Abstract: Explainable Artificial Intelligence (XAI) enhances interpretability in data-driven models, providing valuable insights into complex decision-making processes. By ensuring transparency, XAI bridges the gap between advanced Artificial Intelligence (AI) techniques and their practical applications, fostering trust and enabling data-informed strategies. In the realm of sports analytics, XAI proves particularly significant, as it unravels the multifaceted nature of factors influencing athletic performance. This work uses a rich data analysis flow that includes descriptive, predictive, and prescriptive analysis for the tennis match outcomes. Descriptive analysis uses XAI techniques such as SHAP (SHapley Additive exPlanations) with diverse factors such as physical, geographical, surface level and skill disparities. Top players are ranked; the trend of country-wise winning is presented for the last many decades. Correlation analysis presents inter-dependence of factors. Correlation analysis presents inter-dependence of factors. Predictive analysis makes use of machine learning models, the highest overall accuracy of 80% according to the K-Nearest Neighbors classifier. Lastly, prescriptive analysis recommends specific details which can be helpful for players and coaches as well as for overall strategies planning and performance enhancement. The research underscores the significance of AI-driven insights in sports analytics, particularly for a fast-paced and strategic sport like tennis. By leveraging advanced data analytics methods, this study offers a nuanced understanding of the interplay between player attributes, match contexts, and historical trends, paving the way for enhanced performance and informed strategic planning in professional tennis.

Yuan Zhang, “Multi-Factors Analysis Using Visualizations and SHAP: Comprehensive Case Analysis of Tennis Results Forecasting” International Journal of Advanced Computer Science and Applications(IJACSA), 16(1), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160114

@article{Zhang2025,
title = {Multi-Factors Analysis Using Visualizations and SHAP: Comprehensive Case Analysis of Tennis Results Forecasting},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160114},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160114},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {1},
author = {Yuan Zhang}
}