| # | School | Sailor | TOT | |
|---|---|---|---|---|
| 1 | Boston College | Erika Reineke '17 | 28 | |
| 2 | Brown University | Lindsey Baab '18 | 51 | |
| 3 | U. S. Coast Guard Academy | Dana Rohde '18 | 56 | |
| 4 | Boston College | Isabella Loosbrock '19 | 81 | |
| 5 | Yale University | Louisa Nordstrom '20 | 86 | |
| 6 | Brown University | Hannah Steadman '20 | 87 | |
| 7 | U. S. Coast Guard Academy | Emily Wright '20 | 95 | |
| 8 | Harvard University | Sofia Marie Mascia '20 | 97 | |
| 9 | U. S. Coast Guard Academy | Madelynn Widmeier '19 | 111 | |
| 10 | Connecticut College | Alexandra Maurillo '18 | 113 | |
| 11 | Tufts University | Molly Pleskus '18 | 117 | |
| 12 | Connecticut College | Charlotte List '18 | 132 | |
| 13 | Harvard University | Jessica Williams '20 | 133 | |
| 14 | Connecticut College | Eliza Garry '17 | 183 | |
| 15 | University of New Hampshire | Autumn Becker '20 | 202 |
| School | Sailor | R1 | R2 | R3 | R4 | R5 | R6 | R7 | R8 | R9 | R10 | R11 | R12 | R13 | TOT | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Boston College | Erika Reineke '17 | 6 | 1 | 1 | 1 | 1 | 9 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 28 | |
| Brown University | Lindsey Baab '18 | 11 | 3 | 2 | 2 | 3 | 4 | 4 | 2 | 5 | 3 | 2 | 5 | 5 | 51 | |
| U. S. Coast Guard Academy | Dana Rohde '18 | 4 | 2 | 4 | 3 | 13 | 1 | 2 | 3 | 2 | 6 | 3 | 11 | 2 | 56 | |
| Boston College | Isabella Loosbrock '19 | 3 | 5 | 7 | 7 | 2 | 12 | 5 | BKD | 13 | 8 | 5 | 1 | 7 | 81 | |
| Yale University | Louisa Nordstrom '20 | 2 | 9 | 5 | 8 | 5 | 2 | 12 | 7 | 7 | 4 | 9 | 13 | 3 | 86 | |
| Brown University | Hannah Steadman '20 | 5 | 10 | 6 | 4 | 8 | 3 | 8 | 8 | 11 | 5 | 6 | 7 | 6 | 87 | |
| U. S. Coast Guard Academy | Emily Wright '20 | 8 | 8 | 11 | 12 | 4 | 8 | 3 | 4 | 4 | 10 | 7 | 3 | 13 | 95 | |
| Harvard University | Sofia Marie Mascia '20 | 9 | 7 | 9 | 5 | 7 | 5 | 13 | 5 | 6 | 1 | RAF | 4 | 10 | 97 | |
| U. S. Coast Guard Academy | Madelynn Widmeier '19 | 1 | 4 | 10 | 6 | 10 | 11 | 11 | 10 | 9 | 7 | 12 | 8 | 12 | 111 | |
| Connecticut College | Alexandra Maurillo '18 | 10 | 6 | 13 | 9 | 6 | 10 | 9 | 9 | 8 | 9 | 4 | 12 | 8 | 113 | |
| Tufts University | Molly Pleskus '18 | 12 | 11 | 3 | 11 | 9 | 6 | 6 | 12 | 3 | 13 | 10 | 10 | 11 | 117 | |
| Connecticut College | Charlotte List '18 | 14 | 14 | 12 | 13 | 11 | 13 | 10 | 6 | 10 | 11 | 8 | 6 | 4 | 132 | |
| Harvard University | Jessica Williams '20 | 13 | 12 | 8 | 10 | 12 | 7 | 7 | 11 | 12 | 12 | 11 | 9 | 9 | 133 | |
| Connecticut College | Eliza Garry '17 | 7 | 13 | 14 | 14 | 14 | 14 | 14 | 13 | DNS | DNS | DNS | DNS | DNS | 183 | |
| University of New Hampshire | Autumn Becker '20 | 15 | 15 | 15 | 15 | 15 | 15 | DNF | DNS | DNS | DNS | DNS | DNS | DNS | 202 |
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 | Erika Reineke '17 | Boston College Eagles 1 | 13/13 | 3.83 | 5.06 | +1.23 | 94th → 99th | 4.4 | 2% | 2.2 / 4.6 |
| 2 | Lindsey Baab '18 | Brown University Bears 1 | 13/13 | 3.07 | 3.86 | +0.78 | 83rd → 94th | 2.4 | 8% | 3.9 / 6.1 |
| 3 | Dana Rohde '18 | U. S. Coast Guard Academy Bears 1 | 13/13 | 2.95 | 3.36 | +0.41 | 81st → 89th | 0.7 | 11% | 4.3 / 6.4 |
| 4 | Louisa Nordstrom '20 | Yale University | 13/13 | 2.77 | 2.45 | −0.32 | 74th → 62nd | 0.5 | 49% | 6.6 / 6.7 |
| 5 | Hannah Steadman '20 | Brown University Bears 2 | 13/13 | 2.61 | 2.77 | +0.15 | 68th → 74th | 0.1 | 44% | 6.7 / 7.1 |
| 6 | Isabella Loosbrock '19 | Boston College Eagles 2 | 13/13 | 2.54 | 2.63 | +0.09 | 66th → 69th | 0.0 | 48% | 7.0 / 7.2 |
| 7 | Emily Wright '20 | U. S. Coast Guard Academy Bears 2 | 13/13 | 2.04 | 2.32 | +0.28 | 53rd → 60th | 0.3 | 30% | 7.3 / 8.4 |
| 8 | Sofia Marie Mascia '20 | Harvard University Crimson 2 | 13/13 | 1.73 | 2.68 | +0.95 | 43rd → 71st | 3.3 | 24% | 7.5 / 9.0 |
| 9 | Madelynn Widmeier '19 | U. S. Coast Guard Academy Bears 3 | 13/13 | 1.86 | 1.98 | +0.12 | 47th → 51st | 0.1 | 47% | 8.5 / 8.7 |
| 10 | Alexandra Maurillo '18 | Connecticut College Camels 1 | 13/13 | 2.06 | 2.17 | +0.11 | 53rd → 57th | 0.1 | 57% | 8.7 / 8.3 |
| 11 | Molly Pleskus '18 | Tufts University | 13/13 | 2.09 | 1.76 | −0.33 | 55th → 43rd | 0.5 | 65% | 9.0 / 8.2 |
| 12 | Charlotte List '18 | Connecticut College Camels 2 | 13/13 | 2.34 | 1.37 | −0.97 | 60th → 33rd | 5.1 | 87% | 10.2 / 7.7 |
| 13 | Jessica Williams '20 | Harvard University Crimson 1 | 13/13 | 1.96 | 1.67 | −0.29 | 50th → 41st | 0.5 | 79% | 10.2 / 8.5 |
| 14 | Eliza Garry '17 | Connecticut College Camels 3 | 13/13 | 1.00 | 0.10 | −0.90 | 22nd → 7th | 1.9 | 99% | 14.1 / 10.4 |
| 15 | Autumn Becker '20 | University of New Hampshire | 13/13 | -0.39 | — | — | 3rd → — | 4.5 | >99% | 15.5 / 12.7 |
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 ±0.13 means a rating gap counts about 11% less 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.