2011 MLS Salaries: Down from 2010, Up 60% Since 2006, and in Total Equal to the Torres Transfer Fee

So I am only a few days in to my vacation from the blog, and I already feel compelled to break my vow and make a post.  Who can resist analyzing the 2011 MLS player's salaries when such a treasure trove of information is so neatly presented by the MLS player's union?  The topic has been pretty well covered already, but I've covered this topic in the past, am integrating the data into a wider MLS database, and haven't seen too many neat graphs yet.  So why not make a quick post on the topic?

Rather than just focus on the change versus 2010 salaries, I've chosen to focus on all the publicly available data I have that goes back to 2006.  The trends and associated conclusions are interesting, and shed light on how MLS is attempting to manage its financial position within the US sport's landscape.

Player Salaries

MLS's player salaries are very non-normal, so using an average value to make comparisons is not appropriate.  Rather than use the average, the median is used from each season to make comparisons.  The graph below (click on it to enlarge) shows the median player salary by season, the per cent change versus the previous season, and the overall inflation per season versus 2011.

The annual rate of change from one year to the next peaked in 2009 at 33%, just prior to the latest CBA. The terms of the CBA and expansion since it was signed seem to have reversed the growth trend to the point the median salary has dropped by 13% in 2011 versus 2010.  While salaries are down 13% from last year, the median pay is up 60% since 2006.

Salaries aren't the only thing has changed since 2006 - the number of games in a season has also changed.  The 2006 season saw 32 games, 2007 through 2010 had 30 matches, and the 2011 season will have 34 matches.  While not a perfect predictor of the effort required in a season, the number of matches can serve as a proxy for how hard a player is working for their pay.  The graph below (click on it to enlarge) re-plots the data from the graph above, but normalizes it to a per match basis by season.

Given that the 2007 season saw a reduction of 2 matches versus 2006, the pay per match actually went up 7% while gross pay remained flat according to the first graph.  The next three seasons see pay changes identical to the gross numbers shown in the first graph as the number of matches does not change.  The final season (2011) saw a drop in per match pay even bigger than the gross data - 24% reduction versus 13%.

Jeremiah Oshan at SB Nation details some of the reasons why the median player salary has moved down, so I won't recount them here.  Whatever the reason, the median player is earning less than 2010 and working harder to earn it.

Team Payroll

So what is each team spending on payroll over the years?  The graph below (click to enlarge) shows team payroll from 2006 to 2011.  The numbers have been unadjusted for MLS median player salary (more on that later).

The graph shows a steady increase in the team payrolls from just under $2M in 2006 to over $3M in 2011.  Four teams exceeded $4M in 2011 payroll - Chicago, New York, Los Angeles, and Toronto, although the move of Juan Pablo Angel from NY to LA and Dwayne De Rosario from Toronto to NY have lowered the peaks those four teams experienced in 2010.  Clearly, the addition of roster spots in 2011 has boosted the bottom line cost of fielding a squad in 2011 even if the median income of a player has gone down.

So what happens if we baseline everything in 2011 MLS dollars?  Similar to Pay As You Play, a player pay inflation factor (in this case based on wages and not transfer fees) is required.  The change in median player salary is used for such an inflation factor.  The inflation factor for each season shown in the first graph was applied to each player's salary in that season to provide what their equivalent 2011 salary would be.  Those salaries in 2011 MLS dollars were then totaled by team, and are presented in the graph below (click on graph to enlarge).

The inflated 2011 wages from 2006 onwards show two clear conclusions:

• MLS per team payroll has not increased significantly since 2006 (although the number of teams making such a payroll has).
• MLS per team payroll was declining from 2008 until this season.

What's also interesting is that the real time payroll differential of LA versus the rest of the league shrinks over time with each year of David Beckham's contract.  Adding Landon Donovan's $2.3M DP contract helped stem the rate of decline in 2010, but it still didn't stop NY from passing LA as the most expensive team in the league.  Of personal note to Sounders fans is the virtual erasure of any payroll cost differential they had the last two years.  They've gone from leading the pack of teams outside the Big Four spenders to falling in line with the rest of that pack.  Parity not only rules on the field, but it rules on the payroll as well.

League Wide Expenditures

So where does this leave the league overall?  The graphs below present two summaries of the same date for the league (click on either to enlarge).  The first looks at the sum of guaranteed contracts over time (unadjusted) and the total count of players with contracts, while the second looks at the rate of change of the total expenditure and number of contracts from one season to the next.

Since the last year of the last CBA (2009), overall league expenditures on pay have risen greatly - an increase of 58% to be exact.  Over the same time, the number of players in the league has increased 34%.  From 2010 to 2011, league pay expenditure increases could not keep pace with the addition of two teams and roster spots on every team meaning that overall pay increased by only 12.5% while the number of players increased by 26%.  This was the first time in the six years of recorded salary data that increases in total team payroll were outpaced by the growth in the total number of contracts.  This is likely a one-time event that we won't see again until another two-team expansion like we witnessed with Portland and Vancouver this year.

Conclusions

The above data confirms that MLS's single entity model provides for a very interesting balancing act - almost like a social democratic economic model.  On the one hand, they need to have some provisions to attract older, top talent from around the world as well as keep up-and-coming talent in the league.  This is their method for fueling league growth and exposure.  At the same time, they're clearly trying to grow the professional sport in the United States in a controlled manner to avoid an NASL-style meltdown.  In the last few years, they also seem to be taking the approach of improving the bottom end of the salary range by a modest amount, and are willing to keep wage growth in the median contracts to a minimum.  It's almost as if the league has taken a "reasonable income for the greatest number approach", as there is not doubt an increasing number of players are able to make a modest income in MLS by playing the game they love.

At the same time, the league wide payroll numbers indicate just how far the league has to go to be competitive on the world stage.  The 2011 league-wide payroll of $80.2M equates to the transfer fee Chelsea paid for Fernando Torres.  That's right - the rights to negotiate on a contract with one player in the EPL cost more than the entire wages of MLS for a season!  MLS has more than doubled what they can spend on wages since 2006 - a sign that both average pay and the size of the league have grown.  But they still have a long ways to go if they wish to compete for top talent on the world stage, and attract enough worldwide attention to be able to pay for such talent.

2010 MLS Salaries: No overall movement, Forwards the best paid

I am taking a break from the World Cup this evening to provide some commentary on the 2010 MLS salaries that were released earlier this week. Much has been made of the fact that the injured David Beckham still tops the list with a $6.5 million salary. What is more important to examine is what has happened with MLS payroll from 2009 to 2010 with the new collective bargaining agreement, and to understand which types of players are paid more than others. This is a continuation of a series of posts I made back in March.

It should be noted again that the salary data is not normal. Thus, I use the Mann-Whitney test to compare the 2009 and 2010 data sets to see if there are any differences in their medians. In the case of the team payrolls, the medians are:

• 2009: $2,823,007
• 2010: $2,759,648

Putting the data into the Mann-Whitney test yields the results below.

Mann-Whitney test results for 2009 vs. 2010 MLS team payroll

The results of the test show that the wide prediction (CI) for ETA1-ETA2, with the resultant negative value for the bottom end of the window, means that there is not a statistically significant difference between the payrolls in 2009 and 2010.

After determining there is no difference between the two years, the effects of position on player salary was examined. The MLS players union data was grouped into three bins - F, D, and GK. Any intermediate positions marked with an "M" were binned into the corresponding F or D groups. This yielded the following median values (player position salaries were also non-normal):

• Forwards: $96,000
• Defenders: $78,038
• Goalkeepers: $69,833

Figures 2 and 3 represent the results from Mann-Whitney tests using the F, D, and GK data.

Figure 2: Mann-Whitney test for Forward vs. Defender salaries

Figure 3: Mann-Whitney test for Forward vs. Goalkeeper salaries

As Figure 2 indicates, pay for forwards vs. defenders is higher and by a statistically significant measure, with the predicted gap at $6000. The same is true for forwards vs. goalies per Figure 3, with the predicted gap growing to $8208. It turns out that a test of defenders vs. goalkeepers shows no such statistically significant difference.

To me, it makes sense that goal scorers like the forwards get paid the most money. They not only win games, but also put butts in the seats. What's a bit shocking to me is how goalkeepers end up being the lowest paid position. I would think that the few number of them on a squad and their critical nature in a match would lead them to be better paid than defenders. Perhaps its a quirk of MLS's salary structure, but I would invest more in goal keepers than average defender salary.

On a personal note, it's sad that many technical professionals or people with advanced degrees in our nation make more money than an MLS defender or goal keeper. The attractiveness of MLS is that their players are not spoiled brats like our other professional leagues. Still, we can accomplish that goal while also upping league pay and quality of play.

Payroll doesn't win MLS Supporters' Shields

You can't buy this kind of hardware.

I made posts here, here, and here recreating the Soccernomics' pay-to-win regression, as well as highlighting some hidden results that the authors of that book didn't publish. Previous to that series of posts I highlighted the fact that MLS team payroll disparity isn't increasing, the average MLS team payroll is increasing at three times the rate of inflation, and teams with designated players (DP's) are the ones fueling that rise. The next logical question that arises is:

• Is there a correlation between MLS team payroll and finishing position like there is in the English leagues?

Background

For my international readers, I must take a few sentences to explain the Americanized version of the world's game that MLS plays. We don't award our championship to the team that wins the table. Instead, we give them what we call a Supporters' Shield which qualifies them for the CONCACAF Champions League, and then we break them and the next seven teams into a playoff. That's right - our domestic league uses a knockout round system to determine its champion. This bows to the very American way of determining championships in every other major sport - hockey, basketball, football, and baseball. In all of those other sports except football, playoff teams at least have to win a 5 or 7 game series to advance to the next round - that is, they have to win 3 or 4 games over their opponent. MLS and our football league - the NFL - have largely decided on a single game format. One match between two sides decides who advances to the next round of the playoffs. Pull off one above average performance, and you can easily send a team that consistently outperformed you all season into the off season wondering what all that hard work was for.

Not content to be like every other US sports league, MLS does throw in a home-and-away format in the first round of the playoffs. It's not clear to me why they do this in the first round but not the second round or the MLS Cup. Perhaps it is an attempt to prevent a large number of first round upsets, but it isn't exactly clear to me the purpose it serves.

To keep the playoffs interesting, MLS breaks eight teams every year into the playoff tournament. This presents some interesting challenges for a growing league. The total number of teams in MLS the past few seasons is as follows:

• 2005: 12
• 2006: 12
• 2007: 13
• 2008: 14
• 2009: 15
• 2010: 16

The teams added in 2007 through 2010 are not ones promoted from lower divisions - they are new franchises created for the explicit purpose of playing in MLS. This is another key difference from the rest of the world's game. Because of the rapid growth the last four years, this will be the first year that MLS will have not placed the majority of its teams into the playoff tournament.

All of these factors have a huge impact on the American game. Given that one just needs to make it into the postseason tournament to have a shot at a championship, a number of teams with losing records make it in every year. Indeed, last year's champion, Real Salt Lake, had a losing record and happened to flip a switch at the end of the season to make it into the playoffs and pull off an impressive run of wins once they were in the tournament. Also, given that there are a number of new franchises the last few years looking to make a splash, spending is way up for them yet they still struggle with the usual "expansion team" performance challenges. Finally, there is only the motivation of playoff seeding to push teams to compete for the top spots in the table. All these could potentially affect the drive of teams to spend money and resources to finish first rather than fourth or fifth in the table, thus making a relationship between payroll and performance harder to prove.

The Inputs

For this analysis I used 2005 through 2009 player salary data and MLS final table standings data.

As MLS is divided into two conferences (East and West) for the playoff format, I had to combine the two conferences for each season and assigned finishing positions based upon each team's total points for the season. Where ties in points existed, I awarded the teams the same position in the table and then skipped to the next finishing position for the first team after those that were tied. Once each team in each season had a finishing position assigned, I compiled the average finishing positions for each team.

I used the player payroll data to calculate each team's payroll as a multiple of the league average for the season. The team payroll multiple from each season was then compiled to make an average value for each team in MLS.

The results of these two compilations can be seen in Figure 1 below.

Figure 1: MLS average league position and payroll, 2005-2009

Just like the Soccernomics analysis, the data above is non-normal and must be transformed to perform any correlation studies and regression analyses. To do this, I initially tried the Soccernomics transformations of translating finishing positions to percentages as well as using natural logs and found that they worked. See Figure 2 below, where the p-value is greater than 0.05 and the assumption of normality is a safe one.

Figure 2: Graphical Summary for ln(p/16-p)

The team payroll data was also transformed by a natural logarithm, and we can now explore if there is any relationship between the data.

Correlation Test Results

As in my previous post on regression, the first attribute to check is the Pearson coefficient statistic before doing any regression analyses. Doing so will tell us if there is a statistically significant correlation between the two data sets. Figure 3 shows the results of the tests.

Figure 3: Correlation test between average team finish and team payroll multiple (with and without DP salaries included.

As Figure 3 indicates, there seems to be little chance there is a statistically significant correlation between team payroll and where the team finishes in the table as the p-values are not less than 0.05. What's interesting is that if the DP salaries are excluded (Team% no DP), the correlation statistics actually improves.

I did try a number of other transforms to the data to see if there was one that would generate improved fit. Unfortunately, none of the other transforms I tried improved the correlation statistics. Thus, I conclude there is no relationship between team payroll and table finishing position in MLS.

Reasons for the Lack of Correlation

Given the prominence of the Soccernomics analysis and the different conclusion drawn for MLS, here are some explanations why we might see such a difference in outcomes between the leagues.

• The poor cost/benefit equation of the DP: While the DP sucks up a ton of available pay-roll (MLS salary cap guidelines not withstanding), it represents only a single player on the pitch. As we saw in the correlation statistic comparison, the statistical score actually improves when the DP's salary is removed. This is especially true of the LA Galaxy, whose blowout purchase of David Beckham and his $5M+ annual salary has resulted in two bottom table finishes followed up by an appearance in the MLS Cup in 2009.
• The volatility in the league's makeup: There have been three expansion franchises added to the league in the last three years of the data used in the analysis. Two out of the three have tried to make a big splash by signing DP's, with one experiencing wild success in table position (Seattle Sounders FC) while the other has been in the league basement (Toronto FC). The Soccernomics study used a relatively stable list of teams that fought for positions in a mature league structure, which would provide less "special cause" variation seen in MLS's results the last few years.
• Low sample size: As with all statistical tests, sample size is key. The greater the number of samples, the more forgiving the test is and the lower the threshold for concluding a statistically significant relationship exists. See Figure 4 below for an example of how the number of samples affects the critical test statistic. The highlighted column indicates the critical correlation values that a test must be equal to or greater than to ensure less than a 1% chance of error in assuming a correlation exists between two data sets. In the case of the MLS data I used, n=15 so one must observe a correlation statistic of 0.5923 or greater. As I stated in my regression post, the Soccernomics study had 58 teams included and thus only needed to observe a correlation statistic between 0.2948 and 0.3218 to make a conclusion of correlation.

Figure 4: Critical correlation value vs. sample size and significance level

• The league's salary cap structure: Outside of the league's DP rule, MLS does try to maintain some form of a salary cap like most other American leagues. While America tries to pride itself as one of the most capitalistic societies, it's exactly the opposite in its sports leagues. In some ways, it makes sense. Capitalism fosters a system of cutthroat competition that eventually leads to a few winners and many losers. This can be counterproductive to providing a healthy, competitive league of 20-30 teams. Providing a ceiling on team payroll may help league parity, but it does make it difficult to rationalize expenditures in hopes of future success.

Ultimately, as MLS moves towards a stable 20 team league in the next few years (increased sample size) and the use of DP's becomes more rational with experience (improved cost/benefit equation) we may see the correlation statistic improve.

Next Steps

If the the main goal in MLS is not to win the table but to win the playoff tournament and the MLS Cup, what attributes could be considered in understanding the likelihood of winning the Cup? I will explore this topic in my next post. Until then, enjoy some soccer this weekend!

MLS payrolls: a mixed bag of escalating costs

Not even his $6.5M salary can create

a team payroll disparity within MLS

Much of this past MLS offseason was spent worrying about a potential strike or lockout due to the league and the players union not being able to come to a quick agreement on a new collective bargaining agreement (CBA). Most of the owner's concerns centered around player salaries and policies that would enable them to rise at a faster rate. Along with that concern was one of team competitiveness - we have seen how a lack of any spending cap in the EPL and La Liga has led to a pay-to-win philosophy.

This leads to two fundamental questions that I will answer with statistics.

1. Did the league see an increase in team payroll disparity during the last CBA?
2. Did the league see an overall increase in team payrolls during the last CBA?

Background

MLS and its players union signed a CBA that covered the 2005 through 2009 seasons. It included salary caps, limited player movement, a no strike clause, and a variety of other regulations for the league and its players. Prior to the 2007 season, the league announced the Designated Player rule in a desire to attract talented, visible stars from around the world while providing minimal impact to a team's salary cap. A few teams have taken advantage of that rule, the most notable being David Beckham and his $6.5m salary.

Of note is the salary cap during the 2005-2009 seasons. It has ranged from $2.0M in 2005 up to $2.3M in 2009. The cap has always been a bit of a fungible figure. Seven of the 12 teams ran payroll higher than the cap in 2006, with 12 of the 15 running higher than the cap in 2009. I am sure that MLS has some creative accounting rules that show the teams are under the cap, but it is not clear to me how this is done. The reality is that the clubs are spending a good bit more than the cap, and reality is what I am interested in.

At first blush, there seems to be a good bit of disparity in team expenditures that has grown over the life of the last CBA. Figure 1 shows how much each team has spent as a multiple of the league average for each season.

Figure 1: MLS team payroll, 2005-2009, as a function

of the average team payroll for each year (click to enlarge).

Statistics will tell us whether or not the disparities we see starting in 2006 are real, and whether or not there has been general upward movement in league salaries over the five years of the CBA.

The Prerequisite: Normality

The pre-requisite for many of the frequently used parametric statistical analyses (t-tests, test for equal variances, etc.) is that the data being analyzed be normally distributed. This is because the tests being performed on the data use concepts based upon the Central Limit Theorem to test two sample populations. Thus, normality is a prerequisite and the team payroll data must be checked for this property before beginning any analysis.

An interesting phenomena is observed when each year's distribution of team payrolls is checked for normality. The 2005 and 2006 seasons both test to be normal. That is to say, when a normality test is performed on the data we find a p-value greater that 0.05, which indicates a low risk of accepting the data is normal when it might actually be non-normal. See the Figure 2 as an example of what a "successful" normality test looks like.

Figure 2: Descriptive statistics for 2005 MLS team payroll.

(click to enlarge)

However, once the designated player was introduced in 2007, team payrolls for each season went to a non-normal distribution. That is to say, the p-value for normality tests of the 2007-2009 seasons is much less than 0.05, indicating that we would be at high risk of drawing the incorrect conclusion if a normal distribution were assumed. See the Figure 3 as an example of what such non-normal distributions look like.

Figure 3: Descriptive statistics for 2009 MLS team payroll.

(click to enlarge)

These results have very important implications for the types of tests used to answer the two fundamental questions posed at the beginning of this post. There are now two options:

1. Use a transformation method, like Box-Cox, to transform the 2007-2009 data to make it normal. This makes determining the absolute effects difficult due to the presence of the transformation function.
2. Use a non-parametric statistical test on the original data. Non-parametric tests are far more forgiving of the original data, but end up requiring a far greater spread in two sample data sets to show a statistically significant difference when compared to similar parametric tests.

In this case, I have chosen to pursue course (2) for my study. I am more concerned with testing whether there are differences, as well as easily determining how large a gap there is for tests that show a statistically significant one. Avoiding transforming data via (1) is a huge benefit in this case, especially since the statistics I am interested in don't benefit a whole lot from using parametric tests.

Has team payroll disparity increased?

To answer this first question, I turn to a comparison of the 2009 and 2005 team payroll data. Using this data means I am looking at the bookends of the last CBA. I did this, rather than analyzing each of the five years of the CBA, as it is reasonable to suspect that any change in the distribution of team payrolls will be gradual from year-to-year. Thus, to see any potential difference one must look at the beginning and the end of the CBA, which is what the owners and players really cared about anyways when they were negotiating the new CBA.

To do this, I performed a test for equal variances between the 2005 and 2009 seasons' team payrolls. Performing such an analysis can identify whether the variation in team payrolls between the two seasons is statistically different. If this were so, and if the 2009 variance was higher than 2005, we could conclude that the gap between "rich" and "poor" clubs' expenditures on player payroll was widening. As the 2009 data is non-normal, I used a specific subset of the test for equal variances, Levene's test, to make this evaluation. If the Levene's test comes out with a p-value of 0.05 or lower, we can reject the null hypothesis that variances are equal and safely assume that they are different. Figure 4 displays the results of such a test.

Figure 4: Test for equal variance results,

2005 vs. 2009 MLS team payroll.

(click to enlarge)

The first conclusion that can be drawn from the analysis is that we cannot reject the null hypothesis (p-value from Levene's test = 0.219), thus we cannot declare that there has been a statistically significant increase in the spread in MLS team payrolls throughout the last CBA. We must accept the null hypothesis: there is no difference in the variation in team payroll between 2005 and 2009.

The second conclusion is that this test demonstrates the danger of not performing the initial check for normality. If I hadn't known that the data was non-normal, I might have used the results from the F-test, and it's p-value of 0.00 would have led me to conclude that there was a difference in payroll variation between 2005 and 2009. This would have been the wrong conclusion.

The third conclusion is that not even David Beckham's 6.5M+ salary and the resultant $10M LA Galaxy payroll (the star on the far right of the Levene's test box plots) can generate such a disparity. One might be able to rationalize excluding Beckham's salary from the analysis, or at least cutting it down to the $3M range, as it is more than double the next closest DP salary. This would only close the gap in 2009 salaries, raising the p-value even more and making any test for equal variances more difficult to reject.

Has the average team payroll increased from 2005, and if so by how much?

Now that we know that the variability in team payroll has not increased, it is time to determine whether or not average team payroll has increased from 2005 to 2009 and by how much. To answer these two questions, I used the Mann-Whitney two sample test for non-parametric data. In this case, the null hypothesis is that there is no difference between the two seasons while the alternative hypothesis is that 2009 team expenditures were greater than 2005. Figure 5 shows the results of the test.

Figure 5: Results from Mann-Whitney

test, 2009-2005 team payroll

The results from the test return a p-value of 0.0018, which means we conclude that there is a difference between the 2009 and 2005 season payrolls. The test also gives an estimate for the difference - $746k. Thus, we can conclude that the average team's payroll has increased by $746k over the five year CBA.

General conclusions and new questions

Through statistical analysis, we can see that the gap in team payroll has not grown over the last three years, but the average team payroll expense has increased by almost three quarters of a million dollars. For a league concerned about financial stability and a modest $2.3M salary cap, this had to be part of the reason for the concern in negotiating a new CBA. Teams are spending far more money in 2009 than in 2005, regardless of the good intentions of the CBA.

This analysis leads to a whole new set of questions. Two of which that come to mind are:

1. How much of this increase is due to normal competitive pressures year-over-year in a pro league vs. the unique salary impacts of the DP?
2. Like the EPL and La Liga, can the difference in finishing position year-to-year in the league be partly explained by team expenditures?

I will address these questions in future blog posts.

Why Spanish players are on strike

Hope you enjoyed Messi's performance against

Arsenal. He's on strike next week.

The top two tiers of the Spanish football leagues will be on strike next week.

As it stands, there will be no soccer matches in Spain next weekend after the Spanish players' union, the AFE, Friday called a strike over unpaid wages that will halt games in the country's top four leagues between April 16 and April 19.

The AFE said that 85% of soccer players in Spain's top three divisions either get paid late or not at all. It also says that the Spanish Football Federation still owes the union €8 million ($10.7 million).

So why are players going unpaid or paid late? Here's one reason why.

The reality of the financial situation in Spanish football is that Barcelona and Real Madrid earn more than four times as much from the domestic TV deal alone as any other La Liga side and their turnover is more than six times as high.

Although they are among the wealthiest sporting franchises on the planet, the reality below them is disconcerting. Both clubs have used their weight to accumulate more of the domestic TV revenue to enable them become wealthier at the expense of the remainder of Spanish football.

What is the result of this pay disparity? Barcelona and Real Madrid have won the last 5 league titles, one of them will make it 6 straight this year, and 21 of the last 25 league titles (HT: myfootballfacts). It seems as if the EPL isn't the only league with a pay-to-win problem that's leading to financial problems.

Why the MLS owners fought the players so hard

Note: This is a repost from March 8th, 2010 at my other blog, which is a mix of politics, economics, and containted a few soccer-related posts before this blog was created. I made this post in the middle of the MLS labor turmoil. I have reposted this here as it will provide the background for the first few posts I make of new analysis.

Figure 3.1 from Soccernomics, which shows a substantial

correlation between wage expenditure and where a team
finishes in England's top two soccer leagues.

I mentioned in a previous post that I am reading the book Soccernomics, which is chock full of statistics that clarify or challenge conventional sports wisdom. The authors do a very good job of not only comparing differences in soccer leagues, but also across other sports with a special emphasis on baseball given it's similarly lenient financial rules. One of the more interesting aspects of international soccer versus traditional US sports leagues is the financial and transaction rules governing the game.

Most US professional sports leagues are governed by some form of salary cap to prevent runaway spending by big market teams that translates to widening disparities in competitiveness. The most successful model of this behavior is the NFL salary cap and it's resultant goal of "league parity". Transactions between teams nearly always involve player trades, and are often executed as much for salary cap management as for overall team improvement. This is due to the fact that the teams are often required to assume the terms - both financial compensation and duration - of the contract. This model minimizes costs, and provides a predictable and somewhat stable source of revenue and cost for owners.

International soccer, governed by FIFA rules, is completely different. Largely, there are no salary caps - although that may change as an emerging soccer debt bubble may cause governing bodies like UEFA to place "break even" financial requirements on clubs. Teams are free to spend what they want, where they want. When it comes to transactions, player swaps do not take place. Rather, a "transfer" will take place where the club and player are paid a one time compensation for the transferring of rights from one club to another. Transfer fees can be huge, as speculation plays a big part when rival clubs are bidding for a player's talents. They can be the difference between profits and losses for clubs.

The authors of Soccernomics have found out some interesting things related to transfers and team payroll.

First, there is very little correlation between the money a team spends in the transfer market and their success in the league.

But much of this [transfer] money is wasted on the wrong transfers. In fact, the amount that almost any club spends on transfer fees bears little relation to where it finishes in the league. We studied the spending of forty English clubs between 1978 and 1997, and found that their outlay on transfers explained only 16 percent of their total variation in league position. By contrast, their spending on salaries explained a massive 92 percent of that variation.

What's interesting is that even given this data, clubs still go out and blow huge sums of money on transfers. Soccernomics goes into many reasons why transfers largely fail to improve a club and why clubs continue to pursue them, but I will save readers from me regurgitating all of Chapter 3 of the book. Rest assured - transfers present a bad ROI, and you need to read the book to get the full story why.

Second, there is a good bit of correlation between the money a team spends on annual total player payroll and their success in the league. The graph at the top of this post shows the author's regression results when studying payroll and club success. While there is a substantial correlation, the graph must be deconstructed to understand the true magnitude of the trends given that logarithmic functions had to be used to get the linear fit. If one is to look at what it takes to move from mid-table on average to the top 4 positions, we could examine points that correspond to 0 and 1 on the x-axis. Adjusting both the payroll data and the finishing position data by their logarithmic functions, one finds that a club must spend about 3 times more money than the league average to achieve an average finishing position 5 places higher than the average club. Given a table of 20 teams, that means one must spend 300% more than the average team to move up 25% of the table - diminishing returns indeed! Given that finishing in the top of the league effectively means promotion - either from the Championship to the Premier League or the Premier League to the Champions League - and ever growing revenue, there is huge pressure on team finances to compete for those top few spots.

And thus, we get to the rub of soccer in the United States. The MLS player's union and ownership is locked in a stalemate over the terms of the next collective bargaining agreement (CBA). Players are asking for fundamental changes to the way the league manages their contracts - they are asking for free agency, terms closer to the rest of the world's players when it comes to transfer terms, and for an eventual dissolution of the league's single entity structure so that players can sign contracts directly with teams. The league's ownership is steadfastly refusing these terms - and for good financial reasons. No one expects MLS to turn into the Premier League overnight, but the failure of the NASL looms large as the cautionary tale of what happens when US soccer leagues don't run a conservative financial house. Given the expectation of US sports franchise owners that they should run their team like a business and make a profit (an assumption Chapter 4 of Soccernomics ultimately destroys), the owners can look at results like those from the English leagues and see what the end game is - large sums of money, often spent irrationally, producing unpredictable results and destroying the financial health of the league's teams. Teams like the Seattle Sounders, with deep pocketed owners and fans who set the league record for attendance in the team's first season, would look to use that financial position to build a permanent place at the top of the league for themselves.

As a Libertarian, I can't say I agree with the owners' position. There's a legitimate point to be made for workers' rights and against the business collusion of a single entity. But as a growing soccer fan that wants the MLS to be a stable, long-term venture I also can't disagree with the owners' approach of limiting the financial commitment required of teams. They are doing the rational thing of following the statistics, and recognizing that the league can't support such an arrangement at this time.