영국 프리미어리그 경기데이터 기반 머신러닝을 활용한 경기결과 예측 및 분류모형의 예측 성능 비교 = The Application of Machine Learning Algorithms to Predict English Premier League Match Results

다국어 초록 (Multilingual Abstract)

The accumulation and collection of big data related to sports along with the development of AI algorithms, and computer science has opened a new era of research on the prediction of sports match results based on machine learning. Football, which has the largest market in the world, calls for such research still in its early stages of academic development and practical needs. This study was conducted to predict the English Premier League match to expand machine learning application in sports result prediction research. To implement our research idea, game match variables were collected from various validated sites such as Premier League, Fotmob, Trasfer market, and Capology homepage through web scraping technique. Following the procedure, 16 variables were selected for the research modelling optimized based on a stepwise selection of a total of 123 variables for 1,107 match data (N=2,214) during the 2020-2021 season to May of the 2022-2023 season collected to predict game results based on nine machine learning algorithms. As a result of the analysis, the prediction performance can be depicted in the order of Multi-layer Perception, Multinomial Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Support Vector Machine, Gradient Boosting, Ada Boost, Light-GBM, and Random Forest. The Multi-layer Perception, which showed the highest prediction performance, recorded an F1 score of 86.66. This study has made a significant theoretical and practical contribution to the development of game prediction with the application of machine learning algorithms in the domain of professional football, as it has significantly improved the prediction performance of previous studies recording a F1-score of 86.66 that includes expected goal (xG) variable not frequently applied in the prediction of game results in previous studies.

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