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.
arXiv Machine Learning
Sep 10
Forecasting the Winner of a Live Tennis Match
arXiv:2609.07617v1 Announce Type: new Abstract: With the rise of live sports betting in recent years, tennis forecasting has expanded from pre-match prediction to models that update win probabilities...
arXiv Computation and Language
Sep 14
Information Specialization and Constrained Synthesis in Multi-Agent LLM Forecasting: A Prospective Live-Study of the 2026 FIFA World Cup
The study evaluates a multi‑agent large language model system for forecasting outcomes of the 2026 FIFA World Cup. Two specialist agents—one quantitative and one news‑focused—produce forecasts that are then reviewed by a critic and combined by a meta‑agent. Results show the news specialist performs best, matching betting market accuracy, while the meta‑agent adds little beyond the specialists’ predictions.
Simon Willison
Aug 17
Qwen 3.8 27B scores 52 on the Artificial Analysis Intelligence Index
Qwen 3. 8 27B scores 52 on the Artificial Analysis Intelligence Index That's the same score as GPT-5.
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.