The use of statistical analysis in football has seen a huge increase since the turn of the century and now, more than ever, we are witnessing its effects in the mainstream media and its use by those involved in the game themselves.
It is perhaps right on cue, then, that the excellent Opta Joe’s Football Yearbook lands on our shelves.
Written by Opta’s Chief Data Editor Duncan Alexander, the Football Yearbook sets out to de-bunk the traditional myths surrounding football and, in particular, the Premier League.
An engaging read, with plenty of humour to match, this is a must-buy for the modern-day football fan. Just as it is set out to do, the book brings bring Opta data ‘to life’, offering insight into the stories and intrigues fans have been talking about all season.
This publication doesn’t just focus on the Premier League season in which Leicester City made English top flight history. Indeed, we also gain an insight into some of the world’s greatest teams, as well as the summer Euro 2016 championships, just gone.
And, although the 2015/16 campaign is at the forefront of Duncan’s analysis, the reader is also treated to a taste of the past, with many chapters turning to historical data by means of comparison.
Whether you buy into statistical analysis within football, or not, this is a book you are bound to enjoy reading. Also, if you like a cheeky flutter once every so often, the knowledge provided could certainly help your chances of being significantly more successful.
We were lucky enough to grab an exclusive interview with Duncan following the release of his latest publication, during which we discussed his role with Opta, his motivation behind putting his knowledge into a book, and what he envisages for the future of statistical analysis in the world in football…
Hi Duncan, can you tell us about your role with Opta? What do you do on a day-to-day basis?
I’ve been with Opta for more than 10 years and have largely worked in the data editorial team. What that means, in English, is taking the vast amounts of raw data that is produced and sifting through it to come up with insight and memorable lines for television, newspapers, brands and, since 2009, social media. These days I try to create new ways of using data and shaping it for specific projects, such as the Opta Joe yearbook.
What convinced you to turn your knowledge of data and statistics into writing a book?
Well I was approached rather than hammering at doors demanding a book deal but once I thought about it I realised that there was (hopefully) a sweet spot between traditional yearbooks that have long lists of numbers and records and narrative reviews of the season. This book tries to occupy that middle ground, so covers the big issues of the 2015-16 season, mainly using data, but also veers off into some more unusual areas (such as: why are players called Alan in decline?)
For any of our readers hoping to break into sports analytics, particularly with regard to football, what advice would you give?
The skill-set required has changed over time. I still mainly use Excel like a calculating dinosaur whereas the youngsters are using things such as Python scripts to extract and play with data but ultimately the main thing is that you know about and love football. I could look at a load of numbers about importing paint or the budget deficit in Canada and it would leave me cold, but with football it can inspire me, occasionally, to come up with some interesting ideas and different ways of looking at the game.
How many people (at the same time) do you have analysing some of the biggest matches? And, can you give us a bit of an insight into the data collection process?
Even though Opta has been collecting football data for almost 20 years, the way it happens is still a bit of mystery to many. Essentially, it’s been roughly the same since 2006, with video images of the game piped through some custom-built software that allows two people (one per team, plus a third person to QA the info live) to track and record every touch of the ball in a game. There are roughly 2000 events per match and the x/y position of each is collected live. There’s a pretty good video showing the main elements here:
Do you think that the level of data collected by big clubs makes it harder for smaller clubs with limited finances to compete on the field?
Probably much less so than it does with something like transfer budgets. You could employ an innovative data guy who could give you a competitive advantage for a bit (probably until he was snapped up by a bigger club).
Do you think the role of stats will continue to grow in football and if so, in what ways? Is there anything exciting set to happen within the analytics industry that we should keep an eye out for?
The combination of different datasets is probably the next big step. If you combine positional data with events you could work out who was the best passer under pressure, which defence was the most drilled and which striker consistently finds the most space before shooting. It will be like football stats v2.0, basically.
How effective is the use of statistics for predicting the outcome of future games? Should those who like to bet on weekly fixtures start to have a greater consideration of the data to hand?
As usual, it’s about applying the numbers sensibly. Team X might have won their last five games against Team Y but if the most recent one was on Boxing Day 1931 it’s pretty meaningless. However if Team X have won none of the 14 games that Player A has missed this season and he is out of this match then you should probably take note. That’s without going into metrics such as Expected Goals which can hint at which teams/players are under/over-performing and whether a club’s recent results are realistic or not.
You have a huge Twitter following, with thousands of users sharing each of your posts. What is the secret behind your successful use of social media?
People assume there was/is a grand plan around Twitter but really it has always been a way to use content that wasn’t picked up elsewhere and interact with fans directly (and learn what they do/don’t like). Ultimately, most people don’t really enjoy complicated ‘stats’ but if it is presented in a way that they can use as a rejoinder to their friends it will usually do well. Some people don’t particularly like the one-word summary we do at the end of a tweet but it can often turn a fairly vanilla piece of information into something more amusing, without turning the tweet into some sort of subjective opinion.
If you had to pick your favourite statistic from the 2015/16 season, perhaps one you have included in your new book, what would it be?
Writing the book meant I had to sift through a lot of stats, a lot. One that didn’t make the book that I liked was that players in the Premier League are more likely to be sent off when there’s a full moon, while I keep meaning to get a copy of the Palace v Norwich game from April. The ball was in play for only 45 minutes and 39 seconds of the 98 so if you enjoy seeing linesmen flag morosely for throw-ins, offsides, and goal kicks it is an absolute classic.