Identifying and assessing team-level strategies: 2017 OptaPro Forum Presentation

At the recent OptaPro Analytics Forum, I was honoured to be selected to present for a second time to an audience of analysts and other representatives from the sporting industry. My aim was to explore the multifaceted approaches employed by teams using cluster analysis of possession chains.

My thinking was that this could be used to assess the strengths and weaknesses of teams in both attack and defense, which could be used for opposition scouting. The results can also be used to evaluate how well players contribute to certain styles of play and potentially use this in recruitment.

The video of the presentation is below, so go ahead and watch it for more details. The slides are available here and I’ve pulled out some of the key graphics below.

The main types of attacking moves that result in shots are in the table below. I used the past four full English Premier League seasons plus the current 2016/17 season for the analysis here but an obvious next step is to expand the analysis across multiple leagues.

Cluster Profile Summary.png

Below is a comparison of the efficiency (in terms of shot conversion) and frequency of these attack types. The value of regaining the ball closer to goal and quickly transitioning into attack is clear, while slower or flank-focussed build-up is less potent. Much of the explanation for these differences in conversion rate can be linked to the distance from which such shots are taken on average.

An interesting wrinkle is the similarity in conversion rates between the ‘deep build-up’ and ‘deep fast-attacks’ profiles, with shots taken in the build-up focussed profile being approximately 2 yards further away from goal on average than the faster attacks. Looking through examples of the ‘deep build-up’ attacks, these are often characterised by periods of ball circulation in deeper areas followed by a quick transition through the opposition half towards goal with the opposition defense caught higher up the pitch, which may explain the results somewhat.

EfficiencyVsFrequency

Finally, here is a look at how attacking styles have evolved over time. The major changes are the decline in ‘flank-focussed build-up’ and increase in the ‘midfield regain & fast attack’ profile, which is perhaps unsurprising given wider tactical trends and the managerial changes over the period. There is also a trend in attacks from deep being generated from faster-attacks rather than build-up focussed play. A greater emphasis on transitions coupled with fast/direct attacking appears to have emerged across the Premier League.

EPL_ProfileTimeline

These are just a few observations and highlights from the presentation and I’ll hopefully put together some more team and player focussed work in the near future. It has been nearly a year since my last post but hopefully I’ll be putting out a steadier stream of content over the coming months.

OptaPro Analytics Forum 2016 accepting abstract proposals

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:

https://2plus2equals11.wordpress.com/2012/11/14/assessing-team-playing-styles/ https://2plus2equals11.wordpress.com/2013/02/19/is-playing-style-important/

Square pegs for square holes: OptaPro Forum Presentation

At the recent OptaPro Forum, I was delighted to be selected to present to an audience of analysts and representatives from the football industry. I presented a technique to identify different player types using their underlying statistical performance. My idea was that this would aid player scouting by helping to find the “right fit” and avoid the “square peg for a round hole” cliché.

In the presentation, I outlined the technique that I used, along with how Dani Alves made things difficult. My vision for this technique is that the output from the analysis can serve as an additional tool for identifying potential transfer signings. Signings can be categorised according to their team role and their performance can then be compared against their peers in that style category based on the important traits of those player types.

The video of my presentation is below, so rather than repeating myself, go ahead and watch it! The slides are available here.

Each of the player types is summarised below in the figures. My plan is to build on this initial analysis by including a greater number of leagues and use more in-depth data. This is something I will be pursuing over the coming months, so watch this space.

Some of my work was featured in this article by Ben Lyttleton.

Forward player types.

Forward player types

Midfielder player types.

Midfielder player types.

Defender player types.

Defender player types.