| # | School | Sailor | TOT | |
|---|---|---|---|---|
| 1 |  |
Tulane University | Ava Anderson '27 | 3 |
| 2 | |
Tulane University | Oakley Cunningham '27 | 6 |
| 3 | |
Tulane University | Ashley Hoguet '26 | 9 |
| 4 |  |
University of Texas | Reema Nannapaneni '27 | 12 |
| 5 |  |
Rice University | Yuqing Yang '28 | 15 |
| School | Sailor | R1 | R2 | R3 | TOT | |
|---|---|---|---|---|---|---|
|
Tulane University | Ava Anderson '27 | 1 | 1 | 1 | 3 |
|
Tulane University | Oakley Cunningham '27 | 2 | 2 | 2 | 6 |
|
Tulane University | Ashley Hoguet '26 | 3 | 3 | 3 | 9 |
|
University of Texas | Reema Nannapaneni '27 | 4 | 4 | 4 | 12 |
|
Rice University | Yuqing Yang '28 | 5 | 5 | 5 | 15 |
Performance rating asks, for each skipper: holding every other boat fixed, which rating makes my actual race-by-race finishes least surprising? Boats are valued with the singlehanded rating rather than the fleet rating: 0.79 × the skipper's fleet rating from the week before the event (unrated skippers start at 1.11); the skipper's own singlehanded offset. Strength differences are scaled by 0.80 before they become probabilities, the value that calibrates the model on seasons it had not seen. Non-finishes (OCS, DNF and similar) are left out of the fitting, as in the rating model.
Percentile places a rating among the 309 ICSA sailors with a singlehanded rating who raced the discipline in the last three seasons. Surprise is how many bits more surprising the finishes were at the pre-event rating than at the performance rating. P(this good or better) simulates the event 3,000 times at the pre-event ratings, with the same regatta-form model as the fleet analysis pages, and reports how often the skipper would score this many points or fewer in the races they sailed.
The singlehanded offsets are the current ones (the discipline files keep no weekly history), so a skipper's own offset has already seen this event; offsets are shrunk hard, so the effect is a few hundredths at most. Only skippers are rated.
| Fin | Skipper | School | Races | Rating before |
Perf. rating |
Δ | Percentile before → perf. |
Surprise (bits) |
P(this good or better) |
Avg finish actual / exp. |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Ava Anderson '27 | Tulane University Green Wave 1 | 3/3 | 3.28 | 8.75+ | +5.47 | 86th → 99th | 2.8 | 14% | 1.0 / 1.8 |
| 2 | Oakley Cunningham '27 | Tulane University Green Wave 2 | 3/3 | 2.59 | 2.76 | +0.17 | 68th → 74th | 0.0 | 49% | 2.0 / 2.3 |
| 3 | Ashley Hoguet '26 | Tulane University Green Wave 3 | 3/3 | 0.83 | 1.68 | +0.85 | 18th → 42nd | 0.5 | 40% | 3.0 / 3.4 |
| 4 | Reema Nannapaneni '27 | University of Texas | 3/3 | -0.04 | 0.62 | +0.66 | 6th → 16th | 0.4 | 54% | 4.0 / 3.9 |
| 5 | Yuqing Yang '28 | Rice University | 3/3 | 0.61u | — | — | 16th → — | 7.0 | >99% | 5.0 / 3.5 |
Orderliness is how tightly a fleet race’s finishing order follows the pre-regatta ratings, relative to a typical event (0): positive is the favourites’ day, negative the underdogs’. A value of ±0.08 means a rating gap counts about 7% more than on a typical day. This event is not a fleet race the rating model scores, so there is no actual value for it: this is what this venue’s fleet racing history says about the breeze on the day, read off the chart at the star. How this is estimated · compare venues.
Short write-ups of what the ratings and results show, with the numbers and how they were computed.
