When is a stat a good stat? That is something that I regularly ponder in my role as a play-by-play announcer.
Living firmly in the information age and in a country deeply in love with sports statistics, I have access to a weird and wonderful array of information. When I first started calling games in the late 1990s my job would be to flick through a few reference books, speak to those in the know and to think for myself about which statistics should be included in a broadcast.
Nowadays we are supplied with incredibly detailed information which often runs to hundreds of pages per match, so instead the task is more about deciding what should be filtered out! I hope the following will offer a little food for thought about what I find useful/not so useful as statistics become ever more available.
Stats are a fantastic aid to the understanding of a match but they can never tell the whole story. It is all about the context in which they are used. After all, who remembers the well-worn phrase about lies, damn lies and statistics? If team X beats team Y 3-1 then the obvious assumption is that team X deserved to win – quite simply, they had scored more goals. Team X might also have had more possession and shots on target. But what that doesn’t tell you is the quality of that possession or those shots. Team X may have held the ball inside their own half for 80% of the time, where they were never going to hurt the opposition. Likewise, the majority of their shots could have been weakly hit or from 35 yards out instead of being wide-open chances from three yards.
Soccer is a game less suited to statistics than many other sports. Compare it to baseball, where the pitcher and the batter will always stand in the same positions. In soccer, you have 22 constantly moving pieces, all reacting to who has the ball and where the ball is on the field. That makes it far harder to quantify the effectiveness of an individual because they are always reacting to a multitude of different situations.
Assists are a very useful way of judging which players are able to create chances. Over the course of the season the assists column will pretty accurately represent popular opinion on who is the best at making goals for their teammates (think Mauro Rosales, Graham Zusi, Brad Davis, etc). But again it is worth understanding the context in which those assists have been awarded. For example, a simple three yard pass under no pressure is given equal value to a world class, defense-splitting 65 yard cross-field ball. Sometimes a player who doesn’t even touch the ball can be the most important factor in setting up a goal yet they don’t get an official assist for their efforts. Think of a player who lets a pass run through their legs like Joao Plata did for Real Salt Lake’s second goal against San Jose this last weekend. If Plata hadn’t been there, his marker could have cleared the ball to safety. As it is, Grabavoy gets the goal, Morales gets an assist for the cross but Plata gets nothing.
There is all sorts of data that can be used to measure players’ performance. If you watch the UEFA Champions League you may have noticed that when a player is substituted a graphic will show how far they have run, often marked against his team’s average. If that player is above the average you might think he has worked harder than his colleagues, yet what you don’t know and likely will never find out is whether he has run in useful areas that have benefited the team? Was he spending the match chasing shadows? Similar applies to pass completion percentage (I’m fascinated by heat maps and passing charts – see Seattle v Chivas in glorious detail here) – did the team play a simple passing game or try and hit the ball long, which is of course statistically less successful? Was the passer under pressure from an opponent when he played the ball, and was the intended recipient under any pressure and/or making the right amount of effort to receive the pass? Over the course of the season this kind of data can be hugely useful as there is enough of it to allow anomalies to even out.
The one stat I don’t often refer to is saves. You see, it’s not that I’ve got anything against goalkeepers, as I hope you’ll appreciate given that my color commentator Kasey Keller was rather a good ‘keeper in his day. We’ve often had chats about the use of statistics and are agreed that using the number of saves as a way to measure a goalkeeper’s performance is unfair. After all, he has relatively little control over how many shots an opponent takes. If he is forced to make nine saves in a game it would tend to suggest his team hasn’t defended well enough, while making zero saves would be a much better situation. I’m sure most keepers would prefer not to be top of the saves list! This is where the ‘goals against average’ is far more useful as it is a much more direct measure of how well a team has defended. Happily for the Sounders, Michael Gspurning has consistently been at the top of this category.
As the systems to analyze data grow ever more sophisticated, statistics will play a bigger and bigger role in soccer, both for broadcasters and of course for coaching staff (think Moneyball). But I doubt there will ever be a better substitute for a good old-fashioned pair of eyeballs to interpret what really happens out there on the field. The intangibles of soccer will always see to that.