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# Can Machine Learning Predict the World Cup?

**[Towards Data Science](https://daily.dev/sources/tds)** · 16 min read · 0 upvotes · 0 comments

## Summary

A walkthrough of building a machine learning model in R to predict FIFA World Cup 2026 match outcomes using 49,000 historical international matches. The project compares multinomial regression, elastic-net, and LightGBM models using Elo ratings, rolling form features, and match context. LightGBM was selected as the final model (test log loss 0.873), though it only marginally outperformed simpler regression. A key finding is that all models struggle to predict draws — the selected model correctly identified only 2 out of 1,784 actual draws. The analysis concludes that football forecasting gains come primarily from leakage-safe feature engineering rather than model complexity, and that player-level data would be the most impactful next step.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://towardsdatascience.com/can-machine-learning-predict-the-world-cup>

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Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#r](https://daily.dev/tags/r), [#feature-engineering](https://daily.dev/tags/feature-engineering)

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