Chances are good that your customers are not thinking about AI in sports betting when they place a live wager. More likely, they are wondering how unexpected incidents during the game could affect the outcome — and their odds. Many things can happen during a sporting event that alter its course and the result, including changes in weather conditions, injuries on the field and player ineligibility. Artificial intelligence (AI) and machine learning (ML) use information about such events to change how customers place in-play bets. With AI, users get real-time insights about how different events could lead to a big payday.
These predictive insights are among the many benefits of AI for sports betting. They help players across the iGaming landscape make in-play bets. Despite data security and compliance risks of handling sensitive and personal data, gaming operators are preparing for major upgrades to their AI systems.
Role of AI and machine learning in sports betting
The amount of money spent on AI technology indicates that bookmakers will increasingly use AI for sports betting. The global market for using AI in sports betting was valued at $2.2 billion in 2022. Analysts expect it to grow at a compound annual growth rate (CAGR) of 30.1% between 2023 and 2032, when spending on AI sports betting strategies will reach $29.7 billion. Demand for player data tracking, chatbots and real-time insights is driving this growth. Bookmakers are working quickly to provide these services and keep up with the competition.
Sports betting has historically used static models for analysis. However, these models cannot process real-time data or dynamically adjust to changing circumstances. Machine learning, a subset of AI, uses a variety of algorithms to process real-time data and find patterns in that data that can lead to better insights: trends in player performance, scores from earlier matches and even changes in the betting market itself.
Customers use these AI-powered systems in many ways, including for analysis of earlier games and real-time adjustment of odds. Furthermore, AI systems offer virtual and augmented reality opportunities for a more engaging sports betting experience.
Benefits of using AI in sports betting
Still, those are just some benefits of using AI for sports betting. Other ways that sports betting becomes richer with the use of AI include:
- Better Decision-Making: AI offers comprehensive insights by aggregating and analyzing complex datasets from multiple sources, including player statistics, team dynamics and live game updates.
- Real-time updates: AI models analyze real-time betting data to provide dynamic odds. AI systems quickly adjust odds in response to changing circumstances, such as a player’s condition, team performance or even the weather.
- Efficiency: AI models also enable automation. This helps bookmakers scale their businesses without compromising speed or accuracy as the volume of data increases. It also reduces human error and frees up workers for other tasks.
Types of AI used in sports betting
But before bookmakers realize these benefits, they must develop and train AI and ML models — using algorithms and lots of data — with the help of experienced software engineers. Data scientists call this model training. Once they have trained a model, engineers then incorporate it into a sports betting application. The model continuously improves as more data becomes available for training. In other words, more sports statistics lead to better predictions.
Source: Medium: Introduction to how a Multilayer Perceptron works but without complicated math
A neural network is an example of an ML algorithm. Neural networks perform powerful calculations and process complex relationships within data, mimicking the problem-solving processes of the human brain. The most basic type of neural network is a multilayer perceptron (MLP). It includes input, hidden and output layers, along with a target variable.
One example of a multilayer perception is a large language model (LLM) for GenAI. A generative pre–trained transformer (GPT) takes input from a sequence of data and turns it into a tangible output. They also use natural language processing (NLP), which lets users interact with the GPT in their native language. In sports betting, this is particularly helpful for analyzing unstructured data from social media sites, news networks and even live commentary. From these sources, customers can get details like social sentiment about teams, team morale and the effect of news on the outcome of the sporting event. Operators can learn how to use AI to predict sports betting and refine the odds. GenAI also helps produce content for marketing campaigns.
Reinforcement learning is another use of AI in sports betting. Unlike other AI methods, reinforcement learning algorithms improve by receiving feedback on their performance and then adjusting their strategies. With the development of reinforcement learning, game operators can improve bets based on historical results.
5 use cases for AI in sports betting
AI and ML systems are arming operators with a variety of tools to improve many areas of sports betting. However, some use cases show special promise. Here are five ways to enhance sports betting with AI. 
- More accurate odds: AI models assess a variety of factors such as player statistics, injuries, weather and live game feeds, to create precise odds. For example, FanDuel uses AI sports betting predictions to offer NFL play-by-play betting and adjusts the odds based on real-time game developments.
- In-play betting: This is also known as live betting and involves players placing data-driven bets based on real-time information from the sporting event. DraftKings recently acquired a company to offer an in-play wagering experience.
- Personalized betting experiences: AI analyzes user history and preferences to provide customized betting suggestions. This includes tailored recommendations and notifications about wagers or sporting events that align with individual interests. SharpLink Gaming uses AI in sportsbooks to build personalized experiences by analyzing user behavior, preferences and interactions.
- Fraud detection and risk management: Because AI models process enormous amounts of data, they are ideal for monitoring betting activity and identifying patterns in order to mitigate risk and detect fraud. Sportradar, for example, offers integrity services that use AI to detect betting-related match-fixing and other fraudulent activities.
- Generative AI: When engineers add GenAI to sports betting applications, they offer another way to provide a more engaging user experience. For instance, Stats Perform uses generative AI to personalize betting recommendations.
Strategies for mitigating risks
As the use of artificial intelligence in sports betting becomes more widespread, operators must be mindful of the potential risks associated with handling vast amounts of sensitive data. Mitigating these risks requires robust data security. Sports betting and AI platforms rely on continuous access to user data, transaction histories and real-time game statistics. AI systems offer advanced encryption technologies and multilayer security protocols to safeguard user data and support platform integrity.
The combination of AI and sports betting also presents a unique challenge in terms of fairness and transparency. Companies must consider ethical concerns, including responsible use of data, respect for intellectual property rights and the fairness of the algorithms themselves. Bookmakers must navigate legal frameworks that govern gambling practices and try to stay ahead of changing standards.
Minimizing risks requires regular monitoring and maintenance of sports betting systems. AI models need to be constantly updated and fine-tuned to account for changes. Without proper monitoring, AI systems can become outdated or unreliable. User education is also important for mitigating risks. Users should be well-informed about how bookmakers are using their data and how AI affects the betting process.
Getting started with AI for sports betting
From real-time odds adjustments to personalized betting experiences, AI for sports betting offers a wealth of benefits that were previously unattainable with traditional methods. AI technologies are playing a role in helping many operators stay competitive with more reliable predictions and dynamic odds. As industry adoption of AI technology improves, these systems are your best bet to provide smarter, safer and more engaging sports betting experiences.
If you want to know where to begin with incorporating AI for sports betting into your betting strategy, Intellias can help. We have the right balance of iGaming experience and innovation to bring your project to life.
Contact us to get started on an AI iGaming system that will work for you.
