Teams

Wise Ace GOATs

2022 DC MTL Tri-Level Mixed · DC MTL Tri Level Mixed 4.5, 4.0, 3.5

16 players · average NTRP 4.25 / WASHINGTON D.C.

No matches recorded for this roster in this league — it may be a placeholder registration, or the season may not have started.

View scouting list →
Schedule3 played
2022-09-07vs Grace's AcesWon 10
W#3 DoublesKirah Yen / Phillip Ramil vs Joy Wu / Jose Miguel Sacin6-4, 4-5
2022-08-07vs Grace's AcesSplit 11
L#2 DoublesDian Zhang / Will Jenkins vs Kenneth Fowler / Gianna Kang0-6, 2-6
W#3 DoublesKirah Yen / Phillip Ramil vs Joy Wu / Andrew Park6-4, 6-5
2022-07-26vs Grace's AcesWon 10
W#2 DoublesDian Zhang / William Wong vs Kelly Blackborow / Jose Miguel Sacin7-5, 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.

Flight standings for DC MTL Tri Level Mixed 4.5, 4.0, 3.5 have not been collected yet. Where we have them we show every team in the flight ranked, with this team highlighted. The crawler is still filling in older seasons and flights.

ScoutingWhere each opponent usually plays

Signed-in extra: whether this captain plays strict strength order or mixes it up, from every doubles line they have put out. Create a free account to see it.

Roster averages 3.92 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.

4.49
Leah Noonan
published
4.36
Aravind Krishna
published 5
4.26
Simone Feldman
published
Bavi Sadayappan
published 4.5
3.93
Dian Zhang
usually #2 Doubles 100% · published 4
3.79
Carolyn Healy
published 4
3.78
Phillip Ramil
usually #3 Doubles 100% · published 4
3.76
Thoufiq Kaleemullah
published
3.75
Adriana Rice
published 4
3.60
Kirah Yen
published
3.45
William Wong
published
Kara Stevenson
published
Stephen Gotts
published
Sumeet Chawla
published
Will Jenkins
published
Zachary Errickson
published
Established pairs

Doubles pairs from this roster with three or more matches together anywhere — ordered by performance against what the rating gap predicted, not by record.

Simone Feldman + Phillip Ramil213 matchesnot enough data
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. Lines played in this team’s season are shown beside each name.

Leah Noonan
50W–17L career · no lines in 2022
<1%54%46%
4.49
5
Aravind Krishna
37W–48L career · no lines in 2022
86%14%<1%
4.36
Simone Feldman
79W–66L career · no lines in 2022
2%95%3%
4.26
4.5
Bavi Sadayappan
2W–5L career · no lines in 2022
not enough data
4
Dian Zhang
229W–109L career · 2 lines in 2022
<1%71%29%
3.93
4
Carolyn Healy
49W–17L career · no lines in 2022
1%93%5%
3.79
4
Phillip Ramil
142W–56L career · 2 lines in 2022
2%94%5%
3.78
Thoufiq Kaleemullah
11W–5L career · no lines in 2022
2%94%3%
3.76
4
Adriana Rice
1W–3L career · type S · no lines in 2022
3%95%3%
3.75
Kirah Yen
42W–29L career · no lines in 2022
22%78%<1%
3.60
William Wong
9W–5L career · no lines in 2022
65%35%<1%
3.45
Kara Stevenson
3W–4L career · no lines in 2022
not enough data
Stephen Gotts
1W–2L career · no lines in 2022
not enough data
Sumeet Chawla
23W–12L career · no lines in 2022
not enough data
Will Jenkins
1W–1L career · no lines in 2022
not enough data
Zachary Errickson
1W–1L career · no lines in 2022
not enough data

Three percentages are where each player stands right now: the chance they are below their band, inside it, or above it, on the way they are playing this season. The number on the right is our estimated dynamic rating, which USTA never publishes. This is today rather than a forecast of December, because a forecast has to assume future matches and cannot know whether someone will even be offered them. 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.

Not affiliated with or endorsed by the USTA. Ratings labelled as published are USTA year-end figures; anything we describe as an estimate is ours, not USTA’s. Match data is from USTA TennisLink.