A few days ago I posted my first sports analytics post. Feeling totally attracted to the topic still, here I am again writing about football.
In that post — linked below — I used frequentist stats to demonstrate the randomness of goal events. But I took it further. The random model explained there — influenced by the Poisson distribution — is applicable in many other fields unrelated to football.
Today we’ll move one step forward and, even though it will be football-centered, the process and knowledge we’ll be going through will be relevant for any data scientist.
Football-wise, we’ll focus on defense and try to analyze Barça’s to see where it could have gone better, both on a team and individual level.
As defense is a broad term — it includes tackles, saves, blocks, and many other advanced stats — I’ll be more concrete and focus solely on shots and goals conceded.
In the 2015–16 La Liga, Barça was the second team to concede fewer goals (29), right after Atlético (18). Even though that’s not bad at all, there’s still room for improvement.
The goal is not to provide solutions, that’s the coaching staff’s work. Our goal today as data scientists or sports analysts is to find the problems and hypothesize so that the staff can take this info and solve the problems on the pitch.
So here’s a brief summary of what we’re going to go through today:
- Background and Context.
- Get the data, transform, and prepare it.
- Analyze shots against and goals conceded by FCB.
- Go even deeper by checking shoots and goals conceded on a player level.