From Poker to Predictive Models: How Data Scientists are Revolutionizing Sports Betting

In the realm of sports betting, one individual is leveraging his extensive experience in academia, game design, and social media data analytics to construct comprehensive models across a multitude of sports.

Meet Dr. Darryl Woodford, an adept data scientist, who shares how he combines his unique expertise to gain an advantage in sports betting.

Nick Slade has more.

Data Modeling 

Darryl, co-founder and Chief Technical Officer at Cipher Sports Technology Group, has been developing predictive models for almost two decades. His journey began in the UK, where he studied engineering and game design, culminating in a master’s thesis at the University of Copenhagen on the agency of avatars in virtual environments. Subsequently, he became a research fellow at Queensland University of Technology, focusing on measuring and evaluating audience engagement via social media.

But sports were always on his mind… “I played a lot of Poker in the UK during the poker boom,” Woodford said. “But that got harder so I moved into sports betting where I was a pre-game and in-play punter for a number of years. But that got harder so I started building models instead,” he added.

Utilizing ‘Machine Learning,’ Woodford and his Cipher Sports colleagues created Dimers.com. “First, we founded a site called Fantasy Insider, projecting how individual players would perform in certain games. From there we moved off that into sports betting,” he said.

“We basically took what was a rating for how each player would perform and extrapolated that into a team model, running simulations. If you swap out someone like Patrick Mahomes and put in a different QB, how much impact would that have on the Chiefs for example,” Woodford explained.

Darryl Woodford is continually learning, and the sophistication of his models continues to advance. “When we started off we did the player’s predictions and tried to extrapolate those. We’ve got more sophisticated with sometimes 150-200 features in those models,” he said.

“We’ve also expanded into more simulation models. For sports like tennis point-by-point, which makes sense given the complexities. We currently do 20-25 thousand events a year, and are branching out into fighting sports - and exploring F1 - at the moment.”

When to bet and what is "an edge"?

Woodford discusses the increasing difficulty of finding edges in sports markets at the online sportsbooks.

“Betting markets are becoming more efficient. You are seeing advancements in AI and in data analytics. So edges are harder to find, but you can still find advantages,” he said.

“The US has legalised sports betting which creates new efficiencies. People start betting on sports they don’t know about, or on their home state out of partisanship and that can skew the odds in our favor,” he explained.

“You can look at models on sports that don’t get huge attention, like the MLS for example. If playing bigger markets like NFL or NBA I’d look at smaller player props markets like points, rebounds, assists, first quarter betting for example where edges might pop up.”

MORE: 20 Things Every Sports Bettor Should Know

How to build a model

As Woodford highlights, different sports require different data for accurate results.

“It varies by sport. Team stats in some sports can tell you most of what you need to know. In some sports, individual data combined to run simulations can be better. Once you get the prediction out, you either trust your model or you don’t. If you start second guessing when to bet, you will get into difficulties. Do you trust your process? That is the underlying question,” he said.

“In the feature engineering process that’s when the human judgement comes in. What are the key features you want to focus on? And have you got the right ones? If the answer is yes you should trust your model.”

To capitalize on this expertise Dimers moved to a subscription model, launching Dimers Pro on March 11, 2025. For less than $1 a day, users can get full access to all of the models built and maintained by their team of experienced data scientists.

How to get the data

The United States presents challenges in data gathering, with companies such as Sportradar holding significant control. Woodford offers advice on sourcing relevant information.

“You can buy historical odds data pretty easily these days. If it’s a hobby project you can look at some websites that give you good free data. NBA.com or basketball reference for example,” Woodford said.

“For prop betting they can be quite effective. NFL.com isn’t too bad either for information on a basic level.”

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Sports betting risks

Every facet of the game involves risks, and Woodford stresses the importance of trusting your data source and to always gamble responsibly.

“Where are they getting their information and are they reliable?” he asks.

“In the early 2000s, the models weren’t that sophisticated so you could find edges. Someone was using a model for Wimbledon and CBS had the rights but were showing matches on a 2-3 hour delay and they lined up the website scores with what they were showing on TV, not what was live. As the match went on, the odds started making no sense. It shows you have to know where your data is coming from and what they are doing,” he said.

“A few other points to be cautious of are is the edge too big? If it is you may have missed something in your model. Also has the market overreacted to a player going out? Which creates a bigger edge,” Woodford explains.