I published a survey this week, asking people about their interest in football stats and analytics, their ambitions and skills. I could and probably should have asked a lot more: it’d be cool to know where you’re all based and what teams you support, if only to confirm that statsworld is a sea of Tottenham and Arsenal fans. It would have been good to quantify just how few smart women have a voice in the football stats community.
So I dropped the ball on that, but I think we have some interesting data besides. I’ve only been writing here for a month or so, and I took the somewhat circuitous route into football stats of following Ted Knutson back when he edited a Magic: the Gathering website. Because of that, I’m intrigued as to what’s holding more people back from writing, theorising, and generally contributing to the ruckus. Let’s find out!
Responses
I got 79 responses in the couple of days the survey was up – thanks to everyone that contributed, and to those that retweeted the link! Of these responses, 13 work at clubs professionally, and we’ll look at that in more detail later.
Age
It’s a weird feature of the statosphere that everyone seems to assume everybody else is young. Scamps like the Analytics FC mandem and student-bedroom YouTube sensation Joel Salamon distract us from some of the more venerable members of the community. What’s the truth?
This is pretty left-leaning, and more pronounced when we just focus on the analysts at clubs:
The good news is, if you’re young and interested in football stats and analytics, the only barrier between you and clubs is how good you are and how you can get noticed. It’s also possible that most 35-year-olds don’t sit around all day filling out dumb online surveys because they have tons of work to do, I’m not sure.
Experience
One of the survey’s main motivations was finding out how many people were already involved in doing stats work, how many wanted to be, and what might be holding them back. Let’s look at what our respondents are up to:
I like the blogging numbers – it’s nice to see that people are taking the advice to just get themselves out there – a good 60% of people who can see themselves blogging about stats have already taken the leap. People aren’t lying when they say that if you make good stuff, it’ll get noticed.
People’s ambitions here are pretty clear – getting into professional football clubs is most people’s dream, but one only realised for a few at this stage. More seem to want to do consultancy than take a full-time job at a club, perhaps just because the jobs are thin on the ground – I would still assume the median number of full-time stats people at Premiership clubs is zero.
There are also surprisingly few getting paid to write about stats. Outside of the echo chamber, there clearly isn’t an enormous market for stats-heavy pieces, but it’ll be interesting to see how this number changes as time progresses and the wider media incorporate more stats content.
Also worth noting the smallish numbers of people in academia. Given the dearth of paying jobs in the media, the limited number of jobs at clubs and the generally secretive nature of cutting edge work, I personally think it’d be great to see people in academia taking more of a leadership role in the stats community, but maybe my Twitter feed isn’t representative and I’m missing stuff.
Podcasting is increasingly popular, with Analytics FC hosting a series of impressive guests, and I missed off video as a medium, which is sad because in addition to Joel’s excellent videos (and their very entertaining comments sections), I think we can all agree that this is the single greatest contribution to football analytics.
Barriers
Given the hopes and dreams above, what’s holding us back? The survey asked about the biggest barriers holding back the community:
The two on the left are the most common complaints I see on the Twitter statosphere. Data is the lifeblood of stats work and it’s either very expensive to acquire, or time consuming and of dubious legality. The latter point’s important: even today, WhoScored took out a gun and aimed it at their foot in response to Joel’s latest video:
The situation gets even more complicated when it comes to positioning data, the holy grail for a lot of analysts. Clubs are in an odd situation that they have to opt-in to a sharing agreement to get positioning data about other clubs, and so there’s only a small handful that have any data at all. That’s a function of paranoia and also presumably a lot of clubs not having the resources to do anything useful with the data.
About data, I will just say this: in 10 years time, you will be able to create all the data that Opta and Prozone produce using smartphone-level video and open source computer vision software on your laptop. If someone with the resources of Google wanted to, they could do this in the next couple of years, for every match in the world. I do not believe for a second that the data side of the industry is a valuable long term investment, except in cases of really privileged information like training performances or behind closed doors in academies.
Opta and Prozone will thrive on having the best researchers working for them, in tandem with the best tactical minds at clubs. WhoScored and Squawka will thrive on having the best writers working for them, making this stuff accessible and interesting.
The best way for these companies to find this talent, it appears to me, is to free the data and hire everybody you think does something interesting with it. Maybe that’s naive.
Anyway, enough of that. Elsewhere, there is a lack of stats-focused content in the media. It’s been a year of progress – you’re almost as likely to hear “expected goals” on your TV these days as you are “rainy Tuesday night in Stoke”. It’s also been a year of recurring beef, with Neil Ashton’s seminal air-conditioning piece in the Mail and the fallout from Brentford’s misadventures in the managerial market.
All you can do is keep writing, make it accessible, and hope that narratives in the stats community pan out enough that you can build trust. I certainly think it would have been great for the media to pick up on the West Ham over-performing story, it’d be money in the bank for stats people. Make content that wins people arguments in the pub, and bit by bit people will become more accustomed to thinking about stats.
Getting Data
If data’s the biggest barrier to entry or progress in football stats, how are people getting it today?
The most common thing to do is look at sites with accurate, timely raw numbers like WhoScored. Don’t scrape them and get in trouble, but do note that Squawka’s terms and conditions say this:
You are not permitted to use this website other than for private, noncommercial purposes. Use of any automated system or software to extract data from this website for commercial purposes (“screen scraping”) is prohibited. Squawka reserves its right to take such action as it considers necessary, including issuing legal proceedings without further notice, in relation to any unauthorised use of this website.
So for non-commercial purposes, maybe you’re fine. Ask your lawyer.
Kudos to the 13 people out there manually collecting stats. You can use tools like John Burn-Murdoch‘s pitch tracker to create data, and with enough time maybe you’ll have the best data in the world about set pieces or something.
In addition to these numbers, 44% of respondents to the “how do you manage football data?” say they keep a list of bookmarks to manage data. I suspect given these numbers that most people are able to judge players and teams reasonably well, looking at their shot numbers, or aggregated data like those at Objective Football. That’s a good foundation and indicates a great level of stats literacy in the community. It’s been brilliant to see the amount of stuff Paul Riley‘s been making public, as finally everyone has access to an expected goals model, raising the bar even higher.
It remains a shame that so few people have access to Opta feeds, but hopefully more and more aggregated data and tools can be made public without triggering some sort of retaliation from the owners of the data (who have paid lots of money and put lots of work into collecting it, I should make clear).
Tools
What are the secrets to doing magic with football stats? Well, no secrets, just the usual suspects:
Almost everybody lives inside a spreadsheet of some sort. Tableau is pretty standard at this point, and R is about twice as popular as Python as the language of choice for stats work. Stata gets an honourable mention as it popped up a couple of times.
The SQL number is low, but I guess that reflects the fact that most people aren’t dealing with event data in bulk, or just make do with R dataframes or something. I was the only one that ticked the GIS box, and I think you’re all mad. Being able to do geometry stuff inside SQL is huge: my shot buildup charts are basically a 5-line query that runs in less than a second. If you ask me, everybody should be looking at putting stuff into SQL Server 2016 when it’s released, you get SQL, GIS functionality and embedded R, all in one platform. Get on BizSpark, it’s all free.
Modelling Knowledge
The survey had a big section asking people about the sort of metrics and models they can and do produce. I think this is one of the most important questions, because it shows where we might be falling down as a community in terms of education, but it also points at the areas that are primed for new research because fewer people are working on them.
So on the left of zero you’ll see those that don’t currently know how to calculate a metric or build a useful model. On the right are those that know how, and indeed those that already have working models. Broadly speaking the techniques at the top are better known, and at the bottom are less known.
At the top is the simple stuff, calculating TSR and PDO is fairly straightforward, and it’s good to know how it’s done instead of just consuming the numbers. It also leads on to more advanced stuff, like calculating TSR/PDO but with xG numbers instead of goals and shots.
Strikers are, as ever, dead easy to model. Even just using surface stats like shots on target/90 and various conversion rates, you can get an idea of who’s good, who’s overperforming, and who is sustaining their performances between season.
At the other end of the spectrum, defender ratings obviously make an appearance – this is one of the hardest areas to judge, especially lacking positioning data that is key to so much defensive play.
Right at the bottom is predicting total corners/goals. This isn’t really that analytically useful, but for those of you that bet, these are big markets, and some of the easiest to find value in.
The appearance of goalkeeper ratings near the bottom is a surprise, if only because keepers are more or less the flipside of strikers. Tons of data available, clear metrics for what’s good and bad, even if you’re not using an xG-like model. I will take a moment to push my expected saves model and goalkeeper Christmas Shopping pieces.
A couple of people in the ‘other’ option mentioned working on youth models, or career predictions, which seems like a brilliant area to look into.
Education
I put three questions about education into the survey, mostly because I wanted to make it clear that you can do great stats work without too much formal education, maths or otherwise.
About 40% don’t have a degree, and most that do weren’t necessarily in mathsy subjects, instead doing stats in the social sciences, or taking maths modules in the natural sciences or computer science etc. That said, only 2 of the 13 respondents currently working with professional clubs had less than a batchelor’s degree, so be aware of that.
There weren’t many Sports Science respondents at all, and I’d be interested from anyone with an opinion about whether Sports Science degrees serve you well for work in stats or analytics.
I also asked about coaching qualifications. 9 of you have the equivalent of a Level 1 Certificate in Football, 2 have Level 2, and we were graced by 2 UEFA B Licensed coaches.
The Biggest Issue Facing The Stats Community Today
The proportions remain the same inside professional clubs, and frankly I’m rethinking this whole stats career thing as a result. I’m game for unionising if you are.
Conclusions
You can download a slightly sanitized and anonymized version of the data here.
I don’t see a lot of statistically significant data pointing at surefire ways to get into paid work in football stats. But what I do see is tons of ways that we as a community could help, educate and collaborate with each other. I’d love to think that one day Alan Shearer will wake up every morning and check expected goals tables to see how the season’s going, but that’s a long way off, and in the meantime, it’s clear that there are loads of people that want to contribute more but can’t. I take my hat off to people like Analytics FC, whose podcast is putting important people and their work front and centre, and to Paul Riley, who as much as anyone seems to be trying to put his work (and importantly, his data) out in the open for people to build on. And most of all, huge props to StatsBomb, who I think served as the epicentre and catalyst for a lot of people to either start thinking about stats stuff, or even better to get off their arses and write about it.
So let’s all ask ourselves what we can do to help each other. I know there are tons of smart people out there that have great ideas but perhaps not the programming skill. I know there are great programmers who have no idea where to get data from. If anyone sees my stuff and wants to know how it came to be, get in touch, maybe I can give you some pointers.
In the meantime, one idea that I thought was worth doing straight away, was building a custom football stats search engine. My hope is that this will make it a little easier to find existing research to bring yourself up to speed, find new avenues of research, or at the very least, avoid wasting time redoing work that’s already been done. Annoyingly I’m on WordPress.com here so can’t embed it, but you can bung the following code on your site and get a search box for it:
(function() {
var cx = '018110615440115988629:xtvxg7sucik';
var gcse = document.createElement('script');
gcse.type = 'text/javascript';
gcse.async = true;
gcse.src = (document.location.protocol == 'https:' ? 'https:' : 'http:') +
'//cse.google.com/cse.js?cx=' + cx;
var s = document.getElementsByTagName('script')[0];
s.parentNode.insertBefore(gcse, s);
})();
<gcse:searchbox-only></gcse:searchbox-only>
Or even without script:
<form action="http://www.google.co.uk/cse" id="cse-search-box" target="_blank">
<input name="cx" type="hidden" value="018110615440115988629:xtvxg7sucik" />
<input name="ie" type="hidden" value="UTF-8" />
<input name="q" size="30" />
<input name="sa" type="submit" value="Search" />
</form>
Bookmark it, use it, tell me if there are sites missing that should be indexed. It’s not much, but it’s something I kept wishing existed, so hopefully it helps a tiny bit.
… And Relax
Thanks again to everyone that contributed to the survey, I hope the results are interesting. In six months or a year I’ll probably do this again, so I’d love some suggestions for questions for next time around.
Deep xG 