发现板球雨天目标分计算方法存在性别和赛制偏差，提出可解释校准方案改进
A Fairness Audit of the Duckworth-Lewis-Stern Method: Format-Specific and Gender-Differential Bias, with an Interpretable Calibration Layer for Cricket Target Revision
- 构建8150场比赛的模拟中断场景，量化现有算法偏差范围达137分
- 女性比赛平均高估6.13分，男性仅高估1.51分，差异极显著（p<10^-43）
- 提出轻量级可解释校准层，使女性比赛误差降至0.65分，适合赛事决策参考
自1999年以来，达克沃斯-刘易斯-斯特恩（DLS）方法一直是国际板球雨天中断后目标分修订的标准。尽管已使用超过二十年，但其预测偏差尚无大规模实证审计。本研究基于Cricsheet的8,150场国际比赛（3,095场ODI，5,055场T20I），生成233,550个合成中断情景，按时间切分进行分析。发现两个结构性偏差：第一，DLS预测误差在（剩余回合数，失局数）状态桶中跨度达137分；第二，其在ODI比赛中存在未被量化过的性别差异偏差——训练集上，男性平均高估+1.51分，女性则高达+7.63分，差距为+6.13分（F=195.16，p<10^-43）。我们对比了五种现代替代方法：Bi-LSTM、XGBoost、增强版XGBoost、深度上下文感知模型及堆叠集成模型，并提出DLS-Cal，一种轻量级可解释校准层（27K参数），输出与状态相关的修正值叠加至DLS。DLS-Cal在ODI上降低绝对偏差31%，在T20I上降低19%；性别感知变体将女性ODI残差偏差从+6.19降至+0.65分，而男性校准保持不变。代码、模型与数据均已公开。
原文摘要 · Abstract (English)
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.
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