It’s the World Cup. And I am an Aussie living in London. While it might be wrong, I can’t help but get some sense of pleasure out of seeing the English lose at sport. I have lived through two Ashes losses while in London so it has not all been one-sided but I enjoy it while I can. Unfortunately, there is a downside, the expert analysis and dissection of the game by the non-expert. The ones who claim that England would have won if that goal had of been allowed (and Australia would have been through to the second round if not for the two red cards but I’m not bitter) and who use various statistics to prove their case.

Listening today to comments that Germany’s goals were just counter attacks, that England had more shots, more shots on target, more possession and therefore hadn’t been comprehensively outplayed made me think. Having watched the game, (and I freely admit it is not my sport, I don’t have a complete appreciation of the complexities of it and I was a little biased) I had the impression that Germany were running all over England. Not all game, England were very good for the last 10 min of the first half but outside of that… So what about these key footballing statistics, why didn’t they represent my impression of the game?

My thinking led me to the realisation that common football statistic like shots on target and % possession are actually Football Metrics 1.0. What is needed to clearly represent the difference between the two sides is Football Metrics 2.0. And for the first of these Football Metrics 2.0, can I propose a calculated metric of Goal Scoring Potential (GSP).

Watching the games, you see so many balls kicked softly directly to the keeper due to the pressure on the player with minimal chance of a goal being scored. But that goes down as a shot on target, a top KPI under Football Metrics 1.0. So many corners are kicked into the box, the vast majority are cleared but each has the potential to be headed in. On the flip side, what about the amazing runs leading to a pass to a player with an open goal, except the pass is just a few centimetres wide resulting in a shot not even being possible, such a good chance but not even a shot at goal registered under those Football Metrics 1.0. But this all changes with Football Metrics 2.0.

My proposal is for each potential opportunity to score being graded on a five point scale between A and E reflecting the probability that a goal would result from that opportunity. Grade A opportunities are where you are around 90% likely to score, down to grade E where you would score a goal in only about 1 in 20 times given a similar situation (obviously not a linear relationship between the grades and I admit I am still working this out). Clearly penalties are grade A while corners are grade E, unless a really good cross in which case they might rise to grade D. Then with a number of points assigned to each grade, it is a simple matter to multiply opportunities by points for that grade, sum up the total end up with the GSP for a team.

Without watching the whole game again myself and doing this properly, I would expect that the GSP for Germany would be at least double that of England, reflecting their superiority and dominance. It would reflect the numerous opportunities (grade C?) that Germany had but didn’t take while England had very few chances for goals that didn’t rely on that 1 in 20 shot going in (and actually did in two cases).

So how is this relevant for web analytics? Well, are the metrics that you are reporting on and trying to improve really a good reflection of the performance of your website and/or marketing? When you have a good week, do you rate more highly on these metrics than when you have a poor week? And if not, are these metrics actually meaningful?  So next time your senior football loving manager comes up to you and says the website needs more pageviews, ask them if they think England (or other sporting team) would always win if they just had 55% possession of the ball.

One last thought though – when the siren blows, only one metric really matters in football and that is the number of goals scored. It is the same for web analytics, define all the KPIs and context metrics you want and analyse the hell out of them – but at the end of the day, are you making money?