SuperDraft or Super Daft, Part 2: Who is good at drafting? by Kevin Minkus

Inspired by recent NFL draft analytics articles, I wrote an article developing an expected value curve for the MLS SuperDraft. Using that curve as a baseline for how well draftees in a given slot should do, we can compare that to how well they actually do, across the picks for a given coach or team. This then tells us which coaches and teams have done an especially good or an especially poor job evaluating NCAA prospects over the last few years, by looking at who exceeds and who underperforms expectations.

Here’s how things look at the team level from 2007 to 2015. (I should note that I’m only going up to 2015 because the metric I’m using to measure value is the total number of minutes played by a player in his first two seasons.)

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SuperDraft or Super Daft, Part 1: Determining the Value of an MLS Draft Pick by Kevin Minkus

It’s late April, which means the NFL Draft is here. Unless you’re an NFL fan, or a Union fan faced with kafkaesque traffic closures because of the construction of a ridiculous 3000-seat amphitheater on the steps of an art museum, that probably doesn’t matter much to you. A number of really fascinating articles were written over the last few days, though, analyzing NFL teams’ skill at drafting. To list just a couple - Reuben Fischer-Baum wrote on each team’s ability to appropriately assess the value of prospects given their pick numbers, and Michael Lopez analyzed the efficiency of the league as a whole in its evaluation of prospects. I want to apply some of Reuben’s work to MLS, to determine which head coaches and which teams have done the best and the worst at drafting.

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Who in MLS Plays the Most Vertically by Kevin Minkus

Two excellent articles were written in the past few days that both featured a facet of the game that’s becoming increasingly integral to how MLS plays: verticality. Matthew Doyle looked at verticality as it applies to teams, and Will Parchman touched on about verticality in the context of a specific player, Alphonso Davies. It’s a topic I approached tangentially here, but I don’t think verticality and pace, or directness and pace, are perfect substitutes.

There are a number of metrics already in the public sphere to measure this verticality, and I think looking at them can better inform the current conversation. One of the most intuitive, especially for individual players, is yards run forward while on the ball. Michael Caley was, I think, the first person I’d seen use it widely, and he occasionally looks at these numbers for the Big 5 leagues. I’m doing a bit of interpolation to calculate it here, by inferring forward distance based on the end location of a pass to the recipient, and the starting location of his next pass.

Here are the top ten players so far this season, in yards progressed forward while on the ball, per 90 (data is prior to the New England - San Jose game, and I’ve filtered it down to only those players who have played more than 180 minutes):

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The 22 Stats that Explain the MLS Season so far by Kevin Minkus

We’re a bit more than a month into the 2017 season. While that’s way too early to say anything definitive, it’s probably enough time to get a feel for where teams stand. Here are 22 stats (one per team), that explain something of each team’s season so far.

Eastern Conference

Columbus: $642,500 - combined guaranteed compensation due Ola Kamara and Justin Meram (as of September 2016’s salary release) 

For the money (equal to roughly one Nocerino), Kamara and Meram are the best attacking partnership in the league. Meram has looked good both out wide and in the middle, which bodes well for the Crew as Federico Higuain hits the wrong side of the age curve. And Ola Kamara has picked up exactly where he left off last year, with 3 goals in his first six games. 

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Adrian Heath’s High Risk Approach to Defense by Kevin Minkus

With Jeff Cassar’s firing last Monday and the announcement of Mike Petke as the new RSL coach, part of the conversation among MLS fans and analysts turned to which remaining coach held the hottest seat. The top candidates included Dom Kinnear, Jay Heaps, and Carl Robinson. Also in the discussion, at least somewhat seriously, was Minnesota United’s Adrian Heath, a man who has been at the helm there for four total games. Over those four games Minnesota has conceded a league worst 18 goals, for a goal difference of -12. They've allowed 38 shots from inside their 18, including nine shots from inside the six yard box. Both are the most in the league (and second most on a per game basis). That Heath’s name comes up in the conversation suggests an overall lack of preparedness that, to some, might be damning.

I don’t want to beat a dead horse here. A lot has already been written on Minnesota’s defensive flaws (including from our own Harrison Crow), and I don’t want to pile on. I’m more concerned about answering whether these struggles could've been anticipated in light of Heath’s performance managing Orlando City’s 2015 expansion campaign. Are the problems Minnesota now faces the same that plagued Orlando City that season? And, if so, does Orlando City’s experience point towards a solution?

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Is Minnesota Really The Worst Defensive Team In MLS History? by Harrison Crow

Let me say, first and foremost, I have a fondness for the underdog or down and out. My first true love, the Seattle Mariners, have the longest tenured playoff drought in Major League Baseball. They've missed out on 15 straight seasons of postseason play much due to their own ineptitude.

So I don’t write this to demean what is happening in MLS to Minnesota, as the expansion club is taking body shots both on and off the field with the tremendously rough start they’ve faced over the last month.

Let me say, first and foremost, I have a fondness for the underdog or down and out. My first true love, the Seattle Mariners, have the longest tenured playoff drought in Major League Baseball. They've missed out on 15 straight seasons of postseason play much due to their own ineptitude.

So I don’t write this to demean what is happening in MLS to Minnesota, as the expansion club is taking body shots both on and off the field with the tremendously rough start they’ve faced over the last month.

After their third loss in four games, all with opponents posting five or more goals, most pundits are ready to declare the Loons on the path to having the worst MLS season of all-time. These types of narratives aren’t really anything new for the start of any particular sports season. Especially when they’re so blatant and obvious.

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The Most Important Skills for MLS Goalkeepers and the rise of Joe Bendik by Bill Reno

We’re four weeks into the MLS season and have we learned anything about the goalkeeping crew yet? Tough to say. 2017 brings back some familiar faces while other teams are trying their luck by putting some youth in net. MLS is a tricky league for any rookie to hop into, but goalkeepers specifically need to have a few tools under their belt.

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Examining Pace in MLS by Kevin Minkus

There’s been a decent amount of discussion this week about how the pace of play in MLS looked quicker in week one than it typically does. Teams like Atlanta, New York Red Bulls, Kansas City, and Houston all came flying out of the gate, with fairly up-tempo styles of play both with the ball and without the ball.

Unfortunately, coming up with a metric for pace is pretty tricky, and it depends specifically on what type of pace you’re talking about. Going all the way back to 2013, Ted Knutson looked at pace as the total number of shots taken in a game. More recently, Thom Lawrence looked at pace as the distance covered over time within a team’s possessions. Both of these definitions speak to a certain amount of directness of play that I don’t think meshes with what people currently mean when they say MLS is playing ‘faster’ so far this year.

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Validating the ASA xGoals Model by Matthias Kullowatz

It was more than two years ago that we built the current model for determining the expected goals of each shot, so let’s go back and see how it’s doing. I've included some R code for fitting our generalized linear model (GLM), as well as a gradient-boosted tree model (GBM) for making comparisons. I selected the training dataset to be shots from 2011 - 2014, and the validation dataset to be shots from 2015 and 2016. Actual and predicted goals per shot are shown across each variable of the model.

First, I fit the original model as seen on the ASA website. This is a logistic generalized linear model, which is designed to predict the probability of binary outcomes like shots (goal vs. not goal). Coefficients will differ somewhat from what we posted long ago, as this is a different training dataset.

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