Nonlinear age effects on basketball player performance: insights from Kolmogorov–Arnold Networks in NBA data
Year: 2025
Type: article
Abstract: Introduction: This study utilizes 2,786 NBA player-season samples from 2019 to 2024 to develop a nonlinear modeling approach based on Kolmogorov-Arnold Networks (KAN), applied to modeling the relationship between player age and basketball performance. A novel modeling framework is proposed, integrating interpretable machine learning with age-group-specific feature analysis, aiming to systematically reveal the nonlinear dynamics and transitional mechanisms of performance evolution across age. Methods: Fantasy Points is used as the unified performance metric, and players are categorized into three age groups: Youth (19-23 years), Prime (24-30 years), and Veteran (31-40 years). The KAN model is tuned via Bayesian optimization and evaluated using five-fold cross-validation. Its performance is systematically compared against mainstream models, including Multilayer Perceptron (MLP), XGBoost, Random Forest, and Linear Regression. Results: Results show that KAN achieves the lowest MAE and RMSE across all age groups, with the best or near-best R² values. In the youth group, the model achieves MAE = 0.089, RMSE = 0.115, and R² = 0.986, significantly outperforming all baseline models. Further response function analysis reveals nonlinear structural features in the age-performance relationship. Attribution results indicate that youth performance is driven by multiple interacting variables with strong and volatile marginal effects; in Prime, performance stabilizes and is dominated by key metrics such as points (PTS), assists (AST), and rebounds (REB); in Veteran, performance converges on a few core variables, with a "ceiling effect" and diminishing marginal returns. Discussion/Conclusion: Using a KAN-based nonlinear framework, we reveal the age-group-specific evolution of basketball performance with age, offering new methodological insights for career management, training optimization, and intelligent decision-making in professional sports.
Authors: Yunhan Xiao, Jiahao Wang, Weiping Li, Jiangang Chen, Ning Chang, Yilong Song, Ziying Xu
Institution: Xi'an Physical Education University
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,353
OpenAlex ID: W4415783156