Teams

PLEASANTON 40MX8.0A

2022 MIXED 40&Over · 40&Over MX8.0

20 players · average NTRP 3.86 / NO. CALIFORNIA

No matches recorded for this roster in this league — it may be a placeholder registration, or the season may not have started.

View scouting list →

Flight standings for 40&Over MX8.0 have not been collected yet. Where we have them we show every team in the flight ranked, with this team highlighted. The crawler is still filling in older seasons and flights.

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 3.58 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.

4.31
Debashis Panda
published 4.5
4.06
Sanjay Backliwal
published
3.80
Jennifer Do
published
3.77
Paul Morrow
published
3.77
Shin Koo
published
3.75
Ritesh Bhandari
published
Darien Lum
published 4
3.74
Debbie Soleta
published 4
3.73
Bowyee Gong
published 4
3.66
Paul Lizama
published
3.59
Michael O'Hara
published
3.51
Tanya Lo
published
3.50
Ning Docena
published
3.42
Alan Chan
usually #2 Doubles 43%, also #3 Doubles 43% · published 4
3.36
Erik Amborn
published 3.5
3.17
Salvador Labrador
published
2.88
Jessica Zhang
published
2.81
Dana Takahashi
published 3
Jennifer Dinh
published
Mi Qi
published
Roster

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.

4.5
Debashis Panda
5W–5L career · no lines in 2022
1%92%8%
4.31
Sanjay Backliwal
10W–32L career · no lines in 2022
33%67%<1%
4.06
Jennifer Do
26W–16L career · no lines in 2022
1%93%6%
3.80
Paul Morrow
12W–6L career · no lines in 2022
2%94%4%
3.77
Shin Koo
4W–13L career · no lines in 2022
96%4%<1%
3.77
Ritesh Bhandari
15W–8L career · no lines in 2022
3%95%3%
3.75
4
Darien Lum
4W–0L career · no lines in 2022
not enough data
4
Debbie Soleta
11W–13L career · no lines in 2022
3%94%2%
3.74
4
Bowyee Gong
41W–25L career · no lines in 2022
4%94%2%
3.73
Paul Lizama
3W–15L career · no lines in 2022
11%88%<1%
3.66
Michael O'Hara
9W–19L career · no lines in 2022
24%75%<1%
3.59
Tanya Lo
9W–22L career · no lines in 2022
47%53%<1%
3.51
Ning Docena
13W–26L career · no lines in 2022
51%49%<1%
3.50
4
Alan Chan
80W–67L career · 7 lines in 2022
74%26%<1%
3.42
3.5
Erik Amborn
2W–2L career · no lines in 2022
<1%85%15%
3.36
Salvador Labrador
1W–13L career · no lines in 2022
99%1%<1%
3.17
Jessica Zhang
0W–5L career · no lines in 2022
>99%<1%0%
2.88
3
Dana Takahashi
11W–7L career · no lines in 2022
1%92%7%
2.81
Jennifer Dinh
3W–0L career · no lines in 2022
not enough data
Mi Qi
1W–1L career · no lines in 2022
not enough data

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.