Teams

SAC-Ko

2026 Mixed 18 & Over · 7.0 Mixed

10 players · average NTRP 3.3 · USTA/PACIFIC NW / NORTHERN OREGON

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Scouting

Roster averages 3.09 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.

3.33
Jihoon Bang
usually #2 Doubles 75%, also #3 Doubles 25% · published 3.5
3.30
Sam Ko
usually #2 Doubles 67%, also #1 Doubles 33% · published 3.5
3.18
Shari Levelle
usually #2 Doubles 67%, also #1 Doubles 33% · published 3.5
3.16
Lara Greenberg
usually #1 Doubles 67%, also #3 Doubles 22% · published 3
3.16
Arjun Bilakanti
usually #1 Doubles 67%, also #2 Doubles 33% · published 3.5
3.07
WonKyu Kim
usually #1 Doubles 50%, also #3 Doubles 25% · published 3.5
3.02
Jonathan Lo
usually #3 Doubles 100% · published 3.5
2.94
Grainne Koeberl
usually #3 Doubles 50%, also #2 Doubles 25% · published 3
2.92
Jinmi Kemple
usually #2 Doubles 67%, also #1 Doubles 17% · published 3
2.78
Ruming Yin
usually #3 Doubles 67%, also #2 Doubles 33% · published 3
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.

3.5
Jihoon Bang
57W–41L career
0%99%1%
3.33
3.5
Sam Ko
66W–81L career
0%98%1%
3.30
3.5
Shari Levelle
63W–57L career
3%97%0%
3.18
3
Lara Greenberg
73W–43L career
0%33%67%
3.16
3.5
Arjun Bilakanti
47W–107L career
5%95%0%
3.16
3.5
WonKyu Kim
78W–64L career
18%82%0%
3.07
3.5
Jonathan Lo
31W–59L career
28%72%0%
3.02
3
Grainne Koeberl
56W–65L career
0%93%7%
2.94
3
Jinmi Kemple
75W–78L career
0%93%7%
2.92
3
Ruming Yin
8W–20L career
1%99%1%
2.78

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.