
Can AI Beat the Sports Betting Market? 8 of the Top Models Tried
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...
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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.
Piyasa Dinamikleri
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.
Piyasalara Etkisi
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“Dixon-Coles is an outdated 2000s baseline which doesn’t utilise all available data or account for non-stationarity in a principled way,” the researchers note.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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