Under the hood

Scrape, rate, forecast, publish. Every figure below is out-of-sample.

1.58Mboat-race rows

7,693regattas, 37 seasons

23,824sailors rated

212tests, CI on clean checkout

Pipeline

Pairs of boats ordered correctly, 4 held-out seasons (~16,000 races). Championships: 72.4% (95% CI 71.6–73.3), 166 regattas.

Calibration. x: model said, y: happened (%). Under the dashed line = overconfident; honest to ~85%.

Singlehanded

Match race

Fitted before each season, tested on it. Exact driver named: 63% / 46%. Ratings are today's, so slightly optimistic.

Problem	Change	Evidence
Trainer stopped before converging	rating increments + first-race prior, retuned on week-ahead forecasts	69.6 → 70.4% pairwise
Newcomers have no history	start at school debut median, not league mean	+3.5 pts on newcomer pairs
Races in a regatta are correlated	simulate with regatta effect, drift, same-boat effect	team totals checked at every championship
Most likely squad is a poor rating forecast	price a calibrated, probability-weighted rating	error 0.43 vs 0.55 (SH), 0.44 vs 0.55 (MR)
Strongest-event-first sent specialists to fleet	staff singlehanded / match race first	0.9% of such drivers also sail fleet that week
Availability is uncertain	Monte Carlo weekend, one sailor per boat, 2,000 draws	80% interval shown per entry
Scraped data vs source	re-scrape, compare boat by boat with Techscore	0 dropped boats, all numeric fields match
Page and server must quote the same price	one snapping rule in Python and JS	unit test compares both

Nightly pipelinegated deploy

Optional stagesfail soft

Page generatorsidempotent

Worker restartpkill lswsgi

Import-time threadsnone

Prices (Py = JS)tested

Match-race results on Techscore are one final ranking: no round-robin data.
~5,400 singlehanded rows lack a class year, ~1,300 keelboat boats lack a skipper; neither feeds ratings.
Discipline accuracy uses today's ratings. The play-money fantasy market is a research tool, not gambling.