With most major football competitions finally getting underway in the next few weeks, and the Eredivisie 2011/12 kick off just a few days away, there’s just enough time for one more ‘soccermetrics’ related post. As you are probably aware, ‘soccermetrics’ aims to measure the performance of teams and/or players during a football match. The fact that it’s called ‘soccermetrics’ rather than ‘footballmetrics’ might support the argument that these kind of sports sciences found their origin in the United States, where the beautiful game is referred to as ‘soccer’ and not ‘football’.
The sport most known for the use of statistics is perhaps baseball, and Michael Lewis’ book Moneyball has emerged as a pivotal work, raising awareness for the use of statistics in sports, in particular during recent years among football enthusiasts. The fact that mainstream use of sports statistics started with baseball, rather than with any other sport, seems strongly related to the ‘stop-and-go’ nature of that game. Each pitch starts from the same setup, from the same position on the field and with readily measurable outcomes. Even though, turning baseball data into comprehensible information that accurately represents performance, remained a challenge for many years, earning the term ‘sabermetrics’ in the process.
The acceptance of statistics in baseball has familiarized sports fans with regular use of this kind of information and slowly, but surely, other sports adopted regular use of stats. Among these, American football and basketball, two sports that mix the stop-and-go elements of baseball with the fluidity of football. It’s those two sports that we can use as examples of how to analyze a fluid ball game like football.
Plainly accepting the fact that “football is too fluent a game for any sensible analysis to be made” will serve as a self-fulfilling prophecy. We can learn from the progress made in other (partly) fluent sports. One of the key differences between analysis of basketball or American football and analysis of football is the approach towards possession.
The first statistics to be adopted in the mainstream football world has been possession percentage. Most, if not all, regular viewers of football matches will be aware of the value that’s been attributed to possession of the ball. To cite Johan Cruijff on this subject: “As long as we’ve got the ball, they can’t score.” This early adoption of possession in terms of ‘time on the ball’ is slowly losing ground as recent analyses have shown no clear relation between the amount of possession and match outcome. In other words, Barcelona’s out-and-out dominance in terms of possession and passing against Mourinho’s Inter in the 2010 Champions League semi-final, resulting in them losing the tie might not be the exception, or at least, not anymore.
Skeptics might argue that possession is hard to define within football, as turnovers are everywhere, but this is a matter of convention. Defining the team in possession as the team that last made a passing attempt would work. Any tackle or clearance by the defending team doesn’t interrupt possession unless they make a passing attempt from it. A goal scoring attempt doesn’t automatically lose possession as saves can lead to rebounds and corners are nothing less than a rebound too. Set pieces are passing attempts taken either at predefined positions (corners, goal kicks, etc) or anywhere else (free kicks).
How should we analyze possession?
Again, learning from basketball and American football, looking at the sheer number of times a team had possession of the ball, rather than how long they held onto it!
What is a football match more than two teams trying to put a ball into each other’s net, while preventing that happening to themselves? Well, the objective of each time a team has possession of the ball is exactly that: put the ball in the net.
A problem in drawing upon the examples of basketball and American football is the fact particularly basketball is a high scoring game. With football being an extremely low scoring game (which sports regularly see 0-0 scores anyway?), calculating the amount of goals per possession would not work. To increase the incidence rate of our outcome we could look at shots rather than goals, but this would reward teams opting to quick shots over teams preferring to create the best of scoring opportunities.
Referring to an earlier post, we would best use the chance that any goal scoring attempt produces a goal. An open play shot from just outside the area, just left of the axis of the pitch will on average be a goal in 14.1% of cases, hence would be worth 0.141. A close range header from a corner finds the back of the net in 38.2% of cases, for a worth of 0.382. This way, we could easily present the value created per possession.
In the end
This metric would enable a fair comparison of teams playing different playing styles. Other options of this form of possession analysis are to compare the value created from different ways to start possession. We all know that winning the ball in the opponent’s half is a valuable thing, but on average how many goals does your team score from it?
This metric also enables us to quantify where a team’s possession starts. Again using the example of Barcelona: winning the ball from them is tough, but a big issue is that you’ll almost never do that in their half due to their pressing game. This metric would allow the effect of different tactics in such a situation.
At present, data to carry out this kind of analysis does not seem to be as widely available in football as it is in other sports, but the emergence of stats in football is growing as a whole, which is a step in the right direction. Now let’s try and raise the quality of the information they provide!