← Teams

MPRC-Weaver (Jennifer)

2022 Mixed 18 & Over · 6.0 Mixed

11 players · average NTRP 3.23 · USTA/PACIFIC NW / NORTHERN OREGON

View scouting list →
Scouting

Roster averages 3.13 estimated across 8 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.

3.34
Lupine Swanson
usually #2 Doubles 100% · published 3.5
3.28
Gabriel Weaver
usually #3 Doubles 44%, also #1 Doubles 33% · published 3.5
3.26
Eric Ayers
usually #1 Doubles 100% · published 3.5
3.15
Yan Morrissey
usually #3 Doubles 50%, also #1 Doubles 50% · published 3.5
3.15
Yongping Liu
usually #1 Doubles 67%, also #3 Doubles 33% · published 3
3.13
Patrick Morrissey
usually #3 Doubles 50%, also #1 Doubles 50% · published 3.5
2.87
Dan Blaker
usually #3 Doubles 50%, also #2 Doubles 50% · published 3
2.83
Manoj Agnihotri
usually #2 Doubles 100% · published 3
—
Dipali Dixit
usually #2 Doubles 100% · published 3
—
Keibun Hayashi
usually #3 Doubles 100% · published 3
—
Rachel Connell
usually #3 Doubles 67%, also #2 Doubles 33% · published 3
Results7 ties
2021-12-04Lost 0–2
L#1 DoublesYan Morrissey / Patrick Morrissey vs Harshi Waters / Zachary Wright6-0, 6-4
L#2 DoublesGabriel Weaver / Rachel Connell vs Joshua Husbands / Sara Crate6-1, 6-4
2021-11-21Lost 0–1
L#3 DoublesRachel Connell / Keibun Hayashi vs Theresa Gonzales / Monte Gonzales6-2, 6-3
2021-11-13Won 3–0
W#1 DoublesJane King Ayers / Eric Ayers vs Christie Herkert / Michael McTague6-0, 6-3
W#2 DoublesLupine Swanson / Zack Hegge vs Meegan Fruits / Paul Preston6-1, 6-4
W#3 DoublesJing Li / Yongping Liu vs Hilary Johnson / Dave Sasaki6-4, 7-5
2021-10-17Won 2–1
W#1 DoublesJane King Ayers / Eric Ayers vs WonKyu Kim / KUSUM ASUDANI6-2, 2-6, 1-0
L#2 DoublesLupine Swanson / Dan Blaker vs Colin Looi / Janna McDougall6-1, 6-4
W#3 DoublesGabriel Weaver / Rachel Connell vs Todd McConachie / Jill Boyce7-6, 4-6, 1-0
2021-10-10Won 3–0
W#1 DoublesJing Li / Yongping Liu vs Thomas Bollaert / Rebecca Griffith1-6, 6-4, 1-0
W#2 DoublesDipali Dixit / Manoj Agnihotri vs Mikayla Posey / Scott Learn6-0, 6-4
W#3 DoublesNaun Yvonne Kim / Keibun Hayashi vs Samanta Rodriguez Silva / Dario Soriano6-4, 6-1
2021-10-02Lost 1–2
W#1 DoublesJing Li / Yongping Liu vs Allie Mauck / Devin McDonald7-5, 6-2
L#2 DoublesDipali Dixit / Manoj Agnihotri vs Virginia Martinez / Rodrigo Martinez6-4, 6-3
L#3 DoublesYan Morrissey / Patrick Morrissey vs Hoyun Barr / Kevin Pham6-1, 6-2
2021-09-18Lost 1–2
L#1 DoublesJane King Ayers / Eric Ayers vs Mihye Ko / Chinwoo Kim6-3, 7-6
W#2 DoublesDipali Dixit / Manoj Agnihotri vs Hyekyeong Kim / Jason Lee6-2, 6-4
L#3 DoublesJennifer Weaver / Dan Blaker vs Misug Kim / Daniel M Kim6-2, 6-1
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.

3.5
Lupine Swanson
59W–24L career
0%96%3%
3.34
3.5
Gabriel Weaver
132W–66L career
1%99%1%
3.28
3.5
Eric Ayers
77W–67L career
1%98%1%
3.26
3.5
Yan Morrissey
54W–73L career
5%95%0%
3.15
3
Yongping Liu
68W–49L career
0%40%60%
3.15
3.5
Patrick Morrissey
37W–68L career
13%87%0%
3.13
3
Dan Blaker
35W–48L career
0%97%3%
2.87
3
Manoj Agnihotri
81W–117L career
0%99%1%
2.83
3
Dipali Dixit
40W–48L career
not enough data
—
3
Keibun Hayashi
32W–84L career
not enough data
—
3
Rachel Connell
34W–26L 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.