The game will always precede statistics, but the shadow it casts is gradually shrinking, becoming much less elongated. Since advanced statistics have been generating models and metrics to measure overall performance, reality is much closer to being reliably measured. From general and large-scale comparative models, clear and precise perspectives and panoramas can be extracted.
Let’s delve into one of these assumptions. Which teams generate and concede the clearest chances? We could look at shots on goal for and against, but we would be missing out on the quality of these chances. For that, as in many other cases, the Expected Goals (xG) model and its derivatives come into play. If the model is good and its functions and variables to be used are clear, reality will be very close to being well measured.
xG per shot is an extraordinarily useful tool to quickly grasp a solid statistical impact on the quality of chances. If we are close to the game and its competitions, following them closely and consuming statistical content regularly, it can be a simple method to begin explaining playing styles, performance trends, and luck in front of goal.
In this xG per Shot for and against graph, we can observe, in the bottom right quadrant, the most desired relationship, that is, More xG per Shot and Less xG per Shot Received. There we find the teams that, on average, generate clearer chances and concede fewer clear chances against them: Inter Milan, FC Barcelona, Juventus, Arsenal, and AC Milan.
These types of scatter plots gain value when they debunk some personal sensations or reinforce them. It’s a fact that this season’s Manchester City has conceded much more in defense, both in volume and clarity of chances against. Or how Aston Villa’s playing style and defensive level have led them to generate very clear chances but also be, alongside Luton, the team that has received the most xG per Shot on average.
Finally, it’s very interesting to see how the two teams that have, on average, generated the clearest chances are Girona and Newcastle United. The former with high possession rates but great flexibility in organizing from the back, creating many spaces tactically; the latter thanks to their decision to have less possession, splitting the number of attacks, and ensuring many transitions and fast attacks with ample space ahead.
Founded in 2017 as a consultancy, Driblab has driven innovation through data in all aspects of professional football. Thanks to a transversal model, its database collects and models statistics in all directions. From converting matches and videos into bespoke data for training academies to developing cutting-edge technology, helping clubs, federations and representative agencies in talent scouting and transfer markets. Driblab’s smart data is used by clubs all over the world, with success stories such as Dinamo Zagreb, Real Betis and Girondins Bordeaux among others. Here you can find out more about how we work and what we offer.
