Teams

1WESS-BROWN 6.0 MIXED 40&O SEM F2

2025 aSEM MIX WINTER 40 & OVER · 2025 60 MIXED 40 & OVER SEM F

17 players · average NTRP 3.12 / 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 →
Schedule4 played
L#3 DoublesHeidi Malin / Michel AlKhalil vs David Sills / Yuliana Lionas2-6, 1-6
2025-03-15Lost 01
L#3 DoublesJacopo Alaimo / Mingjie Zhang vs Rebecca Walters / John Walters3-6, 6-4, 0-1
2025-02-23vs 6PEACH-HOCH 6.0 MIXED 40&O SEM F2Lost 01
L#3 DoublesMichel AlKhalil / Heidi Malin vs Thomas Sklenar / Marie Kim Lamb5-7, 4-6
2025-01-25vs 5PH-NEATON 6.0 MIXED 40&O SEM F2Lost 01
L#1 DoublesHeidi Malin / Michel AlKhalil vs Nancy Pfeifer / Kevin Donaldson1-6, 1-6
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 · 2025 60 MIXED 40 & OVER SEM F
TeamWLInd. WInd. LSets lostGames lost
14TROY-VONDELL 6.0 MIXED 40&O SEM F3902527145
21WESS-BROWN 6.0 MIXED 40&O SEM F28118921201
34LIB-GONZALEZ 6.0 MIXED 40&O SEM F172171025221
43LIB-BARDEN 6.0 MIXED 40&O SEM F16318923217
54EAST-MITCHELL 6.0 MIXED 40&O SEM F263171024219
63BEV-MOORE 6.0 MIXED 40&O SEM F354151231244
76PEACH-HOCH 6.0 MIXED 40&O SEM F254121533242
83LIFE-ANDERSON 6.0 MIXED 40&O SEM F245141332255
91WESS-PERES 6.0 MIXED 40&O SEM F33691838280
101CHIP-SCHORER 6.0 MIXED 40&O SEM F13691839283
115PH-NEATON 6.0 MIXED 40&O SEM F237151535290
122HV-MOORE 6.0 MIXED 40&O SEM F127101736278
132WESS-SPENCE 6.0 MIXED 40&O SEM F31852247302
142WESS-JURADO 6.0 MIXED 40&O SEM F20621633199

Ordered by team wins, then losses, then sets lost — the order USTA uses to seed a flight. Individual wins are lines, not ties.

See the full flight, with every team’s record

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 3.04 estimated across 16 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.

3.45
Timothy Kurtz
usually #3 Doubles 40%, also #2 Doubles 40% · published
3.31
Sarkis Toroyan
usually #1 Doubles 55%, also #2 Doubles 36% · published
3.30
Michael Fleischer
usually #3 Doubles 43%, also #1 Doubles 29% · published
3.21
Bonnie Treece
usually #1 Doubles 67%, also #2 Doubles 33% · published 3.5
3.15
Amanda Kill
usually #2 Doubles 100% · published 3
3.13
Michelle Wooddell
usually #3 Doubles 50%, also #1 Doubles 50% · published 3.5
3.06
Melanee Radner
usually #3 Doubles 50%, also #1 Doubles 50% · published 3
3.00
Heidi Malin
usually #3 Doubles 67%, also #1 Doubles 17% · published 3.5
2.98
Jacopo Alaimo
usually #2 Doubles 33%, also #1 Doubles 33% · published 3
2.94
Craig Halseth
usually #1 Doubles 75%, also #3 Doubles 25% · published 3
2.94
Michel AlKhalil
usually #3 Doubles 67%, also #1 Doubles 33% · published
2.90
Dawn Uhl
usually #1 Doubles 100% · published 3
2.88
Nina Brown
usually #2 Doubles 50%, also #3 Doubles 33% · published 3
2.84
Mingjie Zhang
usually #3 Doubles 100% · published
2.80
Rebeca Beliard
usually #2 Doubles 40%, also #1 Doubles 40% · published 3
Andreas Heesch
published 3
2.67
Karen Farra
usually #3 Doubles 60%, also #2 Doubles 40% · published 3
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.

Timothy Kurtz + Karen Farra12070% games+21.3% vs expected
Heidi Malin + Michel AlKhalil0730% gamesnot enough data
Sarkis Toroyan + Bonnie Treece4357% gamesnot enough data
Nina Brown + Andreas Heesch3345% gamesnot enough data
Michelle Wooddell + Michael Fleischer2358% gamesnot enough data
Heidi Malin + Timothy Kurtz5061% gamesnot enough data
Sarkis Toroyan + Amanda Kill4166% gamesnot enough data
Dawn Uhl + Sarkis Toroyan224 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.

Timothy Kurtz
98W–33L career · 10 lines in 2025
<1%64%36%
3.45
Sarkis Toroyan
58W–30L career · 11 lines in 2025
91%9%<1%
3.31
Michael Fleischer
82W–42L career · 7 lines in 2025
2%91%7%
3.30
3.5
Bonnie Treece
106W–43L career · 3 lines in 2025
7%91%2%
3.21
3
Amanda Kill
80W–12L career · 2 lines in 2025
<1%15%85%
3.15
3.5
Michelle Wooddell
143W–150L career · 2 lines in 2025
17%82%<1%
3.13
3
Melanee Radner
52W–36L career · 2 lines in 2025
<1%32%68%
3.06
3.5
Heidi Malin
133W–136L career · 6 lines in 2025
49%51%<1%
3.00
3
Jacopo Alaimo
73W–42L career · 3 lines in 2025
<1%56%44%
2.98
3
Craig Halseth
35W–35L career · 4 lines in 2025
<1%65%34%
2.94
Michel AlKhalil
16W–12L career · 3 lines in 2025
<1%66%34%
2.94
3
Dawn Uhl
81W–56L career · 2 lines in 2025
<1%77%23%
2.90
3
Nina Brown
146W–135L career · 6 lines in 2025
<1%81%19%
2.88
Mingjie Zhang
11W–5L career · 1 line in 2025
1%86%13%
2.84
3
Rebeca Beliard
74W–55L career · 5 lines in 2025
1%90%8%
2.80
3
Andreas Heesch
48W–35L career · no lines in 2025
not enough data
3
Karen Farra
31W–9L career · 5 lines in 2025
12%87%1%
2.67

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.