Evaluating the performance of human is a common need across many applications, such as in engineering and sports. When evaluating human performance in completing complex and interactive tasks, the most common way is to use a metric having been proved efficient for that context, or to use subjective measurement techniques. However, this can be an error prone and unreliable process since static metrics cannot capture all the complex contexts associated with such tasks and biases exist in subjective measurement. The objective of our research is to create data-driven AI agents as computational benchmarks to evaluate human performance in solving difficult tasks involving multiple humans and contextual factors. We demonstrate this within the context of football performance analysis. We train a generative model based on Conditional Variational Recurrent Neural Network (VRNN) Model on a large player and ball tracking dataset. The trained model is used to imitate the interactions between two teams and predict the performance from each team. Then the trained Conditional VRNN Model is used as a benchmark to evaluate team performance. The experimental results on Premier League football dataset demonstrates the usefulness of our method to existing state-of-the-art static metric used in football analytics.

翻译：在许多工程和体育等应用中，评估人类表现是一项常见需求。当评估人类完成复杂交互任务的表现时，最常用的方法是采用已在该情境下被验证有效的指标，或使用主观测量技术。然而，这可能是一个容易出错且不可靠的过程，因为静态指标无法捕捉此类任务相关的所有复杂情境，而主观测量中存在偏差。我们的研究目标是创建数据驱动的AI智能体作为计算基准，用于评估人类在涉及多人与情境因素的困难任务中的表现。我们以足球表现分析为背景进行验证。基于条件变分循环神经网络（VRNN）模型，我们在包含大量球员和足球轨迹数据的数据集上训练生成模型。训练后的模型用于模仿两支球队之间的交互，并预测每支球队的表现。随后，训练好的条件VRNN模型作为评估球队表现的基准。在英超联赛数据集上的实验结果表明，我们的方法对于足球分析中现有最先进的静态指标具有实用性。