Research on Athlete Momentum Based on Linear Regression Algorithm and BP Neural Network

This paper investigates the momentum of athletes using a combination of linear regression and BP neural network algorithms. Firstly, we quantify momentum based on players’ winning streaks, where the momentum increment during a winning streak follows the Fibonacci sequence. Additionally, we observe that players exhibit momentum at every set, game, and point, and these momentums mutually influence each other, consequently impacting the overall outcome of the match. Secondly, employing linear regression, we correlate momentum with players’ current scores and serving performance. Our model reveals significant influences of players’ current scores, ace index, net touch index, and serving and receiving scores on momentum. Finally, we integrate the linear model from the second part to construct a BP neural network for predicting players’ future momentum and winning probabilities. This model demonstrates higher accuracy in prediction.