How Sportsbooks Should Respond as Bettors Embrace Predictive Analytics

The following article is written by Adam Fiske, co-founder and CEO of sports analytics company Cipher Sports, as a special contributor to the Gambling911.com website.

Anyone who's been paying attention will know that there's been a steady uptick in the use of AI-powered predictive models in the sports betting industry. Rapid advancements in the field of AI mean that sports bettors are gaining access to vital predictive tools - like those seen on Dimers.com - that can enhance their sports betting decisions.

Given that the genie is now out of the bottle, this poses a challenge for sportsbooks that will need to navigate a complex landscape in which bettors are becoming increasingly savvy in their betting habits. I’ve seen firsthand how these new predictive models can give bettors a competitive edge, and I don't believe there's any doubt that sportsbooks need to quickly adapt to their increasing usage among bettors.

However, instead of arguing against such models, I believe sportsbooks must embrace them. There are a lot of benefits to be gained from these tools and it’s high time that more sportsbooks recognized this.

Understanding AI predictive analytics

Predictive analytics is a mathematical process that analyzes patterns in current and historical data to predict the likelihood of future events or outcomes. It’s been in use for centuries, with one of the first known applications dating back to 1689, when the Lloyd’s of London insurance market was established. The organization needed a scientific way to assess risks when underwriting an insurance policy. And the company did this by feeding data into a predictive model that could estimate the probability of a particular event occurring.

While the mathematics behind predictive analytics has remained much the same since those early days, improvements in data collection and record-keeping standards have vastly increased the amount of data that an analyst has to play with. But perhaps even more important is that the rise of modern computing has allowed for the creation of programs that can do all the heavy lifting of crunching the numbers. And that's before we even get to talking about the technological advances of the last few years, such as big data and AI.

We at Cipher Sports Technology Group work within the rapidly growing field of predictive betting models, and feel it is no exaggeration to say that the technological leaps of the last few years have been truly revolutionary. Compared to the models that existed just ten years ago, the latest AI-powered ones are on a whole new level in terms of accuracy, speed, and efficiency. Moreover, these new models can use machine learning (ML) and reinforcement learning (RL) algorithms to autonomously learn from the data and improve their predictions over time, capabilities we have deployed across our consumer brands Dimers.com and StatsInsider.com.au.

Given the power of these AI predictive models, it’s no surprise that they’ve generated a lot of attention among avid sports bettors. As a result, the market for sports betting predictive models has soared over the last few years, some paid, some free, and some a combination of the two. Most importantly though, anyone can use these models, and this has huge implications for the sports betting industry.

The need to embrace predictive analytics

On the face of it, bettors using predictive models to improve their bets may seem like a bad thing for bookmakers. After all, if a bettor can now place wagers that have a higher chance of winning every time, wouldn’t that result in more payouts and falling profits for the bookmaker?
 
In short, yes, but such a short-term view ignores the fact that the more certain an outcome is, the lower the payout will be. Experienced bettors know this and will try to improve their odds by calculating more accurate predictions that the bookmaker may have miscalculated. But pulling this off every time requires finding unique variables to include in the model, staying up to date on the latest information, and following your own gut instinct. In other words, it’s a lot of work that only the most committed bettor will be willing to commit to.
 
For a more casual bettor, predictive models are simply a way to save time and have more fun when placing bets. As I see it, this is one of the key reasons why bookmakers should actually adopt predictive models as a service for their customers. By doing this, sportsbooks provide a new experience for bettors and increase engagement. But more than that, predictive models also make sports betting more approachable for new clientele that have never placed a bet before.
 
Another benefit of embracing predictive modeling is that it allows a sportsbook to assess and analyze user data. This data can include things like where they’re from, what their interests are, and where they spend the most time on your site. By feeding this data into a predictive model, sportsbooks can determine where they should focus their marketing, as well as how they can increase both the number of bettors and the number of bets placed.

Some Final Thoughts

As a closing thought, I would ask each reader to imagine a near future in which predictive models are used by almost every sports bettor. In anticipation of such a future, sportsbooks would be wise to learn all they can about predictive analytics and use it to their advantage. These models aren't going away. If anything, they’re only going to become more prevalent and sophisticated as AI technology advances.
 
So, rather than combatting their use – which will only alienate bettors and drive them into the hands of your competition – sportsbooks should view these models as an opportunity to increase their profits and user engagement.