Evaluating sports players based on their performance shares core challenges with evaluating healthcare providers based on patient outcomes. Drawing on recent advances in healthcare provider profiling, we cast sports player evaluation within a rigorous causal inference framework and define a flexible class of causal player evaluation estimands. Using stochastic interventions, we compare player success rates on repeated tasks (such as field goal attempts or plate appearance) to counterfactual success rates had those same attempts been randomly reassigned to players according to prespecified reference distributions. This setup encompasses direct and indirect standardization parameters familiar from healthcare provider profiling, and we additionally propose a "performance above random replacement" estimand designed for interpretability in sports settings. We develop doubly robust estimators for these evaluation metrics based on modern semiparametric statistical methods, with a focus on Targeted Minimum Loss-based Estimation, and incorporate machine learning methods to capture complex relationships driving player performance. We illustrate our framework in detailed case studies of field goal kickers in the National Football League and batters in Major League Baseball, highlighting how different causal estimands yield distinct interpretations and insights about player performance.

翻译：基于运动员表现评估其水平，与基于患者结果评估医疗服务提供者面临着相似的核心挑战。借鉴医疗服务提供者画像领域的最新进展，我们将运动员评估置于严谨的因果推断框架中，并定义了一类灵活的因果性运动员评估估计量。通过随机干预方法，我们比较运动员在重复性任务（如射门尝试或击球轮次）中的实际成功率与反事实成功率——即假设这些尝试根据预设的参考分布随机重新分配给其他运动员时可能达到的成功率。该框架涵盖了医疗服务提供者画像中常见的直接与间接标准化参数，并额外提出了一种专为体育场景可解释性设计的“相对于随机替换的表现”估计量。基于现代半参数统计方法（重点关注基于目标最小损失估计的方法），我们为这些评估指标开发了双重稳健估计器，并引入机器学习方法来捕捉影响运动员表现的复杂关系。我们通过美国国家橄榄球联盟射门踢球员和美国职业棒球大联盟击球手的详细案例研究阐释该框架，展示了不同因果估计量如何产生对运动员表现各异的解读与洞察。