Teams

F 4.0S/8.0D MM Mercuri

2022 NEOTA Fusion · NEOTA 4.0S

18 players · average NTRP 3.79 / NORTHEASTERN OHIO

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 →
Schedule
Flight standings7 teams · NEOTA 4.0S
TeamWLInd. WInd. LSets lostGames lost
1F 4.0S/8.0D CSC Mercer72271845395
2F 4.0S/8.0D MV Miklowski53231739311
3F 4.0S/8.0D MM Mercuri54261943358
4F 4.0S/8.0D PM Perkins54242146378
5F 4.0S/8.0D TP Schaffner45202557433
6F 4.0S/8.0D AS Wimer35152554374
7F 4.0S/8.0D GR Burnett17152555413

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 3.62 estimated across 14 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.

4.41
Jimmy Hyres
published
3.90
Curtis Ottens
published 4
3.85
Motoo Saito
published
3.72
Matthew Perciavalle
published
3.69
Jinu Hwang
published
3.67
Deanne Gibson
published 4
3.66
Marisa Fiucci Rosen
published
3.58
Daniel Palermo
published 3.5
3.51
Frank Mercuri
published 3.5
3.48
David Kolesar
published 4
3.36
Kathleen Aranavage
published
3.30
Patricia Mercuri
published
3.29
Jan Kolesar
published
Elizabeth Caldera
published 3.5
3.23
Laura Hyres
published
Natalia Rosca
published
Ricardo Caldera
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.

Patricia Mercuri + Kathleen Aranavage15762% games+10.6% vs expected
Kathleen Aranavage + Jan Kolesar11266% games+6.8% vs expected
Jinu Hwang + Motoo Saito3255% games+4.0% vs expected
Jimmy Hyres + Jinu Hwang5160% games+3.5% vs expected
Patricia Mercuri + Jan Kolesar16658% games+1.9% vs expected
Deanne Gibson + Patricia Mercuri4253% games−1.8% vs expected
Frank Mercuri + David Kolesar7355% games−3.4% vs expected
Deanne Gibson + Kathleen Aranavage5162% 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.

Jimmy Hyres
96W–46L career · no lines in 2022
<1%75%25%
4.41
4
Curtis Ottens
28W–38L career · no lines in 2022
<1%77%23%
3.90
Motoo Saito
38W–15L career · no lines in 2022
<1%87%12%
3.85
4
Christopher Jarox
24W–31L career · no lines in 2022
not enough data
Matthew Perciavalle
59W–41L career · no lines in 2022
5%94%1%
3.72
Jinu Hwang
82W–66L career · no lines in 2022
7%92%1%
3.69
4
Deanne Gibson
102W–61L career · no lines in 2022
10%89%<1%
3.67
Marisa Fiucci Rosen
61W–59L career · no lines in 2022
11%88%<1%
3.66
3.5
Daniel Palermo
43W–59L career · type A · no lines in 2022
<1%27%73%
3.58
3.5
Frank Mercuri
109W–135L career · no lines in 2022
<1%46%54%
3.51
4
David Kolesar
93W–36L career · no lines in 2022
57%43%<1%
3.48
Kathleen Aranavage
147W–83L career · no lines in 2022
<1%86%14%
3.36
Patricia Mercuri
171W–118L career · no lines in 2022
1%93%6%
3.30
Jan Kolesar
140W–68L career · no lines in 2022
1%94%5%
3.29
3.5
Elizabeth Caldera
64W–45L career · no lines in 2022
not enough data
Laura Hyres
73W–55L career · no lines in 2022
4%94%2%
3.23
Natalia Rosca
31W–10L career · no lines in 2022
not enough data
Ricardo Caldera
88W–48L 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.