Detectability of Sports Betting Anomalies Using Deep Learning-based ResNet: Utilization of K-League Data in South Korea

Kim, Changgyun; Park, Jae-Hyeon; Kim, Daegeon; Lee, Ji-Yong

doi:10.61186/aassjournal.11.2.s1.8

Cited by 1 publication

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the academic world, studies have attempted to detect match-fixing using anomalous match data. Kim et al 26 converted sports dividend odds data into graphs and applied the CNN algorithm to sort normal and abnormal matches by comparing their dividend odds graphs. Ötting et al 24 used the GAMLSS model based on dividend odds and betting volume data to identify differences between fixed and non-fixed matches and evaluated the model's ability to detect fixed matches.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

AI-based betting anomaly detection system to ensure fairness in sports and prevent illegal gambling

Kim,

Park,

Lee

2024

Sci Rep

View full text Add to dashboard Cite

This study develops a solution to sports match-fixing using various machine-learning models to detect match-fixing anomalies, based on betting odds. 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—a model optimized from the previous four. The models classify normal and abnormal matches by learning their patterns using sports betting odds data. The database was developed based on the world football league match betting data of 12 betting companies, which offered 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. We then 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, over 92%, whereas the LR and SVM models were approximately 80% accurate. In comparison, previous studies have used a single model to examine football match betting odds data, with an accuracy of 70–80%.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Other studies have attempted to detect match-fixing through the betting odds 23,24 . This method is considered effective for detecting match-fixing and is accepted by the Court of Arbitration for Sports as the main evidence in sports match-fixing cases 25,26 .…”

Section: Match-fixing Detection Using Betting Odds and Market Price F...mentioning

confidence: 99%

AI-based betting anomaly detection system to ensure fairness in sports and prevent illegal gambling

Kim,

Park,

Lee

2024

Sci Rep

View full text Add to dashboard Cite

show abstract

Detectability of Sports Betting Anomalies Using Deep Learning-based ResNet: Utilization of K-League Data in South Korea

Cited by 1 publication

References 15 publications

AI-based betting anomaly detection system to ensure fairness in sports and prevent illegal gambling

AI-based betting anomaly detection system to ensure fairness in sports and prevent illegal gambling

Contact Info

Product

Resources

About