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Cluster luck numbers for the 2014 MLB regular season

By Dr. Ed Feng 6 Comments

To explain cluster luck, my Grantland colleague Jonah Keri wrote the following.

Joe Peta, a former Wall Street trader, presented cluster luck in his book, Trading Bases. Essentially, the concept boils down to this: When a team’s batters cluster hits together to score more runs and a team’s pitchers spread hits apart to allow fewer runs, that’s cluster luck. Say a team tallies nine singles in one game. If all of those singles occur in the same inning, the team would likely score seven runs; if each single occurs in a different inning, however, it’d likely mean a shutout.

Here are the numbers for cluster luck for the 2014 regular season. For each team, it shows total (offense, defense) for cluster luck. In all cases, a positive number implies good luck, or scoring more runs than expected on offense or allowing fewer runs on defense.

1. New York Mets, 57.29. (9.00, 48.29).
2. Seattle, 48.63. (27.67, 20.97).
3. Cincinnati, 41.94. (12.04, 29.90).
4. Baltimore, 40.00. (-13.53, 53.54).
5. Oakland, 39.71. (44.23, -4.51).
6. Kansas City, 31.46. (16.84, 14.61).
7. Texas, 14.93. (7.64, 7.30).
8. San Diego, 13.98. (-4.81, 18.78).
9. Los Angeles Angels, 11.01. (44.40, -33.39).
10. Minnesota, 9.83. (14.90, -5.08).
11. Toronto, 8.99. (-12.04, 21.04).
12. Washington, 5.12. (-9.92, 15.04).
13. Philadelphia, 4.87. (7.44, -2.58).
14. Atlanta, 4.47. (-29.24, 33.71).
15. Miami, 0.88. (-17.45, 18.33).
16. Boston, -0.39. (-13.36, 12.97).
17. St. Louis, -0.59. (-9.22, 8.63).
18. San Francisco, -1.06. (7.16, -8.22).
19. Milwaukee, -1.29. (-9.09, 7.80).
20. Cleveland, -6.12. (-17.96, 11.84).
21. Detroit, -7.78. (-14.46, 6.68).
22. New York Yankees, -10.36. (-5.51, -4.86).
23. Los Angeles Dodgers, -14.87. (-19.74, 4.87).
24. Arizona, -15.48. (-5.73, -9.75).
25. Colorado, -28.07. (-29.56, 1.49).
26. Chicago White Sox, -28.85. (-10.81, -18.04).
27. Pittsburgh, -40.53. (-42.80, 2.26).
28. Houston, -44.07. (-19.16, -24.90).
29. Tampa Bay, -48.93. (-30.20, -18.73).
30. Chicago Cubs, -67.81. (-20.64, -47.17).

Cluster luck is the deviation of actual runs from Base Runs, the runs created formula of Dave Smyth. The difference in runs scored and runs allowed by Base Runs provides a way to rank teams. The results below give run differential (runs scored, runs allowed). The record denotes a Pythagorean expectation with an exponent of 1.83.

1. Los Angeles Angels, 131.99. (728.60, 596.61). Record: 95-67.
2. Washington, 125.88. (695.92, 570.04). Record: 95-67.
3. Oakland, 117.29. (684.77, 567.49). Record: 94-68.
4. Los Angeles Dodgers, 115.87. (737.74, 621.87). Record: 93-69.
5. Pittsburgh, 91.53. (724.80, 633.26). Record: 90-71.
6. Baltimore, 72.00. (718.53, 646.54). Record: 88-74.
7. Detroit, 59.78. (771.46, 711.68). Record: 86-76.
8. San Francisco, 52.06. (657.84, 605.78). Record: 86-75.
9. Tampa Bay, 35.93. (642.20, 606.27). Record: 85-77.
10. Seattle, 31.37. (606.33, 574.97). Record: 84-78.
11. Toronto, 28.01. (735.04, 707.04). Record: 83-79.
12. Cleveland, 22.12. (686.96, 664.84). Record: 82-79.
13. St. Louis, 16.59. (628.22, 611.63). Record: 82-80.
14. Kansas City, -4.46. (634.16, 638.61). Record: 79-82.
15. Milwaukee, -5.71. (659.09, 664.80). Record: 80-82.
16. New York Yankees, -20.64. (638.51, 659.14). Record: 78-84.
17. Chicago Cubs, -25.19. (634.64, 659.83). Record: 77-84.
18. Atlanta, -28.47. (602.24, 630.71). Record: 77-85.
19. Miami, -29.88. (662.45, 692.33). Record: 77-85.
20. Colorado, -34.93. (784.56, 819.49). Record: 77-84.
21. New York Mets, -46.29. (620.00, 666.29). Record: 75-87.
22. Houston, -49.93. (648.16, 698.10). Record: 75-87.
23. San Diego, -55.98. (539.81, 595.78). Record: 73-89.
24. Cincinnati, -58.94. (582.96, 641.90). Record: 73-89.
25. Chicago White Sox, -69.15. (670.81, 739.96). Record: 73-89.
26. Minnesota, -71.83. (700.10, 771.92). Record: 73-89.
27. Philadelphia, -72.87. (611.56, 684.42). Record: 72-90.
28. Boston, -80.61. (647.36, 727.97). Record: 72-90.
29. Arizona, -111.52. (620.73, 732.25). Record: 68-94.
30. Texas, -150.93. (629.36, 780.30). Record: 65-97.

For my analysis of how cluster luck will affect certain teams in the playoffs, check out my article on bettingexpert.com.

Filed Under: Baseball analytics, Cluster luck

Comments

  1. evo34 says

    September 29, 2014 at 10:04 pm

    FYI — Long before Peta first tried to coin the term, “cluster luck,” Clay Davenport has been publishing essentially the same data, updated daily, for the past 10 seasons:

    http://claydavenport.com/stats/standings.html

    Not affiliated with his site.

    Reply
    • Ed Feng says

      October 1, 2014 at 11:44 am

      Thanks for the clarification. Yes, people have known about cluster luck before Joe. My analysis predates his book as well.

      I try to be careful in saying that he coined the term “cluster luck” but not that he discovered the concept. Because, heck, cluster luck is a good name.

      Reply
      • brendon says

        October 1, 2014 at 2:06 pm

        But it’s a pretty bad name. It ascribes to “luck” what I think others could reasonably ascribe to non-random game elements, like a manager’s ability to construct a worthwhile batting order that responds to the starting pitcher’s strengths and weaknesses. Or individual players’ ability to perform under high-pressure situations (runners on base, two outs) — a quality people with less sabremetric bona fides call “clutch.”

        Reply

Trackbacks

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    […] on-base events? (By the way, this problem has been addressed in other ways — in particular, this post uses a measure which compares the actual runs scored with a runs-created […]

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