2023 MLS Analytics Survey

/
2023 MLS Analytics Survey

Every year, we update the State of MLS Analytics by putting teams into tiers based upon how many analytics staff they have. However, the number of analytics staff members doesn’t necessarily say anything about the quality of work that a club is producing or if analytics is being incorporated into team decision making. And unfortunately, we can never really know what is going on inside a club’s analytics department. This year, though, we decided to do the best we could to get behind the scenes and asked club analytics staff for their input.

Read More

The State of MLS Analytics Ahead of the 2021 Season

/
The State of MLS Analytics Ahead of the 2021 Season

This is an update to last Summer’s article on the state of analytics at MLS clubs. The last few months have been a big one for MLS analytics signings. Harvard’s Laurie Shaw was hired by City Football Group, former Opta and SportLogiq employee Sam Gregory took his talents to Ft. Lauderdale, Cory Jez transferred from the Utah Jazz to Austin FC, Nikos Overheul moved to Vancouver after working for StatsBomb and Smartodds, and American Soccer Analysis’ own Sam Goldberg and Kevin Minkus were hired by New York Red Bulls and the Chicago Fire, respectively. Given this, teams are polarizing into the haves and have nots. In this update I’ve dropped the Tier 1.5 “Definitely Know What xG Is“ as teams in that tier moved up.

Last year, Kevin Minkus wrote Soccer Analytics 101 over at MLSsoccer.com where he defined analytics as “using data and statistics to better understand something.” For the purposes of deciding what MLS teams have an analytics staff member the “something” is player recruitment and tactical analysis. I’m talking about using numbers and mathematical models (e.g. xG, xA, g+) to help evaluate transfer targets and team and player performance.

Read More

MLS according to g+: The Overperforming, the Underperforming, and the Ugly Part 3

/
MLS according to g+: The Overperforming, the Underperforming, and the Ugly Part 3

We have reached the conclusion of the 2020 MLS season, and it happens to coincide with the conclusion of the long, LONG 2020 US election. And the two things share a lot in common - first and foremost among them being the all-important question of “who won?” and “who lost?”

But when the contest is at its end, or a season is nearly over, hand-wringing and analysis is all that’s left - the ‘woulda-shoulda-couldas’ of the world that keep a veritable army of pundits employed in our country. The most important thing for these folks to look at is underperformance: how did we do this thing, expecting it would have a certain result, and not get the desired result? For the election, a few things obviously underperformed; namely, pollsters, who had predicted a robust blue wave that did not manifest; and Democrats, who faced a let down across the country, from the results of the presidential election in Florida to the Maine senate race between Sara Gideon and Susan Collins.

Read More

MLS 2020 According to g+: The Overperforming, the Underperforming, and the Ugly, Part 2

/

On part 2, we begin to get into the numbers you’ve been raptly anticipating - the stuff that you can take to Vegas and possibly blow your next Coronavirus stimulus check on. Vegas likes numbers, but I’m not sure they’ve gone down the internet rabbit hole far enough to discover this website yet.

Read More

MLS 2020 According to g+: The Overperforming, the Underperforming, and the Ugly, Part 1

/

Ever since the first coach had the first microphone stuck in his or her face, or the first pundit expounded upon their local sports team, folks have opined that “we were better than the results” or “we should have won that game.” And for a long time, you would pretty much have to take their word on that.

But the new g+ metric, and the even-newer aggregate measure of g+ that ASA’s Mattias Kullowatz (twitter: @MattyAnselmo) and John Muller (@johnspacemuller) rolled out last week has given us a tool that lets us actually say with real certainty that a team is better, worse, or exactly what the win-loss results show. In other words, g+ is a giant neon sign that blinks “Regression Ahead” or “We wuz robbed, again.”

Read More

The Great Goal Kick Shift

/
The Great Goal Kick Shift

During the Renaissance era, the English language went through a revolution where vowel pronunciation radically changed. This was known as The Great Vowel Shift, and ultimately led to modern English. Similarly, in the late 2010s, goal kicks were revolutionized in what I am calling The Great Goal Kick Shift. Seemingly a worldwide phenomenon, the location where goal kicks were taken rapidly shifted from their traditional location at the corners of the 6-yard box towards the center of the field.

Read More

Cracking the Code: Analyzing the Audi Player Index

/
Cracking the Code: Analyzing the Audi Player Index

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