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Track record

Baseball: what the model predicted, against what actually happened

The model serving this sport has not been backtested.
Nothing here has been checked against what actually happened, so treat every number as untested until you have run python -m pipeline.backtest --sport baseball.
What you are looking at
Two records, answering different questions. Historical replays every prediction the walk-forward backtest made — the model only ever saw matches played before the one it was predicting, so these are genuine forecasts, and there are enough of them to mean something. Live is picks this app actually published, settled after the fact. It starts empty and grows.

Historical uses the raw model probability rather than the calibrated one, because calibration is itself derived from this history — scoring against it would be marking its own homework.
No baseball track record yet. Settle this sport's out-of-sample picks first:
./.venv/bin/python -m pipeline.settle --sport baseball
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Track record

What this page is for

The scoreboard. Not what the model thinks it can do, but what happened when it was asked in advance and the matches were then played.

How to read it

Everything here is out of sample: each prediction was made before the match it describes. Break the record down by market and league before drawing a conclusion from the headline — a model can be genuinely good at one league and useless at another, and an average over both hides it.

Things to watch

Sample size is the trap. A market with thirty settled picks tells you almost nothing; a run of good results over a few weeks is well within what luck produces. Football is low-scoring enough that the better side loses often, and a fair chunk of every result is simply not predictable.

Where to go next

Profit at fair odds, at the foot of the page, is the harshest test on the site: it asks whether the edge survives being paid for.

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