Analyzing Player Momentum in Competitive Gaming: A Data-Driven Approach for Predicting Performance and Outcomes

This paper presents a comprehensive analysis of player momentum in competitive gaming, focusing on the development and application of a momentum scoring model. The study involves the collection of player scores to indirectly depict their momentum, culminating in the creation of a visual map that enables the assessment of player status and the prediction of competition trends. Additionally, a random test is employed to investigate the relationship between player momentum and winning, revealing a significant correlation. Furthermore, a decision tree classification model is developed to predict competition outcomes with 99% accuracy, affirming the impact of momentum on game results. The findings suggest that players can utilize this model to analyze opponents' momentum, forecast trends, and enhance their winning probabilities. Overall, this research underscores the pivotal role of momentum in competitive gaming and provides a robust framework for predictive analysis.