By Kieran Doyle-Davis (@kierdoyle)

I will start this with a disclaimer: this is not how Borussia Dortmund or Manchester United incorporate analytics into their coaching workflow, it’s not even how the Colorado Rapids incorporate data into their coaching. It is a look at the opposite side of the same coin Carl Carpenter examined earlier this week, only for a smaller school without access to the biggest and baddest equipment.

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 over the timeline of a week, which is generally the unit these things work in for us at the University of Toronto. 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!

We play double matchday weekends, with one match Saturday, one match Sunday generally. You have the occasional midweek game, but generally you are preparing your week for two potentially very distinct opponents at the same time, all while working on you and building your game model.

Saturday and Sunday

Our matches are all filmed via a single camera set up from our press box at the stadium, which gives us quite a good angle to analyze video and tactical things accurately. Occasionally our games are streamed and we have access to the broadcast feed as well. On away matches we often don’t have a stadium with press box to film from, so we just use a hill, a bleacher, or whatever vantage point allows for the best camera angle available.

We don’t have a league wide data provider, so every game is hand coded afterwards on review of the match. I’ve been using some public event data trackers, which have a really nice interface. @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. Once we go through, we can start to look at how our evaluation of the performance matches up with what the data starts to say.