I have a character in my book, Michael Caley, who helped popularize expected goals. He worked at SB Nation for a while and has been blogging and tweeting about xG for a long time. He came from baseball. He was a huge Red Sox fan and was on message boards when [baseball analytics blog] Fire Joe Morgan was at its heyday, just arguing with people about stats. And then, watching the World Cup, he started to wonder if the kinds of things that were being applied to baseball could be applied to soccer. In baseball all the initial work is you have runs: You see that runs are being scored. So then you work back from that. You want to find what creates runs. That’s where all the work in early baseball analytics comes from. Famously, when you’re looking at batting average, you’re not seeing the whole picture because you’re not including walks, a big part of how people actually get on base.
With soccer it’s similar. You start with goals. You look to see if goals are predictive of future goals—and they’re not. Like, not at all. But then you turn to shots. You see that shots are more predictive than goals themselves. You run into some problems. Caley found that there were some Tottenham players that took a ton of shots from outside the box. They skewed against the idea that the teams that are better should take more shots. That team also didn’t give up a lot of shots but the ones they did were often pretty good chances. So, you get to OK, maybe the types of shots matter. From there you get into a way of predicting goals. That’s what you want to know: What team created the better chances to score in the game. Managers have been talking about that for a hundred years. That’s how Caley came about it. And so did a handful of other people at the same time. I think that all these people came to it from a different way showed that if you had that analytical mindsight, you’re going to approach it in that same way: Breaking down goals to their component parts and then eventually figuring it out.
It’s important to say that expected goals isn’t reality. It’s not like you are your expected goals. This does not mean that a team like Leeds will just be the twelfth best team automatically—it’s just better at predicting quality than anything else there is. It’s almost like if a baseball season was 38 games long—that’s how I view the soccer season. We know that the playoffs are random as hell and that’s what makes them fun. Let’s take the Champions League final for example: 24 [from Liverpool] shots to 5 [from Real Madrid]. Depending on the model, the expected goals were basically 2 to 0.6 in favor of Liverpool. Obviously, Liverpool didn’t win. Real Madrid converted the one good chance they had, and their keeper played out of his mind. That’s why the game ended the way it did. All the other reasons are very tiny things that happened: Luka Modric made one nice pass. That’s the story of the game. But it goes against stories that a lot of people want to read. It doesn’t give you a reason to be angry, it doesn’t tell you that Liverpool are a team in crisis.
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