A Look into the Efficiency of the Major League Soccer SuperDraft
/by Scott Knuth
Part 1
In this series of articles, we are going to look at the efficiency of the Major League Soccer SuperDraft. For this research, we analyzed the 2013-2019 MLS SuperDraft along with the 2013 MLS Supplemental Draft. We will evaluate draft efficiency by looking at several different success factors for both draftees and teams. Over this 7-year period there were 30 rounds of drafts and 632 allocated draft picks, from which 576 players were selected-approximately 91%. We can deduce that the majority of Major League Soccer teams use their allocated draft picks when available. However, the number of players actually signed to contracts by their drafting club is significantly lower. According to Figure 1, there is a sharp contrast between the number of players drafted versus the number that are signed to MLS contracts. Out of the 576 total selected players, only 269 were signed to an MLS Contract by the club that drafted them.
To further analyze the SuperDraft, another variable was added to observe what college conference a player was from and if there was an outlier in terms of players being drafted versus signed from a specific conference. Figure 2 looks at the top 10 conferences in college soccer by number of players drafted. By looking at these conferences there is one apparent outlier: the Atlantic Coast Conference. Approximately 65% of players drafted from this conference get signed to an MLS contract. The next four conferences with the most selections are the American Athletic Conference, Big East Conference, the Big-10 Conference, and the Pac-12 Conference. These four conferences all have signing rates of around 50%. While they still have a higher proportion of players being drafted than the rest of the conferences, the ACC is way ahead of the pack, with the number of players of being drafted and in turn being signed to an MLS contract approximately 18% higher than the 47% average. The five previously mentioned conferences are traditionally known as the five strongest conferences. A bias may be evident in this scenario, as people have a preconception that these are the top five conferences in the country, in turn having the top teams and the top players. By having preconceptions about these conferences, teams may be more inclined to select players from these conferences.
These five conferences account for 168 players being signed to MLS contracts between 2013-2019. In Figure 3, we observe the number of minutes available versus the minutes played by the signed draftees of each of these five conferences. The number of minutes available by conference is proportionally similar to how many players were signed by their drafting club in Figure 2. There is a noticeable difference between the rate that players are signed compared to the percentage of minutes they play when looking at each conference. While the minutes available to each conference are very similar to the results in Figure 2 in terms of ranking each conference, the minutes played are dramatically different. The American Athletic Conference played 29% of minutes available; the Atlantic Coast Conference played 18%; the Big East Conference played 38%; the Big-10 played 24%; and the Pac-12 Conference played 29%.
One metric to look at a player’s effectiveness is the percentage of minutes played. A Tweet by @Blades_Analytics, a data recruitment analyst for MRKT Insights and formerly of Peterborough Football Club, discussed that an underutilized way to judge a player is how often they get on the field for their club. Using minutes played is an excellent way to initially analyze a player. If a manager values a player, he will play more minutes. There is, of course, the possibility that the manager is incorrectly valuing a player or if there is a squad depth issue. Nonetheless, it is an excellent starting point in trying to value players. Despite this being so, just by observing the minutes played of signed draftees by conference, there is not an overwhelming positive correlation between their college conference and their playing time. We will look further into this later as we individualize the results of this analysis. Next, we will look at the individualized draft results of players.
To continue with the concept of initially analyzing players based on their minutes played, we specifically looked at the percentage of minutes played, a rate statistic due to multiple years being looked at, versus the Overall Draft Number the player was selected at. In Figure 4, we see that the majority of players are located in Quadrant 4. Quadrant 1 is located in the top right corner; Quadrant 2 is located in the upper left corner; Quadrant 3 is located in the lower left corner; and the bottom right corner is where Quadrant 4 is located. Quadrant 4 shows that there is a significant amount of draft picks who are playing less than 50% of their available minutes. There are several recognizable names in Quadrant 1 such as Tim Parker, Walker Zimmerman, Jackson Yueill, and Cristian Roldan, all of whom have received call-ups from the United States Men’s National Team recently. Andrew Farrell is an outlier in this analysis, having been the 1st overall pick in 2013, and having played over 90% of minutes available to him with the New England Revolution, where he has spent his entire professional career. There is also an interesting outlier in Quadrant 2: Dominique Badji, who was drafted 67th overall in the 2015 SuperDraft. He has played around 60% of his minutes available for 3 different clubs: FC Dallas, Colorado Rapids, and Nashville SC. The overall takeaway of this graph is that there are only 18 players that fall into Quadrants 1 or 2, meaning they have played over 50% of available minutes. There are also several players in Quadrant 4: Santiago Patino, Omar Holness, Christian Dean, Hadji Barry, and Romario Williams. This provides evidence of these players being inefficient draft picks, as they were one of the first 25 selections in the draft but have played less than 12.5% of their minutes available.
Another important factor to take into account is the opportunity for players to improve as they become older. To properly analyze the growth of players, we created an age curve that looked at the average amount of minutes played at a certain age, which was further sorted by position. There is a good argument that there is a fairly significant limitation to the analysis in Figure 4 due to the small sample size for some players, either caused by their short time in the league or because of the roster construction of the teams they were drafted by. The data we scraped for this analysis was the entire playing history in Major League Soccer for everyone listed on the 2019 Regular Season Roster of each MLS club. The age curve shown in Figure 5 displays the average minutes played per season for each age group and is color-filtered by position.
There are several different findings from the analysis of the age curve that are interesting. The first is that the average minutes played each year is 1,030. Additionally, when looking at the confidence interval of the average number of minutes played, we have a range of 908-1152. The confidence interval shows that there is a 95% certainty that the average would fall into that range if the analysis was repeated again. The last finding was from the confidence interval of the average age of players. The interval found that the range was between 25.7-28.5 years old. It is also important to note that the age curve will be skewed by players having 0 minutes played in a season, which will affect the average.
The table below contains the percentage change between each age group, also sorted by position.
Figure 5 shows the chronological average minutes played by players throughout their career. After initially looking at Figure 5, we see that players tend to start growing in terms of minutes played around age 22, continue with smaller growth or even plateau around age 29, and finish with a gradual decline in minutes played. When referring to the growth of players from one year to the next, we can surmise from the table that there is incremental growth or decline each year. If we look back at Figure 4, where we previously mentioned several players who were located in the bottom right of Quadrant 4 and there were players who were Top-10 draft picks and only played less than 12.5% of their available minutes, we can conclude those 4 players, Santiago Patino, Omar Holness, Christian Dean, Hadji Barry, and Romario Williams, have the opportunity to keep growing despite not playing much so far in their careers. For example, Santiago Patino, the only player still on an MLS Roster, was 22 last season, so based off the age curve we can expect his playing time to grow -8%, 24%, 0%, 29%, and 6% over the next 5 years, respectively. It is important to note that there are several outliers in the age curve on the upper and lower age limits that will be affected by a small sample size, which is why there are dramatic changes to the curve. For example, there were only two goalkeepers in the data for the ages 36-39 and they both played significant minutes. It is also necessary to take into account the fact that the age curve is not an exact science and that there are many variables that factor into the growth, or decline, of a player. Coming up in Part 2 we will be looking at the individualized performances of players from the SuperDraft.