Teams

It's All A Racquet!

2024 18&Over Mixed · 7.0

20 players · average NTRP 3.73 / 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 →
Schedule7 played
2024-05-10vs Diamond Shark RebornLost 01
L#2 DoublesQiongrong Nie / Mukund Pai vs Liyun Feng / Madhu Talupur2-6, 6-3, 0-1
2024-04-12vs Diamond Shark RebornLost 01
L#2 DoublesQiongrong Nie / Daniel Robaina vs Paul Fang / Roslyn Yang3-6, 4-6
2024-03-22vs Diamond Shark RebornLost 01
L#3 DoublesDaniel Robaina / Qiongrong Nie vs Paul Fang / Liyun Feng6-4, 3-6, 1-0
2024-03-16vs What A RacquetWon 10
W#1 DoublesQiongrong Nie / Harish Kumar vs Sherla Glasgow / Hong Wang6-3, 6-2
2024-03-02vs No Strings AttachedWon 10
W#1 DoublesHarish Kumar / Qiongrong Nie vs Bruno Maguino / Alison North6-3, 2-6, 1-0
2024-02-24vs No Strings AttachedLost 01
L#1 DoublesHarish Kumar / Qiongrong Nie vs Olga Carey / Patryk Hirsz4-6, 4-6
2024-02-17vs Team WassermanWon 10
W#1 DoublesQiongrong Nie / Harish Kumar vs Richard Liu / Yijun Lu7-5, 6-0
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 standings2 teams · 7.0
TeamWLInd. WInd. LSets lostGames lost
1Diamond Shark Reborn43111025208
2It's All A Racquet!34101124209

Ordered by team wins, then losses, then sets lost — the order USTA uses to seed a flight. Individual wins are lines, not ties.

See the full flight, with every team’s record

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 3.52 estimated across 16 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.

3.84
Michelle Kang
usually #2 Doubles 46%, also #3 Doubles 36% · published
3.82
Daniel Robaina
usually #3 Doubles 60%, also #2 Doubles 30% · published 4
3.82
Eric Levitch
usually #1 Doubles 42%, also #3 Doubles 32% · published 4
3.79
Kyle Eng
usually #2 Doubles 35%, also #3 Doubles 35% · published
3.71
Teresa Rim
usually #1 Doubles 38%, also #2 Doubles 38% · published 4
3.67
JUHEE LEE
usually #3 Doubles 40%, also #1 Doubles 40% · published 4
3.61
Mukund Pai
usually #2 Doubles 70%, also #3 Doubles 30% · published 4
3.59
Qiongrong Nie
usually #1 Doubles 55%, also #2 Doubles 27% · published 4
3.59
AUSTIN KIM
usually #3 Doubles 55%, also #1 Doubles 27% · published 4
3.59
Hong Wang
usually #2 Doubles 46%, also #1 Doubles 36% · published 4
3.47
Sherla Glasgow
usually #1 Doubles 57%, also #2 Doubles 43% · published 3.5
3.40
Steve Woo
usually #1 Doubles 71%, also #2 Doubles 14% · published
3.31
Soo Kim
usually #3 Doubles 80%, also #2 Doubles 20% · published
Andrea Chu
published 3.5
3.15
Rommel Serrano
published
3.01
Ingrid Floyd
usually #2 Doubles 50%, also #3 Doubles 33% · published 3.5
3.00
Lynne Ainge
usually #3 Doubles 80%, also #2 Doubles 20% · published 3
Barbara Jacobus
usually #1 Doubles 67%, also #3 Doubles 17% · published 3
Namsoo Joo
usually #1 Doubles 50%, also #3 Doubles 25% · published
PAUL SHIN
usually #2 Doubles 67%, also #1 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.

Hong Wang + Teresa Rim7257% games+11.9% vs expected
Teresa Rim + Daniel Robaina4156% games+10.8% vs expected
Steve Woo + Teresa Rim19562% games+10.1% vs expected
AUSTIN KIM + JUHEE LEE6260% games+9.4% vs expected
Eric Levitch + Barbara Jacobus4152% games+6.2% vs expected
Hong Wang + Sherla Glasgow10359% games+3.2% vs expected
AUSTIN KIM + Soo Kim3348% games+2.0% vs expected
Mukund Pai + Michelle Kang4645% games−1.1% vs expected
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.

Michelle Kang
183W–120L career · 11 lines in 2024
<1%89%11%
3.84
4
Daniel Robaina
78W–94L career · 10 lines in 2024
1%91%8%
3.82
4
Eric Levitch
208W–147L career · 19 lines in 2024
1%91%8%
3.82
Kyle Eng
101W–52L career · 17 lines in 2024
1%93%5%
3.79
4
Teresa Rim
114W–63L career · 16 lines in 2024
5%94%1%
3.71
4
JUHEE LEE
163W–111L career · 5 lines in 2024
10%90%1%
3.67
4
Mukund Pai
179W–130L career · 10 lines in 2024
20%80%<1%
3.61
4
Qiongrong Nie
91W–30L career · 11 lines in 2024
23%76%<1%
3.59
4
AUSTIN KIM
80W–93L career · 11 lines in 2024
25%75%<1%
3.59
4
Hong Wang
186W–67L career · 11 lines in 2024
25%75%<1%
3.59
3.5
Sherla Glasgow
240W–168L career · 7 lines in 2024
<1%60%40%
3.47
Steve Woo
70W–33L career · 7 lines in 2024
<1%77%23%
3.40
Soo Kim
119W–76L career · 5 lines in 2024
1%92%7%
3.31
3.5
Andrea Chu
75W–44L career · no lines in 2024
not enough data
Rommel Serrano
11W–11L career · no lines in 2024
12%87%<1%
3.15
3.5
Ingrid Floyd
201W–228L career · 6 lines in 2024
47%53%<1%
3.01
3
Lynne Ainge
112W–86L career · 5 lines in 2024
<1%50%50%
3.00
3
Barbara Jacobus
8W–21L career · 6 lines in 2024
not enough data
Namsoo Joo
8W–1L career · 4 lines in 2024
not enough data
PAUL SHIN
6W–0L career · 6 lines in 2024
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.