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Paper Type
ERF
Abstract
Although analytics has become commonplace within the sports industry and is growing within the National Football League, there is limited evidence available regarding the construction of a prediction model to determine which factors and more specifically which team statistics have the largest impact on winning. In this study, we strive to generate multiple statistical models to quantify influence of team statistics on regular season wins, evaluate the created models based on accuracy to determine the best predictive model, and validate the created model by applying the regression to the 2018 NFL regular season games and compare to the actual season standings.
Recommended Citation
Gifford, Matthew and Bayrak, Tuncay, "What Makes a Winner? Analyzing Team Statistics to Predict Wins in the NFL" (2020). AMCIS 2020 Proceedings. 35.
https://aisel.aisnet.org/amcis2020/data_science_analytics_for_decision_support/data_science_analytics_for_decision_support/35
What Makes a Winner? Analyzing Team Statistics to Predict Wins in the NFL
Although analytics has become commonplace within the sports industry and is growing within the National Football League, there is limited evidence available regarding the construction of a prediction model to determine which factors and more specifically which team statistics have the largest impact on winning. In this study, we strive to generate multiple statistical models to quantify influence of team statistics on regular season wins, evaluate the created models based on accuracy to determine the best predictive model, and validate the created model by applying the regression to the 2018 NFL regular season games and compare to the actual season standings.
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