Teams

LLW SGV Arcadia Angels Army - Kim- WN/SA 10

2022 Adult Division - 40& Over San Gabriel Valley · SGV - Women 3.5

15 players · average NTRP 3.56 / 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 →

Flight standings for SGV - Women 3.5 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 3.36 estimated across 13 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.

3.56
Kristina Lovato
published
3.50
LINH LEE
published
3.50
Myrna Castanon
usually #1 Doubles 50%, also #3 Doubles 50% · published
3.48
Elena Salinas
usually #1 Singles 46%, also #3 Doubles 18% · published 4
3.48
Sylvia DSouza
usually #3 Doubles 100% · published 4
3.43
Sherie Paulson
usually #3 Doubles 43%, also #2 Doubles 43% · published 3.5
3.34
Jenniffer Wong
usually #3 Doubles 100% · published 3.5
3.29
Melinda Stahl Nix
published
3.26
Kathleen Foy-Asaro
usually #2 Doubles 86%, also #3 Doubles 14% · published 3.5
3.25
Regina Cheung
published
3.20
Virginia Takeuchi
usually #1 Singles 50%, also #2 Doubles 33% · published 3.5
3.20
Pei Fu
usually #1 Doubles 67%, also #2 Doubles 33% · published 3.5
3.18
Ruth McNulty
usually #3 Doubles 50%, also #1 Doubles 50% · published 3.5
Ginger Vasquez
published 3
Jeongyean Jang
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.

Regina Cheung + Sherie Paulson5158% gamesnot enough data
Jenniffer Wong + Kathleen Foy-Asaro4264% gamesnot enough data
Pei Fu + Sherie Paulson3251% gamesnot enough data
Jenniffer Wong + LINH LEE404 matchesnot enough data
Virginia Takeuchi + Kathleen Foy-Asaro213 matchesnot enough data
Virginia Takeuchi + Pei Fu213 matchesnot enough data
Pei Fu + Ruth McNulty123 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.

Kristina Lovato
6W–9L career · no lines in 2022
34%66%<1%
3.56
LINH LEE
18W–6L career · no lines in 2022
<1%48%52%
3.50
Myrna Castanon
161W–81L career · 2 lines in 2022
49%51%<1%
3.50
4
Elena Salinas
140W–158L career · 11 lines in 2022
55%45%<1%
3.48
4
Sylvia DSouza
110W–97L career · 2 lines in 2022
56%44%<1%
3.48
3.5
Sherie Paulson
102W–61L career · 7 lines in 2022
<1%71%29%
3.43
3.5
Jenniffer Wong
200W–169L career · 2 lines in 2022
<1%88%11%
3.34
Melinda Stahl Nix
3W–4L career · no lines in 2022
1%94%5%
3.29
3.5
Kathleen Foy-Asaro
172W–163L career · 7 lines in 2022
2%95%3%
3.26
Regina Cheung
9W–12L career · no lines in 2022
3%95%3%
3.25
3.5
Virginia Takeuchi
112W–106L career · 6 lines in 2022
6%93%1%
3.20
3.5
Pei Fu
85W–49L career · 3 lines in 2022
7%92%1%
3.20
3.5
Ruth McNulty
67W–44L career · 2 lines in 2022
8%91%1%
3.18
3
Ginger Vasquez
0W–1L career · no lines in 2022
not enough data
Jeongyean Jang
3W–0L 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.