Our latest research findings published in the European Sport Management Quarterly. This project was a collaboration with Sangwon Na, who completed his Masters of Research and now joined Mississippi State University as a Ph.D. student, and Yiran Su, who completed her Ph.D. and now joined the University of Georgia as an Assistant Professor.
Understanding fans’ betting behavior
While the influence of sport context knowledge, such as home advantage and team ranking, on prediction accuracy has been discussed in the previous literature, the role of identity-based biases, such as fans’ level of team involvement and the selection of their favourite team, in betting behaviour remain unclear. The main purpose of this study is to develop an understanding of how sport fans’ biases and sport context knowledge influence the accuracy of sport game predictions.
Research methods
A smartphone application enabled us to collect real soccer game predictions and results. A total of 529 football fans participated in 53,943 predictions of 2,353 professional football games within a mobile smartphone application. Chi-square tests and logistic regressions were used to analyse the data.
Results and findings
Chi-square test results indicate that individuals overestimate their favouriteteam to win, as well as theysplit their predictions into dichotomous outcomes by overestimating wins and losses and underestimating draws. Logistic regression analyses indicate that identity-based biases negatively influence prediction accuracy, whereas individuals’ sportcontext knowledge positively contributes to prediction accuracy.
Implications
The study contributes to our understanding of the Psychological Continuum Model (PCM), individual biases, social identity theory and the psychologicalconcept of splitting. Findings have implications for organizations who need to understand fans’ sportgambling behaviour and sport fans who seek to optimize their game prediction accuracy to improve their bets and fantasy team selections.
Keywords:Sports gambling, Social identity, Identity-based bias, Team involvement, Result prediction
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