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Evaluating the performance of human is a common need across manyapplications, such as in engineering and sports. When evaluating humanperformance in completing complex and interactive tasks, the most common way isto use a metric having been proved efficient for that context, or to usesubjective measurement techniques. However, this can be an error prone andunreliable process since static metrics cannot capture all the complex contextsassociated with such tasks and biases exist in subjective measurement. Theobjective of our research is to create data-driven AI agents as computationalbenchmarks to evaluate human performance in solving difficult tasks involvingmultiple humans and contextual factors. We demonstrate this within the contextof football performance analysis. We train a generative model based onConditional Variational Recurrent Neural Network (VRNN) Model on a large playerand ball tracking dataset. The trained model is used to imitate theinteractions between two teams and predict the performance from each team. Thenthe trained Conditional VRNN Model is used as a benchmark to evaluate teamperformance. The experimental results on Premier League football datasetdemonstrates the usefulness of our method to existing state-of-the-art staticmetric used in football analytics.

Deep Generative Multi-Agent Imitation Model as a Computational Benchmark  for Evaluating Human Performance in Complex Interactive Tasks: A Case Study  in Football