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.

Similar to how ESPN does it for the NFL, a 10 question survey was sent to a member of the analytics staff at the 21 MLS clubs that have at least one analytics staff member. Responses were anonymous and teams were allowed to skip any question or comment. After a week, we received responses from 16 clubs which were collated and summarized below.

You can see the survey questions and submit answers yourself here.

1. What are the 5 most analytically advanced MLS teams?

2. What are the 5 least analytically advanced MLS teams?

“It's probably a good thing for the league that it was so hard to select only 5 here.” wrote one club. However, the two teams with the longest and best known analytics departments, Seattle and Toronto, unsurprisingly topped the list of the most analytically advanced teams. Of note, despite being a one person department - Sam Gregory’s Inter Miami are very well regarded in the league.

Multiple clubs commented that choosing teams for either list was very hard as “MLS teams are very resistant to sharing with each other or working together, at least in my experience.” Additionally, a couple clubs didn’t answer who is least analytically advanced because, “It is very possible that some teams that we don't hear anything about publicly are actually doing great work in the analytics space, but perhaps have chosen to not share it.”
That said, there were a few teams consistently ranking in the bottom five for use of analytics. “I imagine/hope that "some" sort of data within their decision making and analysis of performance - However, from what I've seen *coughs* Montreal *coughs* there are a few clubs who do it poorly.” Apparently, Minnesota are held in similar regard. Interestingly, Charlotte fell near the bottom of the list, but while this survey was in the field they announced the formation of a three person department, perhaps their perception around the league will improve in the coming years.

3. What team most incorporates analytics into its decision making?

While having advanced analytics is great, what really can make the difference is if a club incorporates analytical insights into their decision making. You could have the best data scientists in the world, but if your club president makes transfer decisions based on a crowd sourced player valuation website, then it really doesn’t matter.

Here, there were no run away clubs, but again Seattle came out on top. “Red Bulls and Sam have a clear advantage - a worldwide network they can draw from, multiple staffers, and clear profile of player they want which helps their filtering process substantially.” wrote one club. Praise was also given to Miami, “I picked Miami as I believe Henderson's rebuild and Gregory's infrastructure have flipped one of the most difficult situations in MLS history.” and the future of Atlanta, “Atlanta under Lagerwey could really move in this direction.”

4. Is finishing a thing?

One of the foundational truths of American Soccer Analysis is that finishing is not a thing, but do MLS analytics staff agree?

While the headline number is that finishing is a thing, it’s more nuanced than it seems. “Of course it is. It's just not that important.” and “Yes, finishing is very clearly 'a thing', in that some players have greater technical ability than others. However, we place this alongside 2 important considerations: 1) In the long run, the importance of a player or team's 'finishing ability' is less important than the effects of chance volume and chance quality. 2) Measuring players' "finishing ability" is challenging, particularly because of the small sample sizes at a player level.”

5. What position is the easiest to evaluate quantitatively?

Striker (10)
Central Attacking Midfielder (3)
Winger (2)
Goalkeeper (1)

Strikers were the runaway winner here. As one club summed it up: “Goals are important, in my humble opinion.”

However, central attacking midfielders also received a healthy number of votes: “Goalscoring, chance creation and ball progression are all easy to quantify and a good 10 does all of those things.”

6. What position is the hardest to evaluate quantitatively?

Center Back (13)
Center Midfielder (1)
Goalkeeper (1)
Full Back (1)

As you may expect, center backs were the consensus pick for hardest position to quantify performance. “Defensive metrics and data is wishy-washy, so GK could also apply here.”

7. Does your team use analytics for in-game decision making?

Yes (10)
No (5)
Abstain (1)

“New data provider has made that difficult. Previously did.” wrote one club. Another: “Yes, but more so on the side of in-game strategizing beforehand and throughout the season more than live decision making based on data streams.”

8. What programming language/tools do you primarily use?

Python (10)
R (6)
Excel/Google Sheets (0)
Julia (0)
Javascript (0)
Other (0)

Python is the preferred primary language of MLS analytics practitioners, with R being preferred by a substantial minority. In reality, clubs will use many languages or programs depending on what the data is, how it is being delivered, and to whom.

“Selecting only one answer here was hard. It's a battle between Python, R & SQL and the winner depends on the use case.”

“R, Excel, Databricks, Tableau, Microsoft Power BI”

9. How do you think MLS compares in its use of analytics to other soccer leagues around the world?

The general consensus among analytics staff is that MLS as a whole is probably among the world leaders in acceptance of analytics, however, the best in Europe (think Liverpool, Barcelona, etc.) will beat the best MLS analytics departments.

“Third, behind EPL and Bundesliga. Close with [the English] Championship.”

“In terms of the ratio of dollars spent on analytics (data, salaries, tech etc.) vs. dollars spent on player salaries I believe we would have the highest ratio of any league in the world. But we still obviously fall behind the Premier League and other top European clubs in terms of overall investment and sophistication.”

“As a whole, we'd suggest that MLS likely ranks further toward the 'analytically advanced' than the 'analytically naive' end of the spectrum. Given that some of the staff sizes and organizations are smaller than some of the major European clubs, analytical staff may be closer to the major decision makers (or in some cases, becoming a major decision maker). However, in any league there will be teams that land across different parts of the 'analytically minded' spectrum.”

10. How do you think MLS compares in its use of analytics to other North American major sports leagues?

Across the board, MLS staff believed that MLS is behind MLB, the NBA, and the NFL in terms of use of analytics, but may be about on par with the NHL.

“Wayyyyyyyyyy way behind baseball and basketball. Probably equal to hockey but probably the worst of the major 5 leagues.”

“I think as of now it’s last in the major North American leagues in terms of adoption and understanding. It was a struggle to think of five teams that I would say are analytically advanced. Most teams are getting at least one analyst but that is so different than actually having data be a driving force in decision making. To have analytics be a mainstay at your club, it has to start with ownership and most MLS owners are very hands off these days”

“MLB and NBA are clear 1 and 2. NFL and NHL are also ahead of MLS in terms of financial backing and infrastructure, e.g. data engineering/dedicated software developer roles are a lot more common and NFL has things like the Big Data Bowl (Hard to understand why MLS doesn't have a Data Bowl or Hackathon). Front office and coaching buy-in between NFL/NHL/MLS are pretty similar.”