Teams

BAY CLUB SF TENNIS 18MX9.0A

2022 MIXED 18&Over · 18&Over MX9.0

16 players · average NTRP 4.25 / 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 18&Over MX9.0 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 4.07 estimated across 14 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.

4.71
Robin Zhou
published
4.44
Justin Lu
published
4.38
Soumi Gupta
published
4.37
Giacomo Balli
published 4.5
4.18
Lizzie Siegle
published
4.15
Catherine Vowles
published 4.5
4.14
Nikil Pancha
published
4.08
Carolina O'Haren
published 4.5
4.03
Paul Sampognaro
published
3.94
Jana Klein
published
3.83
Bobby Carter
published 4
3.80
Lisa Park
published 4
3.54
Gregory Rojas
published 4
3.40
Nikhil Desai
published
Jonathan Ng
published
Larry Rubin
published
Established pairs

Doubles pairs from this roster with three or more matches together anywhere — ordered by performance against what the rating gap predicted, not by record.

Jana Klein + Justin Lu303 matchesnot enough data
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.

Robin Zhou
10W–4L career · no lines in 2022
6%93%1%
4.71
Justin Lu
24W–10L career · no lines in 2022
<1%67%33%
4.44
Soumi Gupta
9W–5L career · no lines in 2022
<1%82%18%
4.38
4.5
Giacomo Balli
8W–1L career · no lines in 2022
<1%83%16%
4.37
Lizzie Siegle
7W–4L career · no lines in 2022
8%91%1%
4.18
4.5
Catherine Vowles
6W–6L career · no lines in 2022
13%87%<1%
4.15
Nikil Pancha
2W–3L career · no lines in 2022
14%86%<1%
4.14
4.5
Carolina O'Haren
1W–3L career · no lines in 2022
27%73%<1%
4.08
Paul Sampognaro
4W–2L career · no lines in 2022
42%58%<1%
4.03
Jana Klein
6W–8L career · no lines in 2022
66%34%<1%
3.94
4
Bobby Carter
37W–21L career · no lines in 2022
1%90%9%
3.83
4
Lisa Park
27W–29L career · no lines in 2022
1%93%6%
3.80
4
Gregory Rojas
8W–13L career · no lines in 2022
37%63%<1%
3.54
Nikhil Desai
2W–9L career · no lines in 2022
79%21%<1%
3.40
Jonathan Ng
1W–1L career · no lines in 2022
not enough data
Larry Rubin
0W–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.