AI AND PREDICTION MARKETS
optimizing complex predictive processes through the large- scale analysis of data, historical patterns and dynamic probabilities. Nevertheless, the gambling industry has not remained isolated from the use of artificial intelligence systems based on machine learning. Initially, many of these tools were used in marketing processes, user segmentation, customer interaction and commercial optimisation within digital betting platforms. More recently, the evolution of these systems has also started to play an important role in responsible gambling initiatives and the early detection of at-risk players. A study developed by researchers from the International Gaming Institute at the University of Nevada Las Vegas (UNLV), Harvard Medical School, the University of Calgary, the University of Sydney and Washington State University notes that artificial intelligence systems have become central tools in harm prevention efforts within the gambling industry. The study highlights that one of the main attributes of machine learning lies in “…its ability to learn complex and nonlinear relationships in the data” 3 , allowing these models to “…target players according to markers defined by both their gambling patterns…” 4 , thereby enabling the detection of potentially problematic gambling behaviours through the individualised analysis of user conduct.
extract insights from large volumes of data, with the goal of improving the accuracy of predictions in the ever-evolving landscape of college football.” 5 The research compiled thousands of historical data points derived from sports statistics and Las Vegas betting lines, subsequently building a database containing more than 1,700 variables related to performance, historical results and betting behavior. Based on this information, the researchers trained different machine learning models, including neural networks, in order to compare their predictive capabilities against the betting lines traditionally used by the industry. The study concluded that tools such as lasso regression and feature selection 6 , both commonly associated with machine learning techniques, significantly improved the accuracy and robustness of predictive models. However, the study also demonstrated that Las Vegas betting lines constituted one of the strongest predictors within the model, to the extent that removing this variable considerably reduced the performance of the analysed systems. AI in the sports betting context A study entitled “A Systematic Review of Machine Learning in Sports Betting: Techniques, Challenges, and Future Directions” 7 states that “…machine learning has significantly impacted the sports betting landscape by improving both the accuracy of predictions and the efficiency of betting strategies” , further noting that these techniques “…have been employed to identify mispriced odds offered by bookmakers, presenting opportunities for savvy bettors to capitalize on these inefficiencies” . This demonstrates how artificial intelligence is beginning to reshape the traditional understanding of chance within sports betting, opening the discussion as to whether the modern bettor increasingly resembles an analyst of information and
Another example of these predictive capabilities can be observed in gambling verticals such as sports betting. A relevant example is the study developed by Luke Boll, a researcher at the University of Michigan, on neural networks applied to predicting outcomes in college football. In this paper, the author states that “In recent years, neural networks have emerged as a powerful tool for predicting sports outcomes due to their ability to learn complex relationships between statistical inputs and game outcomes. These challenges have led to the development of various prediction models that utilize machine learning and statistical techniques to 3 Kasra Ghaharian et al, “The Need for Benchmarks to Advance AI-Enabled Player Risk Detection in Gambling” , International Gaming Institute (2025). 4 Ibid 5 Boll Luke, “Gridiron Genius: Using Neural Networks to Predict College Football” , Michigan University 6 Lasso regression is a statistical technique used to identify relevant variables within large volumes of data, while feature selection refers to the process of identifying and retaining the most useful variables to improve the predictive performance of a machine learning model. 7 Manassé Galekwa René et al. “ A Systematic Review of Machine Learning in Sports Betting: Techniques, Challenges, and Future Directions” (2024)
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IMGL MAGAZINE | JUNE 2026
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