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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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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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 (from OddsChecker) 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
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This afternoon I thought I’d dust off something I found useful when picking my FPL team last year. While it’s early days and the fixture list hasn’t even been announced yet, I’ve had a look (with Pete Fabianksi‘s help) at which players are proving most popular. The following quick charts rank each club in terms of both their share of
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With the season over it’s time to update 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 taken by each club compares with those they face in return.
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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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Now we’re at the end of the season I thought it was worth refreshing 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
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As we’re at the end of the season, I thought that it was worth updating the long-term trend graphics to see how each club has fared in recent seasons. These are explained in full here and briefly below and this year I’ve expanded the data range from three seasons to five to get a fuller picture of how each club’s
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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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The way the graphic below works is pretty straightforward and it’s exactly the same idea as the weekly permutations: the outer bar shows what’s mathematically possible over the rest of the season while the inner chart is a distribution of what’s actually likely to happen based on my latest model predictions.
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