篮球运动员表现上的非线性年龄影响:Kolmogorov-Arnold网络在NBA数据中的见解
Yunhan Xiao1, Jiahao Wang2, Weiping Li1
1Department of Sports and Health Science, Xi'an Physical Education University, Xi'an, Shaanxi, China.
Frontiers in sports and active living
|November 19, 2025
概括
这项研究引入了一个新的非线性模型,Kolmogorov-Arnold Networks (KAN),用于分析不同年龄组的NBA球员表现. KAN准确地捕捉了与年龄相关的性能变化,优于其他模型.
科学领域:
- 运动分析 运动分析
- 运动中的机器学习
- 性能建模的性能建模.
背景情况:
- 了解运动员表现的年龄相关演变对于职业管理和训练优化至关重要.
- 传统的模型往往无法捕捉不同职业生涯阶段球员表现的复杂非线性动态.
研究的目的:
- 开发和验证使用科尔莫戈罗夫-阿诺德网络 (KAN) 的新型非线性建模框架,以分析球员年龄和篮球表现之间的关系.
- 系统地揭示职业篮球中不同年龄组的表现演变的非线性动态和过渡机制.
主要方法:
- 利用了2,786个NBA球员赛季样本 (2019-2024年) 与幻想点作为绩效指标.
- 将球员分为青年 (19-23岁),高级 (24-30岁) 和老兵 (31-40岁) 的年龄组.
- 采用KAN,通过贝叶斯优化调整并通过交叉验证进行验证,与MLP,XGBoost,随机森林和线性回归进行比较.
主要成果:
- 在所有年龄组中,KAN以最低的平均绝对误差 (MAE) 和根平均平方误差 (RMSE) 显示出卓越的性能.
- 在年轻人群中,KAN实现了MAE=0.089,RMSE=0.115和R2=0.986,显著优于基线模型.
- 响应函数分析揭示了非线性,年轻人的表现由波动变量驱动,主要表现通过关键指标 (PTS,AST,REB) 稳定,退伍军人的表现显示收益下降.
结论:
- 基于KAN的非线性框架有效地模拟了特定年龄组的篮球表现演变.
- 这种方法为职业体育的职业管理,训练优化和数据驱动决策提供了显著的方法进步.
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