Jamon M - MLS and footy analysis

Sure, it's not Stuver that's my concern.

Not gonna blame club PRs for trumpeting when their players do something with an aggregate stat that actually means the team is bad at something, but we've still collectively failed to define what "good" looks like for goalkeepers.

Look for something on Friday Stat Dump this week.

No team better at making slow, lethargic, sideways passes in the final third in MLS 2025.

I would be remiss not to point out that the best version of the Points - xPoints method is done by Eliot McKinley at least once or twice every season with his "good, bad, and unlucky" version.

Take a look and see what's changed since 14 games in:

bsky.app/profile/elio...

If you are wondering why the R-squared values (coefficient of determination) are so low here, I refer you once again to this important work from our analysts at @americansocceranalysis.com:

www.americansocceranalysis.com/home/2022/7/...

No team in MLS 2025 has exactly the same number of points and xPoints.

Leave a reply and let me know why you think your team is doing better or worse than their xPoints this season.

Other than that, enjoy the games!

/end

The tables can still right themselves with 6 or 9 points at stake left in the season for each team. We won't know for sure until after Decision Day.

We can use Points - xPoints to more easily see who is above or below expected right now and by how much.

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Since various team have 2 or 3 games remaining, we'll use points per game (PPG) to compare points to expected points.

Cincinnati and Charlotte show up in the upper left quadrant as over-achievers against their xPoints, while San Jose is the biggest under-achiever of MLS 2025

...so far, anyway.

3/

By just a little bit in a parity league like MLS, xPoints predicts future points better than past points do. Otherwise, it wouldn't be a useful measurement for us. 😉

As we get into this point in the season, the strength of it is less, just like when we use xG to predict future goals.

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Friday Stat Dump: xPoints.

xPlaining...er...explaining xPoints is not easy in this format, but I'll give it a shot:

At its simplest, xPoints is the average points a team gets from 1,000 game simulations based on shots and the xG of those shots vs. their opponent's.

MLS 2025 xPoints table:

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The MLS Journey So Far… Eastern Conference — American Soccer Analysis By Harrison Crow I’m not gonna lie, I wrote the Western Conference and I liked it. I liked it so damn much that I didn’t want to write about the Eastern Conference and you know why, because there...

Vizhub 2, Electric Boogaloo, @harrisoncrow.bsky.social goes east to breakdown maybe the most interesting MLS conference in a decade

www.americansocceranalysis.com/home/2025/9/...

Congrats, Kim!

Life Gets In the Way Friday will supersede Friday Stat Dump this week.

We'll return next week. Enjoy the weekend games.

This is so, so weird, and cutting Caleb is dumb, dumb, dumb.

MLS goals added table

MLS xG table

MLS Goals Added (g+) and xG Leaders ⬇️
(as of: 2025-09-23)

🤖⚽️ | #mls

Late to this, like I am everything these days, but Catalina Bush covered Avg Def Action Height in a little more detail with other contextual metrics in an article for Backheeled last week.

In it, she noted MLS teams with a balance between defensive actions and line height.

bsky.app/profile/cata...

I would say you can come to your own conclusions on that using ASA Viz Hub. But, yes, that is a likely outcome of the analysis.

No, what we are showing is that line height doesn’t affect aggregate xGA but does affect the xGA multipliers of shots against * xG per shot against.

A year ago I did a research project for a class. Using some basic linear modeling, I checked to see whether there was a significant relationship between avg height of def actions and xG/xGA — there wasn’t. Even deviations from a team’s norm didn’t matter.

Jamon does a great job of explaining why 👇

Want to see a particular team's action height details? ASA Viz Hub has you covered! Example below.

Be sure to follow @cata-bush.bsky.social and @americansocceranalysis.com for more about ASA Viz Hub and forthcoming enhancements. I'm really excited for Catalina's project!

Enjoy the games!

/end

This isn't too difficult to figure out: teams play different lines of confrontation with more or less risk. Higher risk faces fewer shots but higher xG per shot and more linebreaking passes.

Does it matter? Avg Def Action Height with xGA says it evens out.

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My hypothesis is that teams with higher lines face more linebreaking passes.

For this test, I'm going to leave ASA Viz Hub and use my own tool that I've been posting visualizations from all season.

Viola! This hypothesis seems correct.

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Which brings another interesting observation: defensive action height, at least in 2025 MLS, seems to affect total shots against. Higher lines overall face fewer shots.

You will recall that the xGA was about the same. What gives? This means that xG per Shot is higher.

Let's test a hypothesis.

4/

The previous chart is an indicator that the line height seems to have had little bearing on the quality of opponent shots in 2025 MLS.

It does, however, seem to have a little more bearing on something like total defensive actions.

I can see it: a team that engages higher could engage more.

3/

If you missed the release of ASA Viz Hub this week, check out this article: www.americansocceranalysis.com/home/2025/9/....

Viz Hub has a team scatterplot with a few comparables. One of them is Avg Def Action Height. Using this we can compare where teams are winning balls to a metric like xGA.

2/

Friday...er...Saturday Stat Dump: Using the new ASA Viz Hub to look at Average Defensive Action Height.

Quite simply this allows us to see how high teams are getting defensive actions. For event data, this is an alternative method to seeing typically how high defensive lines are set.

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Sweet. I like hate-reading.

This is amazing. I really have missed work like this.

Thank you, @harrisoncrow.bsky.social!

I've been out-of-pocket for a couple of days, but here is a great explainer thread from Catalina on the new ASA VizHub app.

I'm still running around today. Hopefully I will fit in a Friday Stat Dump (or maybe tomorrow instead). We'll definitely use this tool, though.

viz.americansocceranalysis.com

MLS "Lowest G+ 11" this season:

ST - Jovelic, KC (-3.9)
W - Sofo, RBNY (-2.3)
W - Carmona, RBNY (-2.1)
AM - Bassi, HOU (-2.6)
CM - Servania, DC (-2.1)
DM - Loturi, MTL (-2.1)
FB - Cannon, COL (-1.2)
CB - Svatok, ATX (-3.0)
CB- Fofana, NE (-1.6)
FB - Petrasso, MTL (-1.2)
GK - Bond, HOU (-6.6)