Player churn: League 2

For those who weren’t aware, Ben Huxley (of @bootifulgame) and I now both form part of the Press Association’s product development team, combining our respective powers to come up with data and graphical innovations, with football very much part of our remit. There are quite a lot of ideas flying around the office which don’t immediately find their way into our products but which at least one of us will doggedly insist is worth pursuing, so we’ve decided to start developing some of these between us.

Here we have the first part of our first such collaboration, born out of a discussion around squad ‘churn’ across the Football League. Having endured the traditional summer babble around transfers we wanted to understand to what extent squads had actually evolved over the summer and whether there were any interesting patterns here. Have the promoted and relegated sides experienced more player turnover as they strengthen and downsize respectively? Who’s been able to keep the core of their squad together and does this get trickier as you move down the divisions?

On the eve of the Football League season we’ve produced a graphic for this season’s League 2 teams, to be followed by versions for the other English divisions, up to and including Premier League, in due course.

The graphic

First of all we came up with a headline measure for continuity: of all the minutes played across a club’s squad in league matches last season, what percentage were accumulated by players who are still at the club? This gives us a more reliable measure than just counting players, as it will be dented more by a first team regular leaving than by the departure of a fringe player. The graphic is sorted using this metric, which is represented by the grey bar and the large percentage next to each team’s name.

We also wanted to see what else we could get into our graphic without cluttering it and decided to look at to what extent each club relied on a core of players. We counted how many players had represented their clubs for at least 25% of possible league minutes, which felt like a sensible definition of a ‘first team’ player. Counting how many players met this criterion for each club gives us a useful measure of squad rotation and we can also count how many of these have remained at the club. The player icons show this – the solid players on the left are still with the club while the faded ones on the right have left.

Finally we added in a measure of what percentage of the club’s total league minutes had been accumulated by their 11 most-used players last season, which gives us a different way of looking at how much or little each manager rotated their squad. This is shown by the thermometer-esque coloured bar overlaid onto the grey one.


As usual, click to enlarge, or go and look at it on Ben’s blog which is better designed to house visual content.


  • What’s really interesting here is the teams relegated from League 1. 3 of the 4 teams that came down have jettisoned the bulk of their squads over the summer – with Bury’s team virtually unrecognisable – while the 4th, Hartlepool, have only moved on 4 of their first team regulars from last season.
  • Despite surviving on goal difference last season, Dagenham & Redbridge’s line-up is the most similar to that of their previous campaign.
  • Wimbledon and Portsmouth’s heavy personnel changes and use of short-term deals during 2012/13 are evident here – their most-used players racked up a significantly smaller proportion of the clubs’ pitch minutes and many have since moved on.


  • Impressive work, another factor to consider for the new season. I’m doing all sorts of data at my club, and I really like the player icons. Any tips where I can do similar ones as you’ve done?
    Great work – keep it up, always a great read

  • Hi,

    Great read as always. Interested to find out how you can produce that kind of player icons. Would really appreciate if you could share that.

  • Have you accumulated any retrospective data for ‘churn?’ Does a nearly relegated team such as Dag & Red fare better with more churn or less? Has there been a correlation between churn and the length of time a manager stays at a club? I’d bet we’d find trends that defy conventional practices.

    • Great suggestions – PA has this data going back over more than a decade, so these are certainly things we can look at. As always it comes down to free time, but it’s on the list!

  • Great work, good luck with the new venture,

    Have you given any thought to where the players are coming from/going to?

    • Many thanks. I have given it thought and it’s something we’re looking at separately. With this one it just came down to time and what we could fit on the page, but it’s definitely something I want to cover.

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