用机器学习预测职业网球发球方向，揭示选手策略与疲劳影响
Predicting Tennis Serve directions with Machine Learning
- 通过特征工程构建预测模型，分析发球选择策略
- 男性球员预测准确率约49%，女性约44%
- 发现顶尖选手采用混合策略，疲劳可能影响发球方向
发球，尤其是第一发球，在职业网球比赛中至关重要。发球方会战略性地选择发球方向以最大化胜率，同时力求难以预测；接发方则试图提前判断发球方向以做出有效回击。发球方与接发方之间的心理博弈是职业比赛决策的重要组成部分。为帮助理解球员的发球决策，我们开发了一种机器学习方法，用于预测职业网球选手的第一发球方向。通过特征工程，该方法在男性球员上平均预测准确率达49%，女性球员为44%。分析表明，顶级职业选手在发球决策中可能采用混合策略，且疲劳可能是影响发球方向选择的因素之一。此外，研究还提示情境信息对接发方预判反应的重要性，可能超出以往认知。
原文摘要 · Abstract (English)
Serves, especially first serves, are very important in professional tennis. Servers choose their serve directions strategically to maximize their winning chances while trying to be unpredictable. On the other hand, returners try to predict serve directions to make good returns. The mind game between servers and returners is an important part of decision-making in professional tennis matches. To help understand the players' serve decisions, we have developed a machine learning method for predicting professional tennis players' first serve directions. Through feature engineering, our method achieves an average prediction accuracy of around 49\% for male players and 44\% for female players. Our analysis provides some evidence that top professional players use a mixed-strategy model in serving decisions and that fatigue might be a factor in choosing serve directions. Our analysis also suggests that contextual information is perhaps more important for returners' anticipatory reactions than previously thought.
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