Teams

SFV - Club Osa

2022 Casual Mixed 18+ SFV · SFV - 8.0 Mixed 18+

8 players · average NTRP 4.33 / SO.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 →
Schedule3 played
2022-08-14vs SFV - Catalina Wine MixersWon 10
W#1 Female DoublesCarrie Gan / Monica Mitchell vs Nickie Huai / Karin Hoesli6-4, 6-7, 1-0
2022-08-06vs SFV - Nice AcesWon 10
W#1 Male DoublesBrian Czerniak / MacLyn McRae vs Jared Mano / Edmond Avedisian6-4, 6-3
2022-07-24vs SFV - Catalina Wine MixersLost 01
L#1 Male DoublesCarlos Garcia / MacLyn McRae vs James Fregia / Bruce Upbin4-6, 2-6
A tie can be incomplete. Tell us if someone is missing.
i
Grouped by tie — one fixture, several lines. A line shows only when a player on it is someone we have indexed, so an early-season tie for a newly-added team can look short. This also means the line count here will not match the header, which counts individual player appearances.

Flight standings for SFV - 8.0 Mixed 18+ 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 3.90 estimated across 7 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.

Carrie Gan
published 4.5
4.12
Saeed Saed
published
4.03
Ashish Bangar
published 4.5
3.97
Jing Lin
published
3.91
Tunde Alele
published
3.85
MacLyn McRae
usually #1 Male Doubles 100% · published 4
3.77
Monica Mitchell
usually #1 Female Doubles 100% · published
3.65
Stephen Kim
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
Carrie Gan
0W–2L career · no lines in 2022
not enough data
Saeed Saed
16W–11L career · no lines in 2022
19%81%<1%
4.12
4.5
Ashish Bangar
5W–5L career · no lines in 2022
40%60%<1%
4.03
Jing Lin
6W–4L career · no lines in 2022
<1%58%42%
3.97
Tunde Alele
5W–5L career · no lines in 2022
<1%76%24%
3.91
4
MacLyn McRae
94W–50L career · 2 lines in 2022
<1%88%12%
3.85
Monica Mitchell
77W–72L career · 1 line in 2022
2%94%4%
3.77
Stephen Kim
11W–18L career · no lines in 2022
13%87%<1%
3.65

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.