How chess and baseball analytics tell a good decision from a lucky result

When an outcome is partly chance, how do you show whether the decision behind it was good?

Lichess grades every move by how good it was, whatever happened next. Baseball Savant’s expected stats show how a hit usually turns out, apart from where this one landed. I’ll study how both separate the decision from the result, then spend a week designing a way to explain a single chance outcome.

Format: Teardown
What I’ll build: An outcome receipt for one random event: what went in, what nudged the odds, the chance of each result, and where the roll actually landed, at a short and a detailed level.
What I’ll reuse: An outcome receipt pattern for simulations, so a result feels earned rather than rigged.
Status: Planned · Dec 2026

What I’ll look at

How Lichess grades a move against the engine’s best move, and shows that without burying a casual player in numbers. How expected stats turn a hit’s speed and angle into the result it usually produces. And how both decide what to show a beginner and what to keep for an expert.

My guess

I expect both work because they compare the result with what usually happens from the same spot, rather than explaining every factor. People seem to accept bad luck once they can see the odds were on their side.

How I’ll prove it

I’ll design receipts for a few random events, from a single draw to one with several things shifting the odds, at both levels of detail.

Then I’ll ask a handful of people to say, for each event, whether the decision or luck decided it, and check that they can tell from the receipt alone.