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Do NFL teams make rational decisions? What factors potentially affect theprobability of wining a game in NFL? How can a team come back from ademoralizing interception? In this study we begin by examining the hypothesisof rational coaching, that is, coaching decisions are always rational withrespect to the maximization of the expected points scored. We reject thishypothesis by analyzing the decisions made in the past 7 NFL seasons for twoparticular plays; (i) the Point(s) After Touchdown (PAT) and (ii) the fourthdown decisions. Having rejected the rational coaching hypothesis we move on toexamine how the detailed game data collected can potentially inform game-daydecisions. While NFL teams personnel definitely have an intuition on whichfactors are crucial for winning a game, in this work we take a data-drivenapproach and provide quantifiable evidence using a large dataset of NFL gamesfor the 7-year period between 2009 and 2015. In particular, we use a logisticregression model to identify the impact and the corresponding statisticalsignificance of factors such as possession time, number of penalty yards,balance between passing and rushing offense etc. Our results clearly imply thatavoiding turnovers is the best strategy for winning a game but turnovers can beovercome with letting the offense on the field for more time. Finally wecombine our descriptive model with statistical bootstrap in order to provide aprediction engine for upcoming NFL games. Our evaluations indicate that even byonly considering a small number of (straightforward) factors, we can achieve avery good prediction accuracy. In particular, the average accuracy duringseasons 2014 and 2015 is approximately 63%. This performance is comparable tothe more complicated state-of-the-art prediction systems, while it outperformsexpert analysts 60% of the time.

Footballonomics: The Anatomy of American Football; Evidence from 7 years of NFL game data