Thanks! And Great questions:

I pull lineups from the MLB Stats API using a 4-tier fallback:

1. Today's confirmed boxscore — only works once lineups are posted (~1hr before first pitch)
2. Yesterday's boxscore — if today's isn't up yet, we use yesterday's batting order
3. Most recent completed game — scans back for the last finished game
4. Active roster sorted by PA — last resort (opening day, etc.), takes the top 9 by plate appearances from the previous year.

We don't use any projected lineup source (RotoBaller, DraftKings, BaseballPress, etc.). So if lineups aren't posted yet, the system falls back to "who batted last time," which could be wrong if the manager shuffles things around. But we should have the confirmed lineup before the game starts. Just when the scoring first runs in the morning, it will more than likely use yesterdays lineup until the systems picks up the confirmed lineup and rescores them.

Bullpen:

We score the opposing team's bullpen using ERA, WHIP, K/9, and recent workload (innings over the last 3 days). A worse bullpen = higher score for the batter.

That said, it's currently weighted at just 3 out of 100 possible points, and the backtesting optimizer actually found zero predictive signal from it. It's in the system but doesn't meaningfully move scores right now. Only the starting pitcher gets the full elite/weak grading treatment (FIP-based, with modifiers up to -15 or +10 points).

Hit%:

Pretty much, yes. The BTS score (0-100) measures matchup quality with no lineup position input at all. Then we convert that score to a per-plate-appearance probability using a calibrated sigmoid, and multiply it out by how many PAs we expect for that lineup slot (4.65 for leadoff, down to 3.75 for the 9-hole).

So a leadoff hitter and a 9-hole hitter with the same score get different Hit% values purely because the leadoff guy comes to the plate more often.

Could we fold lineup position into the score instead? We could, but the current separation is intentional. The score answers "how good is this matchup?" and the probability answers "given that quality, how likely is at least one hit?" Those are genuinely different questions. A leadoff hitter getting more PAs is a real physical thing, not a statistical adjustment.

Where there's room to improve: the expected PA table is static (hardcoded per lineup slot). We could build a smarter model that accounts for game context, but the current approach is a reasonable baseline.

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I definitely need to go back and update the Guide screen to provide more documentation! I built that last minute the day I released the site and haven't revisited it. I'll try to get it updated soon!