Teams

NITA E 80MX40a TAM (Marberry/Sung)

2022 NITA1 ESL Mixed 40 & Over · NITA ESL 40 & Over 8.0 Mixed

14 players · average NTRP 4.00 / NORTHERN ILLINOIS

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
2021-12-19Won 20
W#1 DoublesBruce Thornquist / Sara Marberry vs N/A6-0, 6-0
W#3 DoublesSEAN YANG / Silvia Kusaka vs N/A6-0, 6-0
2021-12-10Won 10
W#1 DoublesBruce Thornquist / Lisa Hans vs N/A6-0, 6-0
W#1 DoublesJoon Sung / Jennifer Grady vs Sophia Koliatsis / James Klein7-6, 6-0
W#2 DoublesSara Marberry / SEAN YANG vs Rochelle Meng / Scott Smith6-2, 6-1
W#3 DoublesNick Paterakos / Huan Chang vs Mary Cooper / Jonathan Cooper6-1, 6-3
L#1 DoublesSilvia Kusaka / Steven Schenker vs Mary Marks / Barry Marks2-6, 3-6
L#2 DoublesSue Jeon / Paul Leonardo vs Rosemary Wheeler / Alex Paz7-6, 1-6, 0-1
W#1 DoublesSilvia Kusaka / Paul Leonardo vs Mary Marks / Barry Marks2-6, 6-3, 1-0
L#1 DoublesBruce Thornquist / Jennifer Grady vs Kevin Podwika / Marilyn Tansey4-6, 2-6
L#2 DoublesJoon Sung / Silvia Kusaka vs John Laurx / Debbi Landorf2-6, 5-7
W#1 DoublesJoon Sung / Silvia Kusaka vs Barry Marks / Mary Marks6-3, 6-7, 1-0
W#2 DoublesPaul Leonardo / Lesley Seeger vs Sophia Koliatsis / Mark Juhl6-2, 6-2
W#3 DoublesLisa Hans / Bruce Thornquist vs Julie Alexander / Alex Paz6-1, 6-3
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 standings4 teams · NITA ESL 40 & Over 8.0 Mixed
TeamWLInd. WInd. LSets lostGames lost
1NITA E 80MX40a TAM (Marberry/Sung)6218614118
2NITA E 80MX40b Midtown AC (Meng/Smith)35101429181
3NITA E 80MX40d Naperville TC (Podwika)3581632221
4WITHDREW NITA E 80MX40c TAM (Aguayo)000000

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 3.70 estimated across 9 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.

3.96
Huan Chang
usually #3 Doubles 100% · published 4
3.88
Lesley Seeger
published
3.87
Joon Sung
published
3.87
Silvia Kusaka
usually #1 Doubles 60%, also #2 Doubles 20% · published 4
3.78
Bruce Thornquist
usually #1 Doubles 75%, also #3 Doubles 25% · published 4
3.64
Paul Leonardo
usually #2 Doubles 67%, also #1 Doubles 33% · published
3.51
El Cid Balitaan
published 4
3.44
SEAN YANG
published
3.34
Lisa Hans
usually #3 Doubles 100% · published
Jennifer Grady
usually #1 Doubles 100% · published
Kim Rust
published
Nick Paterakos
usually #3 Doubles 100% · published
Sara Marberry
usually #2 Doubles 100% · published
Steven Schenker
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.

Sara Marberry + Bruce Thornquist111249% games−13.2% vs expected
Lesley Seeger + Joon Sung14167% gamesnot enough data
Silvia Kusaka + El Cid Balitaan3450% gamesnot enough data
Sara Marberry + Joon Sung1544% gamesnot enough data
Silvia Kusaka + Joon Sung3354% gamesnot enough data
Lisa Hans + Bruce Thornquist4155% gamesnot enough data
Huan Chang + Joon Sung404 matchesnot enough data
Sara Marberry + El Cid Balitaan213 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
Huan Chang
201W–143L career · 1 line in 2022
<1%61%39%
3.96
Lesley Seeger
39W–19L career · no lines in 2022
<1%83%17%
3.88
Joon Sung
91W–45L career · no lines in 2022
<1%83%16%
3.87
4
Silvia Kusaka
232W–160L career · 5 lines in 2022
<1%83%16%
3.87
4
Bruce Thornquist
238W–98L career · type A · 4 lines in 2022
2%94%4%
3.78
Paul Leonardo
91W–64L career · 3 lines in 2022
14%86%<1%
3.64
4
El Cid Balitaan
21W–18L career · no lines in 2022
46%54%<1%
3.51
SEAN YANG
13W–17L career · no lines in 2022
67%33%<1%
3.44
Lisa Hans
71W–103L career · 1 line in 2022
88%12%<1%
3.34
Jennifer Grady
78W–58L career · 2 lines in 2022
not enough data
Kim Rust
14W–24L career · no lines in 2022
not enough data
Nick Paterakos
93W–57L career · 1 line in 2022
not enough data
Sara Marberry
124W–87L career · 1 line in 2022
not enough data
Steven Schenker
28W–24L 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.