At risk: betting-related match-fixing in Brazilian football

Marchetti, Felippe; Filho, Alberto Reinaldo Reppold; Constandt, Bram

doi:10.1007/s10611-021-09971-0

Cited by 15 publications

(8 citation statements)

References 32 publications

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“…Notwithstanding that cooperation between competitors is thus conducted by cyclists, it is actually the UCI’s responsibility to not enable and permit this grey zone. Therefore, we emphasize the responsibility of organizations (institutions) rather than the responsibility of individuals to clarify the rules, and the grey zone regarding collaboration and match-fixing (Marchetti et al, 2021; Tak, 2018).…”

Section: Discussionmentioning

confidence: 99%

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The grey zone between tactics and manipulation: The normalization of match-fixing in road cycling

Hoeven

Constandt

Schyvinck

et al. 2021

International Review for the Sociology of Sport

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Match-fixing has already been studied in various sport disciplines. However, despite some well-known incidents in road cycling, match-fixing has barely been investigated in this unique sport discipline. Drawing on Ashforth and Anand’s theoretical model of normalization of corruption in organizations and on the perceptions of 15 active Belgian road cyclists, this study examines road cyclists’ attitudes towards collaboration and match-fixing in their sport. As the culture of agreements can be seen as part and parcel of road cycling itself, this study also examines whether agreements between cyclists are embedded, perpetuated, and thus normalized in road cycling. Using semi-structured interviews, this qualitative study indicates that road cyclists have a different understanding of match-fixing. By demonstrating that (a) cooperation between competitors happens routinely, (b) cyclists use self-serving explanations to justify these collaborations, and (c) newcomers are induced to engage in and accept collaborations with their competitors, we also indicate how normalized behaviors contribute to a grey zone that can be labelled as match-fixing. To enhance the future credibility of road cycling towards both fans and sponsors, additional clarifying and awareness-raising policy measures should be implemented.

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Section: Discussionmentioning

confidence: 99%

“…Simultaneously a deviant (sub)culture may emerge which normalizes corruption. Marchetti et al (2021), for example, have shown how deeply rooted institutionalized norms may explain betting-related match-fixing in Brazilian football. Third, corrupt practices become routinized and habitual.…”

Section: Literature Reviewmentioning

confidence: 99%

The grey zone between tactics and manipulation: The normalization of match-fixing in road cycling

Hoeven

Constandt

Schyvinck

et al. 2021

International Review for the Sociology of Sport

Self Cite

View full text Add to dashboard Cite

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“…Previous studies have also used data from the Sportradar Fraud Detection System, which detects match-fixing based on global betting activities for soccer games 22 . 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

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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%.

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“…In addition, previous studies have used data from the Sportradar Fraud Detection System (FDS), which detects match-xing based on global betting activities for soccer games [22]. Other studies have attempted to detect match-xing through the betting dividend yield [23,24]. This method is considered an effective way of detecting match-xing and is accepted by the Court of Arbitration for Sports (CAS) as the main evidence in sports match-xing cases [25,26].…”

Section: Match-xing Detection Using Betting Dividend Yields and Marke...mentioning

confidence: 99%

AI-Based Betting Anomaly Detection System to Ensure Fairness in Sports and Prevent Illegal Gambling

Kim

Park

Lee

2023

Preprint

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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%.

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At risk: betting-related match-fixing in Brazilian football

Cited by 15 publications

References 32 publications

The grey zone between tactics and manipulation: The normalization of match-fixing in road cycling

The grey zone between tactics and manipulation: The normalization of match-fixing in road cycling

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

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