There are approximately 530 currently active players in the MLS. Of which, about 200 of them initially entered the league via the MLS SuperDraft.
Using guaranteed compensation, draft selection number and year drafted – a second degree polynomial regression provides a formula that effectively predicts the expected compensation that a player will be paid based upon the number they were selected from the MLS SuperDraft and how many years it has been since they entered the MLS. This is a significant gain over a standard linear regression which results only in a 29% coefficient of determination. This polynomial (non-linear) regression provides an improved 39% coefficient of determination.
The base salary for a player who entered the MLS via the SuperDraft, according to this statistical model, is $158,962. Depending on the player’s selection number in the draft and how many years the player has been in the league, this expected compensation value fluctuates either up or down. For each pick that the player remained undrafted, they lose $6,627.34 off their base salary but pickup $88.43 multiplied by their pick number squared. In other words, a pick’s expected value decreases after each selection, but the size of the decrease lessens exponentially as the pick number grows.
For example, the salary for a rookie player selected with the third pick will have the expected initial salary of:
$139,876.06 = $158,962.21 – $6,627.34*(3) + $88.43*(3^2)
As the player ages, his salary is expected to increase $1,014.40 per year squared, lose $1,552 per year, and gain $106.18 per year multiplied by the player’s initial draft pick number.
For example, after this player has been in the league for two years, his expected salary grows to:
$141,466.24 = $139,876.06 + $1,014.40*(2^2) – $1,552.70*(2) +$106.18*(2)(3)
Using these same formulas, we can develop a table of relative draft pick values, as well as their expected value after multiple years.
Full table is available at: http://dl.dropbox.com/u/380945/mlsSuperdraft.xls
This chart shows that the value of top picks, while initially high, tend not to increase as dramatically as lower draft picks. For example, the compensation of a player selected with a number three pick is expected to rise only $11,293.76 after four years of being in the league. On the other hand, the 38th draft pick’s compensation is expected to rise $26,158.96 over the same period.
According to the chart, exchanging a number three draft pick for any other two draft picks in the first round (given 18 selection picks per round) would be an upgrade. If this hypothetical team was to exchange their number three draft pick for the 17th and 18th draft picks, the expected salary of the two players is expected to be slightly more than the 3rd pick alone. However, their combined value is expected to increase in value by $34,904.40 over four years. In comparison, the 3rd pick in the same draft would have been expected to increase in value only $11,293.76.
Because of the MLS’s single entity structure, maximizing the cultivation of player value increase is perhaps even more important than maximizing the total value of the team. The player market in the MLS is very similar to playing the stock market, but only worrying about stock value fluctuations – not current stock value. According to this model, it may be in a team’s best interest to invest in “penny stocks”. Essentially, what this chart is suggesting is that it is much harder for a good player to double their value than a lesser rated player.
However, there are certainly statistically relevant ramifications of taking this “penny stock” approach. A player’s fluctuation in value certainly correlates very strongly to the amount of minutes that they play during a season. Also, it is much easier for a team to provide one top draft pick playing minutes, than to provide two lower draft picks with a significant share of time. This methodology doesn’t work by letting these investments ride the bench all season.
Also, there are clear salary cap and roster size-limit complications with taking this approach. With a top draft pick you can expect their salary to remain relatively static. With a lower draft pick (who manages to remain rostered), their value is expected to increase by about $10,000 in the first two years. For teams already pushing the salary cap, lower pick investments may not be the best avenue of growth. For teams with confidence in their ability to maximize a young player’s potential and have salary cap room to spare for long-term investments, this avenue is most certainly worth exploring.
Now, by calculating the expected compensation for every drafted player in the league, we quickly learn which players were good draft picks versus players that were not good draft picks. We will classify every player that has a lower actual compensation total than the expected compensation total as a bad pick. Conversely, we will classify every player that has a higher actual compensation total than the expected compensation total as a good pick. Notice, this classification does not imply that a particular player is a good (or bad) investment at this current point in his career.
Using this methodology (determining the difference between the player’s current salary and their algorithmically calculated expected salary), the ten best MLS SuperDraft picks (that are still currently active in the MLS) of all time are:
|Year||Pick||Name||Current Salary||Expected Salary||Difference|
*Freddy Adu is a special case because he has spent a lot of time outside of the MLS before returning. He was also on loan as a designated played and therefore only $415,000 of the player’s salary counted against the salary cap. Even at the league maximum, he is the best draft pick of all time with a positive differential of over $300,000.
By breaking down players based upon position, we can begin to determine what positions tend to do better than others in the draft.
|Average Value Change||$5,805||$9,523||-$13,023||$5,307|
|Standard Deviation of Value Change||$91,058||$49,612||$42,919||$40,609|
It is important to remember that these conclusions are merely a guideline for drafting with potential value in mind. With such a small sample data size of only a decade of MLS SuperDraft results, it remains difficult to consider this guideline complete. As with any guideline, there will always be exceptions to these rules. Hopefully, with this mathematical model, franchises can better understand the risks that they are taking.