← Teams

VTC-Hamlik/Gaylor

2019 Mixed 18 & Over · 7.0 Mixed

9 players · average NTRP 3.56 · USTA/PACIFIC NW / NORTHERN OREGON

View scouting list →
Scouting

Roster averages 3.33 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.

3.66
Dean Gaylor
usually #1 Doubles 50%, also #3 Doubles 50% · published 4
3.33
Andy LaFrazia
usually #3 Doubles 50%, also #2 Doubles 50% · published 3.5
—
Denise Dawkins
usually #2 Doubles 43%, also #1 Doubles 43% · published 3.5
—
Jim Hamlik
usually #1 Doubles 60%, also #2 Doubles 20% · published 3.5
—
Kim Hamlik
usually #1 Doubles 60%, also #2 Doubles 20% · published 3.5
—
Tammy LaFrazia
usually #3 Doubles 100% · published 3.5
3.18
Kelley Gaylor
usually #1 Doubles 40%, also #3 Doubles 40% · published 3.5
3.15
Julie Gorecki
usually #2 Doubles 40%, also #3 Doubles 40% · published 3.5
—
Mark Dawkins
usually #2 Doubles 50%, also #3 Doubles 25% · published 0
Results8 ties
2018-11-18Won 2–0
W#2 DoublesJulie Gorecki / Daniel Gorecki vs Ann-Marie Lundberg / Martin Wells7-5, 6-1
W#3 DoublesKim Hamlik / Jim Hamlik vs James Prouty / Kate Kearney6-1, 6-4
2018-11-17Lost 1–2
L#1 DoublesDean Gaylor / Kelley Gaylor vs Agus Rusli / Debbie Spitzer7-6, 7-5
W#2 DoublesDenise Dawkins / Mark Dawkins vs Jackie Konen / Dan Boren2-6, 6-3, 1-0
L#3 DoublesJulie Gorecki / Daniel Gorecki vs Diana Blebea / John Yu6-3, 6-4
2018-11-10Lost 1–2
L#1 DoublesKim Hamlik / Jim Hamlik vs Jim Nudelman / Tara Delarosa6-0, 6-0
W#2 DoublesKelley Gaylor / Andy LaFrazia vs Ellen Foley / James King6-0, 7-6
L#3 DoublesJulie Gorecki / Daniel Gorecki vs Jolene Neal / Guy Neal3-6, 6-3, 1-0
2018-11-04Lost 0–2
L#1 DoublesJim Hamlik / Kim Hamlik vs Jian Xu / Tony Moreno6-4, 6-3
L#3 DoublesKelley Gaylor / Dean Gaylor vs Shawn Boyce / Sara Shah7-5, 6-4
2018-10-24Won 3–0
W#1 DoublesDenise Dawkins / Mark Dawkins vs Catherine Leedy / Alan Peters6-0, 6-4
W#2 DoublesJim Hamlik / Kim Hamlik vs Kristin Eckelman / Jeffrey Bayless4-6, 7-6, 1-0
W#3 DoublesKelley Gaylor / Dean Gaylor vs Elizabeth Burns / Matthew Casimo6-2, 7-5
2018-10-20Won 2–1
W#1 DoublesJulie Gorecki / Daniel Gorecki vs Benjamin Brewer / Marianne Conroy6-2, 6-0
L#2 DoublesDenise Dawkins / Mark Dawkins vs Chip Rosenfeld / Kathleen Budnick1-6, 6-4, 1-0
W#3 DoublesTammy LaFrazia / Andy LaFrazia vs Michelle Ericson / Douglas Brewer6-1, 6-2
2018-10-06Won 2–0
W#1 DoublesKim Hamlik / Jim Hamlik vs Rolf Ellingsen / Brooke Henderson4-6, 6-4, 1-0
W#3 DoublesDenise Dawkins / Mark Dawkins vs Karen Pickering / Mike Rebar6-2, 7-5
2018-09-30Split 1–1
L#1 DoublesKelley Gaylor / Dean Gaylor vs Holly Kanz / John Kin6-3, 3-6, 1-0
W#2 DoublesJulie Gorecki / Daniel Gorecki vs ANTHONY PEPE / Danielle Cuenco7-6, 7-6
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.
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
Dean Gaylor
88W–63L career
12%88%1%
3.66
3.5
Andy LaFrazia
65W–34L career
4%86%10%
3.33
3.5
Denise Dawkins
59W–54L career
not enough data
—
3.5
Jim Hamlik
33W–46L career
not enough data
—
3.5
Kim Hamlik
24W–44L career
not enough data
—
3.5
Tammy LaFrazia
44W–25L career
not enough data
—
3.5
Kelley Gaylor
68W–64L career
10%89%1%
3.18
3.5
Julie Gorecki
33W–22L career
11%89%0%
3.15
0
Mark Dawkins
24W–15L career
not enough data
—

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.