6HV-KRUMMEL 3.5W TEAM SINGLES FALL SEM F1
2023 SEM FALL TEAM SINGLES · WOMEN 3.5 SINGLES SEM
7 players · average NTRP 3.75 / S.E. MICHIGAN
No matches recorded for this roster in this league — it may be a placeholder registration, or the season may not have started.
Schedule8 played
i
Flight standings14 teams · WOMEN 3.5 SINGLES SEM
| Team | W | L | Sets lost | Games lost |
|---|---|---|---|---|
| 13SCWB-YAGUCHI 3.5W TEAM SINGLES FALL SEM F3 | 8 | 1 | 18 | 193 |
| 23LIB-MARLEY 3.5W TEAM SINGLES FALL SEM F1 | 7 | 1 | 17 | 148 |
| 31DEER-MOWBRAY 3.5W TEAM SINGLES FALL SEM F2 | 6 | 1 | 14 | 145 |
| 45LIFE-LAMAR 3.5W TEAM SINGLES FALL SEM F2 | 6 | 1 | 15 | 136 |
| 54SCWB-CHEN 3.5W TEAM SINGLES FALL SEM F3 | 6 | 3 | 17 | 167 |
| 66HV-KRUMMEL 3.5W TEAM SINGLES FALL SEM F1 | 4 | 4 | 23 | 205 |
| 74VTC-REIC 3.5W TEAM SINGLES FALL SEM F1 | 4 | 4 | 25 | 193 |
| 81BRC-SCOTT 3.5W TEAM SINGLES FALL SEM F3 | 4 | 5 | 29 | 217 |
| 92PEACH-MAZUR 3.5W TEAM SINGLES FALL SEM F2 | 3 | 4 | 13 | 121 |
| 105HV-MCKENZIE 3.5W TEAM SINGLES FALL SEM F1 | 3 | 5 | 25 | 190 |
| 112CHIP-CLIFF 3.5W TEAM SINGLES FALL SEM F1 | 2 | 6 | 27 | 181 |
| 126WESS-BALCEROWSKI 3.5W TEAM SINGLES FALL SEM F2 | 1 | 3 | 15 | 105 |
| 133PEACH-LUKE 3.5W TEAM SINGLES FALL SEM F2 | 0 | 7 | 36 | 227 |
| 142BRC-NOVOTNY 3.5W TEAM SINGLES FALL SEM F3 | 0 | 9 | 42 | 272 |
Ordered by team wins, then losses, then sets lost — the order USTA uses to seed a flight. Individual wins are lines, not ties.
ScoutingWhere each opponent usually plays
Signed-in extra: whether this captain plays strict strength order or mixes it up, from every doubles line they have put out. Create a free account to see it.
Roster averages 3.50 estimated across 5 rated players. No lineup is published in advance, so the column below is where each player has actually been used — a captain's habits are the best available forecast. Sign in and claim your record to see who you have played.
Doubles pairs from this roster with three or more matches together anywhere — ordered by performance against what the rating gap predicted, not by record.
| Nathalie Vandecan + Maribel Weitz | 3–1 | 4 matches | not enough data |
| Stephanie Preston + Maribel Weitz | 3–1 | 4 matches | not enough data |
| Nathalie Vandecan + Jane Coffey | 0–3 | 3 matches | not enough data |
| Jean Wang + Leslie Ehrman | 1–2 | 3 matches | not enough data |
| Maribel Weitz + Jane Coffey | 2–1 | 3 matches | not enough data |
Ordered by our estimated dynamic rating, which is why two players at the same published level are not tied — a published 4.0 says nothing about where inside the band someone sits. Lines played in this team’s season are shown beside each name.
Three percentages are where each player stands right now: the chance they are below their band, inside it, or above it, on the way they are playing this season. The number on the right is our estimated dynamic rating, which USTA never publishes. This is today rather than a forecast of December, because a forecast has to assume future matches and cannot know whether someone will even be offered them. Percentages are blank where a player has too few matches this season, or at a level where the model does not yet beat a base-rate guess; the estimate is a weaker claim than a probability and survives where those do not.
Not affiliated with or endorsed by the USTA. Ratings labelled as published are USTA year-end figures; anything we describe as an estimate is ours, not USTA’s. Match data is from USTA TennisLink.