| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
Wayland High School | Varsity | 0–0 |
| 2 |  |
Noble and Greenough School | Nobles Bulldogs | 0–0 |
| 3 |  |
Lincoln Sudbury RHS | Warriors | 0–0 |
| 4 |  |
Wellesley High School | Varsity | 0–0 |
| 5 |  |
Sharon High School | Varsity | 0–0 |
| 6 |  |
Unregistered-NESSA | Nantasket | 0–0 |
Each side is the sum of its three boats (the skipper's fleet rating times 0.72 plus a team race offset learned from their past matches, plus the crew's offset and a small experience term) plus the school's team race strength and a home-water edge for the host. The win probability is a logistic function of the difference. The forecast was made before the event: the model was refitted on every match sailed before the first day, with fleet ratings as of the week before.
Fitted on the 3,361 team race matches sailed before 2017-04-30, using the line-ups that actually raced. It called 5 of 13 matches (38%) with a log-loss of 0.941 (a coin flip scores 0.693), so the event went poorly for the forecast. Orderliness −3.64 ± 0.94: an upset-heavy event, more orderly than 0% of high school team race events (temperature of the forecast log-odds that best fits the results, relative to the league average; nothing about the field, venue or wind predicts it, see the Weather tab). vs forecast is a team's wins minus the wins the model expected from the matches it sailed: positive means it beat its rating.
| Place | School | Pred. | Strength | Boats (skipper / crew, boat value) | Pred. win % | Record | vs forecast |
|---|---|---|---|---|---|---|---|
| 1 | Wayland High School Varsity | 4 | 0.63 school −0.07 | Emily Wang / Marlee Griffin 0.27 Lucy Poole / Izzy Rubin Sarah Raines / Grace Karbowski 0.32 | 40% | 3–2 (60%) | +1.0 |
| 3 | Lincoln Sudbury RHS Warriors | 3 | 0.75 | Julia Wyatt / Abby Hayes Sarah Alix / Gabby Jones Victoria Larow / Sdyney Blair | 44% | 5–1 (83%) | +2.3 |
| 4 | Wellesley High School Varsity | 5 | 0.61 home +0.11 | Olivia Sowles / Eleanor Bennett Catherine Zhu / Maia Grissino Elizabeth Harrington / Jenna Weiner | 40% | 3–2 (60%) | +1.0 |
| 5 | Sharon High School Varsity | 1 | 2.18 school +0.08 | Gloria Zhang / Briana Chen Alissa Chen / Christopher Anchan Eddie Bielawa / Jason Cloherty | 71% | 2–3 (40%) | −1.5 |
| 6 | Unregistered-NESSA Nantasket | 2 | 1.56 school −0.08 | Anabelle Weises / Jessica Adey Kelley Frumer / Mollie Gilbert Mystery Skipper / Mystery Crew | 56% | 0–5 (0%) | −2.8 |
For each side, the performance strength is the value that would have made its record least surprising: the strength at which the model's expected wins over exactly these opponents equal the actual wins, with every opponent fixed at its pre-event strength. For a skipper, the same shift is applied only to their boat in the matches they sailed, with team-mates and opponents fixed; team-mates who sailed every match together therefore get the same number. Surprise is in bits: -log2 of the probability of the record at the pre-event strength minus the same at the performance strength. P(this good or better) treats matches as independent coin flips at the model's pre-event probabilities, so it ignores form within the day; read it as a rough guide.
Pre-event strength against the strength that would have made each record least surprising, everything else fixed. Δ is in the model's logit units, the same scale as the boat values above (a side is three boats plus the school term); green is a clear over-performance, red a clear under-performance. Skippers need three or more matches.
| Place | School | Strength | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| 3 | Lincoln Sudbury RHS | 0.75 | 3.26 | 5–1 | 2.7 | +2.50 | 3.0 | 6% |
| 4 | Wellesley High School | 0.61 | 1.72 | 3–2 | 2.0 | +1.11 | 0.6 | 31% |
| 1 | Wayland High School | 0.63 | 1.71 | 3–2 | 2.0 | +1.08 | 0.6 | 32% |
| 5 | Sharon High School | 2.18 | 0.47 | 2–3 | 3.5 | −1.71 | 1.5 | 97% |
| 6 | Unregistered-NESSA | 1.56 | -1.44 | 0–5 | 2.8 | ≤−3.00 | 5.3 | >99% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Olivia Sowles | Wellesley High School | -0.04 | 1.07 | 3–2 | 2.0 | +1.11 | 0.6 | 31% |
| Catherine Zhu | Wellesley High School | 0.14 | 1.25 | 3–2 | 2.0 | +1.11 | 0.6 | 31% |
| Lucy Poole | Wayland High School | 0.02 | 1.10 | 3–2 | 2.0 | +1.08 | 0.6 | 32% |
| Sarah Raines | Wayland High School | 0.32 | 1.40 | 3–2 | 2.0 | +1.08 | 0.6 | 32% |
| Gloria Zhang | Sharon High School | 0.73 | -0.98 | 2–3 | 3.5 | −1.71 | 1.5 | 97% |
| Alissa Chen | Sharon High School | 0.66 | -1.05 | 2–3 | 3.5 | −1.71 | 1.5 | 97% |
| Eddie Bielawa | Sharon High School | 0.54 | -1.16 | 2–3 | 3.5 | −1.71 | 1.5 | 97% |
| Anabelle Weises | Unregistered-NESSA | 0.73 | -2.27 | 0–5 | 2.8 | ≤−3.00 | 5.3 | >99% |
Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).
| Rd | # | Winner | Loser | P(winner) | |
|---|---|---|---|---|---|
| 1 | 1 | Wellesley High School | Sharon High School | 22% | upset |
| 1 | 3 | Wayland High School | Unregistered-NESSA | 34% | upset |
| 1 | 5 | Wellesley High School | Wayland High School | 49% | upset |
| 1 | 6 | Lincoln Sudbury RHS | Unregistered-NESSA | 31% | upset |
| 1 | 8 | Lincoln Sudbury RHS | Sharon High School | 24% | upset |
| 1 | 9 | Wellesley High School | Unregistered-NESSA | 32% | upset |
| 1 | 10 | Lincoln Sudbury RHS | Wayland High School | 52% | |
| 1 | 12 | Sharon High School | Unregistered-NESSA | 61% | |
| 1 | 13 | Lincoln Sudbury RHS | Wellesley High School | 52% | |
| 1 | 14 | Wayland High School | Sharon High School | 22% | upset |
| 1 | 16 | Lincoln Sudbury RHS | Wellesley High School | 52% | |
| 1 | 18 | Sharon High School | Unregistered-NESSA | 62% | |
| 1 | 20 | Wayland High School | Lincoln Sudbury RHS | 46% | upset |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Varsity | 1-2-4 | def. | |
Varsity | 3-5-6 |
| 2 | |
Warriors | 1-3-4 | def. | |
Nobles Bulldogs | 2-5-6 |
| 3 | |
Varsity | 1-2-3 | def. | |
Nantasket | 4-5-6 |
| 4 | |
Nobles Bulldogs | 2-3-4 | def. | |
Varsity | 1-5-6 |
| 5 | |
Varsity | 1-2-5 | def. | |
Varsity | 3-4-6 |
| 6 | |
Warriors | 1-2-3 | def. | |
Nantasket | 4-5-6 |
| 7 | |
Varsity | 1-2-6 | def. | |
Nobles Bulldogs | 3-4-5 |
| 8 | |
Warriors | 1-2-3 | def. | |
Varsity | 4-5-6 |
| 9 | |
Varsity | 1-2-3 | def. | |
Nantasket | 4-5-6 |
| 10 | |
Warriors | 1-2-5 | def. | |
Varsity | 3-4-6 |
| 11 | |
Nobles Bulldogs | 2-3-4 | def. | |
Varsity | 1-5-6 |
| 12 | |
Varsity | 1-2-3 | def. | |
Nantasket | 4-5-6 |
| 13 | |
Warriors | 1-2-5 | def. | |
Varsity | 3-4-6 |
| 14 | |
Varsity | 1-2-4 | def. | |
Varsity | 3-5-6 |
| 15 | |
Nobles Bulldogs | 1-2-3 | def. | |
Nantasket | 4-5-6 |
| 16 | |
Warriors | 1-2-3 | def. | |
Varsity | 4-5-6 |
| 17 | |
Varsity | 1-2-3 | def. | |
Nobles Bulldogs | 4-5-6 |
| 18 | |
Varsity | 1-2-3 | def. | |
Nantasket | 4-5-6 |
| 19 | |
Nobles Bulldogs | 1-2-3 | def. | |
Varsity | 4-5-6 |
| 20 | |
Varsity | 1-2-3 | def. | |
Warriors | 4-5-6 |
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
|
Wayland High School | Varsity | Emily Wang '17 Lucy Poole '18 Sarah Raines '17 |
Marlee Griffin '18 Izzy Rubin '17 Grace Karbowski '19 |
|
Noble and Greenough School | Nobles Bulldogs | Ellen Efstathiou '21 Sonia Lingos-Utley '17 |
Reese Dickinson '20 Lily Hicks '18 |
|
Lincoln Sudbury RHS | Warriors | Julia Wyatt '17 Sarah Alix '19 Victoria Larow '18 |
Abby Hayes '20 Gabby Jones '20 Sdyney Blair '20 |
|
Wellesley High School | Varsity | Catherine Zhu '18 Elizabeth Harrington '18 Olivia Sowles '18 |
Maia Grissino '19 Jenna Weiner '17 Eleanor Bennett '17 |
|
Sharon High School | Varsity | Alissa Chen '18 Eddie Bielawa '20 Gloria Zhang '18 |
Christopher Anchan '19 Jason Cloherty '20 Briana Chen '20 |
|
Unregistered-NESSA | Nantasket | Anabelle Weises '17 Kelley Frumer '17 Mystery Skipper '17 |
Jessica Adey '19 Mollie Gilbert '19 Mystery Crew '18 |
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 ±0.02 means a rating gap counts about 1% 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.
