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SAC-Ilcheva/Natarajan

2020 Mixed 40 & Over · 8.0 Mixed

8 players · average NTRP 3.79 · USTA/PACIFIC NW / NORTHERN OREGON

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Scouting

Roster averages 3.47 estimated across 5 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.

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Neerav Shah
usually #2 Doubles 75%, also #1 Doubles 25% · published 4
3.72
Sharon Kim-Geib
usually #1 Doubles 60%, also #2 Doubles 20% · published 4
3.50
Vimal Natarajan
usually #1 Doubles 100% · published 4
3.50
Eric Means
usually #3 Doubles 50%, also #1 Doubles 50% · published 4
3.48
Molly Odonnell
usually #2 Doubles 67%, also #3 Doubles 33% · published 4
3.16
Shari Levelle
usually #3 Doubles 50%, also #2 Doubles 50% · published 3.5
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Diane di Vito
usually #1 Doubles 60%, also #3 Doubles 40% · published 3
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Brady Berry
usually #3 Doubles 100% · published 0
Results8 ties
2020-03-07Lost 0–3
L#1 DoublesIrena Ilcheva / Vimal Natarajan vs Agus Rusli / Angie Lee6-4, 6-1
L#2 DoublesShari Levelle / Rodger DeGeorge vs Deepak Kumar / Kimberly Anderson6-7, 6-4, 1-0
L#3 DoublesSharon Kim-Geib / Brady Berry vs Andre Baran / Donna Kennish2-6, 7-6, 1-0
2020-02-29Split 1–1
W#1 DoublesJulie Hansen / Neerav Shah vs Joshuah Kleiva / Andrea Kropp6-3, 2-6, 1-0
L#2 DoublesMolly Odonnell / David Sefton vs Jake Dawes / Carol Chiu6-4, 6-1
2020-02-23Lost 0–3
L#1 DoublesEric Means / Diane di Vito vs Kyoko Adcock / David Pham6-2, 6-4
L#2 DoublesNeerav Shah / Julie Hansen vs Chizuru Watanabe / Nate Look6-1, 6-4
L#3 DoublesShari Levelle / Rodger DeGeorge vs Kay Dobashi / Donovan Oliver6-3, 0-6, 1-0
2020-02-09Lost 0–3
L#1 DoublesVimal Natarajan / Julie Hansen vs Renato Bernasconi Zuccari / Lily Looi4-6, 6-3, 1-0
L#2 DoublesDavid Sefton / Sharon Kim-Geib vs Agus Rusli / Angie Lee6-4, 6-2
L#3 DoublesBrady Berry / Diane di Vito vs Deepak Kumar / Donna Kennish7-6, 6-1
2020-02-01Lost 0–3
L#1 DoublesIrena Ilcheva / Vimal Natarajan vs Kyoko Adcock / David Pham6-4, 6-2
L#2 DoublesMolly Odonnell / David Sefton vs Michelle Donato / Lee Crockett6-0, 6-3
L#3 DoublesCarla Wheeler / Brady Berry vs Chiho Cronk / Bryan Shieh6-2, 7-6
2020-01-26Lost 1–2
L#1 DoublesVimal Natarajan / Sharon Kim-Geib vs Megan Daniels / Richard Jackson7-5, 4-6, 1-0
W#2 DoublesJulie Hansen / Neerav Shah vs Chris Eddy / Kathryn Eddy6-1, 2-6, 1-0
L#3 DoublesRodger DeGeorge / Diane di Vito vs Craig Hlady / Mary Hlady6-2, 3-6, 1-0
2020-01-17Won 2–1
W#1 DoublesVimal Natarajan / Sharon Kim-Geib vs Marc Alexander / Jill Craven2-6, 6-3, 1-0
W#2 DoublesJulie Hansen / Neerav Shah vs Scott MacDonald / Dana McKillop3-6, 6-3, 1-0
L#3 DoublesMolly Odonnell / Brady Berry vs Mark Stromme / Jaymi Sladen6-4, 5-7, 1-0
2020-01-11Lost 0–2
L#1 DoublesVimal Natarajan / Sharon Kim-Geib vs Chris Collins / Elise Anderson6-4, 7-6
L#3 DoublesCarla Wheeler / Eric Means vs Meshi Chavez / Mary Klinger6-2, 7-5
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
Neerav Shah
23W–18L career
not enough data
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4
Sharon Kim-Geib
76W–53L career
1%99%0%
3.72
4
Vimal Natarajan
78W–95L career
34%66%0%
3.50
4
Eric Means
26W–48L career
36%64%0%
3.50
4
Molly Odonnell
37W–90L career
40%60%0%
3.48
3.5
Shari Levelle
63W–57L career
4%96%0%
3.16
3
Diane di Vito
45W–53L career
not enough data
—
0
Brady Berry
56W–45L career
not enough data
—

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.