Towards Data Science
Jun 6
Who Will Win the 2026 Soccer World Cup?
Building a forecast from Elo, Poisson, and 10,000 simulations The post Who Will Win the 2026 Soccer World Cup? appeared first on Towards Data Science .
I Built 11 Models to Predict the 2026 World Cup. They Crown Four Different Champions.
A single model hands you a single answer and no sense of how much it hinges on the dozens of choices buried inside it. The post I Built 11 Models to Predict the 2026 World Cup.
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arXiv AI
Aug 5
AI World Cup 2026: Benchmarking Large Language Models for End-to-End Football Tournament Prediction
arXiv:2608. 03416v1 Announce Type: new Abstract: Large language models (LLMs) are now regularly asked to forecast real-world events, but comparisons are often difficult because models receive different information, use different tools, and are evaluated under different rules.
Towards Data Science
Jun 9
Can Machine Learning Predict the World Cup?
Building an ML football forecaster in R The post Can Machine Learning Predict the World Cup? appeared first on Towards Data Science .
arXiv AI
Jul 28
LLM-SoccerArena: Benchmarking LLMs on Real-World Predictions in Sports
arXiv:2607. 24573v1 Announce Type: new Abstract: Large language models (LLMs) increasingly support decisions about uncertain future events, yet evaluating their ability to forecast real-world outcomes remains difficult.
arXiv AI
Jul 21
WorldCupArena: Fine-Grained Evaluation of Language Models and Deep-Research Agents on Football Forecasting
arXiv:2607. 18084v1 Announce Type: new Abstract: Predicting a football match before kickoff requires more than knowing past results: a model must use changing information and make a clear prediction before the answer is available.
arXiv AI
Jul 21
FIFA World Cup 2026 as a Contamination-Free Benchmark for LLM Forecasting Agents: Four Models, a Bookmaker, and 104 Matches
arXiv:2607. 17765v1 Announce Type: cross Abstract: We introduce WC2026-Agents, a benchmark and dataset for evaluating large language models (LLMs) as autonomous forecasting agents on real, future events.
Hugging Face Trending Papers
Jul 20
WorldCupArena: Fine-Grained Evaluation of Language Models and Deep-Research Agents on Football Forecasting
Predicting a football match before kickoff requires more than knowing past results: a model must use changing information and make a clear prediction before the answer is available. We present WorldCupArena, a dynamic benchmark for language models and deep-research agents.
Towards Data Science
Jul 8
Information Theory and Ensemble Models
How should we ensemble time-series forecasts better? The post Information Theory and Ensemble Models appeared first on Towards Data Science .
Towards Data Science
Jun 28
I Pitted XGBoost Against Logistic Regression on 358 Matches. The Boring Model Won.
A concrete bias–variance lesson: why the smallest model had the best cross-validated fit, and how to know when to reach for the big hammer. The post I Pitted XGBoost Against Logistic Regression on 358 Matches.
Towards Data Science
Jun 9
The Exact ML Project I’d Build to Get Hired in 2026
Follow this framework to build a project that will impress hiring managers The post The Exact ML Project I’d Build to Get Hired in 2026 appeared first on Towards Data Science .
Hugging Face Trending Papers
Aug 5
From Score Matrices to Football-Aware Match-State Simulation: An Auditable LLM Harness for Exact-Score Reranking
Football score forecasting combines a strong statistical core with a difficult contextual edge. Dynamic Poisson-family models estimate team strength, expected goals, and coherent score probabilities, but do not directly understand roles, tactical matchups, motivation, or how a first goal changes behaviour.
arXiv AI
Jul 15
The One-Word Census: Answer-Choice Conformity Across 44 Language Models
arXiv:2607. 12796v1 Announce Type: cross Abstract: When a language model must pick one answer from a large space of equally valid options, which does it pick -- and how often is it the same answer every other model picks?