Displaying Player Stats By Position

Displaying Player Stats By Position

Previous Data Visuals

Data visualisation is nothing new in football. There are many others out there who do different type graphs to show their data. StatsBomb are well known for their radars and there are several who post bar charts. Ashwin Raman posted this blog post about how he did his bar charts and I’ve taken inspiration from them. All data is from WyScout and using their definitions (more details here), taking players from each position with a minimum of 900 minutes played this season.

Metrics by Position

As each position has different responsibilities, both attacking and defensive, they would need to be judged on different aspects. For example, it wouldn’t be suitable for a central defender to be judged on how many shots they take as it isn’t their main role. Each graphic will show a combination of attacking, defending and ball progression (how they get the ball up the field, either passing or dribbling).

Center-Backs

As a defender, their main job is to protect the goal by preventing shots or limiting shots to low quality chances. Of course winning possession through tackles, interceptions and 50-50 duels is also a key part to being a defender, but there is a debate around the usefulness of these defensive stats as team tactics and game plans can affect outcome. A team who possess the ball a lot less than a team who have a possession-based game plan will likely have more defensive actions.

Note: Still getting WyScout stats figured out, but Van Dijk’s tackling success rate does seem very low.

Full-Backs

While still a defender, they have extra attacking responsibilities and more likely to dribble forward too. As defenders in an wide position, it would be interesting to look at crosses blocked. This would not only look at their ability to close down a cross from an opponent, but if they blocked more crosses, they would be less likely to be beaten in 1v1s. As with most defensive statistics, teams who defend closer to their goal more often will likely face more crosses and if a defender closes an opponent down well, they could prevent a cross by forcing a backwards pass.

Center-Midfielders

This is where style can come into play as the role of this player can be very different. To keep things as simple as possible. Central midfielders can either be involved in the build-up from the back, progress balls into the final third (deep completions), creative play in the final third (final third passing), chance creation (expected assists), shot creation (expected goals) or winning the ball (tackles and interceptions). Some might be a combination of the both and while these graphs might also show a player’s style and role in the team, it will also show their effectiveness.

Attacking Midfielders / Wingers

Looked at as the line between the midfield and the strikers, often looking to link up with the striker and support them, especially in systems that use one striker. While attacking midfielders playing centrally will not cross the ball often and the top teams generally don’t cross the ball a lot, it does have a small look into a player’s style. Maybe in the future these will be separate, but for now they can stay together.

Strikers

As the position most associated with the final third, they will be judged on their end product, either scoring or assisting, as well as their general play in penetration into the penalty area. While tackling and interceptions is a very basic look at what defensive contributions strikers make to a team, it is the best I have available at the moment.

Unsurprising outcome: Messi still ridiculously brilliant

Future Developments

Finding the best way (using data available) to adjust defensive actions would be the most important development. With football statistics and models continuously updating and being created, the metrics used in the bar graphs might be changed to adapt to these. At some point, creating my own metric would be idea, but with limited data and very little experience (at the moment) with using Python and R statistical programs, it isn’t a possibility at the moment.

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