Baseball, modelled — and explained
Pitcher-aware Elo, FIP over ERA, bullpens, closing line value, and how to read the daily MLB board. No tips — the thinking.
What is FIP — and why our MLB model trusts it over ERA
Fielding Independent Pitching strips defence and luck out of a pitcher's record. Here's why FIP predicts the future better than ERA, and how our model uses it.
Modal goes multi-sport: football + MLB, one desk
The sport switcher is live: football and MLB side by side, free during the beta — with per-sport plans and an all-sports Edge tier arriving when the beta ends.
MLB predictions today: how our pitcher-aware model works
Modal's MLB model fuses team-strength Elo with the starting pitcher's FIP, then blends against the market. Here's the full method behind our daily MLB win probabilities.
How to read the MLB board: model, market, blended
Three probabilities per game, starters with FIP, and a gap flag. A two-minute guide to getting real value out of Modal's daily MLB board.
Model vs market: why we blend instead of betting our own number
Our testing showed the market consensus beat the raw model on accuracy — and the blend beat both. Why respecting the market makes predictions better, not weaker.
MLB playoffs 2026: how to actually read win probabilities
October baseball is where casual money floods in and prices get emotional. A guide to reading playoff win probabilities like the market professionals do.
Why the biggest 'edges' in sports betting are usually wrong
Across football and baseball, our graded results show the same pattern: when a model disagrees hugely with the market, the model is usually the one that's wrong.
Elo for baseball: rating teams in a sport built on randomness
Elo ratings came from chess but fit baseball surprisingly well — if you tune them for a sport where the best teams lose 60 times a year. Here's how ours works.
Bullpens: the hidden variable in MLB predictions
Starters get the headlines, but games are decided in innings six through nine. How bullpen quality — and fatigue — moves win probabilities.
From shadow to live: how we test a model before trusting it
Our MLB model ran 79 graded paper bets against sharp closing prices before its first real bet. Inside the shadow-testing discipline every Modal model goes through.
Closing line value: the number sharps check before win rate
CLV measures whether the market moved toward your position after you took it — the fastest honest test of whether a prediction method has real edge.
Reading the receipts: a public, graded track record
An analysis you can't check is just a tip. Here's how we grade every prediction against the real result — in the open — and why calibration matters more than a headline accuracy number.
Model odds vs market odds: what 'value' really means
The edge in football isn't picking winners — it's spotting when the model and the market disagree. Here's how comparing model probability to market odds reveals value, explained plainly.
Your club, every week: the brief a football fan actually wants
Pick your club and the model works for you specifically — one weekly email with your team's next analysed fixture and the freshest transfer news. Here's how it works.
The modal scoreline: why the most likely score isn't the likely winner
A team can be a clear favourite while the single most probable scoreline is a draw. Here's the counterintuitive maths behind the modal scoreline — and why it's the honest way to read a match.