Teams

2SCWB-GAY Team Singles 3.5 Men SEM F3

2023 SEM WINTER TEAM SINGLES · 2023 MEN 3.5 WINTER SINGLES SEM

7 players · average NTRP 3.50 / S.E. MICHIGAN

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 →
Schedule8 played
L#1 SinglesJulian Gay vs Karthik Yadagiri4-6, 2-6
L#2 SinglesMichael Bloom vs Madhup Singh1-6, 3-6
L#3 SinglesMatthias Podzuhn vs Sridhar Perala4-6, 2-6
W#1 SinglesTaek Kim vs Joshua Ewald6-0, 6-2
W#1 SinglesTaek Kim vs Luo Xie6-1, 6-2
L#2 SinglesPatrick Partyka vs Henry Pu0-6, 0-6
L#3 SinglesVimal Saigal vs Hai Xu4-6, 4-6
L#1 SinglesTaek Kim vs Jacob Sa6-2, 4-6, 0-1
L#2 SinglesJulian Gay vs David Sidder0-6, 2-6
L#3 SinglesPatrick Partyka vs Robert Cupples2-6, 3-6
L#1 SinglesJulian Gay vs Scott Lusader1-6, 0-6
L#2 SinglesMatthias Podzuhn vs Timothy Throgmorton1-6, 2-6
L#3 SinglesMichael Fleischer vs Ronald Mills4-6, 3-5
W#2 SinglesMichael Bloom vs Hai Xu6-2, 5-7
L#3 SinglesMichael Fleischer vs Yun Chen1-6, 1-6
W#1 SinglesTaek Kim vs Madhu Dayananthan6-2, 6-2
L#2 SinglesPatrick Partyka vs Ramakrishna Kapilavai3-6, 4-4
L#3 SinglesVimal Saigal vs Madhup Singh3-6, 2-6
L#1 SinglesJulian Gay vs Robert Cupples4-6, 2-6
L#2 SinglesMichael Fleischer vs David Sidder1-6, 0-6
L#3 SinglesMatthias Podzuhn vs Jacob Sa1-6, 2-6
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 standings14 teams · 2023 MEN 3.5 WINTER SINGLES SEM
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 3.23 estimated across 4 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.

3.30
Matthias Podzuhn
usually #3 Singles 67%, also #2 Singles 33% · published
3.30
Michael Fleischer
usually #3 Singles 67%, also #2 Singles 33% · published
3.19
Julian Gay
usually #1 Singles 75%, also #2 Singles 25% · published 3.5
3.15
Patrick Partyka
usually #2 Singles 67%, also #3 Singles 33% · published 3.5
Michael Bloom
usually #2 Singles 100% · published
Taek Kim
usually #1 Singles 100% · published
Vimal Saigal
usually #3 Singles 100% · 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.

Patrick Partyka + Michael Fleischer3243% games−0.2% vs expected
Julian Gay + Patrick Partyka1438% gamesnot 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.

Matthias Podzuhn
45W–22L career · 3 lines in 2023
1%93%6%
3.30
Michael Fleischer
90W–57L career · 3 lines in 2023
1%93%6%
3.30
3.5
Julian Gay
54W–111L career · 4 lines in 2023
7%92%1%
3.19
3.5
Patrick Partyka
33W–35L career · 3 lines in 2023
13%87%<1%
3.15
Michael Bloom
19W–24L career · 2 lines in 2023
not enough data
Taek Kim
25W–11L career · 4 lines in 2023
not enough data
Vimal Saigal
7W–16L career · 2 lines in 2023
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.