4BEV-MOORE Winter 3.0 women singles sem f2
2022 SEM TEAM SINGLES - WINTER · 2022 WOMEN 3.0 WINTER SINGLES SEM
9 players · average NTRP 3.25 / 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 standings9 teams · 2022 WOMEN 3.0 WINTER SINGLES SEM
| Team | W | L | Sets lost | Games lost |
|---|---|---|---|---|
| 16WESS-LI Winter 3.0 women singles sem f2 | 7 | 1 | 12 | 153 |
| 22LIFE-RYAN Winter 3.0 women singles sem f2 | 5 | 3 | 26 | 197 |
| 33FAC-LEWIS Winter 3.0 women singles sem f1 | 5 | 4 | 22 | 212 |
| 42HV-LEONARD Winter 3.0 women singles sem f1 | 5 | 4 | 25 | 228 |
| 54BEV-MOORE Winter 3.0 women singles sem f2 | 4 | 4 | 21 | 176 |
| 61HV-PRESTON Winter 3.0 women singles sem f1 | 4 | 5 | 19 | 202 |
| 74SCWB-GILL Winter 3.0 women singles sem f1 | 4 | 5 | 34 | 249 |
| 81LIFE-ANDERSON Winter 3.0 women singles sem f2 | 3 | 5 | 24 | 197 |
| 95SCWB-LUBIN Winter 3.0 women singles sem f2 | 1 | 7 | 38 | 239 |
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 2.96 estimated across 8 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.
| Michelle Chinnaiyan + Lani Moore | 9–3 | 65% games | +13.0% vs expected |
| Lani Moore + Amy Klamkin | 4–3 | 53% games | +4.5% vs expected |
| Amanda Podgorny + Dawn Uhl | 4–1 | 56% games | not enough data |
| Sara Amaral + Kara Kushiner | 4–1 | 51% games | not enough data |
| Michelle Chinnaiyan + Amy Naum | 2–3 | 47% games | not enough data |
| Dawn Uhl + Lani Moore | 2–2 | 4 matches | not enough data |
| Dawn Uhl + Amy Klamkin | 3–1 | 4 matches | not enough data |
| Amanda Podgorny + Amy Naum | 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.