论文标题
毕达哥拉斯的应用预期在估计团队质量时会赢得百分比和交叉验证方法
Application of the Pythagorean Expected Wins Percentage and Cross-Validation Methods in Estimating Team Quality
论文作者
论文摘要
比尔·詹姆斯（Bill James）开发了毕达哥拉斯（Pythagorean）预期的赢得百分比模式，以估计棒球队预计在整个赛季中赢得了百分比。因此，该模型可用于评估一个赛季中团队的幸运或不幸。从体育分析的角度来看，此类信息很有价值，因为重要的是要了解下一个时期的可再现结果。在竞赛理论（游戏理论）的说法中，原始模型代表（受限的）塔洛克竞赛成功函数（CSF）。我们使用MLB团队Win Data（2003年至2015年）转换，估计和比较了竞赛理论的原始模型和两个替代模型，即串行和差异表格CSF，并执行交叉验证练习以测试替代模型的准确性。与原始模型，模型的优化版本或优化的差异表格模型相比，串行CSF估计器显着改善了赢得估计（减少均方根误差）。我们得出的结论是，串行CSF赢得估计模型平均可以大大提高团队质量的估计。这项工作为替代竞赛表格提供了现实世界考验。
The Pythagorean Expected Wins Percentage Model was developed by Bill James to estimate a baseball team expected wins percentage over the course of a season. As such, the model can be used to assess how lucky or unfortunate a team was over the course of a season. From a sports analytics perspective, such information is valuable in that it is important to understand how reproducible a given result may be in the next time period. In contest theoretic (game theoretic) parlance, the original model represents a (restricted) Tullock contest success function (CSF). We transform, estimate, and compare the original model and two alternative models from contest theory, the serial and difference form CSFs, using MLB team win data (2003 to 2015) and perform a cross-validation exercise to test the accuracy of the alternative models. The serial CSF estimator dramatically improves wins estimation (reduces root mean squared error) compared to the original model, an optimized version of the model, or an optimized difference form model. We conclude that the serial CSF model of wins estimation substantially improves estimates of team quality, on average. The work provides a real world test of alternative contest forms.
