3LIFE-LAMAR Winter Team Singles 3.5 WOMEN SEM F3
2024 zSEM WINTER TEAM SINGLES · 2024 WOMEN 3.5 WINTER SINGLES SEM
14 players · average NTRP 3.50 / 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.
Schedule9 played
i
Flight standings13 teams · 2024 WOMEN 3.5 WINTER SINGLES SEM
| Team | W | L | Sets lost | Games lost |
|---|---|---|---|---|
| 14SCWB-YAGUCHI Winter Team Singles 3.5 WOMEN SEM F3 | 8 | 1 | 14 | 164 |
| 26HV-PRESTON Winter Team Singles 3.5 WOMEN SEM F1 | 6 | 2 | 15 | 162 |
| 34LIB-HAESSLER Winter Team Singles 3.5 WOMEN SEM F1 | 6 | 2 | 21 | 180 |
| 44LIFE-LAGMAN Winter Team Singles 3.5 WOMEN SEM F2 | 6 | 3 | 19 | 177 |
| 53SCWB-CHEN Winter Team Singles 3.5 WOMEN SEM F2 | 6 | 3 | 24 | 191 |
| 62BRC-SCOTT Winter Team Singles 3.5 WOMEN SEM F3 | 6 | 3 | 24 | 185 |
| 73LIB-MARLEY Winter Team Singles 3.5 WOMEN SEM F1 | 5 | 3 | 19 | 177 |
| 81PEACH-MAZUR Winter Team Singles 3.5 WOMEN SEM F2 | 4 | 5 | 28 | 209 |
| 93LIFE-LAMAR Winter Team Singles 3.5 WOMEN SEM F3 | 4 | 5 | 34 | 240 |
| 102CHIP-CLIFF Winter Team Singles 3.5 WOMEN SEM F1 | 2 | 6 | 31 | 204 |
| 112PEACH-LUKE Winter Team Singles 3.5 WOMEN SEM F2 | 2 | 7 | 31 | 227 |
| 125HV-MCKENZIE Winter Team Singles 3.5 WOMEN SEM F1 | 1 | 7 | 30 | 210 |
| 131BEV-MOORE Winter Team Singles 3.5 WOMEN SEM F3 | 0 | 9 | 43 | 279 |
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 3.23 estimated across 13 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.
| Laura Lamar + Julie Boike | 4–1 | 60% games | +3.7% vs expected |
| Kelly Coe + Jennifer Berk-King | 44–21 | 57% games | +3.7% vs expected |
| Jennifer Costabile + AnnMarie Sanford | 3–5 | 44% games | −6.8% vs expected |
| Jennifer Costabile + Lisa Zohoury | 1–4 | 43% games | −7.0% vs expected |
| Laura Lamar + Lisa Zohoury | 3–2 | 47% games | not enough data |
| Lisa Zohoury + Jennifer Berk-King | 0–4 | 4 matches | not enough data |
| Julie Boike + Lisa Zohoury | 3–0 | 3 matches | not enough data |
| Dominika Ronikier-Rangi + Jennifer Prohow | 1–2 | 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.