Read-only research repo that samples resolved Polymarket markets, prices them 7 days before scheduled end, and measures calibration and favorite-longshot bias with Brier decomposition, recalibration slopes and event-clustered errors, broken out by category and volume. The author's findings (self-reported) are that deviations are small and smaller than trading costs; a second part on arbitrage and order books is still collecting data.
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Read-only research repo that samples resolved Polymarket markets, prices them 7 days before scheduled end, and measures calibration and favorite-longshot bias with Brier decomposition, recalibration slopes and event-clustered errors, broken out by category and volume. The author's findings (self-reported) are that deviations are small and smaller than trading costs; a second part on arbitrage and order books is still collecting data. See the source for setup, usage and current status.
Tools: PythonPolymarket public APIs
Difficulty: advanced
Platforms: Polymarket
Read-only research repo that samples resolved Polymarket markets, prices them 7 days before scheduled end, and measures calibration and favorite-longshot bias with Brier decomposition, recalibration slopes and event-clustered errors, broken out by category and volume. The author's findings (self-reported) are that deviations are small and smaller than trading costs; a second part on arbitrage and order books is still collecting data. It is listed under Data & analytics in this directory.
According to its source, it works with Polymarket.
The source lists: Python, Polymarket public APIs.
It was published by aidaman01. The public source is at github.com/aidaman01/prediction-markets.
This directory rates it advanced (beginner, intermediate or advanced).
It had 1 GitHub star as of Oct 18, 2026.
The repository was last updated on Oct 17, 2026, as checked on Oct 23, 2026. Check the source for current status.
Compare all data & analytics tools → · How to build a Polymarket bot
More data: Prediction market datasets and free Kalshi JSON downloads
Framework for collecting and analyzing prediction-market data, including a large public Kalshi prediction market dataset.
Dataset stat Becker Kalshi dataset: 7.68M markets; 23.9% of settled markets resolved YES. Method on detail page.
Scripts to collect Polymarket market and trade data for analysis.
Student research notebook (Rennes School of Business graduating project) that compares the probability distribution implied by SPXW and NDX option prices, using a fitted SVI smile, with the distribution quoted by Kalshi's S&P 500 and Nasdaq-100 range contracts for the same expiry. Uses Yahoo Finance option data and the Kalshi public API, with a saved snapshot in the repo. Findings are self-reported and based on a single snapshot.
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