There is a narrative about Major League Soccer that describes an emerging financial arms race between its teams. The Designated Player Rule, instituted in 2007 with the arrival of David Beckham, has allowed teams additional flexibility to spend larger sums of money on key players. Every team in the league has taken advantage of this opportunity, and the rule itself has been expanded several times in recent years. Teams can currently have up to three such players on their roster, and a new category of expenditure – “Targeted Allocation Money” – was announced earlier this season. This tactic was used almost immediately by the Los Angeles Galaxy, with the end result being the acquisition of Giovani dos Santos.

Surveying this shifting landscape, columnist Steve Davis recently argued at World Soccer Talk that the teams in MLS will effectively split into two groups:

Now [MLS is] like all the other leagues of haves and have nots. We will now march predictably into every season essentially choosing among a handful of big brand clubs as the real title contenders. Everyone else will fight for the scraps.

Is this narrative of financial inequality accurate? I set out to investigate.

Describing the hypothesis

To start with, I note that Davis’ claim that only a handful of big clubs are “the real title contenders” is demonstrably false. Last week I published an analysis of the repeatability of success in Major League Soccer, and concluded that the league’s efforts to enforce parity appear to have succeeded. Team success (or lack thereof) in one season is a very poor predictor of success the following year. While some teams do find success year after year – the Los Angeles Galaxy being one clear example – when looking at points per game from year to year MLS looks very different than leagues (like the Premier League or La Liga) with a near-permanent ruling class.

That piece, however, included no data about team salaries – and so it can shed no light on the question of financial inequalities, or any de factor requirement to “spend big or go home”. Teams around the league have dramatically increased and decreased their salary budget in consecutive years, which may be one reason for significant changes in team performance from year to year. It is this question that has preoccupied me for the last several days.

Before getting into the data, lets define the hypothesis being tested, and talk about what we would expect to find in a league that required big spending. The common hypothesis is that teams in Major League Soccer will gain a significant competitive advantage through taking advantage of rules like the Designated Player and Targeted Allocation spending. The implication of this hypothesis is that if someone were to plot team spending against team performance, there would be a significant relationship between the two. Teams that spend more money, or that take advantage of the DP rule and Targeted Allocations, would tend to perform better than those that don’t. Rarely would you find a team that spends conservatively but ends near the top of the league standings.



The above plot is one example of what you might expect to find in a scatterplot, should the hypothesis prove true. Teams that take advantage of the Designated Player, Targeted Allocation money, and other paths would end up spending more money. Those same teams would be the ones that find success. At the other end of the scale, teams that don’t take advantage of those rules would find themselves constrained in their success – bumping up against an upper limit of success. There may be rare exceptions, but by and large this relationship should be fairly clear.

Collecting data, and a big caveat

Unfortunately, team and league financial data is a closely held secret. Every announcement of a player transaction includes the following boilerplate statement:

Per club and MLS policy, additional details of the deal were not disclosed.

There is only one source of financial data. For the past eight years, the MLS Players Union has been periodically releasing salary data for every player in the league. A number of observers have then re-published the data, including a number of visualizations (2012, 2013, 2014, 2015 and again). The Columbus Dispatch and Orlando Sentinel are among newspapers to have published individual years in a more user-friendly format. Individual analysts have even converted the published PDF documents into spreadsheets or other reusable data formats. With this piece I have joined those ranks, and have published the data in this piece on GitHub.

Before diving too deeply into this dataset, however, it must be pointed out that what the union releases is not exactly the same as what we may be expecting. A close comparison of the most recent release with the league’s public list of Designated Players contains some surprises:

Joao Plata is listed as a current Designated Player for Real Salt Lake, yet both his base salary and guaranteed compensation figures: $150,000 are 9th-highest on the team – and well short of the published maximum salary of $436,250. Some explain that Plata appears on this list because of the transfer fee paid to bring him to MLS.

Fabian Espindola at DC United is in a similar position. Listed at $175,000, his salary would be 9th-highest on the team. Yet he also appears on the list of Designated Players. Espindola should not be subject to any transfer fee rule with DC United, having been traded to the nation’s capital.

Juan Ramirez in Colorado is an even more extreme position. Announced as a “Young DP”, his salary appears to be one of the lowest on the Rapids: $75,000.

Until someone releases a more reliable – or more consistent with league rules – dataset, however, this is the only information publicly available. So, this is what I’ve attempted to use in this analysis.

The last of each year’s salary reports were downloaded, and converted into Excel format using an online tool. These annual datasets are available for download from GitHub.

With two different cost figures provided for each player, I chose to use the Base Salary figure rather than the Guaranteed Compensation figure.

For team performance, I calculated Points Per Game using information found on the MLS website. This was necessary because halfway through the study period, the league switched from a 30- to 34-game schedule.

The combination of these steps results in a final dataset of 153 records that span MLS history from 2007 through the current 2015 season. Each record includes a team’s total salary cost, and the team’s points per game for a given season.

Analysis

This dataset was plotted as a scatterplot, with total salary forming the horizontal axis and points per game comprising the vertical axis. The scatterplot can be found below. Teams at or near the salary cap can be seen in a cluster at the left side of the plot, while teams that spend significantly are laid out to the right. Successful teams, in terms of points per game during the regular season, are found toward the top of the plot, while unsuccessful teams can be found closer to the bottom.

The hypothesis of a barrier between big-spending teams and low-spending teams does not appear to withstand scrutiny.

The hypothesis plot at the top of this article anticipated two clusters of teams, at the upper right and lower left of a plot comparing spending and performance. There should be vacancies at the upper left (low spending, high performance) and lower right (high spending, low performance) in order to verify the hypothesis. While the real data does display a void in the lower right quadrant, the upper left quadrant is quite well populated.

Beyond a visual inspection, the hypothesis is further undermined by examining the best-fit linear trend line through these data points. The slope of the trend line is very nearly horizontal, indicating no significant trend towards higher performance as spending increases. Furthermore, the R-Squared value of 0.0245 indicates very little strength in the (flat) correlation that does exist in the data.

While the overall hypothesis of a competition splitting along spendthrift/frugal lines is not borne out by the data, there are additional conclusions that might be drawn from this data.

It is interesting that, while large budgets are not the only way to team success, it does appear that they may protect against abject failure. The following plot separates teams that missed the playoffs (in red) from those that qualified for the postseason (in gray). With three exceptions (the 2007 and 2008 Galaxy, and 2014 Toronto) teams in the high-spending group have all qualified for the MLS postseason.

Revisiting the caveat

We must revisit the earlier caveat about the distinction between league rules like the Designated Player and the data contained in the players’ union salary data. Examples such as Juan Ramirez and Joao Plata indicate that a significant attribute of team spending – acquisition costs – are not reflected in the union salary figures. It is possible that this mismatch is causing big-spending teams (from the league’s salary-and-acquisition perspective) to be classified as frugal (from the union’s salary-only perspective).

Conclusions

The hypothesis that Major League Soccer is evolving into a league that separates big-spending teams from frugal teams, forcing teams into a financial arms race, does not appear to be validated by the eight years of salary data available to us. It is certainly true that some teams have taken advantage of these rules to spend significant sums. Looking solely at expenditures, MLS does exhibit some teams that spend, and some that do not.

When these two groups of teams are compared by their on-field performance, however, it does not appear that there is a significant relationship between salaries and success. There are many examples of teams with modest budgets achieving success in MLS. The current season includes yet another example, where FC Dallas’ modest roster of 3.7 million dollars had earned a league-best 1.81 points per game heading into this weekend.

Possible directions for further analysis could include:

Expanding the dataset to include non-salary expenses – particularly player acquisition costs,

Categorizing teams not by total salaries but by how many Designated Players they sign,

Tackling the question of playing time, and examining not just players on the roster but which players appear in games, and

Looking specifically at the salary cap, paying attention not to a player’s overall salary but at the more constrained question of how team’s allocate their fixed salary cap funds.

Datasets

The data used to conduct this analysis has been made available on GitHub.