发表机构
St. Xavier’s College (Autonomous)（圣泽维尔学院（自治））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文审计板球目标修正的DLS方法，发现其存在赛制特定偏差与性别差异偏差，提出轻量级可解释校准层DLS-Cal可显著降低偏差，性别感知变体可缩小性别偏差差距。
AI 中文摘要
达克沃斯-刘易斯-斯特恩（DLS）方法自1999年起已成为雨水中断的有限轮板球比赛中修正目标得分的国际标准。尽管已投入使用二十余年，但尚未有针对其预测偏差的大规模实证审计研究发表。我们对来自Cricsheet的8150场国际比赛（3095场ODI、5055场T20I）开展此类审计，生成233550个带时间拆分的模拟中断场景。我们记录到两类结构化偏差：其一，DLS预测误差在（剩余轮数、失球数）的比赛状态区间内跨度达137分；其二，DLS在ODI中存在此前未量化的性别差异偏差：在训练拆分中，男性比赛的平均过度预测为+1.51分，而女性比赛为+7.63分，差距达+6.13分（F=195.16，p<10^-43）。我们将DLS与五种现代替代模型（双向长短期记忆网络（Bi-LSTM）、极端梯度提升（XGBoost）、增强型XGBoost变体、深度上下文感知模型及堆叠集成模型）进行基准测试，并提出DLS-Cal这一轻量级可解释校准层（含27000个参数），其输出作为状态条件修正项添加至DLS。DLS-Cal使ODI的绝对偏差降低31%，T20I降低19%；而性别感知变体将女性ODI的残差偏差从+6.19分降至+0.65分，同时保持男性比赛的校准效果不变。我们公开了代码、模型及数据。
英文摘要
The Duckworth-Lewis-Stern (DLS) method has been the international standard for revising target scores in rain-interrupted limited-overs cricket since 1999. Despite over two decades of operational use, no large-scale empirical audit of its prediction bias has been published. We conduct such an audit on 8,150 international matches (3,095 ODIs, 5,055 T20Is) from Cricsheet, generating 233,550 synthetic interruption scenarios with temporal splits. We document two structured biases. First, DLS prediction error spans a 137-run range across (overs-remaining, wickets-lost) match-state buckets. Second, DLS exhibits a gender-differential bias on ODIs that has not previously been quantified: on the training split, mean over-prediction is +1.51 runs for men but +7.63 runs for women, a gap of +6.13 runs (F = 195.16, p < 10^-43). We benchmark DLS against five modern alternatives: Bi-LSTM, XGBoost, an enriched XGBoost variant, a deep context-aware model, and a stacking ensemble, and propose DLS-Cal, a lightweight interpretable calibration layer (27K parameters) outputting a state-conditioned correction added to DLS. DLS-Cal reduces absolute bias by 31% on ODI and 19% on T20I, and a gender-aware variant reduces women's ODI residual bias from +6.19 to +0.65 runs while leaving men's calibration unchanged. We release code, models, and data.
CommentsAccepted for publication in the Journal of Quantitative Analysis in Sports (JQAS); forthcoming
