Analysis of Race Momentum and Its Influential Factors Based on Logistic Regression and Chi-Square Test

With the rise of sports big data and advanced statistical methods, there is a burgeoning interest in understanding the psychological and tactical impacts of game momentum on match outcomes. This study presents a model that integrates logistic regression with the chi-square test to analyze sports game trends. The logistic regression model, chosen for its effectiveness in binary outcome prediction, was employed to identify key factors influencing momentum, such as serving status and unforced errors. These factors were quantified through a momentum calculation function that reflects player performance in real time. The model’s validity was confirmed by a chi-square test, which provided a p-value of 0.002, indicating a statistically significant correlation between momentum and match outcomes. This analytical framework not only enhances our understanding of sports dynamics but also offers practical insights for coaches and strategists, equipping them with data-driven tools to optimize game tactics and leverage momentum effectively.