Do NFL teams make rational decisions? What factors potentially affect the
probability of wining a game in NFL? How can a team come back from a
demoralizing interception? In this study we begin by examining the hypothesis
of rational coaching, that is, coaching decisions are always rational with
respect to the maximization of the expected points scored. We reject this
hypothesis by analyzing the decisions made in the past 7 NFL seasons for two
particular plays; (i) the Point(s) After Touchdown (PAT) and (ii) the fourth
down decisions. Having rejected the rational coaching hypothesis we move on to
examine how the detailed game data collected can potentially inform game-day
decisions. While NFL teams personnel definitely have an intuition on which
factors are crucial for winning a game, in this work we take a data-driven
approach and provide quantifiable evidence using a large dataset of NFL games
for the 7-year period between 2009 and 2015. In particular, we use a logistic
regression model to identify the impact and the corresponding statistical
significance of factors such as possession time, number of penalty yards,
balance between passing and rushing offense etc. Our results clearly imply that
avoiding turnovers is the best strategy for winning a game but turnovers can be
overcome with letting the offense on the field for more time. Finally we
combine our descriptive model with statistical bootstrap in order to provide a
prediction engine for upcoming NFL games. Our evaluations indicate that even by
only considering a small number of (straightforward) factors, we can achieve a
very good prediction accuracy. In particular, the average accuracy during
seasons 2014 and 2015 is approximately 63%. This performance is comparable to
the more complicated state-of-the-art prediction systems, while it outperforms
expert analysts 60% of the time.Comment: Working study - Papers has been presented at the Machine Learning and
Data Mining for Sports Analytics 2016 workshop and accepted at PLOS ON
