Unsurprisingly, this has generated a lot of excitement among sports bettors, who see predictive modelling as a way to gain a competitive edge.
As for the bookmakers, AI-powered predictive modelling holds the promise of creating more accurate betting odds that will allow for greater consistency in their profits.
This should also bring in new bettors by providing information to customers which takes a lot of the guesswork out of each bet.
Yet, as more companies enter the market offering these predictive models, users are increasingly questioning the accuracy of these predictions and whether they can truly provide a competitive advantage.
Having built and published many of these models myself, I can testify that the leaps forward we’re seeing in underlying technology has led to a significant increase in their accuracy, especially compared to older predictive models.
But I do think that creators need to be more transparent with their users on how these models actually work. They aren’t a crystal ball and should never be portrayed as one.
Instead, they’re simply a tool that can help users place smarter bets. Getting that message across to users will be critical for the future of AI sports betting.
Understanding AI-powered predictive analytics
Bookmakers and their clientele are no strangers to predictive analytics. For years, oddsmakers have used predictive analytics to determine the odds for the sportsbooks that we’ve all become familiar with.
However, in recent years, AI models have emerged that can sort through massive amounts of data and autonomously improve predictions through machine learning (ML) and reinforcement learning (RL) algorithms.
These AI-based models are worlds above traditional methods in both speed and accuracy.
What’s more, these models are not just an advantage for the bookmakers, but also the average bettor.
By using models built with the very latest technology and utilising many of the libraries which underpin the LLM (Large Language Models) that OpenAI’s ChatGPT has brought to the fore, bettors may be able to determine more accurate predictions than the bookmakers may have calculated.
At least for a limited time anyway. This can be a huge advantage for savvy bettors.
Yet this raises a question: if both bookmakers and bettors can now make highly accurate predictions for every sporting event, isn’t that sort of a zero-sum game? Ultimately, yes – but technology will continue to advance for years to come, and there will be edges to be found.
And of course, there will always be an element of chance regardless of how much data the model is provided with. No matter how you crunch the numbers, the real world, to paraphrase Hamlet, defies augury.
What bettors need to know when using these tools
Any experienced bettor will know that a massive number of variables go into determining the likely outcome of a sporting event.
For instance, in an NBA game, some of the top variables may be a team’s defensive rating, offensive rating, rebound differential, and 3-point percentage, to name just a few.
Adding to that, you’ve also got other variables such as betting trends, projected win probabilities, and other public wagering data.
Tracking all this data can be a massive undertaking, even for the most advanced AI predictive modeling software.
This means that savvy bettors should try to balance their odds by comparing the results between different models.
Even then, you’ll want to do your own research, check for any late-breaking news, and recheck the odds before placing a bet.
The takeaway from this is that predictive models are not money-making machines.
Serious bettors can save a lot of time and gain a competitive advantage if they know how to use these models, but to do that still requires a lot of work on the user’s part.
More casual bettors can view predictive models as a way to make betting more approachable and hassle-free.
How bookmakers can build trust in their AI models
For bookmakers looking at launching products based on AI models or trend analysis, I would advise bookmakers to be as transparent as possible on how their predictive models work.
This means broadly sharing the data you’re collecting, the approach taken to building the model, and setting the right expectations.
From there, it’s all about improving your model and marketing it as a new experience for bettors.
For publishers and end users, to overcome the bookmakers, you’ll need to feed your predictive model with an enormous amount of data that is updated in real time.
Also, try to find some hard-to-find or unique variables that you can incorporate. I know from my own experience with putting these models together that you’ll need data from a large number of games just to get started.
For instance, in baseball, you might get away with a couple of seasons. But for sports such as the NFL, where fewer games are played, you’ll need to be looking long-term.
In bigger sports, bettors should be targeting a small edge, and your model will need an accuracy of at least 52.4% to maintain profitability over a bookmaker in against-the-spread or total markets.
In smaller markets, I’ve seen bettors achieve an accuracy of up to 60% with the same modelling techniques. However, those opportunities are becoming rarer and rarer.
And for the bookmakers, it’s time to consider utilising these bettors and modellers rather than shutting them down.
If you can see a user placing bets that follow the pattern of a computer-driven model and they are winning consistently, then by all means monitor their bets, lay off their bets, and use the information elsewhere.
But also try to see it as an opportunity to increase your profits, rather than taking a short-term view and cutting them off.
Final thoughts
In summary, AI-powered predictive models can be a highly useful tool that can save bettors a lot of time when analysing the data of a particular match.
However, at the end of the day, they are still only a tool. Human input will still be necessary to make sense of the data and determine which bet has both a good chance of winning and providing a decent payout.
Ultimately, it’s up to users to decide how much they want to get involved in sports betting predictive analysis. And, most importantly, they shouldn’t forget to have fun along the way.
This article was authored by Dr. Darryl Woodford PhD, CTO at sports betting analytics company Cipher Sports. He is a data scientist and Python developer, with a particular interest in sports betting.
