CHIP-Jang 40s 3.0 NW F3
2026 bSEM ADULT 40 & OVER NIGHT WOMEN · 3.0 WOMEN TUES PM SEM
22 players · average NTRP 2.97 / 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.
Schedule10 played
i
Flight standings19 teams · 3.0 WOMEN TUES PM SEM
| Team | W | L | Sets lost | Games lost |
|---|---|---|---|---|
| 1VTC-Johnston (outdoor) 40s 3.0 NW F3 | 10 | 0 | 30 | 353 |
| 2NAC-Conway 40s 3.0 NW F4 | 9 | 0 | 20 | 202 |
| 3TROY-Vondell 40s 3.0 NW F2 | 8 | 0 | 34 | 330 |
| 4WESS-Reamer 40s 3.0 NW F1 | 8 | 1 | 30 | 280 |
| 5FAC-Lewis 40s 3.0 NW F4 | 6 | 3 | 39 | 323 |
| 6WESS-Studier 40s 3.0 NW F1 | 6 | 3 | 45 | 368 |
| 7TRAVIS- Miller 40s 3.0 NW F3 | 5 | 5 | 48 | 430 |
| 8RCAA-Burns 40s 3.0 NW F3 | 5 | 5 | 60 | 445 |
| 9CHIP-Jang 40s 3.0 NW F3 | 5 | 5 | 64 | 502 |
| 10EAST-Bates 40s 3.0 NW F2 | 4 | 4 | 44 | 331 |
| 11LT- Dejaeger 40s 3.0 NW F2 | 4 | 4 | 50 | 359 |
| 12LIB-Coluni 40s 3.0 NW F3 | 4 | 6 | 57 | 455 |
| 13DCD- Kushiner 40s 3.0 NW F4 | 3 | 6 | 51 | 362 |
| 14PINE-Conroy (inside) 40s 3.0 NW F1 | 3 | 6 | 61 | 416 |
| 15PEACH-Hartel 40s 3.0 NW F2 | 2 | 6 | 51 | 390 |
| 16PH-Neaton 40s 3.0 NW F2 | 2 | 6 | 55 | 401 |
| 17BT-Danowski 40s 3.0 NW F1 | 1 | 8 | 66 | 446 |
| 18HV-Belcher 40s 3.0 NW F3 | 1 | 9 | 86 | 550 |
| 19SCWB-White 40s 3.0 NW F4 | 0 | 9 | 84 | 509 |
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 2.66 estimated across 21 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.
| Joleen Cruze + Najda Robinsonmayer | 3–4 | 49% games | +1.4% vs expected |
| Joan Dallapiazza + Sandra Pogue | 1–5 | 38% games | −0.3% vs expected |
| Fernanda Santos + Nataliya Pryzant | 2–6 | 36% games | −9.1% vs expected |
| Suzanne Thiele + Sandra Pogue | 1–6 | 35% games | not enough data |
| Irmgard Redman + Inok Chung | 3–2 | 44% games | not enough data |
| Cyndy Mickevicius + Joleen Cruze | 0–4 | 4 matches | not enough data |
| Joleen Cruze + Cheryl Cunningham | 1–3 | 4 matches | not enough data |
| Eva Flesher + Najda Robinsonmayer | 2–2 | 4 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.