--- title: We Thought We Had Predicted the World Cup. | daily.dev description: Foresportia's AI model correctly ranked Spain and Argentina as the top two World Cup favorites before the tournament, and they did reach the final. However, a... canonical: https://daily.dev/posts/we-thought-we-had-predicted-the-world-cup--c9xqjvull twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: We Thought We Had Predicted the World Cup. | daily.dev og:description: Foresportia's AI model correctly ranked Spain and Argentina as the top two World Cup favorites before the tournament, and they did reach the final. However, a... og:url: https://daily.dev/posts/we-thought-we-had-predicted-the-world-cup--c9xqjvull og:image: https://api.daily.dev/og/posts/c9XqJvULl.png og:image:alt: We Thought We Had Predicted the World Cup. og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # We Thought We Had Predicted the World Cup. **[Medium](https://daily.dev/sources/medium_js)** · 9 min read · 0 upvotes · 0 comments ## Summary Foresportia's AI model correctly ranked Spain and Argentina as the top two World Cup favorites before the tournament, and they did reach the final. However, a full audit of all 500 predictions across 104 matches revealed important nuances: the model achieved 64.4% accuracy on strict 1X2 match outcomes and 70.4% overall, with much stronger performance on Double Chance (88.5%) and Draw No Bet (79.8%) markets. The main weakness was overconfidence in favorites converting superiority into wins — 20 of 37 incorrect predictions ended in draws. Aggregate goal estimates were accurate at tournament scale but poorly allocated to individual matches. The post-mortem identifies three specific areas for improvement and argues that publishing predictions before the tournament and auditing all failures — not just successes — is what makes model evaluation meaningful. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Football meets machine learning: Forecasting the 2026 FIFA World Cup](https://daily.dev/posts/football-meets-machine-learning-forecasting-the-2026-fifa-world-cup-oqcy3znjp) · R-bloggers · 0 upvotes · 0 comments - [Machine learning meets reality: Forecast evaluation for the 2026 FIFA World Cup](https://daily.dev/posts/machine-learning-meets-reality-forecast-evaluation-for-the-2026-fifa-world-cup-oyrlq2252) · R-bloggers · 0 upvotes · 0 comments - [I Built 11 Models to Predict the 2026 World Cup. 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