As is usual over the summer, I’ve been tracking how much each Premier League and EFL squad has changed since last season. It’s been three weeks since the first version, so I felt like it was worth an update. I’ve harvested the data from the official retained lists, the BBC transfer page, Transfermarkt and a variety of Twitter sources, so
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As is usual over the summer, I’ve been tracking how much each Premier League and EFL squad has changed since last season. I’ve harvested the data from the official retained lists, the BBC transfer page, Transfermarkt and a variety of Twitter sources, so hopefully I haven’t missed anything. You can see the all the data I’ve compiled in a Google
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As usual, I’ve taken the newly-released fixtures for the Premier League and EFL and combined them with an aggregation of leading bookmakers’ odds to visualise where each club’s tough and easy matches appear to be next season. Each row in the matrix below lists a club’s league matches in chronological order, colour-coded based on how difficult they appear to be
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As we’re now at the end of the season, I’ve refreshed the club-by-club attack breakdowns. These are explained in detail here, but in summary they are simple scatter graphics that work as follows: Explanation Each graphic shows a club’s main attacking players: those who have: Featured for at least a quarter of their total pitch minutes in the league this season,
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These graphics are intended to give a quick visual overview of the age of players that each club has fielded in league matches using a technique very similar to “population pyramid” graphs, although I’ll freely admit that they’ve come out with shades of the Rorschach test (or as someone observed on Twitter, the Habitat lighting range). Explanation Hopefully they’re fairly self-explanatory, but
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As is traditional, here’s a look at how each club moved around the league table over the course of the season. This graphic works as follows: The clubs are listed down the side, in the order they finished in the final league table Each row shows how many days that club spent in each league position, based on where they
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What I’ve done below is to measure how long each club has spent in each of the three main “game states” – i.e. winning, drawing and losing. I’ve then converted these into percentages because I include stoppage time, which means that even when every club has played the same number of matches they will have a slightly different total number
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Now that the season is over (barring play-offs which I exclude for consistency), I can create final versions of the scatter graphics. These compare the attacking and defensive performances of every team in a division – each of the four graphics is explained briefly below and at length here. Shot dominance First of all, here is how the number of shots
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These alternative league tables are calculated using expected goals (as seen in my match timelines). There’s a full explanation in the first post here.
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Here are the individual timeline graphics for the latest round of matches, comparing the quality of chances created by each team with goals scored. There’s a full explanation of these here for anyone who’s not seen them before.
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