2025 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. For the third straight 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 25 MLS clubs that have at least one analytics staff member. Responses were anonymous and teams were allowed to skip any question or comment. In the end, we received responses from 17 clubs which were collated and summarized below - note that not all questions were answered by each team. Thanks to all of the analytics staffers who responded for their time and thoughtful responses.
1. Who are the 5 most analytically advanced MLS teams?
2. Who are the 5 least analytically advanced MLS teams?
For the first time in an annual survey, long time analytics leaders Toronto and Seattle have fallen outside the top two analytically advanced teams. Colorado takes the top spot, with almost 70% of analysts counting the Rapids in the top five most analytically advanced clubs, an increase from 25% and 39% in 2023 and 2024, respectively. Columbus is a close second, continuing their ascent up the charts after only receiving a single vote in 2023.
“San Diego did a great job right out the gate of hiring [Data & Insights Analyst] Grant [Rhines] and building a team with data in mind. I think it's oversimplifying it to say their RTD backing puts them in the top 5. It certainly helps but they have top talent there as well.”
San Diego jumped into the top five in their first full season following naming analytics-focused Tyler Heaps as their Chief Soccer Officer and building out a strong analytics department out of the gate.
“Investment. Toronto and Seattle have large head starts, and no club has quite spent enough on analytics to overcome yet.”
Notably, while still well regarded, Toronto has seen their reputation take a hit, being listed on only 31% of ballots compared to 78% last year.
One notable aspect of the top teams is that their use of analytics is publicly acknowledged with the biggest gainer here being Minnesota United. The Loons were listed as least analytically advanced team in all of MLS for 2023 and 2024 but moved into the top 10 with no votes for least analytically advanced in 2025. No doubt their association with some no longer anonymous soccer analytics consultants and prodigious use of long throws, long an analytics hobby horse, have something to do with that.
“I could easily be wrong, since nobody really knows what anyone else does.”
“Infrastructure in place with multiple data-specific focused employees. Also transparency of their use of analytics. Several clubs may have similar department structure but not outward facing to the public.“
As always, we can’t really know but the same handful of teams again find themselves judged as the least analytically advanced. Interestingly, despite having an actual department and being well regarded in previous surveys, Bruce Arena’s hiring in San Jose has caused the Quakes to plummet in 2025.
“No data folks!!!”
“A few clubs in the league still don't have analytics departments (or had excellent individuals in them, and then fired them), which means that the least analytics advanced clubs in MLS are still those that just haven't put resources towards it.”
A handful of teams continue to bring up the rear in their use of analytics. Portland, Montreal, Kansas City, and Dallas still don’t have any known analytics staff.
“Have heard enough about Miami that they are... player-led... in their recruitment.“
Following the departures of Sam Gregory (US Soccer) and Jordyn Kaplan (Boston Legacy), perhaps the only analytics for recruitment that Miami is using is counting the number of DMs between a certain GOAT and his friends.
3. What team most incorporates analytics into its decision making?
While having advanced analytics is great, more importantly 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.
“The decisions they make are clear and well thought out. It's clear when they sign a player, there are data points that back up, as our analytics nearly always like that same player.”
Colorado, Columbus, and Nashville lead the pack as the clubs seen as having analytics most influence their decisions.
“Again, hard to know, but the CSO/GM heavily influences this decision.”
Fran Taylor and a growing team of analysts clearly demonstrate Colorado’s analytics focus.
“I think a few different clubs are near the top here but I'm going to give the edge to [Director of Analytics] Alex Mysiw and the great work he and his team do with Columbus. Alex has been (appropriately) open with how Columbus uses data through presenting at a couple of different analytics conferences. It's clear they incorporate data in how they work and value it highly.”
4/5. What player do you believe is generally overrated/underrated, based on your quantitatively informed opinion?
As solving soccer is notoriously hard and there are a lot of players, it is not a surprise that there was not a lot of consensus among the analysts surveyed on who are the overrated and underrated players in MLS.
That said, Ohio was the home of the most overrated players in Columbus’ Darlington Nagbe and Cincinnati’s Matt Miazga.
Nagbe, who more than once has been deemed most underrated by MLS players, has long been an analytical enigma. While he can control the tempo of games, almost never loses possession of the ball on the dribble or by passing, and has been the heart of four MLS cup squads, the numbers just don’t see it. Since he became a full time center midfielder, he’s had two positive g+ seasons, with Atlanta in 2018 and 2019, and then only just (0.01 and 0.02 g+/96 compared to average), and has been negative his entire time in Columbus. Last season, his -0.11 g+/96 compared to average was the worst among center midfielders. ASA even created a whole new metric, g+ boost, in order to try to figure out why he seemed so good by eye, but the numbers never backed it up.
For the second straight year, Ali Ahmed was seen as the most underrated player in MLS by analytics staffers. Ahmed is on one of the most team-friendly contracts for a non-homegrown player in MLS, making only $150k this season with an option for 2026. His 0.08 g+/96 compared to average is among the top 10 wingers this year.
Vancouver’s Sebastian Berhalter was the only player on the overrated and underrated lists.
“This year - Sebastian Berhalter. Though the recent call up may indicate otherwise [underrated].”
“I guess Seb Berhalter. Like he's really good at what he's good at.. but the MVP and a national team starter?”
6. What recruitment mechanism do you think is the most under-utilized?
While MLS roster rules may not be as complex as those in the NFL or NBA, they are far more complex than the vast majority of soccer leagues worldwide. Rather than just paying more money in transfer fees and salary, clubs have the opportunity to maximize the myriad roster mechanisms to gain an advantage over their in-league competitors.
Two recruitment mechanisms were seen as most under-utilized by MLS analytics staff, MLS Next Pro and better data and methods.
“MLS Next Pro pathways. [There is] no affect on the first team cap gives you basically however many free shots at signing young players with high potential as you have the discretionary spend for. e.g. Micael from Houston. Charlotte is using it, but not too many others.”
MLS Next Pro provides a unique proving ground for young players to prepare for MLS. Some teams, like Columbus, have used it to great effect to identify and develop talent for the first team while others, notably DC and Montreal that don’t even have teams don’t, have not.
As you might expect, MLS analytics staff also put better data utilization at the top of things that MLS teams could do better for recruitment. Examples included better use of tracking data, physical data, positional profiles, and finding outlier characteristics that can better predict player success.
“MLS SuperDraft; understandably disregarded by clubs with strong academies (NYRB and Philly), otherwise I think the league disregards the level of talent that's available on roster-friendly deals.”
7. If you could change one MLS roster rule, what would it be?
Two most popular changes that MLS analysts recommended were to change the roster limits on how players hit the cap (6) and to get rid of discovery lists (4).
For roster designations and the cap, there was a general consensus that the league would benefit from loosening both.
“Flat Cap, with transfer fees hitting less than they currently do. remove TAM and U22 spots and let teams build their rosters however they want to. There are SO many players who could be good in MLS for something like 2-3 million transfer, 500k-1million salary, but you can't sign any of them!”
“Get rid of TAM/GAM. Set a cap number with DP/U22 slots for exemption and just let clubs make sure they get below it. Would ideally coincide with raising the cap and/or increasing the number of exemption slots but you just asked for one rule change so I'll leave it there.”
“Remove the 3 DP +3 U22 restriction and make it more flexible 6 DP/U22”
As far as discovery lists - multiple submissions said to just get rid of them.
After seeing the flurry of cash trades this season, one analyst wants more.
“Raise the number of "trades for financial consideration" that a team can make per year”
8. Does your organization have more or less analytics buy-in than this time last year?
In what will now be an annual question, the majority of MLS analysts (12) said that the level of analytics buy-in at their club was increased (12) rather than decreased (4) from last year.
“Similar resource level - but the use and interest by ownership specifically has improved. Hopefully the resource allocation follows.”
“More, if at all? Changes in the front office did not result in more buy-in on [the] recruitment side, which is disappointing.”
9. What functions are your analytics team involved in?
We know that with limited headcount, MLS teams utilize their analytics staffers for multiple functions.
Unsurprisingly, all 16 of the clubs that responded to this question work on the two main pillars of soccer analytics, player recruitment and match/opposition analysis. Almost all also participate in cap management and roster strategy within their clubs. While sports science can often be a whole different department within a club, the majority of analytics staff at MLS clubs still work with sports science data. A minority of clubs also have their analytics staff involved in player development and business side functions.
10. Are you currently using AI (specifically LLMs or agents) to accelerate your daily work and/or analytics processes today?
Everyone with an MBA loves AI, but it has not been widely implemented at most MLS clubs.
“Definitely useful for coding. The stuff in databricks is pretty nice.“
“Almost exclusively ChatGPT for coding.”
“Yes, a bit. It's helped streamline the development of applications for various uses.”
Like probably most small to medium sized businesses, the use of AI at most clubs is probably mostly restricted to commercial or open source models. Large language models can be very useful for simple or well documented coding tasks (personally, my favorite use case is regex syntax), however results can be buggy and often wrong for more complex coding tasks. Regardless, they can be useful for accelerating the implementation of certain processes which seems to be the case at most MLS teams at the moment.
“I believe LLMs have a long way to go, but if you aren't researching best practices, you may fall behind. They are still quite prone to errors, and in today's MLS, there is no room for that. I'd rather take my time and make sure I am giving accurate answers that are slightly slower than using AI and not taking the time to research it myself.”
Some MLS clubs are being a bit cautious with the use of AI.
“Internally built LLM model built on our proprietary metrics”
And at least one club already has their own model.
Conclusion
At the end of the survey we asked the analysts if they had any other comments, we only got one this year:
“hi”
Hello to you, anonymous MLS analytics staffer. Thanks for answering our questions.