Can AI Beat the Sports Betting Market? 8 of the Top Models Tried

Blockchain ekosistemine dair önemli bir haber gündeme geldi. Can AI Beat the Sports Betting Market? 8 of the Top Models Tried Price data by News Artificial Intelligence Can AI Beat the Sports Betting Market? 8 of the Top Models Tried KellyBench put Claude, GPT-5, Gemini, and Grok through a full Premier League season of betting.

Not one turned a profit. By Jose Antonio Lanz Edited by Guillermo Jimenez Apr 15, 2026 Apr 15, 2026 5 min read Image created by using AI Create an account to save your articles. Add on Google Add as your preferred source to see more of our stories on Google.

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In brief Frontier AI models blew up betting on real-world football markets. They knew the right strategy—but failed to execute it. A simple 1990s model was able to best most of them.

General Reasoning just gave frontier AI its worst report card yet. Eight top models, including Claude, Grok, Gemini, and GPT-5. 4, were each given a virtual bankroll and asked to build a machine learning betting strategy across a full 2023-24 English Premier League season.

Every single one lost money. Several went completely bankrupt. The benchmark is called KellyBench , named after the Kelly criterion, a 1956 formula that tells you exactly how much to bet when you have an edge over the market.

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Every model could recite the Kelly formula. None of them could actually use it. 20 failed all three runs, going fully bankrupt in one, forfeiting mid-season in the other two.

Google's Gemini Flash forfeited two of three runs after placing a single wager of roughly £273,000 on a three-percentage-point historical win-rate edge—and losing it. 6, Anthropic's best model, lost 11% on average and somehow came out looking like the responsible adult in the room. In fact, the research paper mentions that the old Dixon-Coles from the late 1990s outperformed most of the frontier models evaluated — finishing ahead of six out of eight, even with limited data.

“Dixon-Coles is an outdated 2000s baseline which doesn’t utilise all available data or account for non-stationarity in a principled way,” the researchers note. “It is therefore even more surprising that many frontier models, such as Gemini 3. 1 Pro, are unable to beat or match it on KellyBench.

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