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SAC-Sun

2021 Mixed 18 & Over · 7.0 Mixed

10 players · average NTRP 3.65 · USTA/PACIFIC NW / NORTHERN OREGON

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Scouting

Roster averages 3.38 estimated across 10 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.

4.12
Cassia Carpio
usually #3 Doubles 56%, also #2 Doubles 33% · published 4.5
3.64
Paritosh Saxena
usually #1 Doubles 100% · published 4
3.60
Seungja Shin
usually #3 Doubles 60%, also #2 Doubles 40% · published 4
3.53
Matt Zhou
usually #3 Doubles 50%, also #2 Doubles 50% · published 3.5
3.47
Chinwoo Kim
usually #1 Doubles 60%, also #2 Doubles 20% · published 3.5
3.35
Timothea Barnatan
usually #3 Doubles 100% · published 3.5
3.13
Hoon Shin
usually #2 Doubles 67%, also #3 Doubles 33% · published 3.5
3.09
Rajan Madhusudan
usually #2 Doubles 40%, also #1 Doubles 40% · published 3.5
3.05
WonKyu Kim
usually #3 Doubles 50%, also #1 Doubles 50% · published 3.5
2.86
Yogesh Patel
usually #2 Doubles 67%, also #3 Doubles 33% · published 3
Results9 ties
2021-09-25Lost 0–1
L#1 DoublesChinwoo Kim / Honghui Liang vs Bally Bang / Anh Lao6-0, 6-1
2021-09-24Lost 0–1
L#3 DoublesChinwoo Kim / Honghui Liang vs Angela Byrum / Oscar Cedeira6-2, 6-1
2021-05-15Lost 0–1
L#2 DoublesJuan Li / Chinwoo Kim vs Kelly Mohr / Luke Filose6-0, 6-1
2020-11-01Won 2–1
W#1 DoublesCassia Carpio / Rajan Madhusudan vs Sarah Carrato / Jered Cuenco7-5, 7-5
W#2 DoublesAleli Siruno / Matt Zhou vs Henry Kofron / Gina Ossanna6-2, 2-6, 1-0
L#3 DoublesSeungja Shin / WonKyu Kim vs ANTHONY PEPE / KEELEY PLOMBON7-6, 4-6, 1-0
2020-10-17Lost 0–2
L#2 DoublesCassia Carpio / Rajan Madhusudan vs Cindy del Rosario / Saurabh Chauhan1-6, 6-4, 1-0
L#3 DoublesHoon Shin / Seungja Shin vs Adam Oken / Hailey Swisher6-3, 7-5
2020-10-11Won 2–1
L#1 DoublesLei Zhu / Paritosh Saxena vs Holly Kanz / Frank Mathews6-4, 3-6, 1-0
W#2 DoublesSeungja Shin / Hoon Shin vs Sarah Carrato / David Bean6-3, 7-6
W#3 DoublesYogesh Patel / Timothea Barnatan vs Gina Ossanna / Jered Cuenco3-6, 6-2, 1-0
2020-09-26Lost 0–2
L#1 DoublesChinwoo Kim / Aleli Siruno vs Amy Bucher / Kevin Jarvis6-2, 6-4
L#3 DoublesCassia Carpio / Rajan Madhusudan vs Sarah Ferrer / Dylan Wile3-6, 7-5, 1-0
2020-09-18Lost 1–2
W#1 DoublesParitosh Saxena / Lei Zhu vs Ariana White / John Jay2-6, 7-5, 1-0
L#2 DoublesCassia Carpio / Yogesh Patel vs Mary Klinger / William Allers2-6, 7-5, 1-0
L#3 DoublesSeungja Shin / WonKyu Kim vs Kimberly Parks / Kevin Lennox4-6, 7-5, 1-0
2020-09-06Lost 1–2
W#1 DoublesChinwoo Kim / Aleli Siruno vs Ryan Yokoyama / Sherie Briggs6-0, 6-2
L#2 DoublesHoon Shin / Seungja Shin vs Clay Funkhouser / Seona Zimmermann7-5, 3-6, 1-0
L#3 DoublesTimothea Barnatan / Matt Zhou vs Dale Christensen / Erin Doucet6-4, 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.
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.

4.5
Cassia Carpio
256W–91L career
2%98%0%
4.12
4
Paritosh Saxena
35W–31L career
10%90%0%
3.64
4
Seungja Shin
86W–64L career
14%86%0%
3.60
3.5
Matt Zhou
63W–65L career
0%73%27%
3.53
3.5
Chinwoo Kim
90W–54L career
0%86%14%
3.47
3.5
Timothea Barnatan
95W–117L career
0%98%2%
3.35
3.5
Hoon Shin
112W–53L career
13%87%0%
3.13
3.5
Rajan Madhusudan
94W–79L career
13%87%0%
3.09
3.5
WonKyu Kim
78W–64L career
22%78%0%
3.05
3
Yogesh Patel
46W–46L career
0%94%6%
2.86

Three percentages are the year-end projection — chance of moving down, staying, moving up — and the number on the right is our estimated dynamic rating, which USTA never publishes. 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.