Sports analytics, prediction markets, and building trading bots with Python.
Complete tutorial for building an NBA live win probability model in Python using XGBoost. Covers data collection, feature engineering, training, calibration, and live prediction. Working code included.
Comparing the top sports data APIs for prediction modeling and trading in 2026. Coverage, pricing, latency, and what each is best for.
We run automated prediction bots on Polymarket across 7 sports. Here's our verified P&L, the strategy, and what we learned from 938+ live trades.
The same NBA game is priced differently on Polymarket, DraftKings, FanDuel, and BetMGM. Here's the math, the architecture, and the actual disagreements we see.
Our NBA bot had 65% accuracy and was losing money. The problem wasn't the model — it was a calibration bug that left it confidently wrong. Here's how we found it.
We audited every bot, found the gaps between backtest and live performance, and fixed them. CS2, NBA, MLB, LoL, and Tennis — five different problems, five different fixes.
Counter-Strike 2 has the widest mispricings on Polymarket. It also has the worst data infrastructure. Here's how we built a 4-tier model that handles both.
You don't need a quant fund. Our complete trading infrastructure runs on a $7/month VPS plus $5/month for sportsbook odds. Here's the full breakdown.
Our system finds dozens of trading signals per day. We trade 35% of them. The discipline to reject bad signals is worth more than the ability to find good ones.
ATP prediction model results: 8W-4L, 66.7% win rate. ML-powered fair value vs Polymarket prices.
Counter-Strike 2 prediction model results: 19W-25L, 43.2% win rate. ML-powered fair value vs Polymarket prices.
League of Legends prediction model results: 10W-6L, 62.5% win rate. ML-powered fair value vs Polymarket prices.
NBA Basketball prediction model results: 2W-4L, 33.3% win rate. ML-powered fair value vs Polymarket prices.
NHL Hockey prediction model results: 16W-10L, 61.5% win rate. ML-powered fair value vs Polymarket prices.
MLB Baseball prediction model results: 21W-12L, 63.6% win rate. ML-powered fair value vs Polymarket prices.
SOCCER prediction model results: 2W-1L, 66.7% win rate. ML-powered fair value vs Polymarket prices.
College Basketball prediction model results: 4W-0L, 100.0% win rate. ML-powered fair value vs Polymarket prices.
NCAAWB prediction model results: 3W-0L, 100.0% win rate. ML-powered fair value vs Polymarket prices.
ZenHodl week 15 live trading results across 9 sports. 57.8% win rate on 147 trades.
Every one of our bots holds to settlement instead of actively trading. Here's why patience crushes activity on Polymarket — and the failed strategies that taught us.
The same game is priced differently on Polymarket, DraftKings, FanDuel, and BetMGM. Here's how to systematically find and exploit the differences.
Real P&L from 5 live Polymarket bots trading NBA, MLB, NHL, NCAAMB, CS2, LoL, Tennis, and Soccer. Honest about what works and what doesn't.
We backtested 237 trade signals with and without execution constraints. 99% of theoretical profit vanished. Here's what actually kills your trades — and the narrow windows where execution works.
ZenHodl week 14 live trading results across 5 sports. 81.8% win rate on 11 trades.
Tennis prediction model results: 6W-0L, 100.0% win rate. ML-powered fair value vs Polymarket prices.
Step-by-step guide to using the Polymarket CLOB API in Python. Covers authentication, reading orderbooks, fetching market data, and placing limit orders programmatically.
A 6-feature model outperforms a 50-feature model at making money — even with a worse accuracy score. Here's why, and what it means for how you should think about feature selection.
A well-calibrated model makes money even with lower accuracy. Learn how to measure calibration with Brier score and ECE, and fix it with isotonic regression — with full Python code.
How to get real-time sports scores using WebSockets in Python. Compares HTTP polling vs WebSocket streaming, with working code for building a live data feed.
A complete step-by-step tutorial to implement an Elo rating system in Python for sports prediction. Covers the math, K-factor tuning, season resets, and evaluation against real NBA data.
A practical guide to backtesting sports betting and prediction market strategies in Python. Covers the common pitfalls — survivorship bias, look-ahead bias, execution assumptions — that make backtests lie.
A beginner's guide to automated trading on Polymarket. Learn how bots find edges using win probability models, and how to build your own.
How to use Python to scrape live scores, play-by-play data, and win probabilities from ESPN's hidden API. Complete tutorial with working code.
How to implement Elo ratings from scratch in Python for sports prediction. Covers home advantage, K-factor tuning, and season resets.