: information resulting from the systematic analysis of data or statistics.
Hello. The aim of this (what I hope will become) series is to try to explain to people who haven’t really come across football analytics the jargon and metrics it entails in simple terms.
First things first, I’m not a mathematician – my only maths qualification is a GCSE in it several years ago, and I’ve never pursued a higher degree in that subject, so some of you may be in a better starting position than I was when I first discovered football analytics from a maths viewpoint. I’ve learnt everything I know from Twitter and its users’ blogs, which are a fantastic way to see the latest cutting-edge developments in public analytics (we at OSC have a regularly updated list of real life football analytics persons here).
Okay, why is football analytics needed? If we can watch the game with our eyes, see what happened and come to our own conclusions, why do we need numbers?
I was very sceptical of numbers to begin with, no doubt due to the rampant misuse of Squawka’s Comparison Matrix, and wasn’t sure why we needed to quantify ‘everything’. Soccernomics (a must read for anybody interested in football) sums this need up well in its early chapters. I quote it in this extract from a previous article of mine ‘In Defence of Analytics‘:
“There are two main factors which are almost exclusively avoided by the average football fan when it comes to evaluating anything football-related, but both are absolutely critical in the evaluation of anything to do with the beautiful game. The first is given the uninspiring name of availability heuristic. Soccernomics, a fantastic book I’d highly recommend to anyone wishing to get into football and stats, defines availability heuristic as ‘the more available a piece of information is to the memory, the more likely it is to influence your decision, even when the information is irrelevant.’ Basically this means that more recent and/or memorable events tend to stand out in your mind, and therefore influence your opinion about a player/event, even if the information is useless. This means that eyes are hugely fallible when it comes to making judgements, so it makes sense to at least consider the application of something less biased when making said judgements.”
“The other factor is confirmation bias, which is defined as ‘a tendency to search for or interpret information in a way that confirms one’s preconceptions, leading to statistical errors.’ A suitable analogy would be that when watching a game, a player you don’t ‘rate’ makes a poor piece of play. Confirmation bias would strengthen your belief in his lack of quality, even though he might have otherwise had a solid performance. A failure to apply this pair to decision-making is a sure-fire way to make misinformed decisions and judgements, as is part of the reason why decisions based on gut feelings go wrong more often than those based on data, as data can’t lie, it can only be misinterpreted.”
Essentially football analytics is just another layer of information that can be applied to the sport; there will always be room for visual analysis, even the most ardent of the evil number wizards that inhabit Twitter.com would admit that. Taking the old adage “Knowledge is power” and applying it to football puts analytics better into context. Given how much many people have riding on the game of football, from managers to fans to punters, it makes sense to try and understand as much of it as possible, in as many different (plausible) forms as possible. Or at least it did to me.
Numbers are also useful because they can be used to attempt to quantify things that would otherwise be only described with an adjective, and their use in football data means that things not immediately apparent to the eye can be picked up by the stats, allowing us to understand the game even more.
Thanks for reading.
Our next part will focus on one of the key features of football analytics: shots.
If you’ve any questions or suggestions, tweet or DM me @OneShortCorner.