机器学习XGBoost和SHAP模型的整合用于NBA比赛结果预测和定量分析方法的方法
Yan Ouyang1,2, Xuewei Li1,3,4, Wenjia Zhou1,2
1School of Intelligent Sports Engineering, Wuhan Sports University, Wuhan, Hubei, People's Republic of China.
PloS one
|July 23, 2024
概括
人工智能使用XGBoost和SHAP算法准确预测NBA比赛结果. 关键绩效指标,如野外进球率和篮板,在整个比赛中的重要性各不相同.
科学领域:
- 运动分析 运动分析
- 机器学习应用 机器学习应用
- 篮球表现分析 篮球表现分析
背景情况:
- 职业篮球比赛的结果是复杂的,受到众多动态绩效指标的影响.
- 准确的实时预测游戏结果可以提供重要的战略优势.
- 现有的预测模型可能缺乏动态体育环境所需的可解释性和实时适应性.
研究的目的:
- 开发和评估一个人工智能驱动的,对NBA比赛结果实时预测模型.
- 识别和量化影响不同阶段游戏结果的关键绩效指标.
- 加强教练的战术决策,并为利益相关者提供数据驱动的见解.
主要方法:
- 利用了2021-2023赛季的NBA比赛数据.
- 开发了一个实时预测模型,集成XGBoost和SHAP机器学习算法.
- 模拟了各种游戏间隔的预测,并量化了绩效指标的影响.
主要成果:
- XGBoost算法在预测NBA比赛结果方面表现出了很高的效率.
- 野外进球百分比,防守篮板和转盘总是关键的指标.
- 助攻在上半场是关键,而进攻性篮板和三分百分比在下半场占据主导地位.
结论:
- 集成的人工智能模型提供了优秀和可解释的实时NBA比赛结果预测.
- 量化胜利决定因素为教练策略提供了有价值的决策支持.
- 该研究为体育投注者,运动员,经理和赞助商提供了可靠的数据.
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