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：评估人类在完成复杂交互任务中的表现是许多应用程序中通常需要的，例如工程和体育。在评估人类在完成这样的任务时，最常见的方法是使用已被证明在该背景下有效的度量标准或使用主观测量技术。然而，这可能是一个容易出错和不可靠的过程，因为静态度量标准无法捕捉与这些任务相关的所有复杂上下文，而主观测量中存在偏见。我们的研究目的是创建数据驱动的AI代理作为计算基准，以评估解决涉及多个人和上下文因素的困难任务时人类表现的情况。我们在足球表现分析的环境下展示了这一点。我们在一个大型球员和球追踪数据集上基于条件变分递归神经网络（VRNN）模型训练了生成模型。训练的模型用于模仿两个团队之间的交互并预测每个团队的表现。然后，将训练好的条件VRNN模型用作评估团队表现的基准。对英超足球数据集的实验结果证明了我们的方法对于足球分析中现有的最先进的静态度量标准的有用性。