In something a little different today, I'm going to discuss five simple ways data can help football teams gain an advantage. There's this idea among football's old guard that data is complicated and difficult, but the reality is, we try and provide useful insight that is easy to understand and interpret.
1) Corner Touch Maps
This is what we call a corner touch map. Marek and I designed it back in 2014 to help out with the set piece program, and it's probably the dumbest, simplest vis we'll ever build.
What it shows is the first touch by either team after a corner is taken.
Because I can show you a shot map of where teams have had shots off corners, but that only tells you about when they have been successful. These maps more clearly show their plan and - generally - their intended delivery zones.
Check out the map from right-sided corners from Manchester City last season.
This immediately tells you two things. First, they take a lot of short corners, and you need to be ready for those. Second...
City apparently only took outswingers from that side last season, and as a result, neither team had a touch in the box on the left HALF of the six yard box.
And honestly, if I am an opposing coach facing City, my life is nearly impossible as it is, so I am thanking little baby jesus for making my life much easier by allowing me to generally ignore marking that zone (unless there are runners) and overload the zones along the curve. This is just a tiny glimpse of how we use data to help execute set pieces at both ends of the pitch.
2) Arsenal's Left Lane
This is what we call a Defensive Activity Map. Teams are attacking from left to right. The vis attempts to profile where teams are making defensive actions (including pressures), and then compares their defensive activity in each area to the rest of the teams in the league. Zones where they make more actions than average are hotter, and zones where they have fewer actions are greyer or blue.
Arsenal this season are slanted right, possibly because of personnel issues (left back injuries), but maybe as part of a plan? This type of vis doesn't deliver a magical recipe for how to solve/attack tactical issues, but it does help coaches and analysts ask interesting questions. As a coach, I go to the video and try to figure out what is weird. If I am an analyst, maybe I compare the success of attacks down Arsenal's left compared to the right/center and see if there is a vulnerability that way.
3) Similarity Scores
We use these a lot in recruitment, largely because it's easy to talk to coaches about who their ideal player for a position is as opposed to all of the precise things they need that player to do on the pitch.
Once you know which players fill their ideal archetypes, you can then dig into the data for what those players do on metrics you care about, and then plonk down a list of players to scout in the leagues you can afford.
Coach, who is your ideal wide forward?
"I want Lionel Messi."
(Seriously - this always happens. Every coach says this exact same joke.)
And because we are indulgent number wonks who have this already set up in StatsBomb IQ, we can answer the question honestly.
The most similar players to Messi 17-18 in our current data set are:
Neymar Messi (18-19 edition)
and Nicolas Pepe, who has been on fire so far this year.
But the fun part of this is that you can actually narrow down the data to the leagues you can afford to buy players in and still have the exact same conversation.
Who is the Lionel Messi of League One? 2017-18 Bradley Dack, maybe? Or Conor Chaplin?
How about in Austria Bundesliga? Uh... Andrei Ivan?
Look, I'm not saying the data is always right in these situations, but shopping for the poor man's Messi apparently comes with serious limitations.
4) Evaluating Goalkeepers
On Monday, we released phase 1 of the Goalkeeper Module into StatsBomb IQ. It allows teams to profile goalkeepers statistically across a broad range of metrics that haven't really been available before because in other data sets, we never knew where the keeper was when a shot took place.
We were messing around with some of the visualisations in testing and came across this fun one for last year. David De Gea and Joe Hart faced almost exactly the same amount of xG in shots on target last season, but how that xG came about and what happened after that was dramatically different.
The vis above is broken into xG buckets, and you'll notice that the shots Hart had to content with were generally much higher quality than those De Gea dealt with. Sadly, nearly every high xG shot Hart faced also made it into the back of the goal.
When it comes to analysing and evaluating GKs with stats, we're just getting started. Expect to see a lot more from us on this topic in the coming weeks.
5) Passing Tendencies at the Team and Player Level
Stats don't have to be complicated to deliver powerful, useful insight. And often the simple stuff is the most effective IF you know where to find it.
Ted Knutson firstname.lastname@example.org @mixedknuts