根据对手阵容确定11人的比赛:一个IPL的例子

Pub Date : 2023-11-09 DOI:10.3233/jsa-220638
G. Gokul, Malolan Sundararaman
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引用次数: 0

摘要

印度超级联赛(IPL)是全球最受欢迎的T20国内体育联赛。球员的选择对于赢得竞争激烈的印度板球超级联赛至关重要。因此,球队管理层每场比赛从一支15到25人的球队中选择11名球员。分析不同球员的数据,为每场比赛选出最佳球员。本研究尝试了一种方法,其中球员在场上的表现,以确定发挥-11。一名球员在一场比赛中的场上表现是作为一个单一的指标来计算的,该指标考虑了一名球员与对手球队中所有球员的属性。对于这个计算,过去每个球的数据被清理和挖掘,以生成包含球员对球员性能属性的数据。接下来,通过计算性能属性的加权分数,将玩家对玩家组合的各种性能属性转换为玩家的性能评级。最后,提出并开发了一个优化模型,使用计算的性能评级来确定最佳的比赛-11。已开发的优化模型表明,对局的11种打法可以最大限度地提高战胜给定对手的可能性。采用2008- 2020年的过去数据演示了确定IPL比赛的11场比赛的拟议程序。该演示表明,对于联赛阶段的比赛,模型建议的11场比赛和实际的11场比赛在所有球队中具有~ 7%的相似性。剩下的~ 3%与实际选拔的人员不同。然而,这种差异大约导致印度超级联赛(IPL)成为全球最受欢迎的T20国内体育联赛。球员的选择对于赢得竞争激烈的印度板球超级联赛至关重要。因此,球队管理层每场比赛从一支15到25人的球队中选择11名球员。分析不同球员的数据,为每场比赛选出最佳球员。本研究尝试了一种方法,其中球员在场上的表现,以确定发挥-11。一名球员在一场比赛中的场上表现是作为一个单一的指标来计算的,该指标考虑了一名球员与对手球队中所有球员的属性。对于这个计算,过去每个球的数据被清理和挖掘,以生成包含球员对球员性能属性的数据。接下来,通过计算性能属性的加权分数,将玩家对玩家组合的各种性能属性转换为玩家的性能评级。最后,提出并开发了一个优化模型,使用计算的性能评级来确定最佳的比赛-11。已开发的优化模型表明,对局的11种打法可以最大限度地提高战胜给定对手的可能性。采用2008- 2020年的过去数据演示了确定IPL比赛的11场比赛的拟议程序。该演示表明,对于联赛阶段的比赛,模型建议的11场比赛和实际的11场比赛在所有球队中具有~ 7%的相似性。剩下的~ 3%与实际选拔的人员不同。尽管如此,这种差异与现有团队相比,绩效评级大约提高了~ 13.32%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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Determining the playing 11 based on opposition squad: An IPL illustration
Indian Premier League (IPL) is the most popular T20 domestic sporting league globally. Player selection is crucial in winning the competitive IPL tournament. Thus, team management select 11 players for each match from a team’s squad of 15 to 25 players. Different player statistics are analysed to select the best playing 11 for each match. This study attempts an approach where the on-field player performance is used to determine the playing-11. A player’s on-field performance in a match is computed as a single metric considering a player’s attributes against every player present in the opposition squad. For this computation, past ball-by-ball data is cleaned and mined to generate data containing player-vs-player performance attributes. Next, the various performance attributes for a player-vs-player combination is converted into a player’s performance rating by computing a weighted score of the performance attributes. Finally, an optimisation model is proposed and developed to determine the best playing-11 using the computed performance ratings. The developed optimisation model suggests the playing-11 that maximises the possibility of winning against a given opponent. The proposed procedure to determine the playing-11 for an IPL match is demonstrated using past data from 2008-20. The demonstration indicates that for matches in the league stage, the suggested playing-11 by model and the actual playing-11 have a ∼7% similarity across all teams. The remaining ∼3% are different from those selected in the actual team. Nevertheless, this difference approximately yields a ∼ Indian Premier League (IPL) is the most popular T20 domestic sporting league globally. Player selection is crucial in winning the competitive IPL tournament. Thus, team management select 11 players for each match from a team’s squad of 15 to 25 players. Different player statistics are analysed to select the best playing 11 for each match. This study attempts an approach where the on-field player performance is used to determine the playing-11. A player’s on-field performance in a match is computed as a single metric considering a player’s attributes against every player present in the opposition squad. For this computation, past ball-by-ball data is cleaned and mined to generate data containing player-vs-player performance attributes. Next, the various performance attributes for a player-vs-player combination is converted into a player’s performance rating by computing a weighted score of the performance attributes. Finally, an optimisation model is proposed and developed to determine the best playing-11 using the computed performance ratings. The developed optimisation model suggests the playing-11 that maximises the possibility of winning against a given opponent. The proposed procedure to determine the playing-11 for an IPL match is demonstrated using past data from 2008-20. The demonstration indicates that for matches in the league stage, the suggested playing-11 by model and the actual playing-11 have a ∼7% similarity across all teams. The remaining ∼3% are different from those selected in the actual team. Nevertheless, this difference approximately yields a ∼13.32% increase in performance rating compared to the existing team.3.32% increase in performance rating compared to the existing team.
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