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SAC-Majmudar/Saxena

2023 Adult 40 & Over · 4.0 Men

8 players · average NTRP 3.62 · USTA/PACIFIC NW / NORTHERN OREGON

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Scouting

Roster averages 3.38 estimated across 8 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.

3.67
Ashwani Gupta
published 4
3.64
Paritosh Saxena
usually #1 Doubles 100% · published 4
3.56
James Raleigh
usually #1 Doubles 100% · published 3.5
3.33
Shawn Yu
usually #1 Doubles 38%, also #1 Singles 38% · published 3.5
3.27
Shawn Boyce
usually #2 Doubles 40%, also #3 Doubles 40% · published 3.5
3.27
Lei Wan
usually #1 Doubles 42%, also #3 Doubles 33% · published 3.5
3.21
Mubashir Cheema
usually #1 Singles 40%, also #2 Doubles 40% · published 3.5
3.14
Arjun Bilakanti
usually #1 Singles 40%, also #2 Doubles 40% · published 3.5
Results8 ties
2023-06-17Lost 1–3
L#1 DoublesParitosh Saxena / James Raleigh vs Marc DeSantis / Agus Rusli6-4, 6-2
L#1 SinglesShawn Yu vs Bart Borosky6-0, 6-1
L#2 DoublesLei Wan / Mubashir Cheema vs Adam Gamboa / Jason Ruhmann6-0, 6-0
W#3 DoublesShawn Boyce / Nick Majmudar vs N/A6-0, 6-0
2023-06-10Lost 0–2
L#1 DoublesShawn Boyce / Shawn Yu vs Carlin Jackson / Tom King6-1, 6-4
L#2 DoublesArjun Bilakanti / Nick Majmudar vs Lance Berkey / Jonathan Quon6-0, 6-0
2023-06-02Lost 1–3
L#1 DoublesJames Raleigh / Paritosh Saxena vs Donald Pettitt / Eric Hickey6-4, 6-7, 1-0
W#1 SinglesShawn Yu vs Roger Dehoog6-3, 2-6, 1-0
L#2 DoublesNick Majmudar / Arjun Bilakanti vs Patrick Brewer / Ken Avenoso6-0, 6-1
L#3 DoublesShawn Boyce / Basheer Ahmed Muddebihal vs Jonathan Eames / Mark Shih6-3, 6-2
2023-05-26Lost 0–4
L#1 DoublesJames Raleigh / Paritosh Saxena vs Gregg Palmer / Zhitong Chen6-7, 6-4, 1-0
L#1 SinglesMubashir Cheema vs Kris Rosenquist6-4, 6-3
L#2 DoublesShawn Yu / Basheer Ahmed Muddebihal vs Dave Cobban / Jason Dortch6-1, 6-1
L#3 DoublesArjun Bilakanti / Nick Majmudar vs Frank Foti / Paul Stutzman6-1, 6-1
2023-05-21Lost 1–2
W#1 DoublesJames Raleigh / Paritosh Saxena vs Brenner Daniels / Michael Cobb7-6, 3-6, 1-0
L#1 SinglesShawn Yu vs Don Ormsby6-3, 6-0
L#2 DoublesLei Wan / Mubashir Cheema vs Dan Tracy / Matt Brown6-1, 6-2
2023-05-14Lost 0–3
L#1 DoublesParitosh Saxena / Shawn Yu vs Patrick Herbst / Robert Torch6-3, 6-1
L#2 DoublesBasheer Ahmed Muddebihal / Lei Wan vs Brian McDonagh / Walt Crate6-1, 6-1
L#3 DoublesArjun Bilakanti / Mubashir Cheema vs Joe Conyard / James Ringelberg6-0, 7-5
2023-05-06Won 2–1
W#1 DoublesJames Raleigh / Paritosh Saxena vs Michael Kazangian / Jeremy Goodson6-3, 3-6, 1-0
L#2 DoublesArjun Bilakanti / Nick Majmudar vs Timothy Johnson / Aman Wasu6-3, 6-2
W#3 DoublesShawn Yu / Lei Wan vs N/A6-0, 6-0
2023-04-23Lost 0–2
L#1 DoublesJames Raleigh / Shawn Yu vs Chris Thoman / Thomas Turnbull4-6, 6-1, 1-0
L#1 SinglesMubashir Cheema vs Doug Post6-1, 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.
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
Ashwani Gupta
18W–14L career
9%91%0%
3.67
4
Paritosh Saxena
35W–31L career
10%90%0%
3.64
3.5
James Raleigh
23W–51L career · type S
0%61%39%
3.56
3.5
Shawn Yu
27W–43L career
1%97%3%
3.33
3.5
Shawn Boyce
170W–125L career
0%99%0%
3.27
3.5
Lei Wan
25W–28L career
1%98%1%
3.27
3.5
Mubashir Cheema
71W–74L career
3%97%0%
3.21
3.5
Arjun Bilakanti
47W–107L career
6%94%0%
3.14

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.