Chances are good that your customers will not think about AI in sports betting when they place a live wager. More likely, they want to know how unexpected incidents during the game will affect the outcome and their odds. Many things could happen during a sporting event that could change its course and affect the result. They include weather conditions, injuries on the field, and player ineligibility. Artificial Intelligence (AI) and machine learning (ML) use this information 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 the risks of handling sensitive and personal data, operators are preparing for a major upgrade to 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, and analysts expect it to grow at a Compound Annual Growth Rate (CAGR) of 30.1%. By 2032, spending on an AI sports betting strategy will reach $29.7 billion. The demand for player data tracking, chatbots, and real-time insights drive this growth. As a result, 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. That is how ML models, a subset of AI, are different. An ML model uses a variety of algorithms to process real-time data. They find patterns in the data that can lead to better insights. Examples include a trend in player performance, scores from earlier matches, and even changes in the betting market itself.
Customers use these AI-powered systems in many ways. This includes an 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. This includes 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 to changing circumstances, such as updates about 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 ML models with the help of experienced software engineers. Data scientists call this model training. They train AI and ML models using algorithms and lots of data. Once they have trained a model, engineers then incorporate it into software for a sports betting application. The model continuously improves as more data becomes available for training. In other words, the more sports statistics the model has available to it, the better it can make its predictions.
Source: Medium: Introduction to how a Multilayer Perceptron works but without complicated math
Neural networks are examples of an ML algorithm. They are powerful calculations that can mimic the problem-solving processes of the human brain. As a result, they can process complex relationships within the data. The most basic type of neural network is a multilayer perceptron (MLP). It includes input, hidden, and output layers and a target variable.
One example of a applying a multilayer perceptron is developing 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. These models 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, customers get details like social sentiment about the team, team morale, and the effect of the news on the outcome of the sporting event. Operators 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 technique that bookmakers use 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 the bets based on historic results.
5 use cases for AI in sports betting
These AI and ML systems are arming operators with a variety of use cases that are improving 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. As explained earlier, players place their data-driven bets based on real-time information from the sporting event. For instance, 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. In one example, SharpLink Gaming uses AI in sportsbooks to build personalized experiences by analyzing user behavior, preferences, and interactions.
- Fraud detection and risk management: Because AI is processing enormous amounts of data, it is ideal to help mitigate risk and detect fraud. It looks for betting activity and patterns that might be a sign of fraud or risky behavior. Sportradar, for example, offers integrity services that use AI to detect betting-related match-fixing and other fraudulent activities.
- GenAI: When engineers add GenAI to sports betting applications, they offer another way to provide a more engaging user experience. For example, 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. This 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 the integrity of the platform.
The combination of AI and sports betting also presents a unique challenge in terms of fairness and transparency. Companies must consider ethical concerns, including the responsible use of data and respect for intellectual property rights. That includes 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 important for mitigating risks. They 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 you a wealth of benefits that were previously unattainable with traditional methods. This strategy is playing a role in helping many operators stay competitive with more reliable predictions and dynamic odds. As industry adoption of technology improves, AI systems are your best bet to provide smarter, safer, and more engaging experiences in AI for sports betting.
If you need to know where to begin incorporating AI, 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.
