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.