Affiliation:
1. Kangwon National University
2. Korea National Sport University
Abstract
Abstract
This study develops a solution to sports match-fixing using various machine-learning models to detect match-fixing anomalies based on dividend yields. We use five models to distinguish between normal and abnormal matches: logistic regression (LR), random forest (RF), support vector machine (SVM), the k-nearest neighbor (KNN) classification, and the ensemble model, an optimized model of the previous four. The models classify normal and abnormal matches by learning their pattern with sports dividend yield data. The database was built on the world football league match betting data of 12 betting companies, with a vast collection of data on players, teams, game schedules, and league rankings for football matches. We develop an abnormal match detection model based on the data analysis results of each model, using the match result dividend data. Then, we use data from real-time matches and apply the five models to construct a system capable of detecting match-fixing in real-time. The RF, KNN, and ensemble models recorded a high accuracy of over 92%, whereas the LR and SVM models were approximately 80% accurate. By comparison, previous studies have used a single model to examine suspected matches using football match dividend yield data, with an accuracy of 70–80%.
Publisher
Research Square Platform LLC
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