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LLMs struggle with football player career data accuracy

A recent test evaluated eight large language models on their ability to accurately recall football (soccer) player career data, specifically the clubs they played for. The models were prompted to provide this information in a JSON format, with a specific instruction to decline if unsure. However, most models attempted to answer even for less famous players, leading to a high rate of inaccuracies. DeepSeek V4.1 Flash demonstrated the highest accuracy but was significantly slower, while GPT-5.4 mini was faster and named more clubs but with lower precision. Claude Haiku 4.5 declined the most players but still provided many incorrect club associations. AI

IMPACT Highlights LLM limitations in factual recall and the need for careful prompting and verification, especially for niche data.

RANK_REASON LLM evaluation on a specific knowledge domain. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs struggle with football player career data accuracy

How we ranked this

Signal score
29 / 100
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Newsworthiness bucket
Tool
LLM evaluation on a specific knowledge domain. [lever_c_demoted from research: ic=1 ai=1.0]
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Single-source cluster
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model release, product
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High
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Breaking (< 6h)
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Full methodology in our editorial standards.

COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Remdore ·

    I asked eight models which clubs footballers played for

    <p><a href="https://extratime.world" rel="noopener noreferrer">ExtraTime</a>, my football quiz site, has a database of footballers and every club they played for, checked by hand, because the games depend on it. Career Path shows you a player's clubs and you guess who he is. Five…