Estimating the Relevance of First Offensive Shot Tactics in Table Tennis via Simulation Based on a Finite Markov Chain Model

Finite Markov chain modelling is a commonly used type of stochastic modelling employed in performance analysis of net games. Finite Markov chains are based on a state transition model which can be used to depict the game structure of net games as a succession of states which are defined as equivalence classes for game situations, e.g. service and return. Furthermore, the theory of finite Markov chains allows for the calculation of model variables which are of significant interest not only for validation but also for performance analysis, like wining probabilities or expected rally lengths starting from different states. By simulation, of a more-or-less of tactical behaviors one may study the impact of these tactics on overall success. A novel state transition model for table tennis is introduced in this study as extension of an existing model in the literature containing only the first offensive shot. The new model additionally contains subsequent shots since they may be perceived as being influenced by the first offensive shot. A sample of 105 single matches (49 female, 56 male) at the 2020 Tokyo Olympics was examined. The validation of the Markov property resulted in satisfactory results. The relevance of 26 transitions denoting specific tactical behaviors was obtained using simulation and subsequently compared between sexes. Results provide insights concerning the game structure of table tennis with a particular emphasis on the transition from the initial phase of rallies to the first offensive shot.