Subjects: Statistics >> Applied Statistical Mathematics submitted time 2024-02-20
Abstract： This study aims to accurately anticipate the shifts in a match by analyzing the flow of play. To achieve this, our model has been enhanced by scrutinizing the factors that impact its forecasting capabilities. To capture the flow of play, an A-value has been defined and a decision tree model has been developed. Additionally, we have built a Nonlinear Autoregressive Neural Network to fulfill the forecasting function. During the model improvement process, we calculated the Pearson correlation coefficient to gauge the extent of impact. The results indicate that the model performs its predictive function with relative success and that aces, double faults, and unforced errors are the key influencing factors.
Peer Review Status:
Awaiting Review
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