论文标题
通过棒球的订单罚款对时间的贝叶斯分析
A Bayesian analysis of the time through the order penalty in baseball
论文作者
论文摘要
随着棒球比赛的进行，面对特定投手的次数越多，击球手似乎表现得更好。从一次到下一个订单到下一个投手表现的明显下降，称为通过订单罚款（TTOP）的时间，通常归因于游戏内击球手的学习。尽管TTOP在很大程度上被棒球中被接受，并影响了许多经理在游戏中的决策中，但我们认为，估计TTOP大小的现有方法不能消除在游戏过程中投手表现的连续演变，从连续时间之间的不连续性从连续的时间开始。使用贝叶斯多项式回归模型，我们发现，在调整了诸如面糊和投手质量，握手和家庭野外优势之类的混杂因素之后，几乎没有证据表明通过订单之间的投手表现强烈不连续性。我们的分析表明，通过订单第三次开始时，不应将其视为决定是否拉动投手的特殊截止点。
As a baseball game progresses, batters appear to perform better the more times they face a particular pitcher. The apparent drop-off in pitcher performance from one time through the order to the next, known as the Time Through the Order Penalty (TTOP), is often attributed to within-game batter learning. Although the TTOP has largely been accepted within baseball and influences many managers' in-game decision making, we argue that existing approaches of estimating the size of the TTOP cannot disentangle continuous evolution in pitcher performance over the course of the game from discontinuities between successive times through the order. Using a Bayesian multinomial regression model, we find that, after adjusting for confounders like batter and pitcher quality, handedness, and home field advantage, there is little evidence of strong discontinuity in pitcher performance between times through the order. Our analysis suggests that the start of the third time through the order should not be viewed as a special cutoff point in deciding whether to pull a starting pitcher.
