“Not all yards are created equal”. A 3rd and 15, where the running back gains 12 yards is clearly less valuable than a 3rd and 3 where the running back gains 4 yards, even though it will not necessarily show up in the yardage statistics. While this problem has been addressed to some extent with the introduction of expected point models, there is still another inequality omission in the creation of yards and this is the opposing defense. Gaining 6 yards on a 3rd and 5 against the top defense is not the same as gaining 6 yards on a 3rd and 5 against the worst defense. Adjusting these expected points model for opponent strength is thus crucial. In this paper, we develop an optimization framework that allows us to compute offensive and defensive ratings for each NFL team and consequently adjust the expected point values accounting for the opposition faced. Our framework allows for assigning different point values to the offensive and defensive units of the same play, which is the rational thing to do especially in a league with an uneven schedule such as the NFL. The average absolute difference between the raw and adjusted points is 0.07 points/play (p-valueÂ
CITATION STYLE
Pelechrinis, K., Winston, W., Sagarin, J., & Cabot, V. (2019). Evaluating NFL plays: Expected points adjusted for schedule. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11330 LNAI, pp. 106–117). Springer Verlag. https://doi.org/10.1007/978-3-030-17274-9_9
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