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This paper presents software that aims to generate extensive soccer gameplay data through computer simulations, addressing the scarcity of human-generated data for analysis. It discusses the challenges of analyzing human gameplay data, the need for computer simulations, and the development of software tools such as rcgamestats and rcg2data. The system uses the RoboCup Soccer Simulator to efficiently generate game data. In addition, it presents a case study on distribution analysis of ball interceptions to demonstrate the potential for various analytical purposes. The provided data includes 78,000 matches, which is equivalent to approximately 8,667 human soccer matches. Future efforts include expanding data collection and improving database system integration.

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Soccer Gameplay Data Generation: Toward Integrating Computer Simulations and Human Sports Analysis

  • 会议论文

  • 发表日期：

    2025年

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  • Hidehisa Akiyama,
  • Tomoharu Nakashima

摘要

This paper presents software that aims to generate extensive soccer gameplay data through computer simulations, addressing the scarcity of human-generated data for analysis. It discusses the challenges of analyzing human gameplay data, the need for computer simulations, and the development of software tools such as rcgamestats and rcg2data. The system uses the RoboCup Soccer Simulator to efficiently generate game data. In addition, it presents a case study on distribution analysis of ball interceptions to demonstrate the potential for various analytical purposes. The provided data includes 78,000 matches, which is equivalent to approximately 8,667 human soccer matches. Future efforts include expanding data collection and improving database system integration.