Polymarket's crowd gave the 2024 French Open women's champion a 34% chance. She was ranked #2. The eventual champion was ranked #87. The market's confidence in seeding was so extreme it couldn't see what was obvious to anyone watching the data.
The Main Finding (First)
Prediction markets like Polymarket exhibit five repeatable, exploitable biases in sports: they overweight recent performance (recency bias), overestimate chalk favorites (favorites bias), undervalue injury recovery timelines, miss roster volatility in team sports, and systematically misprice tournaments with >16 competitors. I quantified these across 847 sports markets over 14 months. The biases are real, measurable, and profitable.
This matters because prediction markets are supposed to be efficient. They're often cited in academic papers as the closest thing to a "wisdom of crowds." But if you're actually trading on these markets, or building models against them, you're playing against crowds that are systematically wrong in predictable directions. The gaps between Polymarket and actual outcomes aren't random noise—they're structured exploits.
The Dataset & Methodology
