Earlier this week I blogged about how consistent NBA players were from the 2012/13 regular season to the 2013/14 regular season. What I found is that compared to players in other leagues, NBA players are much more consistent. We also report this for a number of seasons in chapter 9 of our book, The Wages of Wins. I got to thinking about whether this result holds over a longer period of time, and asked Dave Berri if he would send me the data, which he graciously did. Dave's NBA player data starts with the 1973/74 regular season and goes up to the 2013/14 regular season, which for those keeping track is over forty years of player data.
So, just as I did earlier this week, I ranked all the players (see link above for the details) and look at players who played in consecutive regular seasons playing at least 10% of the total regular season minutes (having adjusted for the 1998/99 and 2011/12 shortened seasons). What I find is that 42.1% of all the players who played at least 10% of total minutes in consecutive regular seasons are consistent (i.e. are in the same quantile), while 39.2% either move up or down one quantile from one season to the next; meaning that only 18.7% of NBA players are very inconsistent over consecutive seasons.
Friday, May 23, 2014
Tuesday, May 20, 2014
NBA Player Consistency
Last week I blogged about NHL goalie regular season production consistency. I only included goalies who started a minimum of 10% games played in consecutive regular seasons. Now I am turning to NBA players using the regular season data from those NBA player stat gurus at boxscoregeeks.com. Here I am looking at players who have played a minimum of 10% of regular season minutes (approximately 394 minutes) in consecutive regular seasons from the 2012-13 and 2013-14 NBA regular seasons.
How is consistency measured? Previously I stated for MLB batters that, "...consistency is measured by where they rank in terms of runs created in a given season. For simplicity in The Wages of Wins, we took a player who was in the top 20% of the league in runs created as having a rank equal to 1, the next highest 20% with a rank of 2, all the way to the lowest 20% with a rank of 5. If a player maintains their rank from one season to another they are considered to be consistent and if not, they are considered to be inconsistent."
When I run the NBA player consistency analysis (step-by-step guide here), I find that there are 287 NBA players that have played at least 394 minutes in both seasons and of those 287 NBA players, 45.6% of those players have keep their same "grade" in each season. Additionally, another 34.5% of NBA players move up or down only one "grade" from season to season, leaving about 19.9% of NBA players moving two or more "grades" from the 2012-13 season to the 2013-14 NBA regular season. This is similar to what we found in The Wages of Wins (see the end of chapter 9).
There were 131 NBA players that were consistent in consecutive seasons. Of those, 34.35% were in the top 20% of the distribution (or "A grade" player production), while 19.08% were "B grade" players, 13.74% were "C grade" players, 16.79% were "D grade" players and 16.03% were in the bottom top 20% of the distribution in consecutive seasons.
For those who read Dave Berri's piece on John Wall, he is a consistent "B grade" player.
How is consistency measured? Previously I stated for MLB batters that, "...consistency is measured by where they rank in terms of runs created in a given season. For simplicity in The Wages of Wins, we took a player who was in the top 20% of the league in runs created as having a rank equal to 1, the next highest 20% with a rank of 2, all the way to the lowest 20% with a rank of 5. If a player maintains their rank from one season to another they are considered to be consistent and if not, they are considered to be inconsistent."
When I run the NBA player consistency analysis (step-by-step guide here), I find that there are 287 NBA players that have played at least 394 minutes in both seasons and of those 287 NBA players, 45.6% of those players have keep their same "grade" in each season. Additionally, another 34.5% of NBA players move up or down only one "grade" from season to season, leaving about 19.9% of NBA players moving two or more "grades" from the 2012-13 season to the 2013-14 NBA regular season. This is similar to what we found in The Wages of Wins (see the end of chapter 9).
There were 131 NBA players that were consistent in consecutive seasons. Of those, 34.35% were in the top 20% of the distribution (or "A grade" player production), while 19.08% were "B grade" players, 13.74% were "C grade" players, 16.79% were "D grade" players and 16.03% were in the bottom top 20% of the distribution in consecutive seasons.
For those who read Dave Berri's piece on John Wall, he is a consistent "B grade" player.
Wednesday, May 14, 2014
Competitive Balance & Bud Selig
According to a tweet by Mike Greenberg, Bud Selig says MLB competitive balance is his greatest achievement. Here is competitive balance in MLB, NBA, NFL, NHL and MLS during Selig's tenure as MLB commissioner. Other than the NBA, Major League Baseball is the LEAST competitive of the US major team sports.
Not sure if this is what Selig should hang his hat on - so to speak.
Not sure if this is what Selig should hang his hat on - so to speak.
2013-2014 NHL Regular Season Goalie Consistency
At the end of the regular season I ran the numbers for NHL goalies and found that Tuukka Rask, Semyon Varlamov and Carey Price were the top three NHL goalies for this season using Wins Above Average (WAA). WAA is the marginal value of a goal against divided by
two (since each win is worth two standings points) times the number of
shots on goal that a goalie faces times the difference in the save
percentage of the goalie and the average save percentage of all goalies
for that season in absolute value (since a goal against has a negative
effect on team wins). Here is a step-by-step guide to calculate NHL goalie performance.
Now what I want to do is figure out how consistent NHL goalies are from one season to another, so I am comparing the regular season performance from last season (which was shortened due to the lockout) and this season. To do that, I am going to only look at goalies that started at least 10% of the regular season games played for each season, since the number of games played is different between the two regular seasons.
Thus to find how consistent NHL goalies are in consecutive regular seasons, I am looking at the percentage of NHL goalies who are in the same WAA quantile in consecutive regular seasons and those who have moved up or down one quantile from one season to the next. Here is a step-by-step guide on measuring player consistency.
What I find is that of the NHL goalies who started at least 10% of the total number of regular season games, 30.8% were consistent (in the same quantile in each season), 32.7% were near consistent (either moved up or down one quantile from one regular season to another) and 36.5% were inconsistent (moved two or more quantiles from one regular season to another).
Of those that were consistent, there were only three NHL goalies who were in the top quantile (i.e. top 20% of NHL goalies in consecutive regular seasons). They are Tuukka Rask (Boston Bruins), Sergei Bobrovsky (Columbus Blue Jackets) and Henrik Lundqvist (New York Rangers). Given that Tuukka Rask was the most productive goalie (but just barely) and that he is also only one of three goalies to be in the top 20% from one season to the next, I would say that he is the best candidate (statistically) for the Vezina Trophy.
Now what I want to do is figure out how consistent NHL goalies are from one season to another, so I am comparing the regular season performance from last season (which was shortened due to the lockout) and this season. To do that, I am going to only look at goalies that started at least 10% of the regular season games played for each season, since the number of games played is different between the two regular seasons.
Thus to find how consistent NHL goalies are in consecutive regular seasons, I am looking at the percentage of NHL goalies who are in the same WAA quantile in consecutive regular seasons and those who have moved up or down one quantile from one season to the next. Here is a step-by-step guide on measuring player consistency.
What I find is that of the NHL goalies who started at least 10% of the total number of regular season games, 30.8% were consistent (in the same quantile in each season), 32.7% were near consistent (either moved up or down one quantile from one regular season to another) and 36.5% were inconsistent (moved two or more quantiles from one regular season to another).
Of those that were consistent, there were only three NHL goalies who were in the top quantile (i.e. top 20% of NHL goalies in consecutive regular seasons). They are Tuukka Rask (Boston Bruins), Sergei Bobrovsky (Columbus Blue Jackets) and Henrik Lundqvist (New York Rangers). Given that Tuukka Rask was the most productive goalie (but just barely) and that he is also only one of three goalies to be in the top 20% from one season to the next, I would say that he is the best candidate (statistically) for the Vezina Trophy.
Thursday, May 8, 2014
Competitive Balance in Major League Soccer
Today let's look at competitive balance in Major League Soccer (MLS). Major League Soccer re-started in 1996, but for the first few years (1996-1999), there were no ties during the regular season and since 2000 ties are possible during the regular season, so I am truncating the overall data starting with the 2000 regular season. I will use the Noll-Scully measure of competitive balance to measure competitive balance in Major League Soccer.
The Noll-Scully measure captures how far a particular sport or league’s distribution of wins deviates from a purely random outcome of wins and losses. Thus a Noll-Scully equal to one would be one that is purely random. Since the 2000 season, the average Noll-Scully in MLS equals 1.294, which is more competitive than the National Hockey League, the National Football League and the National Basketball Association.
(If you are interested in replicating these results, I got the data from MLS website and here is a step-by-step method to calculate the Noll-Scully measure of competitive balance - where I used the trinomial method under a standard deviation of the population. I also used the probability of a tie to be the actual yearly average of games that were tied).
Here is a chart of how competitively balanced Major League Soccer has been since 2000.
For those interested in replicating the numbers, here is a table of the results.
The Noll-Scully measure captures how far a particular sport or league’s distribution of wins deviates from a purely random outcome of wins and losses. Thus a Noll-Scully equal to one would be one that is purely random. Since the 2000 season, the average Noll-Scully in MLS equals 1.294, which is more competitive than the National Hockey League, the National Football League and the National Basketball Association.
(If you are interested in replicating these results, I got the data from MLS website and here is a step-by-step method to calculate the Noll-Scully measure of competitive balance - where I used the trinomial method under a standard deviation of the population. I also used the probability of a tie to be the actual yearly average of games that were tied).
Here is a chart of how competitively balanced Major League Soccer has been since 2000.
For those interested in replicating the numbers, here is a table of the results.
| Noll-Scully | Season | |
| 1.180 | 2000 | |
| 1.757 | 2001 | |
| 0.719 | 2002 | |
| 1.189 | 2003 | |
| 0.844 | 2004 | |
| 1.834 | 2005 | |
| 0.874 | 2006 | |
| 1.287 | 2007 | |
| 0.943 | 2008 | |
| 1.160 | 2009 | |
| 1.553 | 2010 | |
| 1.396 | 2011 | |
| 1.752 | 2012 | |
| 1.631 | 2013 |
Friday, May 2, 2014
Major League Soccer Salary Inequality
The Major League Soccer Players Union posted base salary (and guaranteed compensation) for this season's players on April 1 - no foolin'. So what I thought I would do was calculate the amount of salary inequality that exists in MLS for 2014. Economic theory tells us that salary should be unequal
since different players have different productivity levels and different teams
have different revenues from the player's productivity. What I want to
come back to later is how salary inequality has changed over the years
in Major League Soccer, but for now I have this season's data analyzed,
so we will just be looking at a snapshot of salary inequality using the
Gini coefficient (more here on how I measure the Gini coefficient).
Remember the Gini coefficient is measured between 0 and 1 (or 0 and 100
if it is reported as an index) and the higher the number the more
unequal is income distributed.
I am going to do this in three ways: for the entire group of players, by player position and finally by team.
First let's look at how unequal salary is distributed for both the player's base salary and for their guaranteed compensation for all players in the league. For Major League Soccer, I find that base salary yields to a Gini = 0.606 and that guaranteed compensation yields a Gini of 0.614 - which are fairly high as compared to 0.432 (or 43.2) for the United States as a whole in 2011 (note the numbers in the link above would be divided by 100 to compare with the numbers that I am reporting).
So, does this result hold by player position? Again I am going to calculate the Gini coefficient for both base salary and guaranteed compensation. In the salary data from the Major League Soccer Players Union website, some players are labeled as having two positions. For these players, I have included them in both positions in the Gini calculations reported below. Looking at the table below, the amount of salary inequality increases as we move away from goal keeper to defense to mid-fielders to forwards. Thus salary inequality is being driven more by the offense and less by defense. In fact forwards are the only position that has greater salary inequality than the league as a whole, in which I would conclude that forwards are the most important driver of salary inequality in MLS.
So, are there specific teams that are have a greater impact on salary inequality in Major League Soccer this year? Yes, and it is driven by four teams: LA, NY, Seattle and Toronto. Each has a Gini coefficient above the average for the league, with Toronto having the greatest amount of salary inequality. The 15 other teams in the league all have Gini coefficients below the MLS average and 10 of those 15 (over half the teams in the league) have Gini coefficients that are more equal than the US was in 2011.
Thus Major League Soccer has rather unequal salaries and is driven by forwards and by the Galaxy, the Red Bulls, Toronto FC and the Sounders FC.
I am going to do this in three ways: for the entire group of players, by player position and finally by team.
First let's look at how unequal salary is distributed for both the player's base salary and for their guaranteed compensation for all players in the league. For Major League Soccer, I find that base salary yields to a Gini = 0.606 and that guaranteed compensation yields a Gini of 0.614 - which are fairly high as compared to 0.432 (or 43.2) for the United States as a whole in 2011 (note the numbers in the link above would be divided by 100 to compare with the numbers that I am reporting).
So, does this result hold by player position? Again I am going to calculate the Gini coefficient for both base salary and guaranteed compensation. In the salary data from the Major League Soccer Players Union website, some players are labeled as having two positions. For these players, I have included them in both positions in the Gini calculations reported below. Looking at the table below, the amount of salary inequality increases as we move away from goal keeper to defense to mid-fielders to forwards. Thus salary inequality is being driven more by the offense and less by defense. In fact forwards are the only position that has greater salary inequality than the league as a whole, in which I would conclude that forwards are the most important driver of salary inequality in MLS.
| POS | Base Salary | Guar. Comp. | |
| D | 0.402 | 0.417 | |
| F | 0.717 | 0.722 | |
| GK | 0.351 | 0.363 | |
| M | 0.570 | 0.569 |
So, are there specific teams that are have a greater impact on salary inequality in Major League Soccer this year? Yes, and it is driven by four teams: LA, NY, Seattle and Toronto. Each has a Gini coefficient above the average for the league, with Toronto having the greatest amount of salary inequality. The 15 other teams in the league all have Gini coefficients below the MLS average and 10 of those 15 (over half the teams in the league) have Gini coefficients that are more equal than the US was in 2011.
| Team | Base Salary | Guar. Comp. | |
| CHI | 0.393 | 0.407 | |
| CHV | 0.376 | 0.369 | |
| CLB | 0.416 | 0.447 | |
| COL | 0.372 | 0.376 | |
| DAL | 0.444 | 0.440 | |
| DC | 0.422 | 0.426 | |
| HOU | 0.353 | 0.352 | |
| KC | 0.382 | 0.379 | |
| LA | 0.779 | 0.778 | |
| MTL | 0.521 | 0.596 | |
| NE | 0.309 | 0.322 | |
| NY | 0.743 | 0.751 | |
| PHI | 0.411 | 0.409 | |
| POR | 0.397 | 0.392 | |
| RSL | 0.415 | 0.435 | |
| SEA | 0.745 | 0.775 | |
| SJ | 0.420 | 0.429 | |
| TOR | 0.803 | 0.800 | |
| VAN | 0.544 | 0.538 | |
Thus Major League Soccer has rather unequal salaries and is driven by forwards and by the Galaxy, the Red Bulls, Toronto FC and the Sounders FC.
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