Teams

MSIL A18&O 3.0M Singhal-Bloomington

2022 MSIL Adult 18 & Over - MSIL West League · West MSIL A18 & Over 3.0M

6 players · average NTRP 3.17 / MID-SOUTH ILLINOIS

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
2022-07-17vs MSIL A18&O 3.0M Arns-QuincyLost 12
L#1 DoublesJohn Webb / Steve Grady vs Jeremy Arns / Mike Wood7-5, 1-6, 0-1
L#2 DoublesMichael Deighan / Ravi Singhal vs Chao Wang / Mike Wilkin1-6, 5-7
W#2 SinglesBob Clay vs Jason Klinner6-2, 6-4
2022-07-17vs MSIL A18&O 3.0M Arns-QuincyWon 21
W#2 DoublesDon Varghese / Steve Grady vs James Peterson / Randy DAVIS7-5, 6-3
L#2 SinglesBob Clay vs Chao Wang3-6, 3-6
W#3 DoublesThomas Smith / Ravi Singhal vs Jim Shumake / Jason Klinner6-0, 6-1
2022-06-04vs MSIL A18&O 3.0M Arns-QuincyLost 12
L#1 SinglesBob Clay vs James Peterson1-6, 1-6
W#2 DoublesRavi Singhal / Thomas Smith vs Jeremy Arns / David Lieber6-2, 2-6, 1-0
L#3 DoublesSteve Grady / Michael Deighan vs Steven Damm / Jim Shumake4-6, 1-6
2022-06-04vs MSIL A18&O 3.0M Arns-QuincyLost 03
L#1 DoublesBharat Kundnani / Ravi Singhal vs James Peterson / Jeremy Arns2-6, 2-6
L#1 SinglesBob Clay vs Steven Damm4-6, 5-7
L#3 DoublesMichael Deighan / Steve Grady vs Jim Shumake / Mike Wood2-6, 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 standings2 teams · West MSIL A18 & Over 3.0M
TeamWLInd. WInd. LSets lostGames lost
1MSIL A18&O 3.0M Arns-Quincy4013715149
2MSIL A18&O 3.0M Singhal-Bloomington0471328190

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 3.11 estimated across 1 rated player. 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.

Don Varghese
usually #2 Doubles 100% · published 3.5
3.11
Joseph McCarron
usually #3 Doubles 60%, also #2 Singles 20% · published
Bob Clay
usually #2 Singles 50%, also #1 Singles 50% · published 3
Bryan Wagler
usually #1 Doubles 67%, also #2 Doubles 33% · published 3
Ravi Singhal
usually #2 Doubles 50%, also #1 Doubles 25% · published
Steve Grady
usually #3 Doubles 50%, also #1 Doubles 25% · 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.

Joseph McCarron + Steve Grady134 matchesnot enough data
Ravi Singhal + Joseph McCarron123 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.

3.5
Don Varghese
16W–12L career · 1 line in 2022
not enough data
Joseph McCarron
53W–55L career · 5 lines in 2022
<1%21%79%
3.11
3
Bob Clay
10W–21L career · 4 lines in 2022
not enough data
3
Bryan Wagler
1W–10L career · type A · 3 lines in 2022
not enough data
Ravi Singhal
34W–34L career · 4 lines in 2022
not enough data
Steve Grady
22W–49L career · 4 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.