Friday, February 28, 2014
MLB Salary Inequality
With the start of MLB Spring Training underway, I wanted to turn my attention to analyzing baseball over the next few blogs. For the first, I decided to look at MLB player salary inequality as measure by the Gini coefficient. So I downloaded Sean Lahman's MLB database and after unzipping the files I used his Salary spreadsheet to calculate the Gini Coefficient for the data that he has from 1985 to 2013. Here are the results since the 1985 season along with the number of players per season.
As you can see since the early to mid-90's MLB player salary inequality has remained fairly stable and fairly unequal - similar to South Africa in 2009, but less equal than the US income distribution, which was 0.477 in 2011.
Season | Gini | n |
1985 | 0.3843 | 550 |
1986 | 0.4888 | 738 |
1987 | 0.5104 | 627 |
1988 | 0.5073 | 663 |
1989 | 0.5328 | 711 |
1990 | 0.5332 | 867 |
1991 | 0.5432 | 685 |
1992 | 0.5728 | 769 |
1993 | 0.6257 | 923 |
1994 | 0.6221 | 884 |
1995 | 0.6787 | 986 |
1996 | 0.6623 | 931 |
1997 | 0.6402 | 925 |
1998 | 0.6353 | 998 |
1999 | 0.6290 | 1006 |
2000 | 0.6066 | 836 |
2001 | 0.6092 | 860 |
2002 | 0.6123 | 846 |
2003 | 0.6251 | 827 |
2004 | 0.6335 | 831 |
2005 | 0.6241 | 831 |
2006 | 0.6189 | 819 |
2007 | 0.6099 | 842 |
2008 | 0.6240 | 856 |
2009 | 0.6185 | 813 |
2010 | 0.6225 | 830 |
2011 | 0.6215 | 839 |
2012 | 0.6196 | 848 |
2013 | 0.6168 | 815 |
As you can see since the early to mid-90's MLB player salary inequality has remained fairly stable and fairly unequal - similar to South Africa in 2009, but less equal than the US income distribution, which was 0.477 in 2011.
Labels:
Income Inequality,
MLB
Thursday, February 27, 2014
UMass Fires Head Football Coach
Charley Molnar was fired as the University of Massachusetts head football coach after the 2013 season, after leading the Minutemen to a combined 2-22 record for the last two seasons they competed in the Football Bowl Subdivision. So I thought that I would take a look at the Minutemen over the last two seasons in terms of their production rankings using the Complex Invasion College Football Production Model, starting with the 2012 season - the first season that I have UMass data analyzed by the model.
2012
Two seasons ago UMass finished at 1-11 overall and 1-7 in the Mid American Conference, while playing against an average strength of schedule as compared to the "league" average. The Minutemen's only victory was a 22-14 decision against #101 ranked Akron and their worst loss of the season was to #114 ranked Miami (OH). In terms of on-field performance, this was the worst team in the Football Bowl Subdivision given their overall rank was #124 out of 124 teams. Why? One reason was their pathetic offense - ranked #124 or worst in the Football Bowl Subdivision and their defense was not much better, ranked #121 out of 124 teams. Surely a season to forget.
2013
Last season UMass finished at 1-11 overall and 1-7 in the Mid American Conference again playing against an average strength of schedule. This season the Minutemen's only victory was over #124 ranked Miami (OH) and their worst loss was to FCS Maine. In terms of the team's on-field performance UMass finished as the #119 ranked team overall with the #121 ranked offense and the #88 ranked defense. While UMass is improving (not much room to decline from last season), this was not enough for the school to retain their head football coach.
Analysis of 2013 NCAA FBS Head Coach Changes
Boise State and Chris Petersen
Texas and Mack Brown
Washington and Steve Sarkisian
Wake Forest and Jim Grobe
Wyoming and Dave Christensen
Eastern Michigan and Ron English
Florida Atlantic and Carl Pelini
Miami of Ohio and Don Treadwell
UConn and Paul Pasqualoni
USC and Lane Kiffen
2012
Two seasons ago UMass finished at 1-11 overall and 1-7 in the Mid American Conference, while playing against an average strength of schedule as compared to the "league" average. The Minutemen's only victory was a 22-14 decision against #101 ranked Akron and their worst loss of the season was to #114 ranked Miami (OH). In terms of on-field performance, this was the worst team in the Football Bowl Subdivision given their overall rank was #124 out of 124 teams. Why? One reason was their pathetic offense - ranked #124 or worst in the Football Bowl Subdivision and their defense was not much better, ranked #121 out of 124 teams. Surely a season to forget.
2013
Last season UMass finished at 1-11 overall and 1-7 in the Mid American Conference again playing against an average strength of schedule. This season the Minutemen's only victory was over #124 ranked Miami (OH) and their worst loss was to FCS Maine. In terms of the team's on-field performance UMass finished as the #119 ranked team overall with the #121 ranked offense and the #88 ranked defense. While UMass is improving (not much room to decline from last season), this was not enough for the school to retain their head football coach.
Analysis of 2013 NCAA FBS Head Coach Changes
Boise State and Chris Petersen
Texas and Mack Brown
Washington and Steve Sarkisian
Wake Forest and Jim Grobe
Wyoming and Dave Christensen
Eastern Michigan and Ron English
Florida Atlantic and Carl Pelini
Miami of Ohio and Don Treadwell
UConn and Paul Pasqualoni
USC and Lane Kiffen
Monday, February 24, 2014
Thursday, February 20, 2014
Monday, February 17, 2014
NHL Goalie Performance at the Olympic Break
Well, I am back. To mark my re-emergence I have decided to look at how NHL goalies have performed up to the Olympic break. Below are the top 20 NHL goalies using our Wins Above Average measure.
Rank | Player | Team | WAA | GS | SA | SV% |
1 | Ben Bishop | TBL | 3.968 | 44 | 1229 | 0.933 |
2 | Jonathan Bernier | TOR | 3.187 | 38 | 1444 | 0.927 |
3 | Tuukka Rask | BOS | 2.877 | 43 | 1210 | 0.928 |
4 | Carey Price | MTL | 2.724 | 48 | 1459 | 0.925 |
5 | Ben Scrivens | LAK, EDM | 2.717 | 21 | 695 | 0.937 |
6 | Semyon Varlamov | COL | 2.436 | 43 | 1436 | 0.924 |
7 | Josh Harding | MIN | 2.228 | 26 | 690 | 0.933 |
8 | Ryan Miller | BUF | 2.096 | 39 | 1373 | 0.923 |
9 | Cory Schneider | NJD | 1.630 | 31 | 800 | 0.926 |
10 | Cam Talbot | NYR | 1.602 | 15 | 449 | 0.935 |
11 | Martin Jones | LAK | 1.547 | 12 | 350 | 0.94 |
12 | Alex Stalock | SJS | 1.440 | 11 | 353 | 0.938 |
13 | Anton Khudobin | CAR | 1.245 | 19 | 564 | 0.927 |
14 | Frederik Andersen | ANA | 1.151 | 16 | 484 | 0.928 |
15 | Marc-Andre Fleury | PIT | 1.081 | 47 | 1278 | 0.919 |
16 | Thomas Greiss | PHX | 1.077 | 10 | 352 | 0.932 |
17 | Philipp Grubauer | WSH | 0.933 | 14 | 458 | 0.926 |
18 | Al Montoya | WPG | 0.924 | 15 | 495 | 0.925 |
19 | Steve Mason | PHI | 0.856 | 43 | 1267 | 0.918 |
20 | Henrik Lundqvist | NYR | 0.839 | 43 | 1243 | 0.918 |
Friday, February 7, 2014
NBA Officials and Racial Bias
In my Sports Economics class, the last topic I go over is the Price & Wolfers paper on racial bias by NBA referees. The authors looked at the 1999 - 2002 NBA seasons and controlling for everything that anyone could think of as to why this would not be true, find that it is true. This is an astounding result - which is why I save the best for last in Sports Economics.
Subsequently, the authors have looked at the 2003-2006 time period and find the same result, but then once the "cat was out of the bag" meaning once that this result was made public, the racial bias effect has disappeared using 2007-2010. This is found in the recent issue of Business Week (h/t Will).
Subsequently, the authors have looked at the 2003-2006 time period and find the same result, but then once the "cat was out of the bag" meaning once that this result was made public, the racial bias effect has disappeared using 2007-2010. This is found in the recent issue of Business Week (h/t Will).
Labels:
NBA
Wednesday, February 5, 2014
Olympics and Incentives
At the start of the 2012 Olympics, four women's badminton doubles teams were disqualified for attempting to lose a round-robin game in order to play an easier opponent in the tournament. As the Winter Olympics start tomorrow, I thought it would be a good time to think about how one would determine if Olympians are shirking or tanking, such as to gain an advantage in later Olympic play. I naturally thought about using data to make this determination (please note I do not have this data, so I am unable to do this type of analysis). So I am going to make some assumptions to walk through this type of analysis.
The first area to look at would be serves. This is an area that should give a clean test of shirking/tanking. If the player has a history of international play, then they would have a series of data on their serves, especially in games that are important (winning a tournament, escaping elimination, winning a game, etc.) where they have an incentive to perform at their best, and then look at how the variation in their performance under instances where they have an incentive to perform their best and compare that to their serves if there is speculation of shirking/tanking. Thus as the article mentions, serving into the net (so that their opponent cannot try to unsuccessfully return the shot) would be a very clean test of tanking.
Second would be in service returns, whether they are able to return the serve and the ability to return the serve in play. Again, one could compare the data series (especially if they have played each other before) and see if there is a statistically significant difference between the two.
Third, one could look at joint tests (given the example above is doubles) and test to see how statistically likely it would be for both players to serve poorly.
Finally (OK, maybe not finally - but finally for this blog), one could look at volleys during the match between the player(s) involved to determine if there is some statistically significant differences in performance during questionable play.
More importantly, designing a tournament such that these types of incentives do not exist is also important; and penalizing players - such as international bans by sport federations might also reduce the need for this type of analysis.
The first area to look at would be serves. This is an area that should give a clean test of shirking/tanking. If the player has a history of international play, then they would have a series of data on their serves, especially in games that are important (winning a tournament, escaping elimination, winning a game, etc.) where they have an incentive to perform at their best, and then look at how the variation in their performance under instances where they have an incentive to perform their best and compare that to their serves if there is speculation of shirking/tanking. Thus as the article mentions, serving into the net (so that their opponent cannot try to unsuccessfully return the shot) would be a very clean test of tanking.
Second would be in service returns, whether they are able to return the serve and the ability to return the serve in play. Again, one could compare the data series (especially if they have played each other before) and see if there is a statistically significant difference between the two.
Third, one could look at joint tests (given the example above is doubles) and test to see how statistically likely it would be for both players to serve poorly.
Finally (OK, maybe not finally - but finally for this blog), one could look at volleys during the match between the player(s) involved to determine if there is some statistically significant differences in performance during questionable play.
More importantly, designing a tournament such that these types of incentives do not exist is also important; and penalizing players - such as international bans by sport federations might also reduce the need for this type of analysis.
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