| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
Georgetown University | Hoyas | 7–2 |
| 2 |  |
Old Dominion University | Monarchs | 6–3 |
| 3 |  |
St. Mary's College of Maryland | Seahawks | 5–4 |
| 4 |  |
Fordham University | Rams | 0–9 |
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 28,305 team race matches sailed before 2026-02-28, using the line-ups that actually raced. It called 15 of 18 matches (83%) with a log-loss of 0.419 (a coin flip scores 0.693), so the event went well for the forecast. Orderliness +0.22 ± 0.43: a typical event, more orderly than 69% of college 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 | Georgetown University Hoyas | 1 | 8.52 school +0.22 | Alexa Shea / Helen O’Neill 1.74 Kelly Bates / Anna Brzezinski 3.26 Emily Doble / Ellie Smith 3.20 | 83% | 7–2 (78%) | −0.5 |
| 2 | Old Dominion University Monarchs | 2 | 7.54 school +0.28, home +0.36 | Gianna Dewey / Petra Humphrey 2.27 Arabella Duer / Morgan Auth Marina Conde / Molly Hanson 2.72 | 59% | 6–3 (67%) | +0.7 |
| 3 | St. Mary's College of Maryland Seahawks | 3 | 6.90 school +0.36 | Cho-Cho Williams / Molly Dowling 2.08 Lina Carper / Lauren Choi 2.15 Maisy Sperry / Indiana Theurer | 42% | 5–4 (56%) | +1.2 |
| 4 | Fordham University Rams | 4 | 5.79 school +0.08 | Anna Robertson / Samantha Hemans 1.45 Lauren Murray / Parker Laing 1.72 Lizzie Cochran / Nora Donahue 2.47 | 15% | 0–9 (0%) | −1.4 |
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 | St. Mary's College of Maryland | 6.90 | 7.62 | 5–4 | 3.8 | +0.72 | 0.6 | 28% |
| 2 | Old Dominion University | 7.54 | 7.95 | 6–3 | 5.3 | +0.41 | 0.2 | 46% |
| 1 | Georgetown University | 8.52 | 8.14 | 7–2 | 7.5 | −0.38 | 0.1 | 83% |
| 4 | Fordham University | 5.79 | 2.79 | 0–9 | 1.4 | ≤−3.00 | 2.1 | >99% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Cho-Cho Williams | St. Mary's College of Maryland | 2.08 | 2.80 | 5–4 | 3.8 | +0.72 | 0.6 | 28% |
| Lina Carper | St. Mary's College of Maryland | 2.15 | 2.87 | 5–4 | 3.8 | +0.72 | 0.6 | 28% |
| Maisy Sperry | St. Mary's College of Maryland | 2.21 | 2.93 | 5–4 | 3.8 | +0.72 | 0.6 | 28% |
| Gianna Dewey | Old Dominion University | 2.27 | 2.68 | 6–3 | 5.3 | +0.41 | 0.2 | 46% |
| Arabella Duer | Old Dominion University | 1.82 | 2.23 | 6–3 | 5.3 | +0.41 | 0.2 | 46% |
| Marina Conde | Old Dominion University | 2.72 | 3.13 | 6–3 | 5.3 | +0.41 | 0.2 | 46% |
| Alexa Shea | Georgetown University | 1.74 | 1.36 | 7–2 | 7.5 | −0.38 | 0.1 | 83% |
| Kelly Bates | Georgetown University | 3.26 | 2.88 | 7–2 | 7.5 | −0.38 | 0.1 | 83% |
| Emily Doble | Georgetown University | 3.20 | 2.82 | 7–2 | 7.5 | −0.38 | 0.1 | 83% |
| Anna Robertson | Fordham University | 1.45 | -1.55 | 0–9 | 1.4 | ≤−3.00 | 2.1 | >99% |
| Lauren Murray | Fordham University | 1.72 | -1.28 | 0–9 | 1.4 | ≤−3.00 | 2.1 | >99% |
| Lizzie Cochran | Fordham University | 2.47 | -0.53 | 0–9 | 1.4 | ≤−3.00 | 2.1 | >99% |
Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).
| Team | Rec. | Hoyas | Monarchs | Seahawks | Rams | |
|---|---|---|---|---|---|---|
| Georgetown University Hoyas | 3–0 | ✕ | W | W | W |
| Old Dominion University Monarchs | 1–2 | L | ✕ | L | W |
| St. Mary's College of Maryland Seahawks | 2–1 | L | W | ✕ | W |
| Fordham University Rams | 0–3 | L | L | L | ✕ |
| Team | Rec. | Hoyas | Monarchs | Seahawks | Rams | |
|---|---|---|---|---|---|---|
| Georgetown University Hoyas | 3–0 | ✕ | W | W | W |
| Old Dominion University Monarchs | 2–1 | L | ✕ | W | W |
| St. Mary's College of Maryland Seahawks | 1–2 | L | L | ✕ | W |
| Fordham University Rams | 0–3 | L | L | L | ✕ |
| Team | Rec. | Hoyas | Monarchs | Seahawks | Rams | |
|---|---|---|---|---|---|---|
| Georgetown University Hoyas | 1–2 | ✕ | L | L | W |
| Old Dominion University Monarchs | 3–0 | W | ✕ | W | W |
| St. Mary's College of Maryland Seahawks | 2–1 | W | L | ✕ | W |
| Fordham University Rams | 0–3 | L | L | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Hoyas | 1-2-4 | def. | |
Monarchs | 3-5-6 |
| 2 | |
Seahawks | 1-2-5 | def. | |
Rams | 3-4-6 |
| 5 | |
Hoyas | 1-2-3 | def. | |
Seahawks | 4-5-6 |
| 6 | |
Monarchs | 1-2-5 | def. | |
Rams | 3-4-6 |
| 9 | |
Seahawks | 2-3-4 | def. | |
Monarchs | 1-5-6 |
| 10 | |
Hoyas | 2-3-4 | def. | |
Rams | 1-5-6 |
| 3 | |
Hoyas | 2-3-4 | def. | |
Monarchs | 1-5-6 |
| 4 | |
Seahawks | 2-3-4 | def. | |
Rams | 1-5-6 |
| 7 | |
Hoyas | 2-3-4 | def. | |
Seahawks | 1-5-6 |
| 8 | |
Monarchs | 1-2-4 | def. | |
Rams | 3-5-6 |
| 11 | |
Monarchs | 1-2-5 | def. | |
Seahawks | 3-4-6 |
| 12 | |
Hoyas | 1-2-5 | def. | |
Rams | 3-4-6 |
| 13 | |
Monarchs | 1-2-5 | def. | |
Hoyas | 3-4-6 |
| 14 | |
Seahawks | 1-2-5 | def. | |
Rams | 3-4-6 |
| 15 | |
Seahawks | 1-3-4 | def. | |
Hoyas | 2-5-6 |
| 16 | |
Monarchs | 1-2-4 | def. | |
Rams | 3-5-6 |
| 17 | |
Monarchs | 1-2-6 | def. | |
Seahawks | 3-4-5 |
| 18 | |
Hoyas | 1-2-3 | def. | |
Rams | 4-5-6 |
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
|
Georgetown University | Hoyas | Alexa Shea '26 Emily Doble '28 Kelly Bates '26 |
Helen O’Neill '27 Ellie Smith '28 Anna Brzezinski '27 |
|
Old Dominion University | Monarchs | Arabella Duer '29 Gianna Dewey '27 Marina Conde '28 |
Morgan Auth '27 Petra Humphrey '29 Molly Hanson '27 |
|
St. Mary's College of Maryland | Seahawks | Cho-Cho Williams '26 Lina Carper '27 Maisy Sperry '29 |
Molly Dowling '29 Lauren Choi '29 Indiana Theurer '25 |
|
Fordham University | Rams | Anna Robertson '28 Lauren Murray '28 Lizzie Cochran '27 |
Samantha Hemans '27 Parker Laing '28 Nora Donahue '28 |
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 ±0.01 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.
