AAREC-Cortina 40s 3.5 NW F1
2026 bSEM ADULT 40 & OVER NIGHT WOMEN · 3.5 WOMEN MON PM SEM
17 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 standings21 teams · 3.5 WOMEN MON PM SEM
| Team | W | L | Sets lost | Games lost |
|---|---|---|---|---|
| 1TROY-Segerlind 40s 3.5 NW F3 | 10 | 0 | 24 | 307 |
| 2HV-King 40s 3.5 NW F2 | 10 | 0 | 30 | 335 |
| 3AAREC-Cortina 40s 3.5 NW F1 | 7 | 2 | 35 | 351 |
| 4WESS-Li 40s 3.5 NW F4 | 6 | 2 | 27 | 275 |
| 5TROY-Shier 40s 3.5 NW F4 | 6 | 2 | 41 | 343 |
| 6LIB-Dawson 40s 3.5 NW F1 | 6 | 3 | 40 | 365 |
| 7DEER-Gibbs 40s 3.5 NW F3 | 6 | 4 | 52 | 447 |
| 8HV-Haessler 40s 3.5 NW F2 | 6 | 4 | 54 | 461 |
| 9NAC-Chen 40s 3.5 NW F2 | 5 | 5 | 58 | 469 |
| 10BT-Fietsam 40s 3.5 NW F3 | 5 | 5 | 60 | 472 |
| 11BOH-Liut 40s 3.5 NW F3 | 5 | 5 | 62 | 445 |
| 12EAST-Thomas 40s 3.5 NW F4 | 4 | 4 | 39 | 309 |
| 13PH-Higgins 40s 3.5 NW F4 | 4 | 4 | 46 | 352 |
| 14SCWB- Ramon 40s 3.5 NW F2 | 4 | 6 | 58 | 456 |
| 15TRAVIS-Skochelak 40s 3.5 NW F1 | 3 | 6 | 59 | 432 |
| 16DCD- Moore 40s 3.5 NW F2 | 3 | 7 | 66 | 468 |
| 17WESS-Brown 40s 3.5 NW F3 | 3 | 7 | 70 | 477 |
| 18BARTON- Geer 40s 3.5 NW F1 | 2 | 7 | 69 | 469 |
| 19CHIP-Roth 40s 3.5 NW F2 | 2 | 8 | 84 | 539 |
| 20LT- Marvel-Nelson 40s 3.5 (outside) NW F3 | 1 | 9 | 75 | 514 |
| 21PEACH-Parrott 40s 3.5 NW F4 | 0 | 8 | 60 | 424 |
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 3.26 estimated across 17 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.
| Ursula Jakob + Jennifer Yuan | 5–2 | 61% games | +12.9% vs expected |
| Padma Nallamothu + Stephanie Preston | 8–2 | 57% games | +9.8% vs expected |
| Stephanie Preston + Kim Lijana | 3–3 | 57% games | +8.8% vs expected |
| Marjorie D Alecy + Yena Kim | 3–2 | 60% games | +6.3% vs expected |
| Marjorie D Alecy + Lori Sella-Armstrong | 6–4 | 57% games | +5.0% vs expected |
| Martha Gove + Kamilah Kelsaw | 2–3 | 48% games | +5.0% vs expected |
| Kim Lijana + Lori Sella-Armstrong | 3–2 | 47% games | +4.6% vs expected |
| Padma Nallamothu + Roopa Gurm | 7–7 | 53% games | +4.0% vs expected |
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.