The interactive graph visualizer for the 2017 Grand Slams tournaments is available.
Single-elimination tournaments are a popular type of tournament among sports, more specifically in tennis. Despite the current state-of-the-art procedures prevent seeded players - or highest rated opponents - from matching in early rounds, match repetitions among other players are possible, even in tournaments very close in terms of time. Therefore, the allocation process for non-seeded players plays a fundamental role in avoiding match repetitions and in increasing the diversity of matches. The thesis develops a methodology for enforcing fairness in single-elimination tennis tournaments in terms of a reduction of match repetitions in consecutive different tournaments, without significantly altering the draw procedure. The considered tournament allocation problem amounts to solving a clustering problem by means of mathematical programming. Several results and solutions are provided for real-life instances related to Grand Slams in 2017 by exploiting the potential of an Integer Programming solver. Moreover, a greedy approach to generate quantitatively good solutions is presented, along with two heuristics. Full tournament simulations are performed to assess the quality of presented methodologies. Among the results, appreciable improvements are obtained for both the expected number of match repetitions and a related measure of fairness. Benchmarks between heuristics, the greedy algorithm, and the optimal solutions are presented. An important outcome is related to the quality of solutions built with the greedy algorithm. Some techniques of data visualization are implemented to highlight the obtained results.
Copyright (c) 2018 Gabriele Dragotto
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