression model to understand the factors affecting an NFL game outcome [ 11 ]. The benefit of our model as compared to the one presented by Cohea and Payton [ 11 ] is that the number of exploratory variables we are using is much smaller, making it easy for a fan to follow. Most importantly though we combine our model with statistical bootstrap in order to facilitate future game predictions (something that the model presented in [ 11 ] is not able to perform). Of course, predictive models for NFL games have been developed by major sports networks. For example ESPN has developed the Football Power Index, which is used to make probabilistic predictions for upcoming matchups [ 12 ]. Software companies have also developed their own models (e.g., Cortana from Microsoft [ 13 ]). Nevertheless, these models are proprietary and are not open to the public.

In this study we are first interested in providing a simple model that is able to quantify the impact of various factors on the probability of wining a game of American football. How much does a turnover affect a team’s probability of winning? Can you really win a game after having turned the ball over 5 times? While coaches and players know the qualitative answer to similar questions, the goal of our work is to provide a quantitative answer. For this purpose we use play-by-play data for the last seven seasons of the National Football League (i.e., between 2009 and 2015) and we extract specific team statistics for both the winning and losing teams. We then use the Bradley-Terry regression model [ 14 , 15 ] to quantify the effect and statistical significance of each of these factors on the probability of wining a game of American football. This model is a descriptive one, i.e., it quantifies the impact of several factors on the success of an NFL team. Similar descriptive models can be useful to the coaching staff since they provide an exact quantification of the importance of each aspect of the game. They can also be helpful for the fans—especially the novice ones—for better understanding of the game. Evaluating the obtained model through cross validation provides an accuracy of 84% in predicting the winning team of a matchup.

The above descriptive model is able to provide accurate predictions when the features are known, i.e., when the performance of the two competing teams of a matchup is known. This can be helpful in post analysis of games by comparing the actual outcome of the game with the expected probability of winning the game for each team given their performance. For instance, one can identify “unexpected” wins from teams that underperformed . However, even more challenging, and one of the most intriguing tasks for professional sports analysts, is predicting the winners of the upcoming NFL matchups, which is the second objective of our work. This task can not be completed simply by the regression model that quantifies the impact of various factors on the probability of winning a game. As we will elaborate on in following sections the majority of the features in the developed model includes performance statistics (e.g., total offensive yards, number of interceptions etc.). Hence, the winner prediction problem involves also predicting the features—i.e., the performance of each team—themselves.

Predicting the upcoming performance of a team can be based on its past performance. A factor that makes this task particularly hard for American football is the small number of games during a season, which translates to high uncertainty. Using a central tendency metric—e.g., mean—is not able to fully capture the variability of the performance. To tackle this problem we propose to use statistical bootstrap. In brief, resampling with replacement the features from the past games of a team will allow us to simulate the matchup between the teams several times and obtain a set of winning probabilities that will allow us to predict the final winner of the game. Our approach, FPM , is shown to exhibit an accuracy of approximately 64% over the past 7 seasons, which is comparable to that of the state-of-the-art systems such as Microsoft’s Cortana and ESPN’s FPI. However, given FPM ’s simplicity it should be treated as a baseline estimation. Simply put the output probability of our model can be thought of as an anchor value for the win probability. Further adjustments can be made using information about the specific matchup (i.e., roster, weather forecast etc.), hence, making it possible to significantly outperform existing proprietary systems. We further discuss this point in detail later in this work.

Our work complements the existing literature by contributing a descriptive and easily interpretable model for American football games. We further provide a prediction engine for upcoming matchups based on statistical bootstrap and the developed Bradley-Terry regression model. We would like to emphasize here that our regression model is rather simple and easy to implement. This, in fact, is one of our main contribution, since we demonstrate that such a simple and transparent approach is able to perform on par with state-of-the-art commercial tools for which due to their proprietary nature we have no telling of how complex they are. We view this as a first step towards exploring how we can maintain a simple and interpretable model that at the same time bears high predictive quality. In the rest of the study we present the data and methods that we used (see Section 2 ). We then present our regression model as well as FPM (see Section 3 ). We finally conclude and discuss the implications of our study (see Section 4 ).

2 Materials and Methods

In this section we will present the dataset we used to perform our analysis as well as the different methodological pieces of our analysis.

NFL Dataset: In order to perform our analysis we utilize a dataset collected from NFL’s Game Center for all the games (regular and post season) between the seasons 2009 and 2015. We access the data using the Python nflgame API [ 16 ]. The dataset includes detailed play-by-play information for every game that took place during these seasons. In total, we collected information for 1,792 regular season games and 77 play-off games. Given the small sample for the play-off games and in order to have an equal contribution in our dataset from all the teams we focus our analysis on the regular season games, even though play-off games are by themselves of interest in many perspectives.

Statistical Bootstrap: In order to perform a game outcome prediction, we first need to forecast the performance of each of the contesting teams. However, we only have a (small) set of historic performance data for each team. Furthermore given that the performance of a team is not stable , using a measure of central tendency (e.g., sample mean) does not accurately capture the variability in the data. To overcome this problem we will rely on statistical bootstrap [ 17 ]. Statistical bootstrap is a robust method for estimating the unknown distribution of a population’s statistic when a sample of the population is known. The basic idea of the bootstrapping method is that in the absence of any other information about the population, the observed sample contains all the available information about the underlying distribution. Hence resampling with replacement is the best guide to what can be expected from the population distribution had the latter been available. By generating a large number of such resamples allows us to get a very accurate estimate of the required distribution. Furthermore, for data with dependencies (temporal or otherwise), appropriate block resampling retains any dependencies between data points [ 18 ]. We will utilize bootstrap in the design of FPM .

3.1 Descriptive Model

In this part of our study we will present our descriptive generalized linear model. In particular, we build a Bradley-Terry model to understand the factors that impact the probability of a team winning an American football game. This model will be later used in our future matchup prediction engine, FPM , as we describe in Section 3.2.

Let us denote with W ij the binary random variable that represents the event of home team i winning the game against visiting team j . W ij = 1 if the home team wins the game and 0 otherwise. As aforementioned our model for W ij will provide us with the probability of the home team winning the game given the set of input features, i.e., y = Pr( W ij = 1| z ). The input of this model is vector z that includes features that can potentially impact the probability of a team winning.

The features we use as the input for our model include:

Total offensive yards differential: This feature captures the difference between the home and visiting teams’ total yards (rushing and passing) produced by their offense in the game.

Penalty yards differential: This features captures the differential between the home and visiting teams’ total penalty yards in the game.

Turnovers differential: This feature captures the differential between the total turnovers produced by the teams (i.e., how many times the quarterback was intercepted, fumbles recovered by the opposing team and turns on downs).

Possession time differential: This feature captures the differential of the ball possession time between the home and visiting team.

This ratio captures the offense’s balance between rushing and passing. A perfectly balanced offense will have r = 0.5. We would like to emphasize here that r refers to the actual yardage produced and not to the passing/rushing attempts. The feature included in the model represents the differential between r home and r visiting .

Power ranking differential: This is the current difference in rankings between the home and the visiting teams. A positive differential means that the home team is stronger , i.e., ranks higher, than its opponent. For the power ranking we utilize SportsNetRank [ 19 ], which uses a directed network that represents win-lose relationships between teams. SportsNetRank captures indirectly the schedule strength of a team and it has been shown to provide a better ranking for teams as compared to the simple win-loss percentage.

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https://doi.org/10.1371/journal.pone.0168716.t001

Based on the Kolmogorov-Smirnov test the features’ ECDFs for the winning and losing teams are statistically different (at the significance level α = 0.01). The probability mass function for the home team advantage is also presented.

https://doi.org/10.1371/journal.pone.0168716.g001

Our basic data analysis above indicates that the distribution of the statistics considered is significantly different for the winning and losing teams. However, we are interested in understanding which of them are good explanatory variables of the probability of winning a game. To further delve into the details, we use our data to train the Bradley-Terry regression model and we obtain the results presented in Table 2 . Note here that, as it might be evident from the aforementioned discussion, we do not explicitly incorporate a feature for distinguishing between the home and the visiting team. Nevertheless, the response variable is the probability of the home team winning, while the features capture the differential of the respective statistics between the home and road team (i.e., the difference is ordered). Therefore, the intercept essentially captures the home team advantage—or lack thereof depending on the sign and significance of the coefficient. In fact, setting all of the explanatory variables equal to zero provides us a response equal to Pr( W ij | 0 ) = 0.555, which is equal to the home team advantage as discussed above. Furthermore, all of the coefficients—except the one for the possession time differential—are statistically significant. However, the impact of the various factors as captured by the magnitude of the coefficients range from weak to strong. For example, the number of total yards produced by the offense seem to have the weakest correlation with the probability of winning a game (i.e., empty yards). On the contrary committing turnovers quickly deteriorates the probability of winning the game and the same is true for an unbalanced offense. Finally, in S1 Text we present a standardized version of our model.

Significance codes: ***: p < .001, **: p < .01, *: p < .05.

https://doi.org/10.1371/journal.pone.0168716.t002

While the direction of the effects for these variables are potentially intuitive for the coaching staff of NFL teams, the benefit of our quantifying approach is that it assigns specific magnitude to the importance of each factor. Clearly the conclusions drawn from the regression cannot and should not be treated as causal. Nevertheless, they provide a good understanding on what is correlated with winning games. For example, if a team wins the turnover battle by 1 it can expect to obtain an approximately 20% gain in the winning probability (all else being constant), while a 10-yard differential in the penalty yardage is correlated with just a 5% difference in the winning probability. Hence, while almost all of the factors considered are statistically significant, some of them appear to be much more important as captured by the corresponding coefficients and potential parts of the game a team could work on. Again, this descriptive model does not provide a cause-effect relationship between the covariates considered and the probability of winning .

Before turning to the FPM predictive engine we would like to further emphasize and reflect on how one should interpret and use these results. For example, one could be tempted to focus on the feature with the coefficient that exhibits the maximum absolute magnitude, that is, the differential of ratio r , and conclude that calling only run plays will increase the probability of winning, since the negative differential with the opposing team will be maximized. However, this is clearly not true as every person with basic familiarity with American football knows. At the same time the regression model is not contradicting itself. What happens is that the model developed—similar to any data driven model—is valid only for the range of values that the input variables cover. Outside of this range, the generalized linear trend might still hold or not. For example, Fig 2 depicts the distribution of ratio r for the winning and losing teams. As we can see our data cover approximately the range r ∈ [0.3, 0.98] and the trend should only be considered valid within this range (and potentially within a small ϵ outside of this range). It is interesting also to observe that the mass of the distribution for the winning teams is concentrated around r ≈ 0.64, while it is larger for the losing teams ( r ≈ 0.8). We also present at the same figure a table with the range that our features cover for both winning and losing teams. Furthermore, to reiterate, the regression model captures merely correlations (rather than cause-effect relations). Given that some of the statistics involved in the features are also correlated themselves (see Fig 3 ) and/or are result of situational football, makes it even harder to identify real causes. For instance, there appears to be a small but statistically significant negative correlation between ratio r and possession time. Furthermore, a typical tactic followed by teams leading in a game towards the end of the fourth quarter is to run the clock out by calling running plays. This can lead to a problem of reverse causality; a reduced ratio r for the leading team as compared to the counterfactual r expected had the team continued its original game-plan, which can artificially deflate the actual contribution of r differential on the probability of winning. Similarly, teams that are trailing in the score towards the end of the game will typically call plays involving long passes in order to cover more yardage faster. However, these plays are also more risky and will lead to turnovers more often, therefore, inflating the turnover differential feature. Nevertheless, this is always a problem when a field experiment cannot be designed and only observational data are available. While we cannot claim causal links between the covariates and the output variable, in what follows we present evidence that can eliminate the presence of reverse causality for the scenarios described above.

Our model is trained within the range of input variable/statistics values on the left table. The figure on the right presents the probability density function for r for the winning and losing instances respectively.

https://doi.org/10.1371/journal.pone.0168716.g002

Correlations between the different variables considered for obtaining the features for FPM . Insignificant correlations are crossed out.

https://doi.org/10.1371/journal.pone.0168716.g003

Reverse Causality: In what follows we examine the potential for reverse causality. To fast forward to our results, we do not find strong evidence for it. To reiterate, one of the problems with any model based on observational data is the direction of the effects captured by the model. For example, in our case teams that are ahead in the score towards the end of the game follow a “conservative” play call, that is, running the football more in order to minimize the probability of a turnover and more importantly use up valuable time on the clock. Hence, this can lead to a decreasing ratio r . Therefore, the negative coefficient for the r differential in our regression model might be capturing reverse causality/causation. Winning teams artificially decrease r due to conservative play calling at the end of the game. Similarly, teams that are behind in score towards the end of the game follow a more “risky” game plan and hence, this might lead to more turnovers (as compared to the other way around).

One possible way to explore whether this is the case is to examine how the values of these two statistics change over the course of the game. We begin with ratio r . If the reverse causation hypothesis were true, then the ratio r for the winning team of a game would have to reduce over the course of the game. In order to examine this hypothesis, we compute the ratio r at the end of each quarter for both the winning and losing teams. Fig 4 presents the results. As we can see during the first quarter there is a large variability for the value of r as one might have expected mainly due to the small number of drives. However, after the first quarter it seems that the value of r is stabilized. There is a slight decrease (increase) for the winning (losing) team during the fourth quarter but this change is not statistically significant. Therefore, we can more confidently reject the existence of reverse causality for ratio r .

Ratio r is stable after the first quarter for both winning (left figure) and losing (right figure) teams, allowing us to reject the reverse causation hypothesis for r .

https://doi.org/10.1371/journal.pone.0168716.g004

We now focus our attention on the turnovers and the potential reverse causation with respect to this feature. In order to examine this hypothesis, we obtain from our data the time within the game (at the minute granularity) that turnovers were committed by the winning and losing teams. We then compare the paired difference for the turnover differential until the end of the third quarter for each game. Our results show that the winning teams commit fewer turnovers than their losing opponents by the end of the third quarter ( p -value < 0.01), further supporting that avoiding turnovers will ultimately lead to a win. Of course, as we can see from Fig 5 , there is a spike of turnovers towards the end of each half (and smaller spikes towards the end of each quarter). These spikes can be potentially explained from the urgency to score since either the drive will stop if the half ends or the game will be over respectively. However, regardless of the exact reasons for these spikes, the main point is that by committing turnovers, either early in the game (e.g., during the first three quarters) or late, the chances of winning the game are significantly reduced.

Turnovers spike towards the end of each quarter, with the highest density appearing during the two-minute warning.

https://doi.org/10.1371/journal.pone.0168716.g005

In conclusion, our model provides quantifiable and actionable insights but they need to be carefully interpreted when designing play actions based on it.

3.2 FPM Prediction Engine

We now turn our attention on how we can use the above model to predict the outcome of a future game. In a realistic setting, in order to be able to apply this regression model we will need to provide as an input the team statistics/features. This is by itself a separate prediction problem, namely, a team performance prediction problem. Hence, we begin by evaluating the prediction performance of the Bradley-Terry regression model itself using traditional machine learning evaluation methods. In particular, we evaluate the prediction accuracy of our model through cross validation. In this way we do not need to predict the value of the features but we explore the accuracy of the pure regression model. Using 10-fold cross validation we obtain an accuracy of 84.03% ± 0.35% . To reiterate this performance is conditional to the input features being known. From the inputs required for our model only two are known before the matchup, namely, the home team (which will allow us to formulate the response variable and the rest of the features appropriately) and the SportsNetRank differential. Thus, how can we predict the rest of the features, since in a realistic setting we will not know the performance of each team beforehand? Simply put, our FPM prediction engine will need to first estimate the two teams statistics/features (i.e., total yards, penalty yards, etc.) and then use the Bradley-Terry regression model to predict the winning team.

The proposed prediction engine consists of 3 modules; a bootstrap module, a regression module and a statistical test module.

https://doi.org/10.1371/journal.pone.0168716.g006

Delving more into the evaluation of our predictive engine we present the accuracy for each season in Table 3 . We also provide the accuracy of a baseline system, where the winner of a game is predicted to be the team with the better running win-loss percentage through the current week. If two teams have the same win-loss percentage the home team is chosen as the winner since there is a slight winning bias for the home team as we have seen earlier. Note here that the baseline is very similar to the way that the league ranks the teams and decides on who will qualify for the playoffs (excluding our tie-breaker process and the league’s rules with respect to the divisions). As we can see our predictive engine improves over the baseline by approximately 9%.

FPM outperforms the baseline prediction based on win-loss standings every season in our dataset. The overall accuracy of our system is 63.4%.

https://doi.org/10.1371/journal.pone.0168716.t003

One of the reasons we utilize bootstrap in our prediction system is to better capture the variability of the teams’ performances. As one might expect this variability is better revealed as the season progresses. During a stretch of few games it is highly probable to have a team over/under-perform [ 22 ]. Hence, the bootstrap module during the beginning of the season might not perform as accurately as during the end of the season. In order to examine this we calculate the accuracy of our prediction system focusing on games that took place during specific weeks in every season. Fig 7 presents our results, where we see that there is an increasing trend as the season progresses.

During the last part of the season the bootstrap engine can exploit the variability of a team’s performance better, hence, providing better prediction accuracy. The linear trend slope is 0.01 (p-value<0.05, R 2 = 0.41).

https://doi.org/10.1371/journal.pone.0168716.g007

Finally, we examine the accuracy of FPM ’s predicted probabilities. In order to evaluate this we would ideally want to have the game played several times. If the favorite team were given a 75% probability of winning, then if the game was played 100 times we would expect the favorite to win 75 of them. However, we cannot have the game play out more than once and hence in order to evaluate the accuracy of the probabilities we will use all the games in our dataset. In particular, if the predicted probabilities were accurate, when considering all the games where the favorite was predicted to win with a probability of x %, then the favorite should have won in x % of these games. Given the continuous nature of the probabilities we quantize them into groups that cover a 5% probability range (with only exception being the range (90%, 100%], since there are very few games in the corresponding sub-groups). Fig 8 presents on the y-axis the fraction of games where the predicted favorite team won, while the x-axis corresponds to the predicted probability of win for the favorite. As we can see the data points—when considering their 95% confidence intervals—fall on the y = x axis, which translates to an accurate probability inference. The corresponding linear regression provides a slope with a 95% confidence interval of [0.76, 1.16] ( R 2 = 0.94), which essentially means that we cannot reject the null hypothesis that our data fall on the line y = x where the slope is equal to 1.

The win probability provided by our model is in alignment with the fraction of the games won by the favorite for the corresponding win probability.

https://doi.org/10.1371/journal.pone.0168716.g008

4 Discussion and Conclusions

Finally, the models themselves can be helpful to many different involved entities associated with the sport. For example, it can facilitate better understanding of the game by novice fans. The impact and importance of ratio r will allow the newcoming fans to appreciate the running game. Similarly, agents and players can use knowledge obtained by similar models for negotiating purposes. It is well-known that running backs are among the least paid players in an NFL roster for a number of reasons (e.g., high risk of serious injuries etc.). Nevertheless, they are extremely important for the success of a team as our model indicates. Moreover, our descriptive regression model can be used by media personnel for a post-game analysis. For instance, “surprising” wins can be identified, while critical parts of the game that led to the final results can also be pinpointed.

Supporting Information

S1 text. standardized fpm ..

https://doi.org/10.1371/journal.pone.0168716.s001

Author Contributions

• Conceptualization: KP.
• Data curation: KP.
• Formal analysis: KP EP.
• Investigation: KP.
• Methodology: KP EP.
• Project administration: KP EP.
• Resources: KP EP.
• Software: KP.
• Supervision: KP.
• Validation: KP.
• Visualization: KP.
• Writing – original draft: KP EP.
• 1. Lamb C, Hair J, McDaniel C (2012) Essentials of Marketing. ISBN-13: 978-0538478342.
• 2. Economist T (2015). Game theory in american football: Defending the indefensible. http://www.economist.com/blogs/gametheory/2015/02/game-theory-american-football . Accessed: 2016-01-12.
• 3. Clark T, Johnson A, Stimpson A (2013) Going for three: Predicting the likelihood of field goal success with logistic regression. In: The 7th Annual MIT Sloan Sports Analytics Conference.
• View Article
• Google Scholar
• 5. Warner J (2010) Predicting margin of victory in nfl games: Machine learning vs. the las vegas line. Technical Report.
• 7. Hochstedler J (2016) Finding the open receiver: A quantitative geospatial analysis of quarterback decision-making. In: MIT Sloan Sports Analytics Conference.
• PubMed/NCBI
• 12. ESPN (2016). A guide to nfl fpi. http://www.espn.com/blog/statsinfo/post/_/id/123048/a-guide-to-nfl-fpi . Accessed: 2016-10-30.
• 13. Bing M (2016). Looking ahead with bing. http://www.bing.com/explore/predicts . Accessed: 2016-10-30.
• 15. Agresti A (2007) An introduction to categorical data analysis. Wiley series in probability and statistics. Hoboken (N.J.): Wiley-Interscience. https://doi.org/10.1002/0470114754
• 16. (2012). Nfl game center api. https://github.com/BurntSushi/nflgame . Accessed: 2016-01-12.
• 17. Efron B, Tibishirani R (1993) An Introduction to the Bootstrap. Chapman and Hall/CRC. https://doi.org/10.1007/978-1-4899-4541-9
• 19. Pelechrinis K, Papalexakis E, Faloutsos C (2016) Sportsnetrank: Network-based sports team ranking. In: ACM SIGKDD Workshop on Large Scale Sports Analytics.
• 20. Tower N (2016). Cortana predictions. https://www.firstscribe.com/blog/bing-predicts-looks-average-in-nfl-week-17-wildcard-weekend-preview/ . Accessed: 2016-02-12.
• 21. Nerd FF (2015). Nfl picks accuracy leaderboard. http://www.fantasyfootballnerd.com/nfl-picks/accuracy/ . Accessed: 2016-01-12.

Roster Survival: An Exploratory Study of College Football Recruits in the Power Five Conferences

Authors: Jeffrey J. Fountain and Peter S. Finley

Corresponding Author: Jeffrey J. Fountain Carl DeSantis Building 3301 College Avenue Fort Lauderdale, FL, 33314-7796 [email protected] 954-262-8129

Jeffrey Fountain, Ph.D. and Peter Finley, Ph.D., are Associate Professors of Sport and Recreation Management at the H. Wayne Huizenga College of Business and Entrepreneurship at Nova Southeastern University.

ABSTRACT This study explored the retention of football players among the Power Five conference universities between 2002 and 2013. A new metric was created to evaluate roster retention beginning at the time players committed to a university as opposed to after matriculation, as is used in more common graduation-rate metrics. Results suggested a large disparity among universities between those that maintain recruits through four or more years of college football and those that have much higher roster turnover rates as well as high rates of commits never appearing on even a single roster. Additionally, the results showed the average number of games football players appeared in during the 12-year time period. The new metric developed and the results of the study are important for various stakeholders, including providing additional information for prospective college football players during the recruiting process. The metric could also provide additional data for athletic department officials when analyzing their own roster management practices as well as the past roster management practices of potential coaches. The NCAA could also benefit from this new metric as it adds information to the conversation about athletes in higher education and it provides a roster based viewpoint on the sheer number of athletes that have moved through “Big Time” college football over the years.

Keywords: Roster Management, College Football, High School Recruits, NCAA, Retention, Power Five.

INTRODUCTION The recruiting process ends for most recruits on National Signing Day, a day recruits can first sign a National Letter of Intent (NLI) with their chosen university. The NLI is a document that “binds a student-athlete to attend the school with which he/she signed” (18, p.238). Division I college football’s National Signing Day has virtually become a holiday of sorts for the millions of fans who are glued to television sets, recruiting websites, social media, and online discussion boards throughout the day (12). In light of the pomp and circumstance that surrounds the culmination of the commitment process as high school football players select their universities little attention is spent on the future of these recruits at their chosen universities (4).

The fans who tune in to see the new signees on National Signing Day may not have an appreciation for the realities college football players face, as the trope about “free education” is so loosely bandied about (29). Southall and Weiler describe the facade that college sports put on,

• The idyllic settings of most major universities add to a sense that “student-athletes” live in an ivy-covered, academic paradise. Such characterizations, represented during most – if not all – game broadcasts help impede fans’ ability to see big-time college sports’ systemic exploitation (29, p. 178).

Beamon (3) identifies a key driver of the exploitation of football players is the pressure to maintain team performance and increase revenue, by recruiting and enrolling superior athletes, further placing at odds the roles of student and athlete. Researchers have also shown that incentive-laden contracts of football coaches, often offer much greater rewards for winning games than for graduating players, which leads some to surmise that players are little more than replaceable parts (9). Fountain & Finley (12) found large turnover rates of football players at top FBS schools and applied the term “roster survival” to label the process of simply maintaining a roster position from year to year, as football teams replenish their rosters with new commits while remaining within the NCAA’s maximum allowable limit of scholarships.

A seminal work by Adler and Adler (1) showed that athletes’ optimism for their academic pursuits can quickly meet a harsh reality, as they become detached from the academic experience and abandon their initial aspirations, settling on an inferior quality of academic experience as they become consumed by their athletic identity. Recent research has focused on academic clustering as athletes in a variety of sports have found themselves clustered into only a few academic majors, often in an attempt to ensure eligibility and sometimes in no way reflecting the distribution of non-athletes across academic majors on the same campuses (6,10,11,19,23,24,26,27). Additionally, the distribution of football players across majors is even more limited at universities that are more highly selective in their admissions criteria (19). This type of behavior should not be unexpected, as other research has found athletic programs serve a variety of uses for their institutions and their commercialization contributes to revenue generation, increased visibility, student recruitment, and alumni support. Thus it is no surprise that the pressure to win is relentless and academics can take a back seat (8,31). Through interviews with former student-athletes, Beamon (3) determined that, in spite of earning a degree, most participants did not believe they had a positive collegiate experience or that their education was emphasized by their university.

In response to concerns about academic integrity in college athletics, the focus has been on graduation-based metrics. The federal government and NCAA each utilize different metrics to measure academic success. The federal government requires all universities that receive federal funds to report the Federal Graduation Rate (FGR) for all students, with completion defined as earning a degree within 150% of the normal time to complete, so a six-year window was established and applied to cohort groups of students entering at the beginning of each academic year (17). The NCAA created the Graduation Success Rate (GSR) as an alternative graduation-rate methodology, crediting institutions for in-coming transfers or midyear enrollees who graduate and excluding athletes that left the university in good academic standing (25). The NCAA also created the Academic Progress Rate (APR) to provide real-time feedback on the progress-toward-degree of athletes, based on the fact that retention and continued eligibility are essential for graduation (14).

The Drake Group, a group that advocates for academic integrity for all students, makes the case that the NCAA’s GSR and APR and any other measure that treats athletes as distinct from the general student population and fails to allow for direct comparison between athletes and non-athletes supports the exploitation of athletes (14). A common criticism of these metrics is that they are intended to shield athletes from comparison to the general student body as part of the NCAA’s marketing campaign, while masking the achievement gap between the groups (28). As Gurney and Southall (15) described in their critique,

• Intentionally or not, the NCAA’s APR and GSR metrics confuse the media, fans and the general public. Using the GSR and APR to tout graduation success and increased academic standards is undoubtedly savvy marketing and public relations, but these metrics are fundamentally nothing more than measures of how successful athletic departments are at keeping athletes eligible, and have increasingly fostered acts of academic dishonesty and devalued higher education in a frantic search for eligibility and retention points (para. 17).

While the aforementioned metrics examine persistence toward graduation and graduation rates, they fail to shed light on the issues of roster management and oversigning. Roster management is the practice of decreasing the numbers of current players on scholarship over the months between National Signing Day and the first day that rosters are finalized. Roster management is necessitated in large part due to the practice of oversigning commits. Bateman (2) defined oversigning as “when a school accepts more signed NLIs than it has student-athletes who are leaving the team before the next season due to graduation, early entry for the National Football League (NFL) Draft, medical reasons, or ineligibility” (p. 11). Staples (30) argued that the coaches that oversign have a competitive advantage because “the coaches who signed more players had a chance to erase their mistakes” (para. 19).

Historically it has been common for some teams to oversign and then need to remove as many as ten players from the scholarship ranks after signing day, by one means or another (16,30). While roster management can be seen as a numbers game, “its implications can be devastating and life-altering for a student-athlete,” (20, p. 1). This is particularly the case for players who have been on the team and are being pushed out, through various questionable means, to make way for incoming players who have been deemed more promising or fit positions of need. Among the means used by coaches to push players out are suggesting that a player will receive little playing time going forward and that, perhaps, he would benefit by transferring to another university. Another practice is claiming a player can’t be medically cleared to play and putting him on a medical hardship waiver, or kicking players off the team for violations of team rules which include minor violations for which a star player would be given a lighter penalty (13,16). Regarding medical waivers, there is some evidence that team doctors are influenced by the coaching staff and know which players are considered expendable (13).

Oversigning was once more prevalent in the Southeastern Conference (SEC). For example, between 2007 and 2011, Auburn signed an average of 30.2 commits per class and Ole Miss and Mississippi State each averaged 28 commits (5). The 2009 recruiting class at Ole Miss had 37 committed players sign and Coach Houston Nutt said, infamously, “There’s no rule that says we can’t sign 80. All I know is we have to have 25 ready to go in August,” (30, p. 1). Kansas State had commitments of 26, 30, 34 and 33 players between 2005 and 2008, totaling 123, meaning at least 38 players had to be managed in some way to fit under the 85-scholarship maximum (20). In 2011, the SEC passed a rule limiting signees to 25, down from 28 per incoming class. The NCAA adopted the rule shortly thereafter. Some conferences hold themselves to a higher standard. The Big 10, for example, adopted a rule in 2002 that limited its member universities to sign a maximum of three more commitments than available scholarship spots and to document how it then arrived at not more than 85 total scholarships when rosters are finalized (30).

PURPOSE OF THE STUDY The purpose of this study was to explore and gain a better understanding of high school football recruits movement from making a commitment to a university’s football program to actually appearing on one and then subsequent rosters. The researchers sought to produce a roster-based retention metric as well as determine the average number of games in which the commits appeared. A term was needed to describe this new retention metric that tracked commits from a high school recruit’s commitment all the way through to their last appearance on a roster at that university. The term for this new metric was labeled the Athlete Commit Life Cycle (hereafter as ACLC). This differs from traditional academic metrics that begin with enrollment at the university by stepping back to track high school recruits from the time they make a final commitment to a university. By doing so this data can account for recruits who make a public commitment but subsequently never make it onto a roster at the university that recruited them.

The researchers took a longitudinal approach by reviewing the football rosters of all the universities in the Power Five conferences (ACC, Big 10, Big 12, Pac 12, and SEC) as of 2016. The study concluded with the 2016 football season which allowed the inclusion of twelve football recruiting classes, spanning from 2002 to 2013. This allowed the study to explore roster retention over a long period of time rather than just a snapshot of a single recruiting class.

The following research questions guided the study:

• Research Question 1: How many rosters did commits appear on during their ACLC?
• Research Question 2: Is there a difference in ACLCs between Power Five conferences and universities?
• Research Question 3: On average how many games did commits play in during their ACLC?

METHOD This study focused on the sixty-four universities in the Power Five conferences as of 2016 and the football players who were published commits to one of the sixty-four universities during the years of 2002 to 2013. Three online sources were utilized to build large datasets that were used for the study. First, a Football Commit dataset was built utilizing the published commitment list for each Power Five university for each recruiting class from 2002 to 2013 from Rivals.com. The 2013 incoming class was used as the final recruiting class in the study because this allowed for tracking of all commits through the 2016 football season, ensuring a minimum of at least four college football rosters (seasons) for each recruit in the dataset. Rivals.com was selected because it was the original provider of recruitment information and is recognized as one of the “Big Four” websites in the recruitment-information industry (7).

The use of the word “commits” was used throughout the study because while a majority of the commits did successfully transition from commit to player (by appearing on at least one roster), some of the commits never appeared on a roster at all. To best represent both the commits as well as the commits that went on to become players, the term commits was applied to everyone in the study regardless if they transitioned into being a player on the roster or not.

A Football Roster dataset was then built utilizing the football-statistics section of the NCAA website. The researchers gathered the rosters that were produced by the NCAA for each Power Five football program for each year from 2002 to 2016. The NCAA statistics rosters also included the official number of games each player appeared in during each season. The Football Roster dataset was much larger than the Football Commit dataset because the NCAA statistics rosters included all players who ever appeared on the roster, not just recruited commits, but also all walk-ons and transferred-in players. The Football Roster dataset’s large size also reflects the multiple roster appearances by players. For example, a football player that played for four seasons would show up on four different NCAA statistics rosters. The NCAA has an eligibility timeline rule that states “you have five-calendar years in which to play four seasons of competition” (21, p.1). The NCAA also has various rules including redshirting and medical exemptions that when combined allow commits to extend the number of years in a program beyond five years. Therefore, a few players appear on a total of six NCAA statistics rosters.

In order to study the ACLC two subgroups in the datasets needed to be removed from the final analysis. The first subgroup was the commits that excelled on the football field during their first several years at the college level and made the decision to leave early (prior to appearing on a fourth roster) for the NFL once they were eligible to do so, which allowed them to continue to participate in their sport at the next level. The NFL website was utilized to build a Drafted Early dataset to determine which commits in the study were drafted into the NFL and in what year. The players who were drafted early were excluded from the final analysis. These players were excluded because they made a personal choice to continue to participate in the sport but at a higher level and the researchers did not want to penalize the universities for commits leaving before completing their ACLC for a roster spot at a higher level.

The second subgroup were commits listed as juniors or seniors on their first roster appearance. This subgroup was treated as Transfer-ins because they did not appear to come in as high school recruits. This subgroup was removed from the final analysis because the ACLC was based on high school commits and how they fared in roster survival at the university they committed to right out of high school. After removing the Drafted Early and Transferred-in subgroups, Excel was utilized to index and match the two datasets to determine how many rosters each football commit appeared on at the university he had originally committed to, as well as how many games he played in.

The researchers then sought to identify commits based on the number of rosters on which they appeared, between zero and six rosters. Based on NCAA’s “four in five years” eligibility rule, commits were sorted into two groups. Those who stopped appearing on rosters prior to a fourth season (appearing on zero to three rosters) were classified as having incomplete ACLCs while those who appeared on four or more rosters were classified as having complete ACLCs.

In terms of games played, as mentioned above, the Football Roster dataset utilized the football-statistics section of the NCAA website. The NCAA Football Statistics Manual defines games played as, “It is a game played if a player is in the lineup for even one play, whether or not he touches the ball,” (22, p. 2). Therefore, the researchers utilized the games played stat provided on the roster list but did not attempt to ascertain the amount of time actually spent on the field by the commits.

Utilizing large datasets from third parties, albeit reliable sources, presented challenges as names of commits were sometimes recorded differently between and/or within the datasets. These differences included spelling variations as well as errors. The researchers utilized the Excel add-in Fuzzy Lookup to assist in finding differences such as full first names as compared to shortened first names, use of nicknames, typos, misspellings, and punctuation differences. For example, the researchers made every attempt to identify commits with the same last name but with one dataset listed with the first name “Mike” and the other dataset listing the first name as “Michael” and determine whether they were the same commit

RESULTS The Football Roster dataset contained 98,731 roster spots and the Football Commit dataset included 17,500 high school football commits recruited between 2002 to 2013 from one of the six-four universities in the Power Five conferences. The subgroups Drafted Early (350) and Transferred-in (1,253) were removed from the final analysis. This reduced the final number in the analysis by 1,603 commits, leaving 15,897 in the final analysis. Table 1 illustrates the percentage breakdown of the roster appearances of commits during their ACLC by conference. The table shows the distribution of the 15,897 commits. The SEC had the most commits, with 3,688, and the Big 12 had the least commits, with 2,484, between 2002 and 2013. The third column, labeled “Zero Rosters” indicates the percentage of commits that never appeared on a single roster for the university that recruited them. As a conference, the Big 12 had the highest percentage of commits that never appeared on a roster at 17.23%. The variance between conferences dissipates slightly once commits appear on at least one roster and then there is roughly a ten-percent loss each year from the first to the third year, across all conferences.

Next, the commits were separated by the number of rosters they appeared on, with those appearing on zero to three rosters placed in the incomplete ACLC group and those who appeared on four or more rosters placed in the complete ACLC group. Table 2 shows the differential between the two groups with 44% of all commits in the study classified as having incomplete ACLCs. The Big 12 conference was the only conference with a greater number of commits with incomplete ACLCs (50.32%) as compared to those classified as completing their ACLC by appearing on four or more rosters (49.68%). The ACC (59.31%) and Big 10 (59.70%) had the highest percentages of commits persist for four or more seasons.

Table 3 displays the ten universities within the Power Five conferences with the largest positive percentage differential between commits classified as completing their ACLCs and those classified as having incomplete ACLCs. The table also provides the total number of commits for each university along with the percentages for the zero to three roster appearances. The breakdown of the zero to three rosters provides a detailed view of when commits with incomplete ACLCs had their ACLCs end. The university with the highest percentage of football commits with completed ACLCs was Northwestern (80.09%) during the 12-year period of the study. This means that only 19.91% of their commits failed to make four or more rosters. This resulted in Northwestern also having the highest positive differential (60.18%) between completed and incomplete ACLCs in the study.

Table 4 displays the ten universities from the Power Five conferences on the other end of the ACLC spectrum, with the largest negative percentage differential between commits classified as completing their ACLCs and those classified as having incomplete ACLCs. The university with the smallest percentage of football commits with completed ACLCs was West Virginia during the studies’ 12-year period. That produced a negative differential of 20.30% between their commits with complete ACLCs (39.85%) and those without (60.15%) for West Virginia. All universities listed on Table 4 had negative differentials, which means that during the 12-year recruiting time period of the study that more than 50% of their commits had their ACLCs end before appearing on a fourth roster. The breakdown of the zero to three rosters percentages on Table 4 depicts when commits with incomplete ACLCs had their ACLCs end and shows that six of the 10 universities had 20% or more of their commits never make it onto a roster.

Beyond roster appearances, the number of games played by each commit was also recorded. Table 5 provides the weighted average of games played for commits by conference. The table breaks down the average games played based on the number of roster appearances from zero to six rosters. The weighted average used the percentages of rosters made for all football commits including those who never appeared on a roster (zero rosters) to reflect the average number of games for all commits at their chosen universities. Overall, the number of games played is similar across conferences, with all commits in the study averaging slightly over 24 games during their ACLC. However, when examined by the number of roster appearances those commits who appeared on only one or two rosters and saw very few games, averaging just under 3 games for those that only appeared on one roster and less than four games per season for those who appeared on only two rosters (7.9 games). A commit that appeared on at least four rosters and survived the many years of roster management saw a large increase in the average number of games played (19.5) compared to those who appeared on three rosters (increasing from 17.1 to 36.6 games).

DISCUSSION AND CONCLUSION This exploratory study took a different approach in examining the progression of college athletes in “Big Time” college football programs. Traditionally the focus has been on team-based graduation rates using academic measures such as the FGR or the GSR to show how many athletes progressed and earned degrees. This study utilized rosters to better identify the year the athletes stopped being a member of the team to see individual athletes’ roster retention rates. The importance of this study is that it moves the point of our attention from matriculation at the university back to the point at which the player had made a commitment to the university, and ostensibly the university to the player. By doing so we see the number of commits that never make a single roster at their chosen university. This method provided raw data for the discussion on the issue of over signing going forward. As the results showed 1,883 (11.85%) of all commits in the study did not appear on a single roster for the university they committed to during the 12-year period of the study.

The issue of over signing and roster management are complex because the reason a commit does not make an initial roster or why a commit does not continue on the team after appearing on one or more rosters varies. There are valid reasons in which the athlete makes the decision to leave the team, there are legitimate reasons for players to be dismissed or retire from a team, and then there are dubious instances in which coaches make decisions to force their once highly-recruited commit off the team (13,16). As an exploratory study with a large dataset, the researchers did not attempt to go back and do in-depth research on why each commit (with an incomplete ACLC) did not progress onto the next roster or why some commits never appeared on a single roster but rather focused on a longitudinal approach to view the movement of commits over time.

The results raise questions of why there was a substantial difference between universities in terms of maintaining the athletes on rosters. The universities with the largest positive differential between complete and incomplete ACLCs had between 63.33% and 80.09% of their commits appear on four or more rosters. While the universities with the largest negative differential between complete and incomplete ACLCs only had between 39.85% and 46.62% of their commits appear on four or more rosters. When comparing Tables 3 and 4 the biggest difference between the largest positive complete ACLC differential and the largest negative ACLC differential is found in the “Zero Rosters” columns. Eight of the ten universities with the largest positive ACLC differential on Table 3 had less than 5% of their commits never appear on a single roster. Whereas, six of the ten universities with the largest negative ACLC differential had 20% or higher of their commits never appear on a single roster. Because there are no rules or regulation that require athletic departments to document what happened to each commit that did not matriculate to the school or never made it onto a roster, further university by university in-depth research would be required to ascertain why these universities had higher numbers of commits never appear on a single roster. Nevertheless, the ACLC data alone could be valuable information for potential recruits weighing their options between Power Five universities. If future recruits have this information during the recruiting process it would force coaches from programs with negative ACLC differentials to try to explain why their football program has had such high turnover rates throughout the years.

The results of the study also provided the average number of games played by commits in each conference. The overall weighted average was just over 24 games, however, those with shorter ACLCs played in far fewer games. Viewing these results could better prepare future recruits to understand the reality of college football and how the years of long hours practicing might not pay off in as many college football games as they think it will.

APPLICATION IN SPORT This study has four distinct application to sport and varied stakeholders. 1) High school prospects would benefit from using the new Athlete Commit Life Cycle (ACLC) metric to help determine which programs are best at retaining players over time and from one roster to the next, as compared to the programs with high turnover and where commits are more likely to never make even a single roster. 2) Athletic department administrators can use this data to evaluate the performance of the football program relative to over signing and player retention, from commitment to the last roster a player makes. 3) The ACLC concept can be studied in other sports, tracking players from the time of commitment to last roster and comparisons can be made by sport, gender, and so forth. 4) The NCAA could adopt new rules that require universities to begin tracking players at the time of commitment as opposed to at the time of matriculation for greater transparency of over signing practices. The NCAA could also require documentation from universities on each athlete that failed to complete their ACLC and the reasons why they never made a single roster or were removed from the team after one, two, or three years.

ACKNOWLEDGEMENTS None

REFERENCES 1. Adler, P., & Adler, P.A. (1985). From idealism to pragmatic detachment: The academic performance of college athletes. Sociology of Education, 58, 241-250. 2. Bateman, J.D., (2011) When the Numbers Don’t Add Up: Oversigning in College Football. Marquette Sports Law Review, 22(1):7-23. 3. Beamon, K. (2008). “Used goods”: Former college student-athletes’ perceptions of exploitation by Division I universities. The Journal of Negro Education, 77(4), 352-364. 4. Big stage, small screen. (2015, February 4). Dallas News. Retrieved from http://res.dallasnews.com/interactives/signing-day-images/ 5. Botkin, B. (2016). Five years later, SEC not hurt by its football recruiting signing cap. Retrieved from https://www.cbssports.com/college-football/news/five-years-later-sec-not-hurt-by-its-football-recruiting-signing-cap/ 6. Case, B., Greer, S., & Brown, J. (1987). Academic clustering in athletics: Myth or reality? Arena Review, 11(2), 48-56. 7. Codrington, K. (2014). An inside look at the complex world of college football recruiting rankings. Retrieved from http://bleacherreport.com/articles/2117325-an-inside-look-at-the-complex-world-of-college-football-recruiting-rankings 8. Donnor, J. (2005). Toward and interest-convergence in the education of African-American football student-athletes in major college sports. Race, Ethnicity, and Education, 8, 45 67. 9. Finley, P. S., & Fountain, J. J. (2010). An investigation of successful Football Bowl Subdivision coaches and the disproportional academic achievement of their White and African-American football players. Academic Leadership Journal, 8(3), pp. 164-176. 10. Fountain, J., & Finley, P. (2009). Academic majors of upperclassmen football players in the Atlantic Coast Conference: An analysis of clustering comparing white and minority players. Journal of Issues in Intercollegiate Athletics, 2, 1-13. 11. Fountain, J., & Finley, P. (2011). Academic clustering: A longitudinal analysis of a Division I football program. Journal of Issues in Intercollegiate Athletics, 4, 24-41. 12. Fountain, J. J., & Finley, P. S. (2017, April). Roster survival: An analysis of retention rates of the top recruiting FBS football programs. Paper presented at the College Sport Research Institute Annual Conference, Columbia, SC. 13. Goodison, B. (2015). Clemson and recruiting and roster management. Retrieved from https://www.shakinthesouthland.com/2015/4/16/8420615/clemson-football-recruiting-roster-management 14. Gurney, G., Lopiano, E. Snyder, D., Willingham, M., Meyer, J., Porto, B., Ridpath, D.B., Sack, A., and Zimbalist, A. (2015) The Drake Group Position Statement: Why the NCAA Academic Progress Rate (APR) and Graduation Success Rate (GSR) Should Be Abandoned and Replaced with More Effective Academic Metrics. Retrieve from https://drakegroupblog.files.wordpress.com/2015/06/academic-metrics-position-paper-2017.pdf 15. Gurney, G.S., & Southall, R.M. (2012, August 9). College sport’s bait and switch. ESPN College Sports. Retrieved from http://espn.go.com/collegesports/story/_/id/8248046/college-sports-programs-find-multitude-ways-game-ncaa-apr 16. Hinton, M. (2013). Oversigning index: On another front it’s still Alabama and everyone else. Retrieved from https://www.cbssports.com/college-football/news/oversigning-index-on-another-front-its-still-alabama-and-everyone-else/ 17. LaForge, L., & Hodge, J. (2011). NCAA academic performance metrics: Implications for institutional policy and practice. The Journal of Higher Education, 82(2), 217-235. 18. Love, A., Gonzalez-Sobrino, B., & Hughey, M.W. (2017). Excessive Celebration? The Racialization of Recruiting Commitments on College Football Internet Message Boards.” Sociology of Sport Journal, 34(3):235-47. 19. Love, A., Watkins, J., & Seungmo, K. (2017). Admissions selectivity and major distribution in big-time college football. Journal of Issues in Intercollegiate Athletics, 10, 1-16. 20. Krohn, A. (2011). Oversigning: An in-depth look into one of college football’s biggest controversies. Retrieved from https://www.gainesvilletimes.com/sports/national-sports/staff-sports-picks/oversigning-an-in-depth-look-into-1-of-college-footballs-biggest-controversies/ 21. National Collegiate Athletic Association (NCAA). (2017a). Transfer Terms. Retrieved from http://www.ncaa.org/student-athletes/current/transfer-terms 22. National Collegiate Athletic Association (NCAA). (2017b). Football Statistics Manual. Retrieved from http://www.ncaa.org/championships/statistics/ncaa-football-statisticians-manual 23. Otto, K. (2012). Demonstrating the importance of accuracy in reporting results of academic clustering. Journal for the Study of Sports and Athletes in Education, 6, 293-310. 24. Paule-Koba, A. (2015). Gaining equality in all the wrong areas: An analysis of academic clustering in Women’s NCAA Division I basketball. International Journal of Sport Management, 16, 1-16. 25. Sack, A. L., & Park, E.-A., & Theil, R . (2011). Watch the gap: Explaining retention gaps between FBS football players and the general student body. Journal of Issues in Intercollegiate Athletics, 4, 55 –73. 26. Sanders, J. P., & Hildenbrand, K. (2010). Major concerns: A longitudinal analysis of student athletes’ academic majors in comparative perspective. Journal of Intercollegiate Sport, 3, 213-233. 27. Schneider, R. G., Ross, S. R., & Fisher, M. (2010). Academic clustering and major selection of intercollegiate student-athletes. College Student Journal, 44, 64-70. 28. Southall, R. (2014). NCAA graduation rates: A quarter-century of re-branding academic success. Journal of Intercollegiate Sport, 7, 120-133. 29. Southall, R., & Weiler, J. (2014). NCAA Division-I athletic departments: 21st century athletic company towns. Journal of Issues in Intercollegiate Athletics, 7, 161-186. 30. Staples, A. (2011). Oversigning offenders won’t be curbed by NCAA’s toothless rule. Retrieved from https://www.si.com/more-sports/2011/01/24/oversigning 31. Upthegrove, T., Roscigno, V., & Charles, C. (1999). Big money collegiate sports: Racial concentration, contradictory pressures, and academic performance. Social Science Quarterly, 80, 718-787.

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Topical Collection on Football Research

• Published: 15 July 2023
• Volume 26 , article number  35 , ( 2023 )

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• Johsan Billingham   ORCID: orcid.org/0000-0002-4130-8429 1 &
• Marcus Dunn   ORCID: orcid.org/0000-0003-3368-8131 2  

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The symbiotic relationship between football, industry, and academia is deepening, creating opportunities to develop every facet of the game. The combination of new technology, research-led approaches, and football expertise is cultivating an environment where ideas can be developed, assessed, and implemented faster than ever. It is important to ensure that the implementation of these ideas benefit the world of football and are supported by evidence.

The Topical Collection on Football Research is a collaborative initiative that was launched to increase awareness of ongoing research that is contributing to new technology developments in football. The collection contains 15 papers that address: current challenges in football, game analysis and player tracking technologies, officiating technologies, playing surface assessment, and football-surface, -player and -environment interaction.

The collection includes an invited paper, which explores why women specific tailoring is needed in football. The paper explores ten questions on football technology and engineering and covers similar themes to the collection itself. It identifies the unique challenges that female players experience due to the design and development of technology and football products around male players, as well as a lack of research for female specific challenges. The paper identifies where focus is needed and calls on industry, and academia to leverage new technologies and research methods to improve performance and health for female players. The Sports Engineering community is keen to explore ideas on how this topic can be further promoted across sport.

The use of player and ball tracking data was a large theme within the collection. The collection addresses how tracking data can be used, how it can be validated, and explores the accuracy behind emerging technologies and techniques. A key paper on the use cases of tracking data describes a novel algorithm to automatically identify football events (such as set pieces, goals, passes, shots, duels, possession, pressures and more) using player and ball tracking data. Currently event data, including that captured in the FIFA World Cup 2022 ™ and FIFA Women’s World Cup 2023 ™ , is collected manually. The lack of affordable data collection solutions means that access to accurate event data is only available to competitions with high budgets. Research presented in the collection has the potential to be used by competition organisers or governing bodies to provide event data where only broadcast cameras are available.

The reliability and effectiveness of a model is inherently linked to the quality of the input data. The relevance, the completeness and in particular the accuracy—amongst others. Research presented in the collection demonstrates the validation of a tool that can be used for ‘full pitch’ validation of player tracking data, generated by commercial technologies. The methodology demonstrated that the tool can be used to establish concurrent validity for a range of Electronic Performance Tracking Systems (EPTS) when used in large areas, with or without gold standard tools such as 3D motion capture.

There are a vast number of player tracking providers used in the football industry, each with their own benefits and challenges. Players will often have their data collected by a variety of different systems. This might occur due to playing for different teams at the same time such as national and domestic teams, or at different clubs over their career, or even when data are collected by the same team using multiple systems. One paper in the collection presents a data clustering approach to quantify and categorize the error of different EPTS systems against 3D motion capture. To improve the precision of the data and the levels of agreement between different systems, multiple methods were explored to decrease the error between the EPTS and the motion capture data. The study found that error reduction of up to 60% could be induced and if applied correctly, practitioners could increase the level of agreements between data from multiple systems.

A technical note presented research in which the researchers assessed the validity of an inertial measurement unit-based approach to categorise physical demands of players into locomotion categories. The paper assessed agreement of the algorithm for 41 players and found that it would be beneficial to establish individual calibration thresholds to improve the algorithm. The final study on tracking data assessed the validity of a LiDAR-based tracking system using three-dimensional motion capture. Previous works have focussed on the validity of position and velocity data; however, this paper also assessed the validity of acceleration data, providing useful information for those working in field settings.

Technology is used heavily in football, from training and talent identification to fan engagement, but it has only recently made its way into officiating. The earliest example of technology use for officiating in football was Goal Line Technology at the FIFA World Cup 2014 ™ . This was followed by the introduction of Video Assistant Refereeing (VAR) at the FIFA World Cup 2018 ™ . VAR is now deployed in over 50 Member Associations across the world with many technology providers offering this service to leagues and competition organisers. In response, work has been conducted to ensure that systems being used by member associations are of a high quality and pass specific, evidence-based assessments. Many of the VAR technology tests developed are based on the requirements of the end users (e.g., Video Assistant Referees). The development and validation of these tests are described by research presented in the collection, which sets out evidence-based pass or fail values. It is important to ensure end users are consulted and involved in research of this nature where possible.

Reliably capturing player perception is important to help understand the player-surface interaction beyond mechanical tests. One study presents research demonstrating the development of a sensory panel to collect reliable player perception data. Results show that targeted training can improve a player’s ability to detect and describe nuances between different playing surfaces, offering additional insights to traditional mechanical testing. The agreement between results of the Rotational Traction Tester (RTT) and the Advanced Artificial Athlete (AAA) were compared to player perceptions of various artificial turf surfaces. Modifications were made to test equipment to improve agreement. For example, the RTT was modified with additional instrumentation, allowing secondary stiffness as well as the operator’s rate of loading to be calculated. The AAA methodology used fewer drops and presents an amended algorithm to estimate Vertical Deformation and Energy Restitution. Ongoing work will inform how the new test equipment can be best implemented into requirements for playing surface assessment, and ultimately drive products to better represent the needs of players.

It is important to understand the performance of playing surfaces and how characteristics change when used. To address this, the collection showcases research exploring the short-term variability of natural-grass surface characteristics during a high-usage tournament. The study identified characteristics that were the most consistent, as well as those with the largest variability, and suggested that better monitoring for high areas of use could allow for improved targeting of surface management applications.

The interaction between the players and the playing surface that they are performing on has an impact on both the performance and the safety. The same can be said for the equipment that is used in the game. Three studies conducted research on the football itself.

To better undertand the behaviour of a football upon impact, it is important to establish sensitive measurement tools to allow comparative assessments. The collection presents research describing a method to reliably quantify impact forces associated with this dynamic interaction. The study concluded that commercial force platforms could be used to detect subtle differences in dynamic impact characteristics. In the future, this understanding could be used to better understand the performance of different footballs, which could inform future strategies for football performance and player safety.

Understanding the action of heading and what is taking place during this player-ball interaction is important to improve performance and safety of footballs. The collection presents research on the effects of football inflation pressure during ball-to-head impacts. Using anthropomorphic head and neck equipment, the study concluded that reducing the inflation pressure of the football may reduce head accelerations during ball-to-head impacts. Further investigation is required to understand whether findings are applicable to the full pressure range described under the Laws of the Game, as well as how reducing the pressure of the ball would affect other impact characteristics, such as performance and playability of the ball.

An important performance characteristic of a football is how it behaves aerodynamically. A study presented in the collection explored the effect of surface features such as seam length and surface roughness on aerodynamic properties through the assessment of 3D printed footballs with varying surface features in a wind tunnel. The study developed novel methods designed to statistically analyze the roughness of the ball and these were correlated against aerodynamic performance. The findings can be used to inform evidence-based design decisions that improve the aerodynamic behaviour of footballs.

The final theme of the collection highlights research that explores the factors that affect a player’s ability to score a goal. One study explored the football-boot interaction, where a protocol was designed to measure the effect of football boot upper padding on shot accuracy and velocity. The study found that additional padding of the boot’s upper had a negative effect on shot accuracy and no effect on the shot velocity. The protocol can be used in future research to inform boot design and provides opportunities to gather empirical evidence for data-driven decisions for manufacturers in the design process. Lastly, a modelling approach was used to explore how environmental factors such as temperature, altitude, and humidity affects the scoring probability for a 25 m free kick. The impacts of these were then modelled to illustrate how the trajectory of the free kick with the same launch conditions would differ in five iconic stadiums.

The wide range of submissions published in the Topical Collection on Football Research demonstrates the impact that research can have to improve the game for the players, coaches, competition organisers and fans. Further Topical Collections on Football Research will be launched to build on what has been achieved so far. We would like to offer a big thank you to all those who have taken part in the process and contributed to the collection’s success and invite those who want to be involved in the process and future collections to get in contact.

The conclusion of the Topical Collection on Football Research aligns strategically with the launch of the FIFA Women’s World Cup 2023™. The timing aims to capitalise on growing excitement surrounding the biggest female football event in history and highlight ongoing research opportunities that are available in football. The tournament will be hosted in 10 stadiums across Australia and New Zealand and kicks-off on the 20th of July 2023. Under the banner of ‘Beyond Greatness’, the event urges individuals to push beyond their comfort zones, overcome uncertainty, and break down barriers. This philosophy resonates strongly within the spheres of sport, industry and academia.

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This article is a part of Topical Collection in Sports Engineering on Football Research, edited by Dr. Marcus Dunn, Mr. Johsan Billingham, Prof. Paul Fleming, Prof. John Eric Goff and Prof. Sam Robertson.

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Billingham, J., Dunn, M. Topical Collection on Football Research. Sports Eng 26 , 35 (2023). https://doi.org/10.1007/s12283-023-00428-2

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The Historical Impact of College Football on Higher Education

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333 Football Research Topics & Essay Titles

Football is a game that millions of people around the world enjoy watching and playing. With 3.57 billion views of the 2018 FIFA World Cup, this sport appears to be the most popular. Besides, each match is more than just a game — football is all about passion, skill, and teamwork.

In this article, our expert team has collected great football topics to write about and research that you can use for your school or college assignments.

🔝 Top 12 Football Topics to Write About

✍️ football writing prompts, 📝 football titles for essays, 🗣️ football speech topics, 💡 football topics for presentation, 🔎 football research topics, ⚽ football essay outline, 🔗 references.

• The history of football.
• Football as the world’s most popular ball game.
• The development of modern football.
• The greatest football moments.
• Why do tactics play a vital role in football?
• Football as a traumatic kind of sport.
• What is football’s most prestigious competition?
• The legends of American football.
• The impact of football on society.
• Advantages of playing football.
• Men’s and women’s football.
• The issue of racism in football.

Are you looking for some prompts on the football topic? Then you are at the right place! Below, you can find ideas for writing your essay.

Why Football Is the Best Sport: Essay Prompt

Football is a global sport that connects practically everyone on the planet. It has the power to bring an entire city or nation to a standstill. In the essay that explains why football is the best game, you can share your own experience or the emotions of your friend who is passionate about this game. Also, you can list the reasons why people love this sport. For example:

• Football connects millions of people.
• There are no age restrictions to enjoy the match.
• The world’s best talents are football players, such as Lionel Messi.
• Watching or participating in football evokes genuine emotions.

Prompt for Panyee Football Club Essay

Have you ever heard about a football club that is floating on water? Panyee FC is one of them! Since there is not enough space on the island, football fans and players built a football pitch in the middle of the sea. Find the answers to the following questions about Panyee Football Club and use this information in your essay:

• What is the history behind Panyee Football Club?
• Why is a Panyee FC pitch built on water?
• What are the core values of Panyee Football Club?
• Can we say that Panyee FC is a symbol of passion for football?

Why Football Is Dangerous: Essay Prompt

The fact that football has the greatest injury rate of any other kind of sport should not come as a surprise. Football players often incur injuries like ankle sprains, knee injuries, concussions, and acromioclavicular sprains. In your essay on the dangers of football, you can raise the following questions:

• Why is it so easy for football players to get injured?
• What types of injuries are most common during a football game?
• What precautions must be taken to prevent trauma?
• How does injury impact the future career of a football player?

Prompt for Essay on Concussions in Football

While every sport has some risk of getting hurt, football, as a high-impact sport , is infamous for causing severe injuries. Concussions are a common injury among football players. They happen when the head is hit hard enough to cause a minor brain injury. To research the topic of concussions in football, write your essay based on the following aspects:

• The effect of concussion on the brain.
• Statistics on concussion in American football.
• Medical concussion protocol.
• The recovery process after a concussion.
• Screening procedures examining football players for brain damage.

If you’re looking for the most engaging football essay titles, check out the ideas we’ve collected below!

Topics for a Descriptive Essay on a Football Game

• The thrill of a last-minute goal in football.
• The intensity of the players’ warm-up and last-minute preparations.
• Sports psychologist: working with athletes .
• The different styles of play in football around the world.
• The rapid movement of players and the choreography of their tactics.
• The role of a coach in football.
• Capturing the joys and frustrations of the players and fans.
• The interaction between players and referees: decisions, protests, and resolutions.
• A description of a football stadium and its architecture.
• The art of dribbling in football.
• How do players and fans celebrate a goal?
• Describing pre-match rituals and superstitions in football.
• How do fans create a supportive atmosphere for their team during the game?
• The joy and excitement of attending a live football match.
• Describing how coaches handle their emotions on the sidelines.
• The description of food served during the football game.
• The magnetic pull of the scoreboard: watching the numbers change.
• The vibrant fan gear and merchandise in a football stadium.
• The drama of penalty kicks: tension, hope, and heartbreak.
• The description of a goalkeeper’s save.
• The sounds of the football match.

Football Argumentative Essay Topics

• Is football too dangerous for young children to play?
• Does football develop leadership skills and teamwork?
• Title IX in the female sports development .
• College football players should be paid for their performance on the field.
• Should football stadiums have stricter security measures?
• Is the use of performance-enhancing drugs in football acceptable?
• Reasons why the NFL should expand to include more teams.
• Why paying college athletes is beneficial .
• Is the NFL doing enough to prevent concussions and other injuries in players?
• Should football games be played on artificial turf or natural grass?
• Is it ethical for colleges to recruit high school football players?
• Should players be allowed to protest during games?
• Does youth sports play a part in the character formation ?
• Reasons why cheerleading should be considered a sport in football.
• Should the Super Bowl be considered a national holiday?
• The economic influence of football: the benefits and costs.
• Is football too focused on commercialization and profit?
• Should football players be allowed to use marijuana for medical purposes ?
• The NFL should have a shorter season to reduce the risk of injuries to players.
• Using performance-enhancing drugs in the world of sport .
• Should college football teams be allowed to schedule games against non-college teams, such as high school teams?
• Should the NFL have a salary cap to ensure fairness among teams?
• Football players should wear full body armor to reduce injuries.
• Is football too expensive for schools and communities to support?
• Should the NFL allow players to use alternative therapies for pain management ?
• Should football players be required to take regular drug tests ?
• Should the NFL have stricter penalties for players who break the rules, such as suspensions or fines?
• Children participation in sports .
• Football players should take classes on financial management to prepare for life after football.
• Should the NFL have a quota for hiring minority coaches and executives?
• High school football players should pass a physical exam before being allowed to play.
• Should the NFL have stricter rules on player conduct off the field?
• College football players should be allowed to transfer to other schools without penalty.
• Should the NFL have a policy on players using social media ?
• Football players should attend media training to prepare for interviews and press conferences.
• Sport psychology: biases and influence of external rewards .
• Should the NFL have a policy on players participating in political activism ?
• Football players should undergo regular psychological evaluations.
• Should the NFL have a policy on players using alcohol and drugs off the field?
• Should football players be required to wear protective eyewear to reduce eye injuries?
• College football teams should provide mental health resources for their players.
• Should high school football teams limit the number of weekly practices to reduce the risk of injuries?
• Paying college athletes: reinforcing privilege or promoting growth ?
• Should college football players be allowed to unionize?
• Should football be banned in schools to protect students from injuries?
• Is playing football in college detrimental to academics?
• Should college football players be allowed to hire agents?

Ideas for a Narrative Essay about Football

• The first time I stepped onto the football field: an unforgettable experience.
• Overcoming adversity: how I bounced back from a football injury.
• A story of teamwork : how football taught me the value of collaboration.
• The most memorable football match I have ever witnessed.
• Coping with stress in athletes .
• The importance of football in building lifelong friendships.
• From underdog to champion: my journey with the football team.
• A day in the life of a football player: behind the scenes.
• The role of football in shaping my identity.
• A tale of rivalry: the intense football match against our arch-nemesis.
• The impact of football on my physical fitness and well-being.
• How a football coach changed my life.
• The thrill of scoring the winning goal: a football victory to remember.
• The evolution of football: from my grandfather’s time to the modern era.
• A football match that taught me the importance of humility .
• The emotional rollercoaster of supporting a football team.
• Lessons learned from defeat: how football taught me resilience .
• A football game that tested my leadership skills.
• Football and community: how the sport brings people together.
• A football camp experience: training, team building , and friendship.
• From fan to player: fulfilling my football dream.

Football Essay Topics: Compare and Contrast

• Regular football vs. American football: a comparative analysis.
• Lionel Messi vs. Cristiano Ronaldo: contrasting two football legends.
• Comparing football and soccer .
• College football vs. professional football: similarities and differences.
• The World Cup vs. the Super Bowl: contrasting two major football events.
• The roles and impact of offensive and defensive players.
• The Premier League vs. La Liga: comparing two dominant football leagues.
• Contrasting playing styles and cultural significance of football in Europe and South America.
• Club football vs. international football: examining the differences in competition and loyalty.
• Football stadiums vs. arenas: comparing the experiences of live football events.
• The similarities and differences between Olympic football and FIFA World Cup.
• Football in the past vs. modern-day football.
• Comparing the roles and responsibilities of quarterbacks and goalkeepers.
• Football fan culture in Europe vs. the US: contrasting fan traditions and behaviors.
• Amateur football vs. professional football.
• Football uniforms vs. gear: analyzing the equipment used in the sport.
• Comparing and contrasting famous football team rivalries.
• Football team dynamics vs. individual brilliance: contrasting the impact of teamwork and individual performances.
• Football referees vs. video assistant referees (VAR).
• Club vs. country: comparing the passion and loyalty for club and national teams.
• Football and injuries: comparing the risk and types of injuries in the sport.
• Football leagues during the pandemic vs. regular seasons.
• Football commentary vs. live match experience: comparing the different ways of engaging with the sport.
• The impact of football on local vs. global economies.
• Football documentaries vs. fictional football movies.
• The role of football in promoting diversity vs. perpetuating stereotypes.
• Football fandom vs. player idolization: contrasting how fans engage with the sport.
• Comparing the traditional grass pitches vs. artificial turf.
• The impact of social media on football vs. traditional media.
• Comparing the challenges of football in different weather conditions .
• Football in mainstream culture vs. football subcultures.
• The health benefits of football vs. injuries and health risks.
• Betting in football vs. gambling .
• The cultural significance of football in different regions.
• Football literature vs. football films: contrasting different forms of storytelling about the sport.
• Football stadiums: traditional vs. modern architecture .
• College football vs. professional football: differences in gameplay and culture.
• Offensive vs. defensive strategies: which is more important?
• Comparing traditional and modern football training methods.
• The history of football in America and Europe.
• Injuries in football vs. soccer: which sport is more dangerous?

American Football Topics

• The evolution of American football: from its origins to the present day.
• The impact of race on American football.
• Concussions and brain injuries in American football.
• The psychology of football: understanding the mental game of players and coaches.
• The role of women in American football: from cheerleaders to coaches and executives.
• The strategies and tactics used in American football.
• The role of coaches in American football: leadership and game planning.
• The significance of the offensive line in American football.
• The impact of college football on the NFL.
• The influence of the media on American football.
• The role of the head coach in American football.
• The importance of physical fitness in American football.
• The impact of technology on American football: from instant replay to virtual reality training.
• The economic impact and financial aspects of American football.
• The history of Super Bowl halftime shows.
• American football and national identity.
• The impact of weather on American football games.
• The influence of player protests on American football.
• The role of American football in the entertainment industry (movies, TV shows, etc.).
• The development of American football youth programs: benefits and challenges.
• The importance of the running back in the offense in American football.
• The role of the defensive line in stopping the run and rushing the passer in American football.
• The influence of American football on sports marketing and sponsorship.
• The impact of fan behavior on American football.
• Exploring the legacy of American football’s great players and their impact on the sport.
• The influence of a new coach on team culture and performance in American football.
• The consequences of player suspensions in American football.
• Player trades in American football: exploring how teams acquire new talent.
• American football and sportsmanship: fair play and ethical considerations.
• The impact of player injuries on American football: exploring the recovery process.
• The role of American football in building teamwork and camaraderie.
• The impact of American football on society’s perception of masculinity .
• The history and cultural significance of American football rivalries.
• The role of American football in promoting community engagement and volunteerism.
• The influence of American football on US pop culture.
• American football and social justice : protests, activism, and athlete empowerment.
• The role of American football in public health and fitness initiatives.
• The ethics of sports gambling in American football.
• American football and sports diplomacy: international relations and competitions.
• The future of American football: challenges and opportunities.

Are you looking for exciting football topics to talk about? Check out our suggestions for persuasive and informative speeches about this sport!

Football Persuasive Speech Topics

• The benefits of playing football for overall physical fitness.
• The importance of youth football programs in fostering teamwork.
• Kids and sports: lack of professional sports guides .
• The positive impact of football on character development and leadership skills.
• The role of football in promoting gender equality and inclusion.
• The economic benefits of hosting major football events like the World Cup or Super Bowl.
• The need for increased safety measures and concussion protocols in football.
• The necessity of providing proper healthcare and support for retired football players.
• The role of football in breaking down cultural and racial barriers.
• Balancing college sports and academic mission .
• The benefits of investing in football infrastructure and facilities for communities.
• The positive influence of football in reducing youth involvement in crime and drugs.
• The potential of football as a tool for empowering disadvantaged communities.
• The role of football in promoting a healthy and active lifestyle among fans and spectators.
• The benefits of including football as part of the physical education curriculum in schools.
• The positive effects of football in promoting national pride.
• Corporate social responsibility in sports organizations .
• The use of football as a platform for raising awareness and funds for charitable causes.
• The importance of football in boosting tourism and international visibility of cities.
• The potential of football in fostering international diplomacy and cultural exchange.
• The importance of providing equal opportunities for females in football at all levels.
• The impact of football on local economies through job creation and tourism revenue.
• The significance of iconic moments in football history.

Football Informative Speech Topics

• The different positions in football and their roles.
• The psychology of football fans and their passion for the game.
• Agencies in the international football industry .
• Famous football stadiums around the world and their significance.
• The rules and regulations of football: understanding the game’s structure.
• The role of referees and their importance in enforcing the rules of football.
• Positive self-talk and its impact on athletes .
• The evolution of football equipment: from leather balls to high-tech gear.
• The most successful football clubs in history and their achievements.
• Exploring the tactics and strategies used in modern football.
• The science behind successful football coaching.
• Sports coaching career and its history .
• Football rivalries: the history and intensity behind classic match-ups.
• The art of scoring goals: techniques and skills of top goal scorers.
• Football and media: the influence of broadcasting and coverage on the sport.
• The psychological aspects of football: mental preparation and performance.
• The cultural impact of football around the world.
• The development and growth of women’s football.
• Physical therapy services for sports injuries .
• The importance of nutrition and fitness in football.
• The significance of football academies in nurturing young talent.
• The role of technology in modern football: VAR, goal-line technology, and more.
• Football hooliganism : understanding the causes and efforts to combat it.
• Famous football managers and their managerial styles: strategies for success.

If you need compelling topics about football for your presentation, here are some ideas you can consider:

• The FIFA World Cup: the most significant event in international football.
• Techniques and skills in football: dribbling, shooting, passing, and more.
• Leadership development in football management .
• The rules and regulations in football.
• Football tactics: exploring different formations and strategic approaches.
• Famous football players of all times: their achievements and impact on the sport.
• Football and sports injuries: common types, prevention, and treatment.
• Steroid use effects on professional young athletes .
• Football stadiums around the world: architecture and unique features.
• The business side of football: sponsorship, transfer fees, and revenue streams.
• Football and social media: the influence of digital platforms on the sport.
• Football documentaries and films: capturing the drama and passion of the sport.
• The effects of football on fashion and popular culture.
• Virtual reality technology in soccer referee training .
• The financial impact of football on cities and regions.
• Football and sports journalism: media coverage and analysis of the sport.
• Football stats and analytics: how data is revolutionizing the sport.
• The causes and consequences of fan violence in football.
• The cultural rituals and traditions associated with football matches.
• Football and the environment: sustainable practices and stadiums.
• The impact of football on tourism.
• Health care site: fitness, sports, and nutrition .
• Football and celebrity culture: players as icons and brand ambassadors.
• Football in video games: the popularity of virtual football experiences.
• The importance of infrastructure in hosting major football events.
• Football tactics in different eras: from Catenaccio to Tiki-Taka.
• Football and broadcasting: the growth of televised matches and media rights.
• Football training drills for improving agility and speed.
• Physical activity and sports team participation .
• Strategies for effective team communication on the football field.
• The importance of proper warm-up exercises in preventing injuries in football.
• Tips for strengthening and conditioning specific muscle groups for football players.
• Defensive formations and tactics for shutting down opponents in football.
• Analyzing football game films to improve performance and strategy.
• Recovering from football injuries: rehabilitation exercises and protocols.
• Sports-related problems and conflicts .
• Sports psychology techniques for boosting confidence and mental resilience in football.
• Nutrition and hydration guidelines for optimal performance in football.
• The connection between globalization and football.
• The role of stretching routines in preventing muscle imbalances in football players.
• Practical strategies for successful penalty shootouts in football.
• Steroid usage in professional sports .
• Football scouting and player evaluation techniques for talent identification.
• The use of technology in football training and performance analysis.
• Football equipment maintenance and safety guidelines for players.
• Preparing and executing penalty kicks in pressure situations in football.
• Advanced passing techniques in football: long passes, through balls, and more.

Do you need to write a research paper about football but don’t know where to start? Consider our list of football research questions and topics:

• How have football tactics evolved over the past decade?
• The impact of technology on decision-making in football.
• Business industry: trend analysis for soccer .
• The psychology of team cohesion and its effects on football performance.
• What is the role of nutrition and diet in optimizing football players’ performance?
• What is the relationship between football and concussions?
• How do FIFA World Cup events affect host countries’ economies?
• What is the carbon footprint of major football events?
• The effects of climate conditions on football matches.
• Shortage of officials at the high school sports level .
• The influence of social media on football players’ image and brand.
• The role of VAR in the fairness of football matches.
• The impact of home-field advantage in professional football.
• How does the football stadium atmosphere affect player performance?
• The rise of women’s football and its impact on gender equality.
• The economic implications of football player transfers and fees.
• The correlation between a team’s wage bill and on-pitch success.
• Factors influencing fan loyalty in football.
• Research handbook of employment relations in sport .
• The role of leadership and coaching in a team’s success.
• The impact of sponsorship deals on football clubs’ financial stability.
• The relationship between player positioning and successful goal scoring.
• The effects of VAR on the emotions and behavior of fans during football matches.
• How does football influence youth development and participation in sport?
• How can big data analytics improve football performance and decision-making?
• The effects of football on cultural identity and national pride.
• How do sports affect disabled people psychologically ?
• The impact of football on the local community and economy.
• The influence of crowd noise on football referee decisions.
• The role of sports psychology in enhancing football performance.
• The impact of financial fair play regulations on football clubs.
• How does football betting affect match outcomes and integrity?
• The cultural significance of football chants and songs in fan culture.
• Steroid abuse in the world of sports .
• The influence of doping scandals on the reputation of football players and clubs.
• The role of football in promoting social inclusion and breaking down barriers.
• How do international football competitions affect tourism?
• The effects of player transfers on team dynamics and performance.
• The correlation between player height and success in football.
• The influence of different playing surfaces on football player performance and injury rates.
• How do referees maintain fairness and order in football matches?
• Achievement motivation theory in sports psychology .
• The impact of football on academic performance and school attendance.
• The role of football hooliganism in shaping public perceptions of the sport.
• The influence of football sponsorship on brand image and consumer behavior.
• The effects of football on social integration and community cohesion.
• How do rule changes affect football game dynamics?
• The influence of football on individual and societal gender norms.
• Sports analysis: steroids and HGH in sports .
• Investigating the impact of celebrity endorsement on football merchandise sales.
• The role of technology in improving football player performance and injury prevention.
• The correlation between alcohol consumption and football-related violence.
• The impact of fan protests and boycotts on football clubs and leagues.
• The effects of retirement on the mental well-being of former professional football players.
• The influence of football on urban development and infrastructure investment.
• How does football affect students’ academic motivation and educational attainment?
• The impact of football on destination marketing in tourism.

Structuring your essay on football is a piece of cake, and we’re going to prove it! Follow our mini guide with valuable tips and examples!

Football Essay Introduction

The first paragraph of an essay is crucial to creating a strong paper. A successful introduction often starts by addressing broad ideas related to the essay’s topic. Follow the steps below to write a compelling introduction:

1. Start with a hook.

Make a good first impression by using a captivating hook . In football essays, it can include a surprising fact, statistics, a question, or a relevant quote. Here’s an example:

What is the one thing that can unite a country and foster its pride? Yes, it is football!

2. Provide background information.

Give essential details on the essay’s main subject. This part can include the history of your topic, an explanation of key terms, and anything that can help your reader understand the context of your issue.

Football is a group of team sports that involve kicking a ball to score goals.

3. End with a thesis statement.

Put a concise thesis statement at the end to outline your motivation for the paper and present central arguments. Let’s talk about this element in detail.

Thesis Statement about Football

The thesis statement is a sentence expressing the primary idea of a piece of writing and guiding the thoughts within the work.

There are several steps that you should take to develop a thesis statement:

• Research information on your issue.
• Limit your topic to a specific area.
• Brainstorm to come up with interesting ideas.

Look at the example of a football thesis statement:

Football offers the chance to feel pride for the favorite team and positively impacts physical, social, and emotional development.

Essay about Football: Body Paragraphs

The main body of an essay is the most crucial part where you deliver your arguments. Here are some tips on writing a good body paragraph:

• Start with a topic sentence to capture the key points.
• Provide additional information to support your opinion.
• Use a transition sentence to get to the next paragraph smoothly.

Here’s an example of what your topic sentence and supporting evidence might look like:

Topic sentence : Football requires effective communication and listening skills since the game will not work without them. Supporting evidence : Communication helps athletes perform and focus better on the pitch and improves the decision-making process.

Conclusion for Football Essay

A conclusion brings your discussion to a close. The following outline may assist you in completing your essay:

• Restate your thesis.
• Explain why your topic is significant.
• Summarize the core points.
• Call for action or provide an overview of future research opportunities

Check out an example of a paraphrased thesis and the summary of the main points:

Rephrased thesis : Football is a fascinating sport with many societal benefits. Summary : To sum up, football can be considered a hobby, a sport, or an obsession. But still, its most important role is to unite people or even entire countries.

We hope you will find our football topics to write about and research beneficial! Want to receive some more ideas? Try our free online title generator ! Just click the button, and the result will not keep you waiting!

• Health and Wellness | The Football Players Health Study at Harvard University
• Sports | Harvard Business School
• Head Injuries & American Football | McCombs School of Business
• Research | Global Sport Institute
• University Archives: History of Football | Marquette University
• NCAA and the Movement to Reform College Football | Library of Congress
• Medical Issues in Women’s Football | National Library of Medicine
• Football Injuries | University of Rochester Medical Center
• Head to Head: The National Football League & Brain Injury | NYU Langone Health

351 Anxiety Research Topics & Essay Titles (Argumentative, Informative, and More)

223 deforestation topics for essays, research papers, & speeches.

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The Football Players’ Health Study at Harvard University: Design and objectives

Ross zafonte.

1 Football Players Health Study at Harvard University, Harvard Medical School, Boston, Massachusetts

2 Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Massachusetts General Hospital, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts

Alvaro Pascual‐Leone

3 Berenson‐Allen Center and Division for Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts

Aaron Baggish

4 Cardiovascular Performance Program, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts

Marc G. Weisskopf

5 Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, Massachusetts

Herman A. Taylor

6 Department of Medicine, Cardiovascular Research Institute, Morehouse Medical School, Atlanta, Georgia

Jillian Baker

Sarah cohan, chelsea valdivia, theodore k. courtney, i. glenn cohen.

7 Harvard Law School, Cambridge, Massachusetts

Frank E. Speizer

8 Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts

Lee M. Nadler

9 Dana Farber Cancer Center, Harvard Medical School, Boston, Massachusetts

The Football Players Health Study at Harvard University (FPHS) is a unique transdisciplinary, strategic initiative addressing the challenges of former players’ health after having participated in American style football (ASF). The whole player focused FPHS is designed to deepen understanding of the benefits and risks of participation in ASF, identify risks that are potentially reversible or preventable, and develop interventions or approaches to improve the health and wellbeing of former players. We are recruiting and following a cohort of former professional ASF players who played since 1960 (current n  = 3785). At baseline, participants complete a self‐administered standardized questionnaire, including initial reporting of exposure history and physician‐diagnosed health conditions. Additional arms of the initiative are addressing targeted studies, including promising primary, secondary, and tertiary interventions; extensive in‐person clinical phenotyping, and legal and ethical concerns of the play. This paper describes the components of the FPHS studies undertaken and completed thus far, as well as those studies currently underway or planned for the near future. We present our initiatives herein as a potential paradigm of one way to proceed (acknowledging that it is not the only way). We share what we have learned so that it may be useful to others, particularly in regard to trying to make professional sports meet the needs of multiple stakeholders ranging from players to owners, to fans, and possibly even to parents making decisions for their children.

1. INTRODUCTION

Over the last 20 years, there has been increasing concern both about the acute injury effects as well as the long‐term consequences to athletes participating in high‐impact contact sports. 1 These are not new concerns. Incidental case reports of acute traumatic injuries resulting in significant incapacitation and even deaths had been reported for over 50 years. More recently, chronic or late‐onset significant morbidity associated with participation in high‐impact sports has become of increasing concern, as reports of significant neurodegenerative diseases occurring in former prominent athletes, particularly related to those who played professional American style football (ASF), have made news in both the scientific as well as the lay press. 2 Other chronic conditions, including musculoskeletal, cardiovascular, sleep disorders and behavioral mental health conditions have also been reported. 3 , 4 However, for the most part, the published literature falls short in providing sufficient data to make informed judgments to quantify the magnitude of the risks associated with ASF for any of these conditions. This has the unfortunate effect of placing a burden on former players, potential players, and their families, as well as other stakeholders to make potentially lifestyle and health‐related decisions without adequate facts. It should be noted that this manuscript is designed to describe a strategic programmatic response to a research need and a series of studies under a large umbrella rather than a single study.

In an effort to better document the potential long‐term consequences of participation in ASF, the National Football League Players Association (NFLPA) in 2014 put forward a nationally advertised major Request for Proposals to study the health and welfare status of retired professional ASF players. The proposal asked for studies to assess and develop potential new preventive, diagnostic, and therapeutic interventions that would mitigate potential long‐term consequences of participation in the sport. In response, Harvard University developed the Football Players Health Study (FPHS), which was designed as a multidisciplinary investigative team approach to address these issues. This effort was formally funded in 2014 . The goal of the Football Players Health Study at Harvard University (FPHS) is to further understand the benefits and risks of participation in ASF, identify those risks that are potentially reversible or preventable, and develop interventions or approaches to improve the broad array of issues impacting the health and wellbeing of former ASF players.

From the onset, it was clear that the success of this program would be dependent on understanding and being committed to the concept of ongoing engagement with the population of interest in a participatory approach throughout the research process. 5 We initially conducted a number of focused meetings with representatives of the NFLPA as well as former ASF players from a variety of other player associations. These sessions provided input into prioritizing clinically meaningful targets for assessment, intervention, and potential functional improvements. Follow‐up meetings led to working groups of Harvard University faculty who came together to design a comprehensive set of studies, as indicated below, around the theme of “the whole player, the whole life.” Issues considered included, but were not limited to, identifying factors that could mitigate risk of having an injury; understanding consequences of injury as well as other factors associated with participating in the sport at the professional level on short and long‐term health impacts; and, to the degree possible, understanding the long‐term consequences for both physical and social impacts of having participated in the sport. In addition, we proposed to explore potential new approaches to therapeutics to lessen long‐term consequences of the unique exposures and putative injuries to which players are exposed. Not the least of our objectives was to determine the magnitude of the risk rates of a wide variety of outcomes. Such data would give all stakeholders better estimates for making potentially life‐changing decisions regarding participating in ASF. Because of the complex nature of player‐team relationships, a group of bioethics and legal scholars formed an additional unique component of our studies to explore and address the ethical and legal implications of the way professional ASF is organized. Finally, a significant component of our efforts is to keep the former players informed of our progress. This has been done through both a series of former player advisor group meetings and social media efforts to both inform former players on the progress of the studies and encourage participation in the ongoing efforts.

This paper describes the components of the FPHS studies undertaken and completed thus far, as well as those studies currently underway or planned for the near future. We present our initiatives herein as a potential paradigm of one way to proceed. We fully acknowledge that our approach is not the only way, but believe that what we have learned may be useful to others, particularly in regard to trying to make professional sports meet the needs of multiple stakeholders ranging from players to owners, to fans, and possibly even to parents making decisions for their children.

2. SCOPE OF THE STUDIES

We initially established two important Advisory groups. The first was made up of former NFL players who represented a spectrum of regional areas of the US, positions played, and different age groups. These former players provided essential insight into the concerns and questions that were most germane to the former player groups. The second was a group of local physician/scientists representing the range of research domains believed to be important to consider. Both groups have continued to evaluate and provide input into the research designs undertaken.

The range of studies can be divided into three broad categories (Figure ​ (Figure1). 1 ). Within each of these categories, there are a number of substudies, some of which have been completed, some which are ongoing, and some which are still in the planning or early implementation stages. In addition, an important component is communication and return of results to the participating former players and other stakeholders.

Range of studies undertaken, ongoing, and planned [Color figure can be viewed at wileyonlinelibrary.com]

2.1. Former player studies

A major effort of the FPHS focuses on studies of former NFL players with the goal of assessing risk factors associated with participating in professional ASF and the putative long‐term consequences to their health and wellbeing. This plan requires a coordinated set of studies. Initially, we needed to assess components related to the exposures that are or have been a necessary part of the game. Further, we are attempting to quantitate the current physical, social, and neurocognitive state of former players, and testing within nested case‐control subgroups newer diagnostic techniques and remote assessment tools. Eventually, we would hope to introduce potential new therapeutic modalities that may enhance the lives of former players after they leave the game.

2.1.1. Cohort questionnaire studies

Beginning in 2014, we sought to enlist the participation of an as large as possible cohort of former ASF players who had participated in the NFL (or former American Football League). Our criterion for enrollment was “formerly played professional football at any point from 1960 to present.” “Formerly played” was defined as having received compensation as a player from an NFL team. The year 1960 was chosen because by that time the transition from the soft, leather helmet to the hard, plastic helmet had been established throughout the league. The eligibility to join the cohort is a dynamic one in which younger players are invited and encouraged to enroll as they declare themselves retired. In addition, as subsequent substudies identify former players who had not enrolled in the initial round of cohort data collection, they are invited to provide baseline data.

To determine the topics to include in the initial standardized questionnaire, we held focus group meetings with both former players and research advisors. We first identified the parameters that would permit us to measure some of the characteristics of “exposure” in professional football (eg, position played, years of play, nature of some of the injuries during active playing years, essential demographics, etc). We also identified a number of health‐related domains of concern, for which we believed, by using well‐validated questions, we could establish baseline health status for the proposed cohort. Because a significant portion of the eligible cohort had either a home address or an email address, but not both, we needed to assess the potential difference in response patterns that might occur using one rather than both methods for contact. We selected approximately 500 former players at random who had both home addresses and emails to assess the response rate and degree of completion of the various components of the questionnaire. We determined that the response patterns and degree of completeness were no different between administering the questionnaire by email (REDCap C ) vs Scantron c paper questionnaires, and thus both methods were used for those for whom we had appropriate contact information.

The original main sources for defining former players were lists provided by the NFL Players Association, supplemented by NFL Profootball Reference. 1 Additional sources, many overlapping, included a number of philanthropic associations formed by former player groups, wives of current and former players, and other regional and local groups. These groups were asked to communicate with their members and to inform them of the study. 2 Figure ​ Figure2 2 describes the sources and number of former players for whom we initially believed we had obtained a contact address. Initially, we estimated that approximately 20 000 individuals played for one or more of the approximately 30 teams over the years starting in 1960. Of these, we estimated that approximately 4000 had died before the beginning of the follow‐up period. In February 2015, at the time of our first effort to contact the former players, and over the initial 3 years of follow‐up, we were able to confirm 14 538 individuals who met the criteria as former active players. Other members of the initially constructed lists had included coaches, management staff, and others who were not active players. We were able to confirm anticipated valid home addresses for approximately 12 713 players. In addition, we had available potential email addresses for 8542. Using combined mailings for both paper questionnaires and web‐based methods, we estimated that 13 403 former players with appropriate years played eligibility received our questionnaire in one or both forms (only the first method used to respond was counted). At present, the cohort is made up of 3785 former players who have completed our initial questionnaire. Newly retired players are continuing to enroll and plans exist to follow them over time.

Flow chart of former player contacts [Color figure can be viewed at wileyonlinelibrary.com ]

2.1.2. Content and purpose

Our standardized questionnaire captures demographic and football exposure information that allows us to characterize the former players by age, race, football experience, and other usual characteristics of behavior related to lifestyle and general risk factors for injury and illness associated with participating during their playing years as well as postplaying years. In addition, we use well‐established questions to assess outcomes in a number of health domains. 3 Other illnesses, surgeries, and medical conditions since retirement, which are self‐reported as confirmed by health practitioners, also are collected.

The data obtained from the cohort by questionnaire serves two essential purposes. The initially obtained data provides cross‐sectional estimates of the frequency of occurrence of health outcomes in a variety of domains. Along with these questions on outcomes, we are obtaining detailed information on exposure in organized football as well as general lifetime risk and behaviors that may affect these outcomes.

2.1.3. Limitations

The overall response rate for the entire eligible cohort to date is approximately 30%. This is not dissimilar to other pro athlete cohorts. 6 In selected exposure groups, we have found response rates by important demographic characteristics such as age, position played, and the number of seasons to be relatively comparable to the overall eligible population. In addition, although we can identify the prevalence of different health outcomes in our baseline data, the possibility of bias in estimating rates of these conditions among all former ASF players must be considered if players with certain conditions were more or less likely to join our cohort than those without the conditions. Importantly, though, this is not as problematic for studies of exposure‐health outcome associations with the cohort data, as such outcome‐dependent (or exposure‐dependent) participation or nonresponse does not create spurious associations between an exposure and outcome if no true association exists. 7

The prospective component of the population cohort study began in January of 2019, with follow‐up questionnaires sent to all former players who previously responded, and with newly reported medical conditions documented by follow‐up to the players’ health care providers. The relation between exposures identified at baseline and the occurrence of new outcomes after baseline identified by such cohort follow‐up is much less susceptible to any biases related to participation in the baseline questionnaire. In addition, those former players who did not respond to the initial questionnaire will be given another opportunity to join the study. A few remote‐based substudies among the cohort members have been undertaken, either based upon the initial questionnaire responses or de novo ancillary hypotheses that are currently underway and are described in detail under the Targeted Studies section below.

2.2. In‐person assessment studies

An additional purpose of the cohort baseline questionnaire was to use the collected data on health status to identify former players who self‐reported to be either “afflicted” or “nonafflicted” in four important health‐related domains. A limited number of these participants, in matched groups by age and race, have been selected to be invited to come to Boston to undergo detailed specialized testing using both best‐available technology and some novel or exploratory technology to assess how self‐reported status in the domains of neurocognitive, cardiac, musculoskeletal pain, and sleep relate to objective measures in each of these domains. The first objective will use a multimodality protocol designed to provide a comprehensive phenotypic assessment of former players, with an emphasis on clarifying the link between subjective complaints or prior diagnoses (or lack thereof) identified on the questionnaire and gold‐standard objective assessments of disease status. Depending on the observed correlations, these objective measures, although in relatively in small numbers of subjects, can be used to test several hypotheses related to exposure in football by using nested case‐control designs.

For those participants who come to Boston for the in‐person assessments (IPA), we have developed a medical navigation system. At the end of the session, each subject who participates in the IPA will meet with an appropriate study team member to have the opportunity to discuss all clinical findings. Follow‐up for any incidental, clinically relevant findings will be recommended. When appropriate and desired, the subject will be helped with referral to clinical care facilities in his local community. This medical navigation also serves as an educational and guidance program geared toward the specific needs of participants across the study.

2.3. Targeted studies

While the development of the cohort of former players was being established, a number of opportunities to investigate a wide variety of issues related to the nature of the kinds of injuries and potential long‐term outcomes were identified through a local process of request for proposals among all of the Harvard institutions. These targeted studies are funded from the original grant proposal award. These were initially identified as Pilot Studies and more recently characterized as Targeted Studies. These studies take advantage of local ongoing work in the Harvard community by requesting research proposals for modest efforts that would provide data that could lead to substantial outside funding to expand the research and/or have significance to the former ASF player population. The proposals are competitively evaluated and considered for 1‐ to 2‐year funding periods. The criteria for funding are whether the study is considered innovative, feasible, and achievable in a limited funding period, considering the potential roadblocks to success and whether the proposers had the requisite skills and experience to conduct the research. These criteria are rated competitively by external, independent, domain‐specific experts, and funding decisions are made by the study leadership based on evaluation scores and programmatic needs. The studies are divided into basic science or pathophysiologic studies in both nonhuman and human (though not necessarily football player) populations, and studies in both active and retired ASF players. The targeted studies are each summarized by domains and described briefly in Table ​ Table1 1 .

Targeted Studies

Abbreviations: BCH, Boston Children's Hospital; BU, Boston University; BIDMC, Beth Israel Deaconess Medical Center; BWH, Brigham and Women's Hospital; DFCI, Dana‐Farber Cancer Institute; HSL, Hebrew Senior Life Institute for Aging Research; McLean, McLean Psychiatric Hospital ; MGH, Mass General Hospital; MSM, Morehouse School of Medicine; NEU, Northeastern University; Wyss, Wyss Institute at Harvard.

A wide variety of Targeted Studies were developed over the initial 3 years of the study (Table ​ (Table1). 1 ). These include very basic immunologic assessments of impacts resulting from an acute injury, outcomes related to repeated mild traumatic brain injury in animals, cardiac assessment in active and retired players, video analysis of the biomechanics of exposure and the development of mechanical preventive strategies for stress to the knee during active exercise, among others. Similar additional basic and applied studies that are currently underway and being planned are also included in Table ​ Table1 1 .

More recently, we have begun to develop remote assessment and intervention studies.

Because of the practical limitations of bringing large numbers of former players to Boston, it is clear that to the degree we can obtain standardized data remotely from former players living across the country we can increase our power to test a variety of hypotheses. Several such attempts are already underway. For example, using a smart phone‐based application specifically developed for the FPHS, we conducted a remote study of a neurological function using the effects of dual tasking on measures of balance (The Team Study). 17 Participants who downloaded our Team Study app 4 provided repeated measures over several weeks of balance and walking while doing mental arithmetic, with the data being transmitted remotely. Assessments of the results of this effort are currently underway. A second example is the Brain Health Study that uses a standardized series of cognitive assessment tools, remotely administered on an encrypted website, providing detailed assessments in several brain function domains. At the end of the procedures, the results are compared to a large standardized database, and we are able to provide individual participants with a personalized assessment of their cognitive function and styles. To date, 349 former ASF players have completed this assessment.

Other remote cohort substudies are currently just getting underway or are in the planning stages. These include the development of a scalable sleep intervention program to improve pain, quality of life, and health in former players; a goal‐directed resilience training study to mitigate chronic pain in a group of players living in the greater Atlanta area; and a study of personal networks with the potential to inform the development of tools to enhance health‐positive networking.

As a result of the initiation of a detailed follow‐up questionnaire study of all responders to the first round of baseline questionnaires, starting in 2019, we are collecting prospective incidence data over a 4‐year period in the established cohort and have increased opportunities for further remote studies in selected subgroups of the population. Those former players who did not respond to the initial questionnaire will be given another opportunity to join the study.

The basic population we are studying is largely a public and relatively easily identifiable cohort of former players. Therefore, one of the unique challenges of the study is to maintain the confidentiality of all medical information being gathered. Essential to gaining the trust of the former players, every possible effort to protect the security and confidentially of all health‐related data provided is made. To this end, we secured a Certificate of Confidentially from the National Institute of Health for each individual research protocol developed which includes former player participants. The Certificate prohibits disclosure of identifiable, sensitive research information to anyone not connected to the research (with certain exceptions; see https://humansubjects.nih.gov/coc/index ). In addition, all data obtained are held in secure, custom‐built data repositories. All identifiable data are coded and removed from any working files. Access to identifying data is on a need‐to‐know basis, and only by specifically trained and vetted personnel.

2.4. Law and ethics initiative

At the onset of this effort, we recognized that because of the nature of the potential competing stakeholders’ interests in the NFL (owner, players, agents, physicians, families, fans, etc.), the interactions of these stakeholders raised myriad potentially complex legal and ethical considerations. Our Law and Ethics team's first task was to determine not only who these stakeholders were, but also to map the nature of their interactions. This effort encompassed a variety of distinct projects with the primary goal of understanding the legal and ethical issues that may enhance or impede players’ health and welfare. In keeping with the mantra, “the whole player, the whole lifetime,” this component of the study examined issues at various points of a player's lifetime—from competing for a spot in the NFL Combine, to active years of play, to retirement planning, and the way players and family members dealt with health issues after their playing years were over.

A series of studies were undertaken to assess how stakeholders’ perspectives interact (Table ​ (Table2). 2 ). We identified who the stakeholders in player health were, evaluated their legal and ethical obligations, and assessed the current successes as well as gaps and opportunities for each stakeholder in protecting and promoting player health. 20 , 21 In addition, we applied a series of legal and ethical principles to arrive at recommendations for positive change where needed. 22 A second effort compared the NFL's policies and practices to those in place in Major League Baseball, the National Basketball Association, the National Hockey League, the Canadian Football League, and Major League Soccer, to assess best practices and make recommendations for areas deemed in need of improvement. 23 In addition to these major reports, a series of additional studies on the legal and ethical aspects of the game were carried out over the first 3 years of the study. These include an examination of team doctors’ conflicts of interest and the ethical management thereof, an examination of the applicability of the Americans with Disabilities Act and the Genetic Information Nondiscrimination Act to NFL football and the NFL Combine, and an analysis of the applicability of workplace safety laws and guidelines (such as the Occupational Safety and Health Agency) to professional football. We also conducted a qualitative assessment project with one‐on‐one interviews of approximately 50 current and former players and another 50 of their family members. The output of this study is ongoing. The goal was to better understand the perspectives of these key stakeholders on the following topics: overall professional football experience, improving player safety, health, family, and social issues, support as a professional athlete, life after football, risk disclosure, and risk‐taking, health care and club medical staff, medical screenings, and injury and pain management. These data produced a theoretical evaluation of the path that a hypothetical college football player might face, from a legal and ethical perspective, in trying to enter the sport. 24

Law and ethics studies

2.5. Communications and return of results

A significant effort to inform the former ASF player community about the overall study and to, when possible, provide them with updates that relate specifically to their concerns in specific domains, is being carried out. To date, these efforts have focused on a variety of digital media pathways including emails; social media such as Twitter, Facebook, and LinkedIn; informational videos on the Study website; and status reports. Some of the remote player studies that are web‐based provide opportunities for rapid feedback of results to the participants (eg, Brain Health Study, Personal Networks Study). In addition, we have developed a process for providing contextual information from subject matter experts to not only interpret individual results appropriately but also to potentially offer suggestions to improve the participants’ daily lives and health outcomes. Similar approaches apply to results return for other emerging cohort analyses which are positioned within the framework of and providing motivation for, the player taking a proactive approach to their health. In addition, player advisors meet with investigators approximately once per year in person and participate in regular telephone conference calls to both discuss results and to communicate player issues of which they have become aware.

3. DISCUSSION

To date, we have established the largest ongoing study of living ASF former players. Because we continue to recruit both medium‐term and long‐term former players, as well as newly retired players, we anticipate that the cohort will grow in both size and significance as we move forward over the years. The current overall response rate to our questionnaire is approximately 28% across all position player groups. However, with regard to the distribution of responses within this group across age, years played, and positions played, our responders are similar compared to the entire potential cohort of former players who have not yet responded (Table ​ (Table3). 3 ). While there are limited studies for comparison among professional, team sport athlete populations, a recently reported study in a cohort of professional rugby players, with a response rate of 28%, is consistent with our own results. 6

Cohort responders with data in NFL Pro‐Reference (PFR) database compared to non‐responders in the database (preliminary assessment as of September 2018)

Clearly, there are limitations to drawing definitive conclusions from what is essentially a voluntary participatory sample from the fully defined population of former players. These include issues of both sampling bias as well as generalizability. There are also issues of comparability of this cohort of essentially former super athletes to other men of comparable size, age, and race who were not as athletic. Thus, comparing the generalizability of the findings in these studies of former ASF players to the general population can only be done with caution. With regard to outcomes, we were able to show that the actual number of reported Anterior Cruciate Ligament (ACL) tears among our participating former players’ years of play 25 corresponded almost exactly to that which would be predicted from data in the literature in two reports on ACL tears in active players summarizing the last 20 years. 26 , 27 In spite of this initial evidence of representativeness, any estimates of prevalence among our current responders can only be considered preliminary and used with caution. As we move forward with our 4‐year follow‐up assessment, we would anticipate our incidence data would become more generalizable within the total cohort.

One of the major objectives of initiating these studies was to define the nature of the risks these former players experience in their postplaying years, from the time they are no longer actively playing through the remainder of their lives. Too often, both the peer‐reviewed scientific literature and the lay press consists of anecdotal, unique, and often dramatic case reports. Even in the studies that have used collected samples from a series of cases, one cannot make estimates of the actual levels of risk. There would appear to be no question that there are potential long‐term health risks associated with participation in professional ASF, but the magnitude of these risks remains elusive. In addition, most published studies of health risks offer limited potential avenues for mitigation or prevention. The future health and well being of potential players both during the time they are playing as well as in their post play lives will be enhanced by developing a more quantitative understanding of the risks and possible benefits associated with life in football.

No less important in our studies is the possibility of understanding potential pathophysiologic mechanisms associated with or related to some of the variety of injuries and medical conditions associated with ASF. Because of the way the various studies described above have been undertaken, we have the opportunity to explore some of these mechanisms of injury and repair both in human and other animal species. In one case, a pilot/targeted study has led to a formal phase 3 clinical trial. 18 One of our original cardiac pilot studies was instrumental to a multiyear NIH‐funded effort to examine mechanistic underpinnings of pathologic heart remodeling in football athletes. Two targeted studies designed to assess the potential for intervention for reducing chronic pain or hypertension are also underway. If newly designed brain contrast assessments in our IPA studies correlate with standardized neurocognitive testing, these efforts may provide a further understanding of the chronic repetitive head injury.

4. TURNING SCIENCE INTO ACTIONABLE INSIGHTS

Future studies linked to biological targets will hopefully yield quality of life improvements for players and former players, but predicting who might become affected, what specific exposures increase that risk, whether there are postplaying activities that will mitigate that risk, and whether, once affected, the condition can be treated or its impact minimized, remains to be determined. Even while waiting for definitive studies, there may be opportunities to demonstrate potentially important interventions. Several analyses on the initial data have resulted in publications or manuscripts related to domains of interest. For example, our data strongly suggest that voluntary or forced weight gain during active playing years increases downstream risk in a variety of domains, providing valuable information for player education and clinical practice. 28 Similarly, a recent analysis of long‐term outcomes from having torn an ACL during active play suggests that, besides increased risks of significant arthritis and subsequent knee replacement, there is a need for assessing former players for potential cardiac risks, as there appears to be a modest excess of myocardial infarction among these former players. 25 These findings suggest that former players should undergo cardiovascular risk assessment, and that certainly for those with post ACL injuries and resulting chronic knee pathology, or chronic significant weight gain during playing years, consideration of custom designed exercise programs is warranted. Evaluating the impact of providing specific information in a focused form to former players is an obvious and important component that is being developed as part of our long‐term follow‐up plans.

5. CONCLUSIONS

The Football Players Health Study at Harvard University is a broad and ambitious research and translation program that attempts to securely capture data from all aspects of former ASF players’ lives. We anticipate that such data will help to quantify the potential long‐term risks associated with ASF. As more pathophysiologic data and risk quantification are obtained, we anticipate the information will be useful to drive more informed player decision‐making. We would also anticipate that results will lead to appropriate interventions as these men age, and thus enhanced health and wellbeing outcomes.

CONFLICT OF INTERESTS

Dr. Zafonte was partially supported by the National Institute on Disability, Independent Living, and Rehabilitation Research (90DP0039‐03‐00, 90SI5007‐02‐04, and 90DP0060), the National Institutes of Health (4U01NS086090‐04, 5R24HD082302‐02, and 5U01NS091951‐03), and US Army Medical Research and Materiel Command (W81XWH‐112‐0210). He also serves as Co‐PI on a T‐32 and receives funding from the Football Players Health Study at Harvard University, which is funded by the NFL Players Association. Dr. Zafonte received royalties from (a) Oakstone for an educational CD—Physical Medicine and Rehabilitation: a Comprehensive Review; (b) Demos publishing for serving as coeditor of the text Brain Injury Medicine. Dr Zafonte serves on the Scientific Advisory Board of Myomo, Oxeia Biopharma, BioDirection, and ElMINDA. He also evaluates patients in the MGH Brain and Body—TRUST Program, which is funded by the NFL Players Association.

Dr. A. Pascual‐Leone was partly supported by the Sidney R. Baer Jr. Foundation, the National Institutes of Health (R01MH100186, R21AG051846, R01MH111875, R01MH115949, R01MH117063, R24AG06142, and P01 AG031720), the National Science Foundation, DARPA, the Football Players Health Study at Harvard University, and Harvard Catalyst| The Harvard Clinical and Translational Science Center (NCRR and the NCATS NIH, UL1 RR025758). Dr. A. Pascual‐Leone serves on the scientific advisory boards for Neosync, Neuronix, Starlab Neuroscience, Neuroelectrics, Magstim Inc, Constant Therapy, and Cognito; and is listed as an inventor on several issued and pending patents on the real‐time integration of transcranial magnetic stimulation with electroencephalography and magnetic resonance imaging.

Dr. Baggish has received funding from the National Institutes of Health/National Heart, Lung, and Blood Institute, the National Football League Players Association, the American Heart Association, the American Society of Echocardiography and receives compensation for his role as team cardiologist from US Soccer, US Rowing, the New England Patriots, the Boston Bruins, the New England Revolution, and Harvard University.

All authors in this study are or were either partially or fully supported by the Football Players Health Study at Harvard University which is in turn sponsored by the NFLPA.

AUTHOR CONTRIBUTIONS

All authors participated in the conception and design of the study, acquisition, analysis, and interpretation of the data, participated in drafting and revising the manuscript and approved the final version submitted to AJIM. All authors agreed to be accountable for all aspects of the study to ensure that questions related to its accuracy and integrity are appropriately investigated and resolved.

ETHICS APPROVAL AND INFORMED CONSENT

The work was performed at Harvard Medical School. The institutional review board of the Beth Israel Deaconess Medical Center, Boston, USA approved this study and all participants provided written consent before participating in the research. We secured a Certificate of Confidentially from the National Institute of Health for each individual research protocol developed which includes former player participants. The Certificate prohibits disclosure of identifiable, sensitive research information to anyone not connected to the research (with certain exceptions; see https://humansubjects.nih.gov/coc/index ).

ACKNOWLEDGMENTS

The authors wish to acknowledge and thank the former ASF player community, our participants and advisors, and the past and present staff of the Football Players Health Study at Harvard University for their outstanding commitment to improving former ASF player health. The authors would like to specifically thank Kelsey Palmer and Chloe Young who provided extensive administrative and editorial support on the manuscript. All authors this study are or were supported by the Football Players Health Study at Harvard University which is in turn sponsored by the National Football League Players Association.

DISCLAIMER BY AJIM EDITOR OF RECORD

John Meyer declares that he has no conflict of interest in the review and publication decision regarding this article.

Zafonte R, Pascual‐Leone A, Baggish A, et al. The Football Players’ Health Study at Harvard University: Design and objectives . Am J Ind Med . 2019; 62 :643‐654. 10.1002/ajim.22991 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

1 NFL Profootball Reference, an on‐line website that follows NFL player statistics both currently and historically.

2 Most of these sources overlapped with the main sources of identified contact information obtained from the NFL Players Association, and the NFL Alumni Association.

3 2008‐2012 PROMIS Health Organization and PROMIS Cooperative Group.

4 The applications was designed specifically for Apple iPhones and participants were invited to access many functions that measured a number of physiologic parameters while, standing, walking, and dual tasking.

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Article Contents

• FOOTBALL NARRATIVES AND THE ISSUE OF SOURCES
• AN UNDERUSED SOURCE: THE MATCH REPORT
• EXCERPTS FROM MATCH REPORTS
• THE MAKING OF MODERN ASSOCIATION FOOTBALL: THE MOTLEY ORIGINS
• MODERN FOOTBALL AND THE FOLK GAMES
• MAKING MODERN FOOTBALL: THE ROLE OF CUPS AND CLYDESIDE
• CONCLUSIONS
• APPENDIX: GRAPH AND METHODOLOGICAL NOTES
• Notes and References
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The Origins of Football: History, Ideology and the Making of ‘The People’s Game’

Gavin Kitching is Emeritus Professor of Politics at the University of New South Wales, Sydney, Australia, and Visiting Research Fellow at the International Centre for Sports History and Culture, De Montfort University, Leicester. His current research on the origins of football is the first stage of an attempt to write a social history of his native North-East of England through the lens of football.

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Gavin Kitching, The Origins of Football: History, Ideology and the Making of ‘The People’s Game’, History Workshop Journal , Volume 79, Issue 1, Spring 2015, Pages 127–153, https://doi.org/10.1093/hwj/dbu023

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Recent scholarship on the origins of association football has been marked by a highly ideological debate on its ‘class’ nature. The traditional story – of a game created by ‘gentlemen’ but taken up, and ultimately dominated, by ‘ruffians’ – has been challenged by a revisionist account which presents football as an ancient ‘people’s’ or ‘plebeian’ game, briefly hijacked by upper-middle class men in the mid-Victorian period, before returning to its ‘popular’ roots from the 1880s onwards. This article suggests that, as currently conducted, the debate is both conceptually confused and bedevilled by paucity of sources. The conceptual problems derive partly from an endemic vagueness in the historical use of the term ‘football’, and partly from a persistent tendency to conflate football play with rules of play. The paucity of sources is well-known in the study of football as a medieval and early modern folk pastime. But it is also an issue in studying early forms of club football. This article uses a hitherto underused source – the match reports of the earliest amateur football clubs in Britain – as part of an attempt to address the conceptual confusion and also to present a genuinely new account of the impact of traditional ‘folk’ football on the modern game. It is suggested that the impact was both real and very short-lived.

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Commission Research and White Papers

Study: Literature Review of Division I Athletics Reform , October 2020 The Knight Commission on Intercollegiate Athletics commissioned this work to compile and assess relevant proposals about reform of Division I athletics, over the last decade, and provide a summary of those proposals which can inform the Knight Commission’s work in assessing the NCAA and Division I organizational, regulatory and governance structure and in developing possible new alternative models.

NCAA Division I Governance and Organizational/Competitive Structure Survey , October 2020 The vast majority of NCAA Division I campus and sports leaders believe that college sports reform should be focused on “big solutions,” a new survey from the Knight Commission on Intercollegiate Athletics shows. The groundbreaking survey reveals far-ranging dissatisfaction with current Division I governance. The survey, conducted June 18 – July 14, 2020 for the Knight Commission by Shugoll Research of Bethesda, Maryland, found that nearly 80 percent of respondents agreed with the statement that “Division I reform should look for ‘big solutions’ rather than incremental changes.” View survey highlights, the complete report, and the appendices:

• Executive Summary
• Complete Report
• Survey Report — Appendix B
• Survey Report — Appendix C

An Assessment of Football Bowl Subdivision (FBS) Football Factors on National Collegiate Athletic Association (“NCAA”), Division I 2018 Revenue Distribution , September 2020 A new Knight Commission-sponsored study by CliftonLarsonAllen (CLA), a national professional services firm, released on September 30, 2020 analyzes the NCAA’s distribution of revenues from the NCAA Division I Men’s Basketball Tournament, focusing on schools that field Football Bowl Subdivision (FBS) football teams. Based on CLA’s report, along with the Knight Commission’s longtime analysis of related issues, the Commission concludes that the current NCAA distribution methodology disproportionately rewards FBS schools.

Assessment of Intercollegiate Athletics Financial Reporting , March 2020 This report was completed by Collegiate Financial Partners, LLC with support from the Knight Commission on Intercollegiate Athletics to conduct nonpartisan analysis, study and research and prepare an educational document on the subject of the reporting obligations applicable to college sports financing.

The NCAA and “Non-Game Related” Student-Athlete Name, Image and Likeness Restrictions Professor Gabe Feldman, Tulane Law School and Director, Tulane Sports Law Program presented a white paper at the May 2016 meeting of the Knight Commission on Intercollegiate Athletics. In it, he proposed a model that would eliminate some of the current restrictions on college athletes using their celebrity for financial gain by signing autographs or engaging in commercial endorsements using an athlete’s non-game related name, image, or likeness (NIL).

Knight Commission Studies Interest in Alternative Division I Competition Models A Knight Commission on Intercollegiate Athletics study reveals interest among university presidents, athletics administrators, faculty and head coaches in exploring alternative models for Division I competition and administration of different sports that may reduce missed class time and travel costs. The study was conducted to assess interest in whether different structures in various sports might offset the challenging effects of some conferences’ newly enlarged geographic footprints.

Knight Commission memorandum to NCAA President Mark Emmert and NCAA Board of Directors on NCAA governance and related issues, August 2013 The Commission officially launched its governance review in 2012 following a decision reached at its October 24, 2011 meeting that such an examination was needed despite recent progress toward achieving important academic reforms. The Commission believed then—as it does now—that significant issues continue to challenge the operation and integrity of Division I intercollegiate athletics. Many of these issues are outside of the NCAA’s control and/or beyond the scope of the NCAA’s reform agenda launched in late 2011. The objective of the Commission’s review was to assess whether different approaches in the Division I model and governance might improve accountability and better serve both institutions and college athletes. The fragmented oversight for the highest level of college football, and for the billions of dollars in revenue it produces, was a key element in this examination.

The review focused on in-depth interviews with nearly 50 higher education and college sports leaders. The interviews were conducted in spring 2013 by Art & Science Group, the education research firm that conducted the Commission’s 2009 survey of presidents at Football Bowl Subdivision (FBS) institutions. Several current and past members of the NCAA Executive Committee and Division I Board of Directors participated in this new study.

Changing the game, Kirwan, W., and Turner, G. (2010) In the 2010 September/October issue of Trusteeship , read about how rising athletic expenses are becoming a destabilizing force for many institutions. William E. “Brit” Kirwan and R. Gerald Turner show you how the game is changing.

Presidential Survey on the Cost and Financing of Intercollegiate Athletics, July 2009 The survey reports the views of presidents for the Football Bowl Subdivision (formerly Division I-A) universities on the costs and financing of intercollegiate athletics. The findings are based on 95 quantitative telephone interviews and 22 qualitative follow-up telephone interviews with presidents. The quantitative component achieved an 80 percent completion rate. The study was conducted by Art & Science Group of Baltimore, MD, from March to July 2009. For Report Appendices , link here .

Executive Summary of Faculty Perceptions of Intercollegiate Athletics Survey, October 2007 In a national survey of more than 2,000 faculty members at universities with the country’s most visible athletic programs, a striking number of professors say they don’t know about and are disconnected from issues facing college sports. More than a third say they don’t know about many athletics program policies and practices, including the financial underpinnings of their campuses’ athletics programs. Furthermore, more than a third have no opinion about concerns raised by national faculty athletics reform groups. …

Faculty Perceptions of Intercollegiate Athletics The main goal of the Faculty Perceptions of Intercollegiate Athletics Survey is to examine professors’ beliefs about and satisfaction with intercollegiate athletics. The investigation also identifies faculty members’ primary concerns about intercollegiate athletics and gathers preliminary data on whether they would join campus-based initiatives aimed at ameliorating these concerns. Further, the survey assesses whether professors think such activities would lead to meaningful change on their campus…

Public Opinion Poll, Jan. 2006 The Census-balanced and representative telephone poll of 502 adults among adults 18 years of age and older was conducted in December 2005 for the commission by Widmeyer Research and Polling of Washington, D.C. The margin of error for the poll is +/- 4.4%. Poll findings suggest the following: Americans believe college sports are like professional sports…

Public Opinion Poll, Dec. 2005 A recent Census-balanced and representative telephone poll among 502 American adults completed in late December 2005 for the Knight Commission on Intercollegiate Athletics by Widmeyer Research and Polling of Washington, DC found that: Americans say the NCAA should “stay the course but remain diligent.” …

Challenging the Myth: A Review of the Links Among College Athletic Success, Student Quality and Donations An integrative review of the economic literature on intercollegiate athletics by Cornell University economist Robert Frank. May 2004.

Executive Summary of Division I-A Postseason History and Analysis The summary of discusses the proposed changes to the Division I-A postseason football system being discussed in Spring 2004 and designed to remedy some of its current problems. It emphasizes that the lack of a governing authority able to consider and address all the key issues – business, educational, and political – is a material weakness. The report does not offer specific solutions, but focuses on facts and data for stakeholders’ reference when considering what is “arguably the most visible face of higher education to the U.S. public at large.”

Division I-A Postseason History and Analysis John Sandbrook’s 2004 report, “Division I-A Postseason Football History and Status,” prepared at the request of the Knight Foundation Commission on Intercollegiate Athletics, offers a comprehensive examination of Division I-A postseason football, from the historical roots of bowl games as civic events designed to promote tourism to today’s environment where games are viewed primarily as television properties. The report provides supporting data for critical aspects of the bowl system and its participating institutions, including scheduling information compared to the academic calendar, television and sponsorship arrangements, financial results, and the distribution of participation opportunities by each Division I-A conference and institution. The report examines the overwhelming role economic factors continue to play in every facet of the bowl system, including the critical issue of its governance and the negotiation and administration of its largest revenue factor – television rights – as separate properties rather than as a consolidated package or sets of packages.

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Peer-Reviewed Football Research: A Comprehensive Collection contributed to technology at the FIFA Women's World Cup™

All technological innovations used at the FIFA Women's World Cup™ underwent thorough scientific evaluations to ensure their integration into the game has positive benefits.

The Topical Collection on Football Research is part of the drive to ensure that football research is peer-reviewed and made publicly available to the benefit of the football community.

The collection consists of peer-reviewed papers and an executive summary which identifies ongoing research in the football technology space.

Harnessing technology in football to reshape the way that the sport is both presented and perceived, as well as unlocking greater efficiency for football’s administration, was one of the core goals of FIFA’s vision 2020-23. Simultaneously, FIFA’s role has been to facilitate the cost-effectiveness of those tools and ensure they are accessible globally to mark a uniform improvement of the football experience around the world – both for players, and for fans.

The symbiotic relationship between football, industry, and academia is deepening, creating opportunities to develop every facet of the game. The combination of new technology, research-led approaches, and football expertise is cultivating an environment where ideas can be developed, assessed, and implemented faster than ever. It is important to ensure that the implementation of these ideas benefit football globally and are supported by scientific evidence. Over the last decade, FIFA and the International Sports Engineering Association (ISEA) have collaborated on a number of projects. The Topical Collection on Football Research in the Sports Engineering Journal was a natural progression of this relationship. The primary objective behind this initiative was to capture and present the most recent research advancements in football while also raising awareness about upcoming topics in the field within academic, industry, and public circles. The collection contains 14 papers that address current challenges and highlight recent scientific and technological developments in football, such as game analysis and player tracking technologies, officiating technologies, and football-surface, -player and -environment interaction.

Showcase the game | Football Technology at the FIFA Women’s World Cup™

14 Sept 2023

Within the collection, an invited paper explores why women-specific tailoring is needed in football. The paper identifies the unique challenges that female players’ experience due to the design and development of technology and football products around male players, as well as a lack of research for female specific challenges. In addition, the paper highlights where focus is needed and calls on industry, and academia to leverage new technologies and research methods to improve performance and health for female players. The Sports Engineering community is keen to explore ideas on how this topic can be further promoted. "Research plays a pivotal role in the exploration and development of new technologies and products for football, providing empirical evidence to quantify benefits, potential risks, and challenges. Every technological innovation introduced at the FIFA Women's World Cup™ had to undergo rigorous scientific evaluations to ensure it had a positive impact on the game” explained Johannes Holzmuller, FIFA Director of Football Technology Innovation. The conclusion of the collection aligns with the ongoing FIFA Women’s World Cup 2023™, capitalising on the excitement surrounding the biggest female football event in history and highlighting important and ongoing research opportunities in football.