AI-powered sports prediction tools are everywhere right now, but Staicy is taking a slightly different approach. Instead of asking users to blindly trust an algorithm, the company combines millions of data points, roughly 1,500 daily predictions, and continuous AI analysis with a professional handicapper making the final call. Staicy CEO Ryan Robey and COO Jesse Spause sat down exclusively with Trade Handle to explain how that system works, where prediction markets fit in, and why they are willing to admit when their models get something wrong.
The timing is important for Staicy. Robey said the company began building the platform roughly 20 months ago and only recently reached the point where its Apple and Android apps, models, and dashboard are all fully operational. That means the 2026 season will be Staicy's first full football season with the entire product working together.
Staicy Starts With Around 1,500 Predictions a Day
Staicy does not simply generate a handful of picks each morning. Robey told us the system produces around 1,500 predictions per day before comparing its projections against available market prices and identifying where its models see the largest differences. Only a small percentage of those predictions eventually become suggested plays.
"We get 1,500 predictions. We take what our prediction is versus the Vegas line and what fair odds should be based on our modeling, and we look for edges."
Robey said Staicy typically looks for edges above 10%, which can turn those 1,500 predictions into suggested plays. The company's AI continues to analyze performance and make small adjustments based on millions of data points. A professional handicapper then reviews the information and makes the final call, keeping a human involved rather than allowing the model to operate completely on its own.
When the Model is Wrong, Staicy Pulls It
One of the more interesting parts of our conversation had nothing to do with Staicy's successful models. Robey openly discussed an MLB pitcher-outs model that simply wasn't producing the results the company wanted. Staicy tried adjusting it but eventually determined that variables such as pitch counts, injuries, matchups, and managerial decisions made the category too difficult for the system to model consistently.
"We looked at it and said, 'We're not good at this. We're not going to do it anymore.'" That led to another line Robey repeated throughout our interview. "We're not trying to win a science contest; we're trying to win money."
Staicy has also found categories where its models have performed much better. Robey pointed to certain baseball hit markets and pitcher strikeout unders, while Spause specifically highlighted the platform's ability to identify situations where fading a player can provide value. Those results influence what Staicy's backend system prioritizes moving forward.
Spause Says Staicy isn't Afraid to Fade the Hype
Staicy COO Jesse Spause pointed to another part of the platform's approach that can easily get overlooked. The system isn't designed to only find exciting plays where it expects something to happen. Staicy has also found success identifying players and statistics where its models believe the market has gone too far in the other direction. "Staicy's big on the fades, too. She loves fading pitcher strikeouts and batter hits, runs, RBIs on players."
That fits with what Robey told us: more than half of Staicy's suggested plays sometimes land on unders. It might not always be the most exciting way to follow a game, but Staicy isn't building its recommendations around what is most entertaining. If the model consistently sees value going against a popular player or statistical expectation, Spause said those are exactly the opportunities Staicy wants to identify.
There is also a useful philosophical point here. Knowing what to avoid can be just as valuable as knowing what to play. Staicy's approach is less about finding action in every game and more about letting its data determine where an opportunity actually exists, even when that means fading the more obvious side.
Transparency is a Big Part of the Pitch
Anyone can advertise their best day. Staicy wants users to see what happened on the bad ones, too. Robey told Trade Handle that Staicy publishes every selection, whether it wins or loses. He said the company had not recorded a negative monthly ROI during roughly 18 months of tracking at the time of our interview, while making clear that individual losing days absolutely happen. "We lay it all out there. There will be days where we lose, and that's just how it is."
That transparency matters even more in an industry filled with eye-catching performance claims. Historical performance cannot guarantee what happens next, but publishing both sides of the record at least gives you something tangible to evaluate.
Prediction Markets Are Already Part of Staicy
Prediction markets were one of the main reasons we wanted to catch up with Robey and Spause. Staicy already incorporates them when searching for the best available prices rather than treating them as a completely separate product. "Oftentimes the best line is available at the prediction markets."
Staicy still displays prediction market prices in a format familiar to traditional sports users because Robey believes a learning curve remains around understanding what a 64-cent contract means. Underneath that presentation, however, the objective remains the same: compare Staicy's model against available prices and find where the model sees value.
That crossover could become increasingly important as prediction markets grow. More venues mean more prices to compare, but fees and execution costs must be considered before assuming a better displayed price means a better return.
A Human Still Gets the Final Say
Staicy is leaning heavily into AI, but Robey does not want the company to become completely dependent on it. The models undergo regular checkups and revisions, while a professional handicapper reviews reports and retains final control. "We are very much embracing the power of AI and the power of that computing to make sure we stay ahead of the curve."
Users can also interact with the human side of the operation. Staicy built a community chat directly into its mobile app where members can discuss picks, strategy, and individual games. Robey said users can even ask the company's handicapper about a game that did not produce an official suggested play and get his personal view.
Football is Staicy's Next Big Test
Football presents Staicy with a much different data challenge than baseball. Robey said the company expects the opening weeks to require some patience as models begin processing new rosters, quarterbacks, offensive lines and coaching tendencies. Receiver usage is one area Staicy believes its deeper analysis could become particularly useful once enough current-season information becomes available.
The company is already using deep-learning analysis around receiver route running and related information. Robey expects that picture to become much clearer after teams have played two or three games and actual usage patterns begin replacing preseason assumptions.
"It's going to take a week or two to get it fully rolling. With new teams, new rosters, new quarterbacks, new offensive lines. But after that, we're going to be cooking."
The Trade Handle Prediction Markets Take
Plenty of companies can attach AI to a sports product. What makes Staicy interesting to us is how much attention Robey and Spause put on what happens after the algorithm produces an answer.
Generating roughly 1,500 predictions every day sounds impressive, but filtering them, tracking every result, recognizing weak models, and occasionally admitting, "We're not good at this," tells us considerably more about the product. Staicy is also entering football season with prediction markets already incorporated into its search for value, giving us another reason to keep watching how the platform develops.
The next few months should provide a much better test. Baseball gave Staicy its first full-season dataset, while football will test the complete platform at a much larger scale. If the models perform, the company has the records to show it; if they don't, Robey made it clear that Staicy isn't afraid to change them. That is what makes Staicy.win worth a try if you are looking for help with your sports trades.