A40 3.0W NC Peters
2022 NEOTA Adult 40 & Over · NEOTA A40&O 3.0 Women
17 players · average NTRP 2.83 / NORTHEASTERN OHIO
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 standings22 teams · NEOTA A40&O 3.0 Women
| Team | W | L | Sets lost | Games lost |
|---|---|---|---|---|
| 1A40 3.0W LT2 Sirak | 10 | 0 | 14 | 269 |
| 2A40 3.0W PM1 Sloan | 9 | 1 | 24 | 322 |
| 3A40 3.0W NC Peters | 8 | 2 | 40 | 393 |
| 4A40 3.0W MV2 Hunter | 7 | 1 | 21 | 266 |
| 5A40 3.0W CC Becker | 7 | 1 | 26 | 297 |
| 6A40 3.0W PW2 Scott | 6 | 2 | 39 | 332 |
| 7A40 3.0W CV1 Fisher | 6 | 2 | 40 | 342 |
| 8A40 3.0W TP1 Dean | 6 | 4 | 50 | 436 |
| 9A40 3.0W AVB Pallante | 5 | 5 | 64 | 478 |
| 10A40 3.0W AO Swanson | 4 | 4 | 50 | 381 |
| 11A40 3.0W WR2 Strobach | 4 | 6 | 60 | 466 |
| 12A40 3.0W GR Iams | 4 | 6 | 64 | 458 |
| 13A40 3.0W WR1 Miller | 4 | 6 | 66 | 488 |
| 14A40 3.0W TP2 Farrell | 4 | 6 | 70 | 485 |
| 15A40 3.0W CV2 Morgan | 3 | 5 | 46 | 382 |
| 16A40 3.0W NR Carbeck | 3 | 5 | 54 | 384 |
| 17A40 3.0W LT1 Barker | 3 | 7 | 54 | 442 |
| 18A40 3.0W PM2 Whitlam | 3 | 7 | 69 | 475 |
| 19A40 3.0W WE Allan | 2 | 6 | 59 | 419 |
| 20A40 3.0W MV1 Cunningham | 2 | 6 | 61 | 410 |
| 21A40 3.0W PW1 Forkin | 0 | 8 | 68 | 441 |
| 22A40 3.0W AS Miller | 0 | 10 | 99 | 597 |
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 2.81 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.
| Lisa Mears + Angela Dimos | 5–7 | 51% games | +7.0% vs expected |
| Lora Kilkenny + Wendi Preston | 27–9 | 59% games | +5.7% vs expected |
| Lisa Mears + Mary Slee | 4–9 | 46% games | −2.4% vs expected |
| Wendi Preston + Jo Ann Cecchini | 7–8 | 51% games | −9.8% vs expected |
| Laurie Craven + Jing Zhou | 7–0 | 74% games | not enough data |
| Janice Hall Moore + Suzanna Racine | 4–2 | 52% games | not enough data |
| Debra Wilkinson + Suzanna Racine | 1–4 | 36% games | not enough data |
| Debra Wilkinson + Cindy Peters | 0–5 | 32% games | 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.