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 in his prime, only arguing with people about stats. And then, as he watched the World Cup, he began to wonder if the kinds of things that were applied to baseball could be applied to football. In baseball, the initial work is to have runs: you see, runs are counted. Then you work back from it. You want to find out what generates runs. That’s where all the work in early baseball analysis comes from. As you know, when you look at batting average you don’t see the whole picture because you don’t include walks, a big part of how people actually get to base.
It’s similar in football. You start with goals. They look to see if goals predict future goals—and they don’t. Like, not at all. But then turn to the shots. You see that shots are more predictable than goals themselves. You run into some problems. Caley found there were some Tottenham players taking a lot of shots from outside the box. They resisted the idea that the better teams should fire more shots. This team didn’t fire many shots either, but the ones that did make were often pretty good odds. So, you come to OK, maybe the type of recording is important. From there you get into a way of predicting goals. What you want to know: Which team created the better scoring chances in the game. Managers have been talking about this for a hundred years. That’s how Caley got involved. And so did a handful of other people at the same time. I think all of these people came in a different way and showed that with that analytical mindset, you’re going to approach it the same way: breaking down goals into their component parts and eventually figuring them out.
It is important to say that expected goals are not reality. It’s not like they are your expected targets. That doesn’t mean a team like Leeds are automatically 12th best team – they’re just better at predicting quality than anything else. It’s almost as if a baseball season is 38 games long – that’s how I see the football season. We know the playoffs are random as hell, and that makes them fun. 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 took the one good chance they had and their goalkeeper went crazy. That’s why the game ended the way it did. All other reasons are very small things that happened: Luka Modric made a nice pass. This is the story of the game. But it goes against stories that many people want to read. It doesn’t give you any reason to be angry, it doesn’t tell you that Liverpool are a team in crisis.
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