5 Use Cases for Machine Learning in Sports Betting
Operators and bettors continually look for an edge in anticipating the outcomes of sporting events. This has given rise to machine learning in sports betting. Among the outcomes that machine learning in sports can contribute to the impact of weather on athlete performance, how well specific team compositions perform, the risk of an athlete injury, and recent athlete performance.
Alongside finding more accurate ways to anticipate sporting event outcomes, machine learning in sports betting also covers ways to improve many operational processes for operators. Let’s investigate each of the cases.
Forecasting Matches
From an operator’s perspective, machine learning offers the tantalizing possibility of automating the generation of accurate predictions — more accurate than human analysts (oddsmakers) could reliably generate. Hence, sports betting operators can create automated odds that maximize their average returns from events.
Such machine learning models are multivariate, meaning they factor in many variables when making predictions. In some models, this can be a quantitative analysis of historical performance by an athlete or a team, such as average possession and pass rates in soccer or the history two players have in matchups in games like tennis. In other models, machine learning prices in factors such as the likelihood of player injury or how probable weather conditions can affect outcomes.
The results of these models can be stark. Since the 1990s, computer scientists and machine learning researchers have developed models across a range of sports that have provided above average accuracy for match outcomes. The accuracy of these models range from 50 up to 90 percent depending on the sport. In the case of tennis, the prediction accuracy of models has averaged 70–75 percent. This can mean reliable returns for bettors and sports betting operators (if odds are adjusted properly), with the effectiveness of models only increasing as they are exposed to more data.
THE USE OF MACHINE LEARNING IN PREDICTING OUTCOMES OF SPORTS EVENTS
Machine learning, however, is still a nascent technology, and it is essential to mitigate the risks of using models. One challenge is that of “explainability.” There is a substantial risk of models becoming “black boxes” with predictive reasoning that is incomprehensible to outside observers. Tech teams need to offset this and ensure that their models and infrastructure include “explainability” functionality so that non-technical stakeholders can make sense of the rationale for a model’s decision making. Basically, converting 1’s and 0’s to layman terms.
Another challenge is the risk of concept drift. Concept drift weighs new data heavily which could prompt a model to redefine the concepts it uses in such a way to reassign the classification of existing data points incorrectly and systematically misallocate new data points to the wrong categories. The risk posed by concept drift in models for sports betting operators is that they can generate inexplainable and systematic errors in their predictions. To put it simply, as new data points are added to the dataset, there is a risk of misclassification which has to be mitigated.
It is also worth mentioning that most of the underlying technology and code for machine learning in sports betting is open source, meaning that everyone has access to the code and can also employ similar models. Therefore, operators also have to consider bettors who may pick up a systematic advantage from machine learning models.
Offsetting this risk requires both human and automated monitoring of bettors’ performance, identifying those whose pattern of betting outcomes is significantly beyond the norm, and looking into the types of bets they place. Betting operators can then limit the amount of money a bettor can place on a particular outcome, limit the number of bets they place for a particular game or sport, or impose deposit or withdrawal limits to discourage high volume automated bets.
Fan Segmentation
Beyond the direct use of machine learning for sports betting, operators can also benefit from machine learning to overhaul their marketing, sales, and content functions. Machine learning models can analyze customer journeys on operator websites, a customer’s betting patterns with particular teams or sports, and the customer’s appetite for risk and return based on historical bets.
Using this data, it is possible to leverage machine learning to personalize a bettor’s experience to reflect their preferences. This can include advertisements and banner content that is better suited to a bettor’s interests and values or automated and personalized incentives to encourage them to place more bets. It can also include targeting a particular club, fanbase, or community for platform onboarding based on their advertisement clickthrough rates or analyzing historical betting volumes.
Automated Content
Rapid engagement with audiences is an excellent way to drum up real-time interest in a sports team or a betting operator. Leading up to and during match time, the creation of live content to mirror the ebbs and flows of a game can generate massive fan engagement and direct traffic towards a brand.
Of course, the challenge here is being able to rapidly create content on-the-fly, given how short the window can be to generate content that quickly resonates with the viewing public. But alas, machine learning can play a significant role with companies like WSC Sports pioneering machine learning to generate clippings of high intensity moments for near-instant distribution on social platforms.
Automated content can help sports betting operators ride the crest of high momentum sporting moments, generating traffic and building up a reputation as an authority on a given sport with fans and the general public.
Simulations and Fantasy Sports
The rise of fantasy sports and simulated sporting events has also proved an enormous opportunity when to use machine learning models. Machine learning can be used to improve the selection of team formations in fantasy sports
There are some fantastic opportunities here as such platforms can provide simulations in real-time. This means bettors can enjoy a more realistic and exciting simulated experience, and betting operators can have a product that will encourage trust from the public while optimizing long-term revenue.
One example of such a platform is Betradar’s simulated reality soccer product, which plays out simulated fixtures in real-time to maximize engagement. It also provides pre-match and live betting markets on simulated fixtures, encouraging bettor participation in fantasy sports.
Sports Data Integrity
Another crucial way that machine learning can support sports betting operators is by minimizing fraud and reducing the risk of match fixing. Models can detect and flag particularly anomalous match outcomes and any peculiar trends in athlete performance over time.
While operators have been increasing the number and types of wager offerings over the years, machine learning solutions have become increasingly important in this regard. One example of machine learning being used to flag anomalous match outcomes to sporting authorities is Sportsradar. In 2020, the Sportsradar team flagged over 521 suspicious matches to relevant sporting authorities, providing crucial third-party oversight for sports events.
Operators face some notable challenges when deploying machine learning for fraud detection; these include problems such as regulatory inconsistencies and lack of data mandates. You can listen to our recent webinar on sports data integrity to find out how they are responding to these problems.
The Future of Machine Learning in Sports Betting
Machine learning can help sportsbooks improve crucial business functions like fraud detection, audience personalization, and marketing while unlocking new business functions, such as real-time simulated and fantasy sports games.
The challenge facing operators is to ensure they move to deploy machine learning in their operations swiftly and effectively while also ensuring machine learning is deployed in a robust, responsible manner to mitigate risks for customers and themselves.
If you have any questions or if you need a professional consultation on implementing machine learning capabilities into your sportsbook, drop us a line.
By Russell Karp,
Vice President of Media and Entertainment Practice at DataArt
