标题： 评估MLB胜场预测模型中的胜利强度 显示英文标题

标题： Assessing win strength in MLB win prediction models

摘要： 在美国职业棒球大联盟中，策略和计划是决定比赛结果的重要因素。 以往的研究通过构建机器学习模型来预测任何一场比赛的获胜队伍，从而对此进行了帮助。 我们通过使用一个共同的数据集来训练一系列全面的机器学习模型，扩展了这项工作。 此外，我们将这些模型产生的胜率与通过得分差衡量的胜势联系起来。 在此过程中，我们证明了最常用的机器学习模型确实表现出预测胜率与胜利强度之间的关系。 最后，我们分析了将预测胜率作为决策机制用于运行线投注的结果。 我们展示了在使用适当投注策略时可以获得正收益，并表明对机器学习模型的简单使用会导致重大损失。 显示英文摘要

摘要： 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.