用机器学习预测棒球胜率，发现胜率与得分差正相关，合理投注可盈利
Assessing win strength in MLB win prediction models
- 基于统一数据集训练多种机器学习模型，预测比赛胜率
- 模型预测胜率与实际得分差存在显著正相关关系
- 合理运用预测结果可获投注收益，盲目使用则会亏损
在美国职业棒球大联盟（MLB）中，策略与规划是决定比赛结果的重要因素。以往研究已通过构建机器学习模型来预测比赛胜方。本文在统一数据集上训练了多种机器学习模型，并将模型输出的胜率概率与实际得分差所衡量的胜势强度进行关联分析。结果表明，主流机器学习模型确实表现出预测胜率与胜势强度之间的正相关性。此外，我们评估了将预测胜率用于运行线投注（run-line betting）的决策效果，发现采用合适投注策略可实现正收益；而直接盲目使用模型进行投注则会导致严重亏损。
原文摘要 · Abstract (English)
In Major League Baseball, strategy and planning are major factors in determining the outcome of a game. Previous studies have aided this by building machine learning models for predicting the winning team of any given game. We extend this work by training a comprehensive set of machine learning models using a common dataset. In addition, we relate the win probabilities produced by these models to win strength as measured by score differential. In doing so we show that the most common machine learning models do indeed demonstrate a relationship between predicted win probability and the strength of the win. Finally, we analyze the results of using predicted win probabilities as a decision making mechanism on run-line betting. We demonstrate positive returns when utilizing appropriate betting strategies, and show that naive use of machine learning models for betting lead to significant loses.
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