By The Numbers: An Introduction
In Luukas' first piece for Stumptown Footy, he takes us to soccer stats school
Hey Everyone!
Welcome to ‘By The Numbers’ — I’m happy you are here. Since I am the new kid on the block, I just thought I’d take a second to introduce myself.
My name’s Luukas, I’m 22, and I am a current university student. I’m from the PDX area, but currently live just outside of Salt Lake City, where I attend school. I have an internship with a USL club, where I currently work in their youth academy as a team administrator and I dabble in video analysis when I have the time. I’ve known that I’ve wanted to work in sports my entire life, but I found soccer to be the sport I was drawn to the most 7 years ago. I want to pursue a careers initially as either a video analyst or scout, and then progress to become a sporting director or general manager one day.
I believe that the implementation of video and data analytics is the future of sports, and specifically within the beautiful game. There is evidently a rift between teams who strongly believe in using data in their workflow versus teams who do not. Understanding data properly can be a difficult skill to master, and it has taken me significant amounts of time to learn what different metrics mean.
As I write my ‘By the Numbers’ series, I hope to give you, the reader, a deeper insight into the numbers that make up the Portland Timbers performances on the field. I aim to do this by implementing video and data analysis into my writing, and getting insight from those who work around video and data on the team, so that way we may all gain a deeper insight into the team that we all love.
What happens on the field isn’t 100% indicative of the full picture. As analysts, it is our job to combine what is happening on the field with data insights that we find, and provide solutions to help the team move forward in its season. As you read my series, I hope you approach your viewership of the boys in green and gold with a slightly more academic and analytical lens. If I change the way that you view the beautiful game even slightly, I know that I have accomplished my job.
Lets get started, I hope you enjoy the ride, class is now in session.
Soccer Analytics 101
I think before we dive into the numbers, it’s important that we gain an understanding of some of the key performance indicators (otherwise known as KPI’s) that are often associated with Association Football Teams. Common KPI’s include shots on and off target, pass completion percentages, speed and distance data, among others.
Expected Goals, or xG
Expected Goals is commonly associated with the number of goals that can be expected to be scored, dependent though on where and how the player took the shot.Expected Goals Against, or xGA
xGA is estimated as the number of goals that a team may concede. It is dependent on the number of chances that they give to their opposition. xGA is a good metric to gauge a goalkeepers performance.
Expected Assists, or xA
xA is the numbers of assists that a player can be expected to have dependent on the number of shots taken at the receiving end of their passes.
Passes Per Defensive Action, or PPDA
This metric is commonly used in measuring the effectiveness of a team’s press. Typically, this metric is measured in the opponents half and in the middle third of the pitch.
Possession Value
The likelihood that a goal will be scored anytime a team has posession of the ball.
While the usage of video is not a new concept within sports, the reliance on data to gain an insight into team performance is still in its youth. As time progresses and teams become increasingly more data driven, performance indicators will only advance and get better, thus allowing people, teams and fans to gain a deeper insight into the teams that they follow.
Thats all for this first instalment. I truly hope you enjoyed. Tune in for tactical and data analytics content from time to time. Remember to question everything, for that is the best way to learn.
This was such a good essay, and I never new soccer could go into such detail. I don’t think I can ever look at the sport the same way again!
Welcome aboard! This is an exciting addition and I'm really looking forward to seeing your contributions.
I have a question, that is admittedly a pet peeve of mine: why do people - not saying you, or anyone here, do this, just the general soccer community - place so much value on single-game xG? There's so much noise in that data in a 90 minute sample that it's essentially useless, and yet broadcasts and analysts report xG at half time and full time as if those numbers are significant. Can you talk a little about why that is, and how long it takes for xG data to actually have meaning?
It feels like it takes about a half season or more for xG to stabilize, but I can't quite explain why with any sort of eloquence.