The analysis of discrete data by machine learning : ---Taking momentum in tennis match as an example

In tennis matches, comebacks often occur, and the existence of rules such as breaking and holding serve increases the probability of such situations. When a comeback happens, there is often a shift or fluctuation in “momentum.” However, tennis data is mostly discrete, making it difficult to quantify the fluctuations in “momentum.” We will use the random forest algorithm model in machine learning to quantify these fluctuations and assess the main influencing factors of the fluctuations through the method of feature importance index. Subsequently, we will summarize the factors affecting the “momentum” of different athletes and analyze the data through the comprehensive scoring index method, observing the correlation between “momentum” factors. After that, we will conduct a sensitivity analysis of the model and find that the model remains stable even when adjusting 0-5 factors. Finally, we will provide relevant suggestions based on the influencing factors of “momentum” fluctuations summarized for different athletes.