Finding Optimal Strategies in tennis from Video Sequences

The analysis of tennis data from broadcast video in order to identify tactical information is broadly an unexplored field, and the existing analytical approaches usually consist of simply providing general statistical information. In this paper, we present a tennis model based on Markov decision processes (MDPs), which describes the dynamic interaction between the players and we introduce a novel Monte Carlo-based method with an aim to extract optimal strategic information. In order to test the approach with real tennis data, we also present a system that transforms broadcast video tennis sequences into discrete temporal data that is fed into the model. We show that this framework, based on states, actions and rewards, allows for the identification of optimal strategies.

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