KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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Prediction of English Premier League Game Using an Ensemble Technique

앙상블 기법을 통한 잉글리시 프리미어리그 경기결과 예측

  • 이재현 (숭실대학교 융합소프트웨어학과) ;
  • 이수원 (숭실대학교 소프트웨어학부)
  • Received : 2019.11.11
  • Accepted : 2020.01.22
  • Published : 2020.05.31
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Abstract

Predicting outcome of the sports enables teams to establish their strategy by analyzing variables that affect overall game flow and wins and losses. Many studies have been conducted on the prediction of the outcome of sports events through statistical techniques and machine learning techniques. Predictive performance is the most important in a game prediction model. However, statistical and machine learning models show different optimal performance depending on the characteristics of the data used for learning. In this paper, we propose a new ensemble model to predict English Premier League soccer games using statistical models and the machine learning models which showed good performance in predicting the results of the soccer games and this model is possible to select a model that performs best when predicting the data even if the data are different. The proposed ensemble model predicts game results by learning the final prediction model with the game prediction results of each single model and the actual game results. Experimental results for the proposed model show higher performance than the single models.

스포츠 경기 결과예측은 전반적인 경기의 흐름과 승패에 영향을 미치는 변인들의 분석을 통해 팀의 전략 수립을 가능하게 해준다. 이와 같은 스포츠 경기결과 예측에 대한 연구는 주로 통계학적 기법과 기계학습 기법을 활용하여 진행되어 왔다. 승부예측 모델은 무엇보다 예측 성능이 가장 중요시된다. 그러나 최적의 성능을 보이는 예측 모델은 학습에 사용되는 데이터에 따라 다르게 나타나는 경향을 보였다. 본 논문에서는 이러한 문제를 해결하기 위해 데이터가 달라지더라도 해당 데이터에 대한 예측 시 가장 좋은 성능을 보이는 모델의 선택이 가능한 기존의 축구경기결과 예측에서 좋은 성능을 보여온 통계학적 모델과 기계학습 모델을 결합한 새로운 앙상블 모델을 제안한다. 본 논문에서 제안하는 앙상블 모델은 각 단일모델들의 경기 예측결과와 실제 경기결과를 병합한 데이터로부터 최종예측모델을 학습하여 경기 승부예측을 수행한다. 제안 모델에 대한 실험 결과, 기존 단일모델들에 비해 높은 성능을 보였다.

Keywords

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