Authors: Joshua Blinkoff1, Michael Voeller1, Scottie Graham2 and Jeffrey Wilson3
1Barrett Honors College, Arizona State University Tempe, AZ
2Arizona State University, Sun Devils Athletics, Tempe, AZ
3Department of Economics, Arizona State University, Tempe, AZ
Jeffrey R. Wilson, BA, MS, PhD
Department of Economics CPCOM 465D
Arizona State University/Tempe AZ 85287
Dr. Jeffrey Wilson is a Professor of Statistics and the Faculty Athletics Representative to the PAC-12 and NCAA. His research includes binary logistic regression models and hierarchical data with random effects.
Predictive Modeling of 4th Down Conversion in Power 5 Conferences: Football Data Analytics
In the sport of football, coaches are faced with critical decisions at different times in the game. Often the coach makes the decision based on a gut feeling or the advice of an assistant. However, if each decision can be supplemented with data, it is possible to increase the chances of success. This paper uses data (2015-18) from the games played between the 65 teams in Division I in the Power 5 conferences of the NCAA, to present a prediction model useful for 4th down determinations.
A predictive logistic regression model is used in the determination of 4th down options. In particular, a model based on a logistic regression model with random effects, capable of predicting the likelihood of converting on 4th down decision is presented. The adequacy of the model is estimated through calibration, discrimination, and bootstrap samples.
Distance-to-go, pass or run, line of scrimmage, and the week of season are significant factors in predicting a successful 4th down with team as a random effect.
The paper demonstrates the use of analytics to increase the decision-making in football. It increases the precision in decision making by 36% in these data.
Applications in Sport
Teams can use the model to facilitate similar decisions in other parts of the game. This can also be used in the recruiting of players.(more…)