Prediction of tennis match results based on logistic regression

Abstract

More and more people like tennis, and the demand for the accuracy of predicting the results of tennis matches in sports betting is getting higher and higher. In this paper, a model is built to predict the results of tennis matches. In this paper, we first use the random forest model to analyze the factors that will have an impact on the outcome of the game, and the authors have selected out five factors that may have an impact on the outcome, which are: break of serve, Serve, Score, Net points , And they were assigned proportionally and the distribution was Break of serve 34.10%, Serve 24.30%, Score 38.00%, Net points 3.60% and using these proportions the influencing factors were calculated numerically, in this paper, the calculated value is named “momentum”. Then Pearson's correlation coefficient was used to analyze the relationship between momentum and match results, and the analysis found that the correlation between momentum and match results was as high as 0.807. Finally, a logistic regression model was used to predict and test the results of tennis matches. The final test results found that the model established in this paper has a high accuracy rate, and the research results also provide a new idea for tennis betting industry.