As analytics have become more wide spread throughout the footballing world, it is becoming more and more obvious about the potential gains to be had. Are you actually creating as good chances as you think you are? Did you really deserve to win that game? Not everyone has access to an analysis department to inform and impact your coaching from a data perspective, but there are some things you can do.

I am an assistant coach and data analyst with the University of Toronto Women’s Soccer program. I’ve been in my role for about a year now, in which we went from finishing 6th in our division (barely making the playoffs), to finishing 4th and going on a Cinderella story run to a national bronze medal. This corresponded with a totally new staff who deserve all of the credit. We’ll look at some of things we do and use to inform our training, our game model, and our tactical decisions. We are one of the lower budget programs (particularly in comparison to NCAA D1 schools), so this is written with the hope that you may be able to adopt it for yourself, whatever level you work at!

Video Analysis

The first tool in the toolbox is absolutely video analysis. Unfortunately, our memory of the game just isn’t what we think it is. Some academic work has shown coaches aren’t even able to recall 40% of the pertinent information surrounding a goal from memory, and if you can’t remember what actually happened, how can you possibly use the match to inform your training/play?

If you have access to more expensive coding software like Sportscode or Nacsport, you can draw up pretty elaborate templates to tag and clip every moment you value. But you don’t need the super expensive software to still have an impact, even with iMovie or Windows Movie Maker you can create impactful, actionable video work. Start with finding the moments that you value according to your game model, clip them down, and evaluate: are we accomplishing what we want to accomplish? If yes, great! Now you know it, and you can show your players “Look, this is exactly what we’re talking about”. If no, now you can look and identify what’s missing and adjust instruction or training accordingly.

For example, we consistently speak about “playing across the game”, finding the player outside of pressure who can spring numbers up situations the opposite side, or play against the oppositions movement to find space. We consistently use this clip as our gold star example of what the whole attacking side of the game model looks like.

You can do the same thing in terms of opposition analysis, you have your principles of play and sub-principles of play, but you adjust and tweak your strategies to implement them on a given week. Analysing opposition video allows you to A) identify THEIR game model and principles, both going forward and defensively and B) theorize how to adjust and tweak and manipulate your way of playing to be successful. Having this information gives you the opportunity to inform your players, visually and verbally, to put them in the best position to succeed.

Record your own data!

There’s a lot of data work floating out in the world and it can get pretty overwhelming to think about how to apply pass maps and heat maps and machine learning and so on to your club. But the reality is you just need to find what’s valuable to you, and what’s realistic for you. We don’t have a league wide data provider, so any data we get is hand collected from the video. There are a few really useful (and free) public data collection tools, @PeterMcKeever’s Open Football Club is an excellent one which allows you to add whatever context flags you want for shots (and also has a nice xG model built in!), but be sure to fill out every single field if you want it to work. Similarly FC Python’s event tracker is quite good, and Ben Torvaney has a simplified shot tracker as well. They all are useful in their own way.

Brief comment: This will talk some about expected goals (xG), at its core xG is a framework to identify the probability of a shot becoming a goal, based on some contextual factors around the shot. You can look at all the shots to ever match that description and see exactly how many went in. The tools above distil that down for you and just tell you the probability without any of the hard work. For example, a 0.07 xG shot has a 7% chance of becoming a goal.

We are quite big on collecting shot maps (and consequentially expected goals (xG) maps, because at the end of the day goals are the most important thing, and shots give you goals. Knowing where you are shooting from/conceding shots from and the context around it is extremely important to us. Here’s an example, we use the Open Football Club example above to record the shot data from our match against Queens away. The data goes into an excel sheet and we do a bit of graphing and end up with something like this. Queens here are yellow, Toronto is blue, goals are pink. As you can see, we created very few opportunities, but they were mostly central (a big plus for our game model). However, we gave up too many opportunities, and they were also mostly central (a big minus for our game model).

xG Map of a game away to Queens, Toronto are blue, Queens are yellow, goals are in pink. The size of the circle corresponds to the probability of it being scored. The rectangle percentage bars correspond to Win-Draw-Loss probabilities based on the shots in the match, and can be calculated here.

Then, we pair the video to the data and try to answer “Why did they get so many central chances?” Once we have our answer, we plan our sessions, adjust our periodization plan, the topics of our video sessions with the players, our conversations during training to try and better equip the team to deal with the next match. Luckily for us, we got to apply it directly to the same opponent the next weekend, and it went quite a bit better.

At the end of the day, you have to measure what you value, and not value what you measure. For us, shots and shot locations and context are important, so we keep track of those things. We keep track of all of the times we “play across the game”, when we hit certain triggers in the build phase, when we hit certain actions in the oppositions build phase, because those things are important to us and the way we want to play. Liverpool care about pressing and pressure regains, so some fans from The Anfield Wrap recorded their own data for every time Liverpool pressed someone or regained pressure possession off a pressure. Andy Hargreaves said it best “measure what you value, instead of valuing what you can measure”.

Player Development

Another big benefit of applying analytics to your coaching is its use in player development. For example, we had a striker who underperformed her xG significantly during the regular season, but her numbers were pretty great all around. She was putting up ~2/3 of an expected goal every 90 minutes but was doing a tonne of other work for the team. Holding it up, running the channels, connecting with other attackers, getting into good spots to finish. We were really happy with her performance, and not at all worried that the goals wouldn’t come. But for a player it can be really hard to constantly get good looks and not finish them, even if it’s just a cold streak. So we looked at the data, and the majority of her misses came from a combination of 1v1’s when she was played through and first time finishes on cutbacks. As such, we tailored her extra finishing work to focus on those scenarios. Now, the obvious comment is that this is a super small sample size and any improvements are just her dropping the cold streak for more normal finishing, which is true. But players aren’t ball-kicking robots and feeling like you’re working to fix the thing that’s going wrong is almost as important as continuing to do the great things and knowing the goals will eventually come. In our winter season (pre-COVID19 cancellation), she’s scored in every match.

Having that data on hand to consult and work with is important. You can do the flip side and use it to work with your goalkeepers, maybe they are conceding more goals than expected from cutbacks and need to work on different scenarios in training addressing their set position or their footwork to address it or just need more reps. Using tools like this allows you to provide directed, actionable feedback. Everyone wants their striker to finish better, but how? What types of finishes should they work on?

Applying Data to your Coaching

Applying data to your coaching doesn’t have to be a complicated process of collecting every pass and every tackle and offside there is, it just has to be actionable and representative of what you value. Record your own data using the tools available to you, pair it with your video, and use it to inform your training, your tactical approach, and your player development. It’s never going to replace watching the game and it’s never going to replace you thinking about the game, but it augments, supports and informs those things to make you and your players better.

Kieran is an Assistant Coach at University of Toronto Women's Soccer and a PhD candidate at Western University

You can follow him on Twitter at @KierDoyle


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