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. For the second year, we decided to do the best we could to get behind the scenes and asked club analytics staff for their input.

A 10 question survey was sent to a member of the analytics staff at the 24 MLS clubs that have at least one analytics staff member, including 2025 MLS expansion club San Diego FC. Responses were anonymous and teams were allowed to skip any question or comment. In the end, we received responses from 18 clubs which were collated and summarized below. Note that both of these are an increase from the 2023 version where only 21 clubs could be contacted and 14 responses were recorded. Thanks to all of the analytics staffers who responded for their time and thoughtful responses.

You can see the exact form of the survey 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?

“Tough question, as we feel the number of advanced teams has increased considerably over the last year. We have a list of 10ish teams now who we consider advanced analytically so it is hard to narrow this list to just 5.”

While the overall level of analytics practice has increased across the board, Toronto, New York Red Bulls, and Seattle are at the top of the list of the most analytically advanced teams like last year.

“As long as Seattle and Toronto keep committing resources and nailing their hiring, it's really tough to make up ground on their 5+ year head starts while only keeping pace in terms of headcount. No MLS club in this new spending era has really leaned into analytics to the extent they're building departments sized like NHL or NBA teams. Other than the multi-club groups who can compete off the backing of much larger orgs.”

However, Toronto has climbed over Seattle for the top spot and the Red Bull’s analytics setup has gained more recognition. Following their MLS Cup victory, Columbus went from being seen as the 15th most analytically advanced team to the 4th, quite an ascent in a single year.

“I'm not certain how things have changed at Miami since Sam's left - but they were doing some pretty cool stuff while he was Director.”

Following the departure of Sam Gregory to US Soccer, Inter Miami’s analytical reputation took a large hit, going from the 4th most advanced to now getting 3 votes as least analytically advanced.

“The list of potential selections here is getting smaller and smaller. That probably now means that just having an analytics department at all is table stakes, and the teams that differentiate themselves will do so on sophistication of methods, on processes, and on overall buy-in.”

The teams seen as least analytically advanced last year, Minnesota, Dallas, Montreal, and Portland, continued into 2024. With Minnesota hiring an analyst, perhaps their reputation will rise in future surveys.

Of note, after hiring former US Soccer and Monaco analytics head Tyler Heaps and soliciting applications for a number of analytics positions, San Diego got a vote for most advanced team a year before their inaugural season.

We also want to re-emphasize the it is impossible to know what a club is doing from an analytics standpoint from the outside. Furthermore, “most analytically advanced” and “least analytically advanced” are subjective and could be interpreted differently by individual survey takers. A club that is seen as not analytically advanced may actually be very advanced and just don’t make it known to the outside world.

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

While having advanced analytics is great, more important 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.

“This is of course hard to say from the outside, especially given all the different areas analytics can be impactful, many of which are not visible.”

Colorado, despite being 5th most analytically advanced in the eyes of MLS data practitioners, came out on the top for putting analytical insights into practice. With Fran Taylor as Sporting Director and a team of analysts on staff, it is not hard to see why the Rapids would come out on top here.

“Also thinking maybe Houston? Feels like Pat Onstad has really been open to listening and incorporating SRC into decisions even if they do not have the infrastructure the top teams have.”

4. What player do you believe is generally underrated, based on your quantitatively informed opinion?

As solving soccer is notoriously hard, it is not a surprise that there was not a lot of consensus among the analysts surveyed on who are the underrated players in MLS. That said, four players received two votes. Vancouver’s Ali Ahmed has already almost eclipsed his minutes from last year and is sporting a 0.04 g+ above average per 96’ from the wing all on a $100k contract. Gaston Brugman is obviously a better known quantity on a TAM deal for the Galaxy. However, he may get a little overlooked by his midfield comrade, Riqui Puig, despite his 0.10 g+ above average per 96’ being 2nd in MLS among defensive midfielders this season. Matt Freese went from career backup to starting and having a Turner/Petrovic-esque season for NYCFC. Minnesota’s Robin Lod has always been an above average MLS player, but this year is putting up g+ on par with the 2022 version of Hany Mukhtar who was MLS MVP.

5. What player do you believe is generally overrated, based on your quantitatively informed opinion?

Similar to the underrated list, there was little consensus on what players are overrated based on analytics. As you might expect, this list is full of Designated Players and big free agent signings. Interestingly, one staffer included analytics darling Brian White, which may be a reflection on him being an analytics darling.

6. With the league data provider transitioning from Opta and Second Spectrum to Sportec, how satisfied are you with the state of the data provided to teams via this partnership?

Prior to the 2023 season, MLS announced that Sportec would become the official event and tracking data provider for the league, replacing Opta and Second Spectrum, respectively. With over a year since the transition, it did not go well, although it appears that it has gotten a bit better.

“Tracking data quality once it is cleaned is very good quality. But there have been many hiccups along the way with inconsistencies in reliability during matches and lack of awareness in simple answers to questions from the analysts. Event data from matches that connects to the tracking data is getting improved and is beginning to connect to the tracking data, but again, there are some inconsistencies that make it very frustrating at times to use. We generally use tracking paired with our data provider to keep consistency in our stories.”

However, many clubs are buying data from outside providers rather than relying on what is provided from Sportec.

“The scope of the metrics that STS covers compared to other providers seems extremely low with most being largely useless.”

“We don’t use their synced feed either because we have infrastructure built on sb [StatsBomb] events.”

7. What public resources are most helpful for evaluating teams and/or players?

OK, so we may have baited this question a bit (though the response is freeform!), but American Soccer Analysis was the runaway response for what public resource MLS data analysts use. Following closely behind is FBRef, which was noted for its speed and ease of accessing data on a player or team. Despite popularity in Twitter arguments over which player is better based upon a crowd sourced value, Transfermarkt isn’t considered all that helpful.

“We rely more heavily on internal resources than we do public sources, but at different times we have looked to Transfermarkt, ASA, Opta Team Ratings, and (before it disappeared) the 538 Global Soccer Rankings.”

8. Does your team use raw player-tracking data to create proprietary in-house metrics?

It seems that tracking data may be making the transition from data type of the future to data type of the present. The vast majority of teams are currently using tracking data to generate in-house metrics.

“Yes, and its used quite consistently throughout the first team to make decisions for match preparation”

“Using tracking data to analyze our team's compactness in/out of possession and team formations in/out of possession.”

“Not currently but work in progress.”

9. Would increased data and/or code sharing across clubs have a net positive impact on the league?

Well, everyone knows clubs don’t share methods. What this question presupposes is: maybe they should? And overall analysts think it would be a positive thing for the league, although they don’t think it will ever happen.

“A net positive, but I don't see this happening. We are way too competitive with extremely tight margins that I don't see teams being okay with the sharing aspect.”

“I have doubts on whether code-level collaboration would ever happen between competitive parties, but I imagine we've all ended up using similar tools already because we're trying to solve the same/similar problems. We're (probably) all riffing in our own bespoke ways off a fairly rich community of open-source soccer data enthusiasts.”

“To the point of collaboration in general: more opportunities to network with and learn from our counterparts at other clubs (organized by the league, a la the NFL technology conference) would be appreciated.”

“This isn't a yes/no question dweebs. Net positive.” [When the survey first went out this question was worded poorly and this response gently let us know]

10. If you could wave a magic wand to improve analytics usage at your club through one of the following, which would you choose?

When asked what would make analytics usage at their club better, most of the analysts asked for more staff.

“We have so many projects to complete at the club in the long term with even more questions that arise throughout the day. Having a bigger staff helps us to narrow the scope for each staff member and makes sure that we are hitting the questions for everyone at the club.”

“We have been fortunate at our club to scale up our resources and team as we've integrated data throughout several domains at the club. At the same time, the scale of analytics teams in sports like baseball show how analytics/R&D departments can grow beyond what any MLS team has currently built to my knowledge.”

“Perhaps a bit idealistically, I think that the ideal approach for data in a football club is a central data science department that is responsible for facilitating data informed decision making across the entire organization, including soccer operations and business operations. As one football example, I think of what Sevilla is doing. However, if a club chose to adopt an approach like this.. then it would take a larger team than what exists in many professional clubs.”

“Having a dedicated data engineer would also be amazing.”

However, increased buy-in from decision making was still something that would improve a club.

“Increased buy-in being using data at the start of our processes as opposed to at the end to confirm biases.”

“You might add "Coherent organizational structure", as an additional option. It seems to be the case that many clubs don't have a rigorous way to incorporate data into their workflows. Or maybe they don't have any structured ways of working at all. So there the roadblock is less "buy-in" and more an environment that lacks overarching strategy to govern which and how decisions get made. It's incredibly hard to do good analytics work without a strong point of view as to how the club can ultimately succeed in its goals.”

And getting data can still be an issue, especially for recruitment purposes.

“MLS sucks for buying data. They all charge by the league and were at a level where there’s a lot of potentially interesting leagues of equal relevance. A list of 20 leagues I want, the 21st is no less important than the 10th.”

“SkillCorner would be valuable if we could get the budget for it.”

Conclusion
At the end of the survey we asked the analysts if they had any other comments, one summed up the ongoing process of data driven decision making:

“Our club generally makes a lot of good decisions both on/off the field, which the "analytics" generally agree with, but [that] doesn't mean we can’t better leverage data at every level.”