explainer: btvc g+ pass networks & shot charts

Since, pretty much, ever, there’s been a significant lack of data to analyze and women’s soccer. This was exacerbated when FIFA and Opta made an agreement that torpedoed data once housed with FBref. This was a massive loss, as it was the only easily accessible purveyor of both archival and real-time data. That sucks.

Thankfully, my friend at American Soccer Analysis are here to help. With their data access, I’m able to provide Shot Charts and Goals Added (g+) Passing Networks for every NWSL match. Below is a sample of what these visualizations look like, and the data that’s packed into each one. They’re fun tools for analyzing and understanding a match, and I am thankful to Cata Bush for helping me provide them.

shot charts

To me, soccer is a game about trying to generate quality chances to put the ball in the opponent’s net. Not every team plays with that as its primary focus, but for the most part that’s the game. So instead of xG race charts, which are useful in understanding the flow of a game but minimal details beyond that, this season I’m switching to a fully loaded shot chart.

You will often hear me talk about shot quality a lot. So the box to the right of the graph will be a list of good/great shots, as calculated by xG. The average shot quality across the NWSL was about .10, so we set this to list all shots over .15, who took it, at what minute, and the end result of the shot.

These charts give a lot of information about team’s shots throughout a match, and I’m excited about using them throughout the season.

g+ passing networks

The pass networks include custom data from American Soccer Analysis in order to spotlight passing and receiving impact. These are measured using ASA’s goals added (g+) metric, which calculates how much each touch—in this case passing & receiving—changes their team’s chances of scoring and conceding across two possessions.

(If you want to fully nerd out, here’s a deep dive into the methodology of g+.)

When assessing these charts I look for the average passing position of each player, and the average positioning of the team. Are players high up the pitch or closer to their own goal? This can reveal either a tactical choice, or impact of pressure from the opponent. You can also get a sense of a team’s width. This can also suggest tactical decisions or impact of pressure (player bubble colors and passing volume between players can usually provide further hints as to whether a team is finding success, or struggling against a team’s pressure and/or out of possession shape).

These are fun data visualizations that help in understanding a match just a little bit more deeply. I drop these in every subscriber’s inbox 24-48 hours after the final match of the weekend. They’ll also come with blurbs from me about each graphic. It’s a good time, hope to see you there.

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