17 posts tagged “tutorial”.
Step-by-step 2026 walkthrough for US users signing up to Kalshi — eligibility check, account creation, KYC with SSN, ACH funding from your bank, finding...
Practical architecture guide for building a Polymarket trading bot in Python in 2026 — the eight components you need, how they fit together, what to build...
Practical 2026 walkthrough for first-time Polymarket users — geographic access, wallet setup, USDC funding on Polygon, finding sports markets, placing...
Pragmatic seven-step workflow for integrating a sports prediction API into your trading bot, dashboard, or research pipeline — auth, polling cadence, edge...
Step-by-step guide to building a sports prediction API in Python, from live data collection and feature engineering to model calibration and FastAPI...
Prediction markets are the fastest-growing alternative to traditional sportsbooks. This 2026 guide explains what they are, how they differ from...
Prediction market APIs compared: Polymarket CLOB, Kalshi REST, ESPN scoreboard/summary endpoints, The Odds API, auth, fees, rate limits, and Python.
How win probability models actually work — from Elo ratings through logistic regression to XGBoost. Includes calibration, feature engineering, and the...
Train and evaluate a calibrated NBA win probability model in Python using play-by-play game states, walk-forward validation, XGBoost and isotonic regression.
Step-by-step Polymarket CLOB API guide in Python: authentication, reading orderbooks, fetching market data, and placing limit orders programmatically.
A 65%-accurate model can be a money loser. A 53%-accurate well-calibrated one prints money. Here's how to measure calibration with Brier score and ECE...
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...
A practical guide to backtesting sports betting and prediction market strategies in Python. Covers the common pitfalls — survivorship bias, look-ahead...
Python examples for ESPN scoreboard and summary endpoints: event IDs, winprobability fields, playId joins, missing sports and data limits.
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 implement Elo ratings from scratch in Python for sports prediction. Covers home advantage, K-factor tuning, and season resets.