论文标题
使用反事实虚拟模拟在棒球中估算团队打击策略的效果
Estimating the Effect of Team Hitting Strategies Using Counterfactual Virtual Simulation in Baseball
论文作者
论文摘要
在棒球中，对场上的每场比赛进行了定量评估，并对个人和团队策略产生影响。加权的基础平均值（WOBA）众所周知，是击球手的贡献的量度。但是，这项措施忽略了游戏状况，例如基础上的跑步者，在采用多次击球策略时，已知教练和击球手认为，这些策略的有效性尚不清楚。这可能是因为（1）我们无法获得击球手的策略，并且（2）很难估算策略的影响。在这里，我们提出了一种使用反事实击球模拟估算效果的新方法。为此，我们提出了一个深度学习模型，该模型在改变击球策略时会改变击球能力。该方法可以估计各种策略的影响，这些策略传统上很难使用实际的游戏数据。我们发现，当可以忽略击球策略的转换成本时，使用不同策略的使用就会增加运行。当考虑开关成本时，增加运行的条件受到限制。我们的验证结果表明，我们的模拟可以阐明使用多种击球策略的效果。
In baseball, every play on the field is quantitatively evaluated and has an effect on individual and team strategies. The weighted on base average (wOBA) is well known as a measure of an batter's hitting contribution. However, this measure ignores the game situation, such as the runners on base, which coaches and batters are known to consider when employing multiple hitting strategies, yet, the effectiveness of these strategies is unknown. This is probably because (1) we cannot obtain the batter's strategy and (2) it is difficult to estimate the effect of the strategies. Here, we propose a new method for estimating the effect using counterfactual batting simulation. To this end, we propose a deep learning model that transforms batting ability when batting strategy is changed. This method can estimate the effects of various strategies, which has been traditionally difficult with actual game data. We found that, when the switching cost of batting strategies can be ignored, the use of different strategies increased runs. When the switching cost is considered, the conditions for increasing runs were limited. Our validation results suggest that our simulation could clarify the effect of using multiple batting strategies.
