I’ve been working almost a year and a half now in a full time role as a data scientist at Opta and Perform Group working in football analytics for a variety of clients both in the pro and media spaces.
I’m not going to lie it’s a pretty cool gig. Which is why I guess it isn’t too surprising the question I get most often is “how do I get a job in football analytics?”
A few years ago I was asking a lot of “more established” analysts this question too. Most of the answers you get are things like: do more work publicly, show you know how to ask a good question and approach the problem in a meaningful way. When I was asking these questions and getting these answers I was always a bit disappointed — I get that you have to do work but what else should I be doing?
As time went on I wrote more and more, got good feedback on my work from people in the analytics community, sort of stumbled my way into a freelance writing gig (from what I understand of the journalism game I think this was a pretty lucky break if I am being honest, less to do with me and more just being in the right place at the right time), and eventually started to get in contact with performance analysts at clubs doing the odd piece of work here and there. It was probably about three or four years after I had first started in the football analytics community that I graduated from university and lo and behold I found myself in a full time job in football analytics.
Obviously there was lots of hard work along the way but it wasn’t like I had a concrete plan of going from step one to step two and ending up at Opta. So when people ask “how do I get a job in sports analytics” I don’t really have a satisfactory answer either beyond what was told to me or an in-depth and overly specific life story.
But because this is a question I get so much I thought I could give at least a few tips that I’ve picked up along the way that have either helped me or people I know in the industry.
Start doing work now and make it public
As of today I have done work in Python, R, SQL, Stata, Apache Spark and MatLab, I have experience with general linear models, supervised and unsupervised machine learning models, bayesian models and more.
I don’t say that to brag. I say that because when I started writing about football analytics I would have only known what about 20% of the words in that sentence were.
When you get into an industry like this you’ll inevitably compare yourself to the people who are better than you, and there are always people who are better than you. When I started most of my work was in excel with data I’d copy+pasted. I compared myself to people who knew how to code and wished I could be as good as them. As I started learning how to code I’d compare myself to people who’s math ability made their modelling skills significantly better than mine. Today my coding and math backgrounds are both pretty good and I compare myself to people who make fancier web-apps than I can or academics who build complex models with tracking data.
The point is you are never going to be the best so don’t wait until you are. I hear from a lot of people that they want to learn how to do x before they start writing. If you do that you’ll be waiting forever. Start now. Doesn’t matter how simple or silly you think the idea is. If you get your work out there, even if you just demonstrate you know how to ask a good question and your methodology is suspect people will still find it. The feedback may not be what you want all the time, but you’ll learn from it. I grimace when I look back at some of my early stuff, but I know if I’d never done it or put it out there I certainly wouldn’t be where I am now.
So don’t let the gatekeepers keep you out. Publish something and you’ll have an audience.
Sports Analytics isn’t a degree (and it doesn’t need to be)
To be fair the sentence “sports analytics isn’t a degree” probably isn’t true anymore. Actually I think now there are several degrees offered in sports analytics, but that’s beside the point. One of the follow up questions that comes with how to get into sports analytics is “what should I study to work in sports analytics?” This one I think I have a better answer to: study something you are interested in.
I studied economics (with lots of political science and math thrown in), because it’s something I was — and still am — really interested in. People I know have gotten jobs or are big names in sports analytics who have studied history, philosophy, chemistry, meteorology, physics, theology and just about everything in between. A lot of sports analytics is about thinking intelligently about a problem and communicating complex concepts in terms people can understand. These are skills you will pick up across a myriad of academic disciplines. Going into a degree because you think it will get you a job in sports analytics is a) incredibly limiting and b) probably not true, the first thing a potential employer in sports is going to look at is never going to be what your degree was in.
Saying that you should start publishing work as soon as possible and study what you are interested in doesn’t mean you shouldn’t learn skills specifically for sports analytics. The learning part is often a bit more intimidating but it really shouldn’t be.
The first is learn your sport. There is a tendency in some areas of the media to frame analytics as something which is at odds with the expertise of experts in the field, and sometimes analysts themselves fall into this trap, but there is so much you can learn about the sport from people in it. Analytics gives you a new way to approach and sometimes challenge these ideas, but it is important to learn from experts in the sport to even be able to start discussing these ideas.
The second is learning to code. Chose a language (I’d recommend python or R) and learn how to code in it. This is often the most intimidating step for newcomers, but regardless of your educational background coding is something you can learn.
One thing I would suggest is learn with a project you are interested in, yes it’s important to learn how to print “hello world” but it can also be a bit tedious. If you start with a project you want work on, preferably a simple one to start with it can make the process of learning new a new language and inevitably getting frustrated with it along the way bit more palatable. Again don’t get fed up if you aren’t an expert right away, because the truth is you’ll never be one: there will always be people better than you who you’ll be learning from.
Finally I think if you really want to advance and work in a more technical field it’s important to learn a bit of math and get an underlying understanding of probability and statistics. Again there are plenty of places to learn online and you don’t need a degree in a mathematical field to necessarily be a good analyst.
Working in a club isn’t the only job
In analytics circles working in a club or team has come to be seen as the pinnacle, when an analyst has really made it. The truth is club jobs are only a fraction of the jobs out there. There are people like myself and many others who work for data companies or consultancies. As analytics gains more traction in the mainstream the demand for journalists and people in the media who have a good understanding of analytics work will increase as well. In fact many people work in clubs and don’t enjoy it finding that they prefer working in media or consultancy spaces.
The roles are all different but the skill sets are similar: you need to understand the sport, what the problems that the sport presents are, how data can be used to approach these problems and how to communicate all of this in a succinct and easy to understand manner.
Don’t limit yourself to looking at clubs, the demand for smart people working in sports is much broader than just clubs.
You still might not get a job and that’s okay
I worked hard to get where I am and following some of the steps above helped me get a job in the industry. I’m also a very privileged straight, white guy who had lots of support from family + friends and moved across an ocean on my path to working in football. It’s no secret sports jobs are pretty highly sought after so I can’t guarantee that by following these steps you’ll get a job.
That being said the skills I’ve outlined are things that are useful in many fields and it’s not like if you don’t get a job in sports analytics it will have all been for naught. If you learn how to code, how to use data and how to communicate mathematical concepts effectively you will be more employable in any field.
Also of course — this stuff is fun! We do it because we love the sport and we want to learn more about it. If you aren’t enjoying it, stop because there are industries that will pay you more.
While this isn’t a blueprint of “how to get into sports analytics” I hope some of this helps. And if you don’t think any of this is useful then ignore it all — you may well have better ideas!
It still feels surreal that a sport I grew up loving, playing (poorly), coaching and refereeing is now my full time job. I still have imposter syndrome all the time when I am in meetings with coaches, analysts, players, broadcasters and journalists, but slowly as more and more people listen to me and realise I genuinely have something to offer it becomes more natural. It isn’t like there was ever a breakthrough moment when I became a proper sports data analyst^TM, so hopefully talking about all of this helps in some way to de-mystify the process.
So now that I’ve written all of these ideas out somewhere I hope that next time instead of messaging me to ask for advice on how to break into the industry you’ll message me with your first blog post or an example of some public work!
Thanks for reading and good luck!