Strategy Analysis in NFL Using Probabilistic Reasoning

Many devices and cameras collect different sports data, and discovering useful insights from this data is crucial for increasing the chances of winning for professional athletes. In this paper, we introduce a novel application of probabilistic reasoning to the field of sports analytics, with a focus on the National Football League (NFL). By leveraging the Probabilistic Model Checking (PMC), we build probabilistic models using historical match data to predict the outcomes of NFL drives and generate strategy recommendations. Our approach simplifies the complex, strategic nature of NFL plays into discrete, sequential events, enabling us to explore the impact of team strategies on the likelihood of scoring touchdowns and field goals. This research not only sheds light on the intricate dynamics of American football but also opens new avenues for using probabilistic modeling in sports analytics, offering insights that could significantly influence team decision-making and resource allocation.