Understanding the dynamics of player performance in professional tennis, particularly how quantifiable metrics such as momentum influence match outcomes, is crucial for advancing sports analytics and enhancing coaching strategies. This study investigates performance metrics and momentum in professional tennis. The research introduced an innovative method of quantifying "momentum" in the sport, analyzing its impact alongside other performance indicators. We applied the Analytic Hierarchy Process (AHP) to determine the relative importance of various performance factors, revealing technical skills as the predominant influence. The study employed Random Forest algorithms to model match outcomes, contrasting predictions with and without momentum as a variable. Our results significantly demonstrated that momentum plays a critical role in the dynamics of match outcomes, challenging the notion that player success sequences are merely random. The robustness of the Random Forest model was further validated through sensitivity analysis, highlighting its effectiveness as a predictive tool in sports analytics. This research not only enhances the understanding of player performance in tennis but also supports the incorporation of momentum in sports performance evaluations.