OptaPro are inviting proposals to present at their Analytics Forum, which according to their announcement:
aims to connect football clubs with analytical communities and experts working outside of the professional game
This will be the third year that the forum has taken place and an impressive number of clubs and other football organisations are represented at the forum, along with plenty of laptop gurus with no relevant playing experience.
I was lucky/skillful enough to have my proposal accepted last year, so I thought it might be useful if I posted my abstract as an example. I’m told that the judges liked it as it was tailored to the audience i.e. club analysts.
When I wrote it, my aim was to define a clear and (hopefully) relevant question and give some idea of how feasible it was and how it could be used. I posted the slides and video of my presentation here if you want to check it out.
If you’re thinking of submitting, then I would highly recommend it. The forum is a great way to meet others working in football analytics and as a member of the online analytics community, it was great to properly meet people I had ‘known’ via Twitter. Presenting was a valuable experience also and led to interesting discussions with people during and after the event.
The closing date for submissions is midnight Sunday 18th October. My abstract is below and good luck with your submissions.
Finding square pegs for square holes: identifying player types for scouting
Proposed area of study: player evaluation
Proposed method: Principal component analysis and cluster analysis of on-ball player data
One consideration when scouting potential player signings is how well they will fit into their new team environment. A common criticism of a perceived failed player transfer is that the player was a “square peg for a round hole”. This study will aim to identify certain player types based on their statistical output to aid finding the “right fit” when scouting players.
I propose using Principal Component Analysis (PCA) to distinguish players based on their underlying performance data (specifically Opta’s on-ball data). PCA is an ideal method for exploring datasets with multiple variables in order to discern patterns in the underlying data. This study builds on my previous analysis that used a similar method to study playing styles at the team level1. I will further extend this by applying cluster analysis to the data to group the players into certain types based on their attributes.
I have already explored the feasibility of this method using publically available Opta data from WhoScored.com and the results are promising. In order to extend the analysis for the forum, I would look to apply the method to more granular data, with a focus on player actions in open-play; the current dataset I have used groups all on-field actions together, which is not ideal. Furthermore, inclusion of location data would provide additional context for the analysis and aid differentiation of players and styles.
The persistence of player traits and classification will be assessed. Providing the dataset is large enough, it should be possible to test this persistence for players staying at the same team and for those who transfer to a new one. This will be a crucial aspect of the analysis and its utility.
The output from the analysis can serve as an additional tool when identifying potential transfer signings by categorising players according to their team role and providing statistical baselines for their performance compared to their peers. For example, the method separates different styles of central midfielders, such as deep-lying playmakers and defensive midfield “destroyers”. Players can then be compared against their peers in that style category based on the important traits of those player types.
By applying these techniques, this study will aim is to provide a more robust “apples-to-apples” comparison technique and find the appropriate square peg for the square hole in question.
1Relevant blog posts available here: