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

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  • Yazan F. Roumani

    (Oakland University)

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.

Suggested Citation

  • Yazan F. Roumani, 2023. "Sports analytics in the NFL: classifying the winner of the superbowl," Annals of Operations Research, Springer, vol. 325(1), pages 715-730, June.
    • Handle: RePEc:spr:annopr:v:325:y:2023:i:1:d:10.1007_s10479-022-05063-x
    DOI: 10.1007/s10479-022-05063-x
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    References listed on IDEAS

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    1. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    2. Jared Quenzel & Paul Shea, 2016. "Predicting the Winner of Tied National Football League Games," Journal of Sports Economics, , vol. 17(7), pages 661-671, October.
    3. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    4. Adrian Gepp & Kuldeep Kumar & Sukanto Bhattacharya, 2010. "Business failure prediction using decision trees," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(6), pages 536-555.
    5. Baker, Rose D. & McHale, Ian G., 2013. "Forecasting exact scores in National Football League games," International Journal of Forecasting, Elsevier, vol. 29(1), pages 122-130.
    6. Yazan F. Roumani & Yaman Roumani & Joseph K. Nwankpa & Mohan Tanniru, 2018. "Classifying readmissions to a cardiac intensive care unit," Annals of Operations Research, Springer, vol. 263(1), pages 429-451, April.
    7. Delen, Dursun & Cogdell, Douglas & Kasap, Nihat, 2012. "A comparative analysis of data mining methods in predicting NCAA bowl outcomes," International Journal of Forecasting, Elsevier, vol. 28(2), pages 543-552.
    8. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
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