The experiment
How it works
We started with a simple question: how good is AI at sports analytics? So we built a model, we run it on public stats to grade its predictions. Here is exactly how it works.
It starts with public stats
We use the same free, public numbers anyone can pull up: pitching and hitting stats, batting orders, box scores. No insider info, no leaked data. If you wanted to, you could look at the exact same numbers we do.
We make a real prediction
For each game the model projects a player's stat, like a pitcher's strikeouts. It comes from two simple things: how good the player has been (their rate) and how much chance they'll get (their opportunity, like how many batters they'll face). Rate times opportunity gives a number we can stand behind.
We check it against the market
The market sets a number for most of these stats, and that number is sharp, it soaks up a lot of information. We compare our prediction to theirs to see how the market reads the same public stats, and how well the model lines up. We track every difference so we can learn where the model is strong, where it is off, and keep improving it.
We keep score, honestly
Every prediction goes on the record and gets graded against the closing number, the last and sharpest one before the game starts. We show the wins and the losses. We also track whether the market moved toward our side after we made the call, which is the truest test of whether a prediction was actually sharp.
Keeping score
The data is public. The real work is doing it well every day and keeping score, wins and losses included. The record shows where the model is strong and where it needs work.
Follow along as the record grows, and judge it for yourself.
The fantasy tools and live scores are free.