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

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%.

Match-Fixing Causing Harm to Athletes on a COVID-19-Influenced Gambling Market: A Call for Research During the Pandemic and Beyond.

Match-fixing, although not a new problem, has received growing attention during the COVID-19 pandemic, which has been reported in the media to have increased the risk of match-fixing events. Gambling is a well-documented addictive behavior, and gambling-related fraud, match-fixing, is a challenge to the world of sports. Most research on match-fixing has a judicial or institutional perspective, and few studies focus on its individual consequences. Nevertheless, athletes may be at particular risk of mental health consequences from the exposure to or involvement in match-fixing. The COVID-19 crisis puts a spotlight on match-fixing, as the world of competitive sports shut down or changed substantially due to pandemic-related restrictions. We call for research addressing individual mental health and psycho-social correlates of match-fixing, and their integration into research addressing problem gambling, related to the pandemic and beyond.

Betting Practices Among Players in Portuguese Championships: From Cultural to Illegal Behaviours.

The growth of the online sports betting market has generated new risk areas and threats to sport integrity, such as match-fixing. In recent years, institutional concern to fight against the phenomenon has been intensified and a set of countermeasures has been adopted. One of the most widely implemented measures to protect integrity in sport is to ban athletes and sports players from betting on the competitions in which they are involved. In some countries, such as Portugal, this practice has become a crime under the new legislation. Despite the legal and sporting restrictions and the prevention programmes carried out for sport institutions to explain the gambling rules, there are many athletes who are putting bets on their own competitions and even in their own games. Through interviews with key informant actors and ethnographic fieldwork, this article describes betting patterns among sports actors in Portugal and explains the perceptions and incentives that lead them to bet in their own sports, competitions and games. The results show different conceptions of integrity between the normative discourse on legal and sport governance institutions and sports actors' opinion, essentially, the premise that suggest a direct link between betting on one's own games and manipulation of these games. In some cases, betting in one's own games helps to strengthen fair play values. However, the spreading of online betting, together with the perception of inefficient controls in the implementation of sporting and legal regulations, creates opportunity structures for fixing matches and taking financial profit though gambling activities.

Performance Profiling-Perspectives for Anti-doping and beyond.

Performance profiling is a new area of research that could potentially open new frontiers in the fight against doping. Even beyond exposing unnatural and pharmacology aided performances, there are other potential applications and benefits of performance modeling for the protection of the integrity of sports. The backbone of performance modeling in anti-doping is the individual tracking of performance through competition results or other metrics of sporting achievements. Since performance improvement is the primary goal of doping, it is expected that doping will affect competition results. Thus, individual tracking of performance could potentially expose suspicious cases that deserve more scrutiny from anti-doping officials and help to adjust targeted testing. On the other hand changes in performance levels could also be used to assess the efficiency of new anti-doping strategies. Another application of performance analysis is to develop unified classifications of athletes according to their level of performance. This classification has numerous practical meanings, but from anti-doping perspective it provides an opportunity to set exact criteria for athletes belonging to national and international testing pools and thus estimate the number of tests needed in different countries based on the number of athletes at ascertain performance level. At the moment, in the absence of unified and comprehensive criteria for national and international testing pools, there are no definitive regulations regarding exact doping test numbers needed. Thus, it creates inequality between nations and affects the credibility of the anti-doping system worldwide. Such classification would allow a more efficient use of anti-doping resources. Since doping is not the only threat to the integrity of sports, performance modeling can also help to reveal cases of other misbehavior in sports, like match fixing or result manipulation. In summary, performance modeling and its application to various fields is a new method to improve the efficiency of systems to safeguard the integrity of sports at different levels.