A Guide to the ASA Vizhub

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What to know about the newest ASA platform

Catalina Bush

Have you ever wished that you could picture the game in front of you? Not as a broadcast, or a highlight video, but on a sheet of paper? All of a midfielder’s passes, your striker’s shots, an opponent’s tendencies, visualized? Previously, that required access to data, coding knowledge, and hours spent reinventing the wheel, just to see the most fundamental aspects of our game in graphic form. Not anymore.

Introducing American Soccer Analysis’ newest public platform: ASA VizHub. Making use of ASA’s extensive database, VizHub does the hard work for you, allowing for easy generation of industry-quality graphics.

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MLS Roster Profile Releases Now Available on Github

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This week, we published extracts of Major League Soccer’s roster profile releases to our GitHub. In addition to parsing the information the league provides about each club’s roster construction and each player’s contract status, we’ve also matched the teams and players to their respective American Soccer Analysis ID. For our fellow programmers and spreadsheet enthusiasts, this provides a link to performance, salary, and other publicly available data, which continues to be accessible through resources like itscalledsoccer.

In the same repository, we’ve open-sourced the code used to produce these extracts. Users are encouraged to report any bugs, recommend improvements, and/or contribute themselves.

Looking ahead, we also intend to incorporate this data into the application, as well as keep pace with future releases from MLS HQ. Stay tuned for those updates.

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State of NWSL Analytics: August 2025

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State of NWSL Analytics: August 2025

Over the last several years, the team here at American Soccer Analysis has been assessing the State of MLS Analytics by combing through team websites, scouring LinkedIn, and asking those who would know about how many people each team employs. This year, we’ve taken our talents to the women’s game to determine the Tiers of NWSL Analytics.

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2025 MLS Analytics Survey

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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.

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State of MLS Analytics: July 2025

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Ten years ago, if an MLS club had one person “doing analytics” it was a big deal. Now a majority of clubs now have multiple full time employees in actual analytics departments and a team is seen as backwards if it don’t have at least one staff member primarily focused on game analytics. Every year, we comb team websites, LinkedIn, and ask those who would know about how many people each team employs to determine The Tiers of MLS Analytics.

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Using Goals Added Subcategories for Player Evaluation

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By Paul Harvey, Mike Imburgio, Ben Bellman

Since it was introduced, Goals Added has been superior to most of the publicly available algorithmic rating systems, such as those provided by WhoScored, SofaScore, or Fotmob. The additional context of just how much each action is contributing to the likelihood of scoring provides more information than simply evaluating a player based on the number of certain actions (the value of those actions also weighted by a human being, introducing bias). Similarly, basing scoring on a known currency of goals is simply much more intuitive than some nebulous 0-10 rating scale.

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Breaking it down: g+ Sub-categories

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By Paul Harvey

When Goals Added first entered the public sphere, it marked a major shift in how soccer could be interpreted using data. Although similar models had been developed, it was hard to get data and turn that data into insights. In terms of a unit of account, there are few expected value models as accessible and understandable as g+.

Despite the initial success, though, g+ is still an opaque measure in many ways. Much like it’s spiritual father figure, WAR, you lose some of the how of a player's game. Although on the broad level the numbers are clear, even a simple comparison between two players can be hard to do just with the six Goals Added categories. Sure, we can say that one player has added more value via passing than another one. But what kind of passes led to that value? What’s the actual on field difference between the two players?

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