Analytics Blog
Methodology notes, model deep-dives, and field guides to using AI4GAMEDAY across the leagues we support.
- ·10 min readSports Prediction ModelsAlgorithmsMethodologyMonte CarloPoisson
Sports Prediction Models and Algorithms: A Transparent Guide
How modern sports prediction models actually work — Monte Carlo simulations, Poisson distributions, Elo, logistic regression, and ensembles — and how AI4GAMEDAY uses them as an alternative to black-box prediction sites.
- ·8 min readAI Sports PredictionsMethodologyExplainable AI
How AI Models Predict Sports Outcomes: A Guide to Explainable Analytics
A transparent deep-dive into how AI sports predictions are built — the factors AI4GAMEDAY weighs (player rest, lineup changes, travel), how the model produces calibrated probabilities, and how the Accuracy Ledger proves it.
- ·6 min readNHLPlayoff SimulatorMonte Carlo
NHL Playoff Odds 2026: How Our Monte Carlo Model Works
How AI4GAMEDAY simulates the rest of the 2026 NHL season tens of thousands of times to produce calibrated playoff odds — and how to read them.
- ·5 min readWin ProbabilityLive Analytics
Win Probability, Explained
What live in-game win probability actually measures, why it jumps on big plays, and how to use it without overreacting.
- ·5 min readNFLPlayoff Simulator
NFL Playoff Simulator: How to Read the Bracket
A short field guide to reading NFL playoff probabilities — division odds, wild card race, seeding, and Super Bowl percentages.
- ·4 min readPower RankingsMethodology
Power Rankings, Explained
How AI4GAMEDAY builds power rankings from possession-level data, what the tier badges mean, and why they sometimes disagree with the standings.