Waiting for Dabo: A machine learning model for predicting Power 4 college football coaching hire success

TL;DR

This paper develops a statistical machine learning model to predict the success of Power 4 college football coaching hires based on various background and performance factors, achieving 66% accuracy.

Contribution

It introduces a predictive model using regularized linear regression with 29 variables, highlighting key but weakly predictive factors for coaching success.

Findings

66% accuracy in predicting successful hires

Important factors include previous head coaching experience and team quality

No strong combination of factors reliably predicts success

Abstract

Using data on 103 recent P4 college football hires, we built a statistical model for predicting a coach's success at their new school. For each hire, we collected data about their background and experiences, the previous success as a head coach or coordinator and their success since hiring. Over 50 variables on these factors were recorded though we used 29 of these in building our predictive model. Our measure of success is based upon Bill Connelly's SP+ team ratings relative to the performance on the same metric of the school in the 15 year prior to their selection as head coach. Using a cross-validated regularized linear regression, we obtain a predictive model for coaching success. Among the important factors for predicting a successful hire are having been a previous college head coach, leaving a job as an Offensive Coordinator, age and quality of the hiring school's team in the previous 15 years. While we do find these factors are important for the prediction of a successful coaching hire, the trends here are weak. With 66\% accuracy, the model does identify coaching hires that will outperform team performance in the 15 years before the hire. However, no combination of these factors leads to high predictability of identifying a successful coaching hire. All of the data and code for this paper are available in a Github repository.