UCLA

Data analytics and machine learning have become vital applications in the world of sports, providing glimpses of statistically probable, mathematically formulated future states while helping teams and individual athletes make smarter decisions. The sport of tennis, featuring a relatively low number of moving parts, a bevy of readily available data and a global interest, is ripe for advanced forecasting analysis. In the following, the method of adaptive least squares is leveraged in conjunction with Kalman filtering to create time-variant match statistics for professional tennis players. Once the adaptive model is constructed, the sigmoid function is utilized to transform forecasted delta set values into forecasted probabilities of winning for any given matchup. The most successful model constructed from the web-scraped pool of data is continuously improving and correctly forecasts tennis match outcomes 63.54% of the time, exceeding the prediction accuracy if outcomes were chosen solely based on professional rankings.