Eds note: Predicting the results of the MLS SuperDraft is a fool's errand. If you need evidence for that claim (and with all due respect to Mel Kiper), see the 10 completely different mock drafts that have been released over the past few days. Instead of trying to predict the unpredictable, we just want to arm our readers with as much information as possible so that they can back up what they're seeing at the combine and know what they're getting from the players called to the podium on draft day. A.J. and Aaron both identify talent professionally using conventional scouting means, but they also have a deep appreciation for the contributions that analytics and stats can provide to give context and meaning to what their eyes tell them. We hope you'll use this guide when watching the combine next week and when these players are taken in the draft.
This Saturday, the 2017 MLS Player Combine kicks off in Carson, CA, and leads into next Friday’s SuperDraft. To date, 66 players have been invited to showcase themselves to the league’s coaches, scouts, and front offices. This begins with physical testing on Saturday, followed by matches on Sunday, Tuesday, and Thursday. Matches will be live streamed on the MLS site.
Most of the combine invitees recently finished their NCAA careers, and are not tied to clubs via the Homegrown Player rule. If you’re an avid follower of the college game, many of these players will be familiar to you, and you may already have an idea of how they’ll fit into the league. If you’re less familiar with these prospects, it may be a bit more difficult to draw conclusions from three days of matches featuring players thrust into a situation where they don’t know their teammates and there’s no real tactical system. Frankly, that’s a really difficult task for the clubs and those of us who DO know the college game!
In either case, the more information and insight we can gather on players, the better chance we’ll have of identifying the talents that will be on MLS rosters in a few weeks’ time. The combine frequently allows lesser-known players to improve their draft stock, and the bigger stars to grow their list of potential suitors. To that end, we present profiles and data on the majority of combine invitees based on who we’ve seen in our work as it relates to NCAA soccer. Read More
The ball is placed on painted grass and a tall bright goalkeeper strides backward toward the goal. Now lunging forward the leg swings like a pendulum through the ball sending it bravely toward the sky. The onlookers lift their gaze as the ball reaches the peak of its flight. Two gladiators lock limbs below, jostling for position. They leap together in an effort to possess the falling ball. There is a deflection and the gladiators separate. They rejoin the play.
Long kicks by goalkeepers are a staple in soccer matches and they are a beautiful sight to behold, but that doesn't mean they are a good idea. By the end of this article I hope to convince you of that fact, even if the data isn't entirely perfect. Read More
In my Sports Analytics class at Saint Joseph's University, my professor would always stress the importance of having a valid data source; “Put garbage in, get garbage out,” he would tell the class. If the data has a bias, isn’t random, or is miscalculated, then any resulting conclusion is not credible. In order to have a sound analytic method, it is imperative that the data source is not “garbage.” For the course’s final project, I chose to analyze players’ cost efficiency and also use binary integer programming to build an optimal lineup. Ironically enough, I decided to have my data source be none other than the Audi Player Index.
More after the jump. Read More
Over the course of a year, the Columbus Crew have gone from runner-ups in the 2015 MLS Cup to bottom dwellers, finishing ninth in the Eastern Conference in 2016. After losing a number of games off conceding late goals and tying over a third of their games, Crew fans are right to be polarized when evaluating their team, especially the defense. In 2015 the Crew conceded 53 goals and another 58 in 2016, both getting close to most in the league. Currently all eyes are on Steve Clark, with debate on both sides on if he is the right goalkeeper for Columbus.
Every team needs a goalkeeper that fits their style of play. For example, if a defense is bleeding crosses, they may want a strong goalkeeper to handle the dangerous lobs into the box. When looking at Columbus, it’s easy to look at the number of goals they’re giving up and think they need a better shot stopper, when in reality, Clark is above average on saving shots in both expected goals and save percentage. The Crew don’t need a spectacular shot stopper. Instead, there are two main things they require from their goalkeeper. Read More
Toronto FC and Philadelphia Union enter the playoffs in undesirable form and have experienced opposite trajectories in the regular season.
After a stale first half of the season, TFC has regained talented players from injury (welcome back, Jozy & Giovinco!) and have lost only three times since mid-season. This form tailed off as they closed out conceding six goals in three games.
The Union began their campaign proving most people wrong by winning with an up-tempo, athletic, well disciplined style. But the wheels fell off around mid-season and the Union are historically bad for a playoff team.
More after the jump. Read More
The topic of “finishing” is always a fun one in the analytics world, and, last April, it’s one I studied using data going all the way back to the beginning of the league to see if I could find evidence for a statistically significant gradient of repeatable finishing skill in MLS. Click the link to read the piece in full, but the short of it was, while there were many instances where a forward outperformed their xG by a wide margin or converted an unusual number of their shots on goal, these seasons were rarely repeated within a player’s career as you would expect if such numbers were tied to a skill.
After such a long and arduous study, you can imagine my consternation any time I read a piece praising or criticizing a player’s finishing skill within the league. In fact, when Jordan Morris told the New York Times, “my finishing is still raw,” I nearly had an aneurysm. Doesn't anyone read long winded statistical articles anymore? (Answer: no) But read more after the jump. Read More
Following an underwhelming inaugural season under Jason Kreis, New York City FC’s ownership sought a dramatic change. They enlisted the help of Arsenal legend Patrick Vieira to oversee their team’s sophomore campaign. Opting for a foreign manager raised more than a few eyebrows among MLS media. The former French National Team captain has proved doubters wrong but still 2016 has been anything but smooth for NYCFC.
After New York City FC’s first game against the Chicago Fire, it appeared as though history may repeat itself. They edged out with a 4-3 victory but defensively they looked as shaky as in 2015. Since that game, however, Vieira has taken great strides. By implementing a unique style of build-up play Vieira has managed to maintain NYCFC’s strong attack while addressing their porous defense. New York City’s 1.56 xG against/game sits at 5th worst in the league but they’ve improved from 2015 by .26, the highest mark over that time.
More after the jump. Read More
We’re a little over a month from MLS announcing MLS Goalkeeper of the Year. Even though they haven’t announced the nominees, there are only two goalkeepers worthy of winning the award. Read More
If you like goal scoring then tuning in for the 2nd half of MLS games will bring you 32% more pleasure than watching the 1st half. There have been 478 goals scored in the 2nd half versus just 363 in the 1st half through September 29th. Finishing rates improve from 10.3% to 10.9% between halves but the primary driver of the increased goal scoring is a 24% increase in shots attempted. Why do shots increase so much?
Possible answers after the jump. Read More
You may have noticed that yesterday we debuted our playoff probabilities for 2016! It will also show up as an option in the upper right corner until the end of the season.
As in our 2015 iteration, playoff probabilities come from a combination of where teams are now in the tables, what their remaining schedule is, and how good our model thinks they are. The remaining games of the 2016 season were simulated 10,000 times based on win-loss-draw predictions for each game. The probabilities and averages given below are calculated from those simulations. A bit more in-depth explanation follows: Read More