Mapping Defensive Actions: A Spatial Analysis of Where Teams Focus Their Efforts

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Mapping Defensive Actions: A Spatial Analysis of Where Teams Focus Their Efforts

Every team has its own “style”. Some teams bunker, some teams high-press, some clog the middle, some work the wings. Where they defend is a major part of what defines their style. The recipe for a team’s defensive shape is one part tactics and 11 parts players on the field. Certain players seem to naturally gravitate their efforts to particular areas, be it the wing they’re assigned to, their preferred foot, their favorite partner-in-crime or how they’re instructed to approach the opposition. In the end, the action happens in consistent general areas of the field, but in complex patterns.

One could take an Opta map from any particular game and examine the defensive spatial patterns. You can see the clusters of defensive actions as well as voids where a team hardly seems to find themselves defending at all. But that’s just one game. We all know that teams are forced to adapt their style of play to their opposition, and whatever flukey circumstances played out in that game might not be totally indicative of a team’s overall style. What would really be telling is the aggregate over multiple games.

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MLS Capology: Constructing the Ideal MLS Roster

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MLS Capology: Constructing the Ideal MLS Roster

Major League Soccer is thankfully becoming more and more transparent every year, and as they peel back the curtain fans can understand (and challenge) the strategy of roster compositions of their favorite teams. Ultimately the printed rules allows fans to become more intimate with their teams. This year MLS published rosters that allocated players between the senior roster, supplemental and reserve roster positions. They also shared details about General Allocation Money (GAM). At this point it’s worth the effort to take stock of the various roster rules and funding agreements between the team and the league to determine some paths to building an MLS team.

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MLS Goalkeepers or: How I Learned to Stop Worrying and Love the BOB

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MLS Goalkeepers or: How I Learned to Stop Worrying and Love the BOB

It seems like every week I see multiple goalkeepers launch a hopeful goal kick to a teammate close to the sideline, only to overhit it by about twenty yards. While fans may appreciate the invitation to be a part of the game, they’d rather not see their goalkeeper concede possession so easily. MLS goalkeeping standards aren’t the same as La Liga, but surely there is some standard, right?
 
I accessed the secret scrolls of passing statistics dating back to the 2015 season to see just how often MLS goalkeepers launch a ball straight out of bounds. For this exercise, we’ll be using the stat BOB, which stands for for “Ball Out of Bounds” because having a stat acronym with two O’s would jump ASA’s rating from G to PG and I couldn't bring myself to be the sole reason for that. We don't have the data to separate punts, throws, passes, and goal kicks but I think this still addresses the topic at hand. As such, the below BOB pulls in all 'keeper distribution, be it a goal kick launched towards midfield or a toss to a nearby teammate a foot away.
 
Over a total of 1587 BOB in 1622 games puts the average BOB/gm for a goalkeeper at .978.

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NYCFC, Expected Goals, and Fantasy Sports

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NYCFC, Expected Goals, and Fantasy Sports

It’s no surprise that expected goals is finally being talked about in the fantasy sports realm. This is great and it’s really entertaining to me because, as you might expect, it’s where we at ASA often use it the most. It’s an incredibly useful tool that can provide some quick tools for judging players when needed.

Now, let’s talk about how we’re using it.

Expected goals is, as we have well documented over the years, a measure of the opportunities and chances created by a player and their team. Porting that to the fantasy soccer realm there are terms and conditions on this that we need to consider.

Expected goals isn't a one-stat-fits-all for all metrics. Rather it’s a sum of many parts. Looking over at NYCFC and the fact that they’re killing it with the highest expected goal differential is great! But—realizing how they’re doing is even more important as that speaks to the sustainability of their success.

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Shots Not Taken: Exploring the propensity of teams to shoot from good positions

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Shots Not Taken: Exploring the propensity of teams to shoot from good positions

Do you ever find yourself yelling “JUST SHOOT THE BALL!” at the TV screen? Of course you do, you watch soccer! Sometimes it can be maddening to see your star striker make his/her way into the box, only to futz around with a pass or dribble. At times it doesn’t even matter whether that pass or dribble was successful. Does it seem like your team does it particularly bad? You’re probably not alone.

Psychologists will be quick to point out a thing called negativity bias. Basically, we probably all think our team dilly-dallies in the box more than others because we remember it better. The existence of this bias, by the way, is supported by a convincing amount of experimental evidence. But it begs the question, who is empirically more likely to shoot when they can?

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

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

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

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

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

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Who in MLS Plays the Most Vertically

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

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The 22 Stats that Explain the MLS Season so far

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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