Journal of Industrial Convergence (산업융합연구)

DAEHAN Society of Industrial Management (대한산업경영학회)
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Predicting Soccer Players' Wage Grades Using Big Data and Artificial Intelligence

빅데이터 및 인공지능을 활용한 축구선수 연봉등급 예측

  • 정현성 (서강대 메타버스전문대학원) ;
  • 김진화 (서강대 메타버스전문대학원) ;
  • 현대원 (서강대 메타버스전문대학원)
  • Received : 2024.07.19
  • Accepted : 2024.08.20
  • Published : 2024.08.28
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Abstract

This study proposes a new method for predicting the wage grades of soccer players using big data and artificial intelligence. Predicting the salaries of soccer players is a crucial task that involves accurately assessing players' performance and potential, and reflecting this in their salaries to enhance the economic efficiency of the soccer industry. This research analyzes player ability data provided by FIFA 22 and employs various big data and artificial intelligence techniques to predict players' salary grades. Key methodologies used include decision trees, artificial neural networks, random forests, and boosting, which were utilized to compare the accuracy of the salary prediction models. The results show that the random forest and boosting methods exhibited the highest prediction accuracy. This study demonstrates the process and utility of using big data and artificial intelligence technologies to predict soccer players' salary grades, offering a new perspective on the soccer industry.

본 연구는 빅데이터와 인공지능을 활용하여 축구선수의 연봉등급을 예측하는 새로운 방법을 제안한다. 축구선수의 연봉 예측은 선수의 성과와 잠재력을 정확하게 평가하고, 이를 연봉에 반영함으로써 축구 산업의 경제적 효율성을 높이는 중요한 과제이다. 본 연구는 FIFA 22에서 제공하는 선수 능력치 데이터를 분석하여, 다양한 빅데이터 및 인공지능 기법을 통해 선수의 연봉등급을 예측한다. 주요 연구 방법으로는 의사결정나무, 인공신경망, 랜덤 포레스트, 부스팅 등을 활용하였으며, 이를 통해 연봉등급을 예측하는 모델의 정확도를 비교 분석하였다. 연구 결과, 랜덤 포레스트와 부스팅 기법이 가장 높은 예측 정확도를 보였다. 이 연구는 빅데이터와 인공지능을 이용해 축구선수의 연봉등급을 예측하고, 축구 산업에 새로운 관점을 제공한다.

Keywords

Acknowledgement

This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the Graduate School of Metaverse Convergence support program(IITP-2023-RS-2022-00156318) supervised by the IITP(Institute for Information & Communications Technology Planning & Evaluation)

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