Accurate prediction of performance fluctuations in competitive environments is essential for optimizing strategies and decision-making processes. This study introduces a computational framework employing Principal Component Analysis (PCA) and regression techniques to evaluate dynamic performance and momentum impact. The proposed model quantifies the influence of critical factors, integrating data-driven insights to predict outcome variations. Experimental results indicate a strong correlation between identified momentum shifts and performance success, with the fluctuation prediction model achieving over 95% accuracy. The framework’s applicability is further extended to diverse competitive scenarios, demonstrating its robustness and generalizability in predictive modeling of performance dynamics. The analysis results indicate that the above models have good universality.