← Teams

All Mixed Up

2022 40&Over Mixed · 7.0

15 players · average NTRP 3.56 / NEW JERSEY REGION

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 →
Schedule6 played
2022-06-30vs NJ: Fusion TigersLost 0–2
L#2 DoublesTeresa Murphy / David Coppola vs Kai Xu / Tina Tang2-6, 3-6
L#3 DoublesLynne Ainge / Pranjal Shukla vs Michelle Deng / Derek Jin4-6, 3-6
2022-06-26vs NJ: Fusion TigersLost 1–2
L#1 DoublesJosephine Chan / Justin Kidman vs Hong Wang / Liang Chao3-6, 4-6
L#2 DoublesPranjal Shukla / Lynne Ainge vs Rie Yonemochi / Kevin Wang2-6, 2-6
W#3 DoublesTina Gonzalez / Anil Ghelani vs Songbai Yan / Hsiaoli Ma7-5, 4-1
2022-06-20vs NJ: Fusion TigersLost 0–1
L#1 DoublesDevang Sodha / Lynne Ainge vs Derek Jin / Jean Zhang2-6, 0-6
2022-06-16vs NJ: Fusion TigersLost 0–2
L#2 DoublesGaurav Malik / Lynne Ainge vs Rie Yonemochi / Kevin Wang5-7, 1-5
L#3 DoublesDawn Donnelly / Anil Ghelani vs Tina Tang / Derek Jin6-7, 0-5
2022-06-05vs NJ: Fusion TigersLost 1–2
L#1 DoublesAnil Ghelani / Josephine Chan vs Alicia Shieh / Hong Wang1-6, 2-6
L#2 DoublesGaurav Malik / Tina Gonzalez vs Mary Chion / Aris Cheng4-6, 2-6
W#3 DoublesDevang Sodha / Lynne Ainge vs Michelle Deng / Jerry Zhang6-0, 6-3
2022-06-02vs NJ: Fusion TigersLost 0–1
L#2 DoublesTeresa Murphy / David Coppola vs Alicia Shieh / Hong Wang2-6, 3-6
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 7.0 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 3.29 estimated across 12 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.

3.61
Mukund Pai
usually #2 Doubles 50%, also #3 Doubles 25% · published 4
3.59
Devang Sodha
usually #3 Doubles 50%, also #1 Doubles 50% · published 4
3.51
Indranil Das
usually #3 Doubles 50%, also #1 Doubles 50% · published 3.5
3.41
Gaurav Malik
usually #2 Doubles 67%, also #3 Doubles 33% · published 3.5
3.35
Anil Ghelani
usually #3 Doubles 67%, also #1 Doubles 33% · published 3.5
3.30
Teresa Murphy
usually #2 Doubles 100% · published —
3.29
Tina Gonzalez
usually #2 Doubles 50%, also #3 Doubles 50% · published —
—
Elme Schmid
usually #1 Doubles 71%, also #2 Doubles 14% · published 3.5
3.16
Dawn Donnelly
usually #3 Doubles 50%, also #1 Doubles 50% · published 3.5
3.15
Justin Kidman
usually #1 Doubles 67%, also #3 Doubles 33% · published —
3.10
Josephine Chan
usually #1 Doubles 100% · published —
3.01
Ingrid Floyd
usually #3 Doubles 38%, also #2 Doubles 38% · published 3.5
3.00
Lynne Ainge
usually #3 Doubles 40%, also #2 Doubles 40% · published 3
—
David Coppola
usually #2 Doubles 100% · published —
—
Pranjal Shukla
usually #3 Doubles 67%, also #2 Doubles 33% · 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.

Mukund Pai + Ingrid Floyd2–547% gamesnot enough data
Justin Kidman + Elme Schmid1–23 matchesnot enough data
Lynne Ainge + Pranjal Shukla0–33 matchesnot enough data
Dawn Donnelly + Indranil Das1–23 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.

4
Mukund Pai
179W–130L career · 4 lines in 2022
20%80%<1%
3.61
4
Devang Sodha
19W–13L career · 2 lines in 2022
25%75%<1%
3.59
3.5
Indranil Das
52W–21L career · 2 lines in 2022
<1%46%54%
3.51
3.5
Gaurav Malik
42W–31L career · 3 lines in 2022
<1%76%24%
3.41
3.5
Anil Ghelani
126W–133L career · 3 lines in 2022
<1%86%13%
3.35
—
Teresa Murphy
93W–63L career · 2 lines in 2022
1%93%6%
3.30
—
Tina Gonzalez
172W–137L career · 2 lines in 2022
1%93%6%
3.29
3.5
Elme Schmid
78W–67L career · 7 lines in 2022
not enough data
—
3.5
Dawn Donnelly
81W–139L career · 4 lines in 2022
11%88%<1%
3.16
—
Justin Kidman
61W–66L career · 6 lines in 2022
13%86%<1%
3.15
—
Josephine Chan
54W–86L career · 2 lines in 2022
23%77%<1%
3.10
3.5
Ingrid Floyd
201W–228L career · 8 lines in 2022
47%53%<1%
3.01
3
Lynne Ainge
112W–86L career · 5 lines in 2022
<1%50%50%
3.00
—
David Coppola
0W–2L career · 2 lines in 2022
not enough data
—
—
Pranjal Shukla
2W–23L career · 3 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.