Understanding Stats in Women’s Soccer: xG, xA, and Per 90
Modern football is not what it was 10 to 15 years ago. The age of analytics has crept into a sport that has seemingly never needed it. However, with more money being pumped into the game to go with advances in technology, there was no avoiding the use of analytics and statistics.
As we enter a new era, stats are going to be used more and more to tell a story about how the match went. It’s important that we use a combination of watching the game for ourselves and then compare stats to see if that aligns with what you’re seeing in each match. Let’s dive deep into understanding soccer stats and what you need to know.
Traditional Statistics
Traditional stats are stats that most soccer fans will pay attention to, such as possession, shots, shots on target, saves, fouls, yellow cards, red cards, offsides, and corners. There are inferences you can make based on these stats, but it’s important to remember that unless you’re watching the game yourself, the stats can be misleading. For example, possession is a stat that fluctuates based on game flow.
Take the USWNT’s recent run through the Gold Cup, for example. If you hadn’t watched any of the matches and just looked at the score and possession, you might think the USWNT wasn’t as dominant as usual. In reality, they ended each match against Brazil, Canada, and Colombia with less possession, but all three games resulted in wins. Now, after having seen each of these matches, we know that all three teams were trailing the United States, which means they had to chase the game, which led to more possession of the ball. The other factor is the USWNT high press, which doesn’t require as much possession of the ball; it’s about forcing the other team into a mistake.
That’s why it’s important to watch the game and use stats as an auxiliary piece, something to aid us in understanding the game better. The issue is when people use stats to tell the whole story or to just fit the narrative they’re trying to push about a team, coach, or player.
I’ve made this mistake in the past myself. Flashback to Alex Morgan’s end in Orlando around 2017-2018, where in her last 25 games with the club, she scored just five goals. I was ready to write her off and say she wasn’t the same player and that she was past her prime. What happened instead? Alex Morgan gave birth, came back to the NWSL, and joined a brand new San Diego Wave franchise, scoring 15 goals in 17 games in the club’s first season.
How could I possibly ever doubt Alex Morgan’s greatness? Yet, for a brief period, because of how the numbers looked, it seemed plausible. The point here is that no matter how many stats you put together, you’ll be able to gain insight into certain situations regarding a player or coaching strategy, but you’ll never get the full story.
Getting into the advanced stats can be incredibly daunting. Unless you’re completely obsessed with stats and a natural with numbers, or you want to understand parts of the game better, it’s worth checking out this full glossary. There are three important stats I’ll focus on: xG, which is expected goals; xA, which is expected assists; and Per 90, which is goal output per 90 minutes.
xG (Expected Goals):
This might be the most popular advanced metric to use, and it’s incredibly accurate because it accounts for the different types of chances teams can create. We’ve all seen the kind of game where your favorite team is dominating the other, but they just can’t score. A good example is Angel City’s home opener against Bay FC. Angel City had at least three to four close chances where the ball was either cleared off the line or hit the post/crossbar. If you go to fbref.com and check out this link you’ll see what the expected goals are for the game. Bay FC won the game 1-0 but Angel City’s expected goals were 1.9. Meaning if things went according to plan and Angel City took their chances, they would’ve won this game 2-1.
This helps because it confirms the idea that Angel City was dominant, created more chances, and should have won the game. However, it also hurts knowing that they should’ve won the game according to the stats but didn’t. This metric helps identify what the score of the match should’ve been but also highlights why we still have to play the game; games aren’t won on paper.
xA (Expected Assists):
This means the same as what expected goals mean but for assists. It’s a stat for the playmakers and creators who passed the ball to a teammate before the shot was taken. If you take a look at the current top 3 in the NWSL in xA, you’re going to see Trinity Rodman, Claire Emslie, and Carson Pickett. Based on how this season has started and having watched all three players extensively, the xA metric is spot on.
We’ve seen Trinity Rodman create a ton of chances already, and the same can be said for Pickett and Emslie, who put in a boatload of crosses and are constantly playing through balls to teammates.
Per 90
Per 90 means per 90 minutes, which is the length of a match. This metric is used to determine a player’s output for the entire match and acts as a per-match average. It creates a level playing field for the players who have gotten more minutes to have the opportunity to either score more goals or dish out an assist. As an example, we can use goals per 90 minutes to highlight Sophia Smith’s dominance last season and why she won the Golden Boot. Sophia Smith edged out Kerolin by one goal, scoring 11.
Looking at the goals per 90 minutes, the gap should’ve been even bigger. Smith had a ridiculous 0.85 goals per 90, whereas Kerolin was the next closest at 0.54. Based on those numbers you can say that Kerolin’s scoring at least one goal in every other game. Whereas Sophia is almost at a full goal per 90 minutes, meaning if she starts the game there’s an even higher chance she’s going to score compared to Kerolin.
Now, Kerolin won MVP because she truly was more valuable for North Carolina; they likely wouldn’t have made the playoffs without her. However, when you look at the stats, and you see how Smith terrorized defenses last season, she just as easily could’ve won MVP for the second straight season.
There are so many great metrics out there that we can use to better understand the game. Expected goals, expected assists, and per 90 stats are a few of the countless advanced statistics we have at our disposal. While these stats are an incredibly useful tool, it’s important to find the perfect combination of using stats with what you see on the pitch yourself. The numbers can sometimes mislead as well, it’s important for you to determine how you want to use statistics and analytics to your advantage when talking about the game with coaches, teammates, friends etc.
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