Sports analytics in the NFL: classifying the winner of the superbowl

Abstract

Sport teams’ managers, coaches and players are always looking for new ways to win and stay competitive. The sports analytics field can help teams in gaining a competitive advantage by analyzing historical data and formulating strategies and making data driven decisions regarding game plans, play selection and player recruitment. This work focuses on the application of sports analytics in the National Football League. We compare the classification performance of several methods (C4.5, Neural Network and Random Forest) in classifying the winner of the Superbowl using data collected during the regular season. We split the data into a training set and test set and use the synthetic minority oversampling technique to address the data imbalance issue in the training set. The classification performance is compared on the test set using several measures. According to the findings, the Random Forest classifier had the highest recall, AUC, accuracy and specificity as the oversampling percentage was increased. Our results can be used to develop a decision support tool to assist team managers and coaches in developing strategies that would increase the team’s chances of winning.