Teams

OPunjabi/WParker (Queens)

2023 Mixed Doubles 40 & Over League · 40 & Over Mixed 8.0 - Thurs PM

1 players · average NTRP 4 · USTA/SOUTHERN / NORTH CAROLINA

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Scouting

Roster averages 3.98 estimated across 1 rated player. 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.

3.98
Michele Sutherland
usually #3 Doubles 57%, also #2 Doubles 43% · published 4
Results7 ties
2023-08-07Won 10
W#2 DoublesDuane Johnson / Michele Sutherland vs N/A6-0, 6-0
2023-07-27Won 10
W#3 DoublesLance King / Michele Sutherland vs Trista Holwager / Mike Haynes6-1, 6-1
2023-07-20Lost 01
L#2 DoublesLance King / Michele Sutherland vs Meghan Bartholomew / Jacob Huffaker6-4, 7-6
2023-07-13Won 10
W#3 DoublesDuane Johnson / Michele Sutherland vs Tamara Gallagher / Richard Gallagher6-0, 6-0
2023-07-06Won 10
W#3 DoublesRavi Mujumdar / Michele Sutherland vs N/A6-0, 6-0
2023-06-25Won 10
W#2 DoublesLance King / Michele Sutherland vs Mark Griffey / Kelley Montroy6-4, 6-1
2023-06-15Won 10
W#3 DoublesLance King / Michele Sutherland vs Margaux Karagosian / Rich Pietrykowski6-1, 6-4
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
Michele Sutherland
332W–175L career
0%87%13%
3.98

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.