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# I Built 11 Models to Predict the 2026 World Cup. They Crown Four Different Champions.

**[Towards Data Science](https://daily.dev/sources/tds)** · 12 min read · 0 upvotes · 0 comments

## Summary

A data scientist built 11 different predictive models for the 2026 FIFA World Cup using the same match dataset and tournament simulator, covering rating systems (Elo, Colley, PageRank), goal distribution models (Poisson, Negative Binomial), and five ML classifiers (logistic regression, KNN, random forest, XGBoost, neural network). The models crown four different champions — Spain, Argentina, France, and Netherlands — illustrating how model choice, information source, and bias-variance tradeoffs drive divergent predictions. The key insight is that the disagreement between models is more informative than any single forecast, and that simpler models outperform flexible ones on the small 358-match dataset due to overfitting.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://towardsdatascience.com/i-built-11-models-to-predict-the-2026-world-cup-they-crown-four-different-champions>

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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#python](https://daily.dev/tags/python), [#xgboost](https://daily.dev/tags/xgboost)

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