足球网:一个基于Gated Recurrent Unit的模型来预测足球比赛的赢家
Jassim AlMulla1, Mohammad Tariqul Islam2, Hamada R H Al-Absi1
1College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
PloS one
|August 1, 2023
概括
预测足球比赛的结果至关重要. 一个深度学习模型SoccerNet通过分析球员表现数据,在预测卡塔尔明星联赛冠军方面实现了超过80%的准确性.
科学领域:
- 运动分析 运动分析
- 在足球中的机器学习
背景情况:
- 赢得足球比赛是全世界俱乐部的主要目标.
- 预测比赛结果对于战略规划和绩效改进至关重要.
- 现有的研究往往侧重于球员表现指标,以预测获胜者.
研究的目的:
- 开发和评估一个用于预测足球比赛赢家的深度学习模型.
- 分析球员表现及其对卡塔尔明星联赛 (QSL) 比赛结果的影响.
- 确定关键的比赛部分和影响比赛结果的球员角色.
主要方法:
- 利用了十年的卡塔尔明星联赛 (QSL) 比赛数据 (2011-2022).
- 开发了SoccerNet,这是一个基于Gated Recurrent Unit (GRU) 的深度学习模型.
- 整合了来自STATS平台的15分钟时间段的比赛和球员数据.
主要成果:
- 足球网在预测比赛赢家方面取得了超过80%的准确性.
- 与中场和前相比,防守者在QSL比赛中发挥了更为主导的作用.
- 比赛的最后15-30分钟显著影响了比赛的结果.
结论:
- 拟议的深度学习模型为MENA地区的足球比赛预测提供了一种新的方法.
- 调查结果可以帮助QSL教练人员和管理层做出战略决策.
- 分析强调了特定球员位置和比赛后期表现对比赛结果的关键影响.
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