A Systematic Review of Machine Learning in Sports Betting: Techniques, Challenges, and Future Directions

For example, in horse racing, the research by Terawong and Cliff (2024) emphasizes the need for high quality datasets to develop profitable betting strategies using machine learning. Without reliable data, the predictive power of the models diminishes, leading to potentially costly betting decisions. Expanding on this concept, Paerels (2020) used three types of models—logistic regression, gradient boosting, and generalized additive models (GAM)—to calculate expected goals (xG) in the NHL.

Figure 2 illustrates a comprehensive system for predicting sports betting outcomes using machine learning. Platforms like dfsPro.ai leverage machine learning to refine their models over time, increasing prediction accuracy while simultaneously recalculating odds based on live inputs. Live betting has experienced a remarkable surge as AI-driven tools have enabled sportsbooks to process vast amounts of real-time data. According to a report from Optimove, analysis of 3.8 million players, shows that 54% of global bets are now placed in-play—with peaks reaching 70% in Greece, 57% in Italy, 55% in Spain, 52% in the U.S., and 34% in the U.K. Machine learning (ML) is playing a key role in shaping the future of sports betting by turning raw data into smarter decisions. By analyzing millions of data points in real time, ML helps sportsbooks improve prediction accuracy, adjust odds instantly, and offer more personalized user experiences.

Among these technologies, machine learning (ML) has emerged as a game-changer, allowing bettors and businesses to make data-driven predictions that minimize risk and maximize potential returns. Intellias guided EveryMatrix through this transformation, helping them build a global sportsbook platform that processes over 25,000 events each month across 2,000 betting markets and 50 sports. The platform leverages AI and machine learning for sports betting to enhance its services with advanced insights and deliver engaging, top-notch experiences to users.

The dataset used in this study included 1,056,766 price change signals across 73 markets and 10 events, with messages transmitted every 50 milliseconds, sourced from Betfair’s PRO package. Many platforms offer free trial versions or affordable subscriptions, allowing more people to get started with sports betting. As the market grows and competition intensifies, betting platforms that invest in innovation, whether through better data handling, efficient backend tools, or smarter risk management, will thrive.

If a certain team has beaten another one ten times in a row by an average margin of 9.5 points and the spread is eight, you’re typically going to bet on the team to cover the eight points due to doing so in the past. Expand your iGaming, sports betting, poker, bingo, baccarat, and other products with Intellias, your iGaming software solutions provider. I am excited to share a prediction model I have been working on for a couple of years, and that so far looks very promising. It hopefully gives people an idea of how machine learning can be used for soccer betting. However, with its continuous development and evolution, the betting industry has to ensure ethical engagement in responsible wagering practices.

Shin and Gasparyan (2014) used virtual data from FIFA 2015 to predict the outcomes of soccer matches, achieving up to 80% accuracy with a linear SVM model. Machine learning software learns from historical sports data and can identify patterns over time, providing insights that may improve bets on complex sporting events. It works similarly to how algorithms analyze the stock market to make financial decisions. The effectiveness of machine learning models is heavily reliant on the quality and comprehensiveness of data. For example, missing player statistics or inconsistent match records can skew model outputs, undermining their reliability. Instead of generic bets like “who will win,” bettors can now choose from micro-moments such as “Will the next play be a pass or run?

AI-Driven Odds Setting and Dynamic Pricing

• With the increasing interactivity and gamification of betting, personalized experiences driven by data are defining the future of the industry.
• No one would have imagined that these things would be how they currently are, but now, the future is showing new heights and a lot of action.
• These tools work together to streamline betting workflows, enhance user experience, and ensure the platform runs smoothly across all touchpoints.
• These algorithms use statistical models to analyze these factors and create probability estimates for the outcome of a game.
• This holistic approach provides a more comprehensive picture, improving the accuracy of predictions.
• To make these systems even smarter, many platforms now use machine learning and neural networks.

The primary dataset utilized in this study was the ShotLink dataset provided by the PGA Tour. Complementing these findings, Cintia etal. (2016) employed a dynamic graph analysis framework to evaluate the performance of the rugby team by constructing multilayer networks representing passes and disruptions (tackles). The analysis used data from 18 rugby matches collected by Opta, covering the 2012 Tri-Nations championship, the 2012 New Zealand Europe tour, the 2012 Irish tour to New Zealand, and the 2011 Churchill Cup. Key metrics included connectivity, assortativity, number of strongly connected components, and clustering of the pass network, along with similar measures weighted by disruption events. The results indicated that high connectivity in the pass networks was correlated with more meters gained, highlighting the importance of maintaining multiple pathways for the ball.

This disparity can create a competitive imbalance in the betting market, where only those with access to advanced computational resources can leverage sophisticated models effectively. Golf predictions, as studied by Laaksonen (2023) and Leahy (2014), focus on the use of advanced analytics and proprietary data to predict player performances. These studies illustrate the challenges of predicting results in individual sports and emphasize the need for detailed player statistics and environmental factors to improve the accuracy of the model. Similarly, Lord et al. (2023) analyzed the entropy and spatial distribution of ball movement patterns in international field hockey using video footage from the 2019 Pro League tournament, covering 131 matches. The notational analysis system in SportsCode was used to capture the start and end locations of ball movements and their outcomes. The metrics included game possession, entropy, possession per zone, and progression rates.

The Increasing Use of Machine Learning and AI in Sports Betting

This process is often referred to as identifying value bets—where the odds provided by a bookmaker are higher than the algorithm’s probability of a certain outcome. Sportsbooks utilize machine learning to monitor betting patterns and identify potential risks or fraudulent activities. By detecting anomalies in real-time, operators can mitigate threats and maintain the integrity of their platforms. AI-based anomaly detection systems have been developed to ensure fairness in sports and prevent illegal gambling.

Sportsbooks can then take that data and personalize a bettor’s experience to reflect their preferences and personality better. The business can do things like placing banner advertisements and promotional content reflecting the bettor’s background and values. Most crucially, they can also create personalized incentives to encourage customers to bet more. Or, in the case of churned or inactive customers, they can create incentives to bring them back to their business – and thus prevent them from switching to the competition. We’re here to help you understand The Best NFL Betting Algorithms with expert analysis, guides, popular strategies, and more.

This is particularly useful for live bets on in-game events, helping bettors stay ahead of the changing trends in the sports world. For instance, neural networks, a subset of machine learning, can be used to predict outcomes by analyzing the relationships between variables such as team strength, player fatigue, and weather. Machine learning also allows algorithms to make real-time adjustments based on in-game developments, making them highly adaptable in dynamic environments.

Make sure to verify the regulations on any site or platform you use for betting and check for any restrictions before placing bets. In the Philippines, betting via PAGCOR is regulated, with strong data privacy rules under the Data Privacy Act of 2012. Read below the biggest challenges operators and bettors face when relying on algorithmic tools. Understanding these risks helps set realistic expectations and shows where human judgment still plays a vital role.

Vaknin (2021) compared Poisson-based models and classification models to predict events related to scores, with classification models showing superior performance. Geurkink et al. (2021) identified key variables predicting match outcomes in Belgian professional soccer using Extreme Gradient Boosting. The 576-game dataset was tracked by the SportVU system, achieving an accuracy of 89.6%. While these ensure sportsbooks stay competitive, they can also lead to aggressive odds shifting, making it harder for bettors to lock in favorable bets. Additionally, AI’s ability to track bettor behavior raises data privacy concerns, as sportsbooks use machine learning to predict and influence betting habits. AI-powered tools assist bettors in sports betting picks, helping them identify the best-value bets by comparing odds across multiple sportsbooks.

The model aimed to provide feedback to players, focusing on key body parts such as the right knee, ankle, shoulder, and eyes. The dataset comprised 50 goal-kick videos from different angles, expanded to 4,000 data points using data augmentation techniques. The initial model trained on 50 data points had an accuracy of 48%, which improved to 74% with the augmented dataset. In addition, a domain knowledge-based model was tested using joints deemed crucial by expert advice and player surveys, achieving comparable accuracy. The dataset used roobet india in this study was OpenPose-extracted posture data from goal-kick videos. The dataset, obtained from OPTA, included 26 PIs such as tackle ratio, clean breaks, average carry, lineouts won, penalties conceded, and missed tackles.

These technologies are not only changing how bets are placed but also how odds are set, predictions are made, and the overall experience for bettors is being enhanced. Traditional methods of setting odds are being complemented (and sometimes replaced) by AI algorithms that can process more variables and data points than ever before. This evolution is making the betting market more efficient and dynamic, benefiting both bookmakers and bettors. Machine learning models have achieved impressive accuracy rates, with some platforms reporting up to 95% accuracy for specific predictions.