Advanced Real-time Analysis and in-Game Prediction of Tennis Matches

In competitive sports, there exists an intangible phenomenon known as “momentum” that significantly influences the dynamics of a game. Through the application of data analysis and machine learning techniques, this elusive concept of momentum can be quantified and leveraged to forecast game outcomes. Focusing on the domain of tennis, a linear model incorporating four parameters, which are selected based on the regulations and other intrinsic attributes of tennis matches, is devised to encapsulate player performance. By definition, the exponential weighted moving average of player performance serves as a robust metric for measuring momentum, validated through correlation testing. Subsequently, utilizing logistic regression and simulation algorithms, a predictive model is constructed to forecast game progression at both discrete points and the ultimate match result. Experimental findings indicate a high degree of alignment between the model predictions and the actual flow of the game.