2026 Club Standings

2026 San Diego Backgammon Club rankings through January 14, 2026.
For previous year’s standings, see: 2023, 2024, and 2025 final rankings.

RANK

PLAYER

MATCH WINS

MATCH LOSSES

WIN PCT

z-score

EVENTS

1ST PLACE FINISHES

2ND PLACE FINISHES

3RD PLACE FINISHES

Elo

SDBC Ranking Points

1

John Turner

7

1

87.50%

2.1

2

1

0

1

    1,656

23

2

Sam Rishmawi

7

2

77.78%

1.7

3

0.5

1

0

    1,674

21

3

Kutaiba Almukhtar

5

3

62.50%

0.7

3

1

0

0

    1,534

17

4

Frank Frigo

4

0

100.00%

2.0

1

1

0

0

    1,872

16

5

Blake Beber

4

0

100.00%

2.0

1

1

0

0

    1,737

16

6

Raid Westin

5

3

62.50%

0.7

2

0

1

0

    1,629

13

7

Allen Tish

4

3

57.14%

0.4

2

0

1

0

    1,795

12

8

Bijan Rahmani

3

1

75.00%

1.0

1

0

1

0

    1,578

11

9

Justin Sheftell

5

2

71.43%

1.1

2

0

0

1

    1,763

9

10

Dar Khatibi

5

4

55.56%

0.3

3

0

0

1

    1,632

9

11

Michael Showkati

3

1

75.00%

1.0

1

0

0

1

    1,749

7

12

Roger Alves

2

1

66.67%

0.6

1

0

0.5

0

    1,710

6

13

Paul Skolnick

2

1

66.67%

0.6

1

0

0

0.5

    1,567

4

14

Mark Weiner

4

3

57.14%

0.4

3

0

0

0

    1,716

4

15

Peter Nolan

4

3

57.14%

0.4

3

0

0

0

    1,526

4

16

Serdar Altok

3

2

60.00%

0.4

2

0

0

0

    1,740

3

17

Manny Armesto

3

3

50.00%

0.0

2

0

0

0

    1,483

3

18

Halle Hedayat

3

3

50.00%

0.0

3

0

0

0

    1,453

3

19

Anthony Niakiani

2

1

66.67%

0.6

1

0

0

0

    1,765

2

20

David Gray

2

2

50.00%

0.0

2

0

0

0

    1,703

2

21

Avo Fronjian

2

2

50.00%

0.0

1

0

0

0

    1,617

2

22

Homer Farsad

2

2

50.00%

0.0

1

0

0

0

    1,508

2

23

Jimmy Lakdalwala

2

3

40.00%

-0.4

2

0

0

0

    1,678

2

24

Fahri Cimen

2

3

40.00%

-0.4

2

0

0

0

    1,559

2

25

Kenny Lavigna

2

3

40.00%

-0.4

2

0

0

0

    1,534

2

26

Michael Pines

2

4

33.33%

-0.8

2

0

0

0

    1,630

2

27

Kevin Duc

1

1

50.00%

0.0

1

0

0

0

    1,583

1

28

Alireza Rafigh

1

1

50.00%

0.0

1

0

0

0

    1,512

1

29

Erik Kipperman

1

2

33.33%

-0.6

1

0

0

0

    1,590

1

30

Tony Renda

1

2

33.33%

-0.6

1

0

0

0

    1,455

1

31

Amin Mazaeri

1

2

33.33%

-0.6

1

0

0

0

    1,424

1

32

Byron Georgiou

1

2

33.33%

-0.6

1

0

0

0

    1,331

1

33

Jon Vietor

1

3

25.00%

-1.0

2

0

0

0

    1,670

1

34

Susan Mazarei

1

3

25.00%

-1.0

2

0

0

0

    1,473

1

35

Abbas Anvar

1

4

20.00%

-1.3

2

0

0

0

    1,580

1

36

Matt Schoonmaker

0

2

0.00%

-1.4

1

0

0

0

    1,751

0

37

Tom Cox

0

2

0.00%

-1.4

1

0

0

0

    1,584

0

38

Robert Burns

0

2

0.00%

-1.4

1

0

0

0

    1,537

0

39

Mackenzie Mee-Lee

0

2

0.00%

-1.4

1

0

0

0

    1,524

0

40

Amy Renda

0

2

0.00%

-1.4

1

0

0

0

    1,459

0

41

Karen Newell

0

2

0.00%

-1.4

1

0

0

0

    1,353

0

42

Jim Delpy

0

2

0.00%

-1.4

1

0

0

0

    1,314

0

43

Tom Keisler

0

4

0.00%

-2.0

2

0

0

0

    1,768

0

44

Paul Palomino

0

4

0.00%

-2.0

2

0

0

0

    1,412

0

Z-SCORE – a statistic used to measure results more accurately with consideration of total matches played, and reflects the number of standard deviations from the mean performance.  The mean is assumed to be a 50/50 coin flip.

SDBG RANKING POINTS – Each match win is worth 1 point. For a standard sixteen person bracket, 1st place receives 12 points, 2nd place receives 8 points, and 3rd place receives 4 points. Splits of 1st/2nd each receive 10 points, and splits of 3rd each receive 2 points. For an eight person bracket, ranking points for 1st/2nd/3rd are halved. Forfeited matches and byes are not scored.

Elo – A rating system that measures the relative strength of a player. A player’s Elo rating increases when they win, and decreases when they lose. The amount of the increase/decrease depends on the strength of their opponent, how long the match is (e.g. 7 points or 5 points), and how many matches the player has played. The difference in Elo ratings between two players can estimate each player’s win chances in the match based on a formula. For example, a player whose rating is 100 points greater than their opponent’s is expected to win a 7 point match 58% of the time. Every player starts with a rating of 1500.

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