Martínez Johann H, Garrido David, Herrera-Diestra José L, Busquets Javier, Sevilla-Escoboza Ricardo, Buldú Javier M
Biomedical Engineering Department, Universidad de los Andes, 111711 Bogota, Colombia.
Grupo Interdisciplinar de Sistemas Complejos (GISC), 28911 Madrid, Spain.
Entropy (Basel). 2020 Feb 2;22(2):172. doi: 10.3390/e22020172.
We quantified the spatial and temporal entropy related to football teams and their players by means of a pass-based interaction. First, we calculated the spatial entropy associated to the positions of all passes made by a football team during a match, obtaining a spatial entropy ranking of Spanish teams during the 2017/2018 season. Second, we investigated how the player's average location in the field is related to the amount of entropy of his passes. Next, we constructed the temporal passing networks of each team and computed the deviation of their network parameters along the match. For each network parameter, we obtained the permutation entropy and the statistical complexity of its temporal fluctuations. Finally, we investigated how the permutation entropy (and statistical complexity) of the network parameters was related to the total number of passes made by a football team. Our results show that (i) spatial entropy changes according to the position of players in the field, and (ii) the organization of passing networks change during a match and its evolution can be captured measuring the permutation entropy and statistical complexity of the network parameters, allowing to identify what parameters evolve more randomly.
我们通过基于传球的互动方式,对与足球队及其球员相关的空间和时间熵进行了量化。首先,我们计算了一支足球队在一场比赛中所有传球位置的空间熵,得出了2017/2018赛季西班牙球队的空间熵排名。其次,我们研究了球员在场上的平均位置与其传球熵量之间的关系。接下来,我们构建了每支球队的时间传球网络,并计算了其网络参数在比赛过程中的偏差。对于每个网络参数,我们获得了其时间波动的排列熵和统计复杂度。最后,我们研究了网络参数的排列熵(和统计复杂度)与一支足球队传球总数之间的关系。我们的结果表明:(i)空间熵根据球员在场上的位置而变化;(ii)传球网络的组织在比赛过程中会发生变化,其演变可以通过测量网络参数的排列熵和统计复杂度来捕捉,从而能够识别哪些参数的演变更具随机性。