As usual, I’ve taken the newly-released EFL fixtures 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 and the clubs themselves are
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I want to focus more on the impact of individual players next season but I realised that I didn’t even have a good way of visualising who was on the pitch and when, so today I’m road-testing another new graphical template. Explanation What I’ve done for every club is to plot out their league campaign minute-by-minute and then shade in a bar
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Shortly after the regular EFL season had finished I compiled a set of club-by-club graphics which tracked how their league position changed over time compared with my model’s predictions. When doing so I noticed that even the pre-season predictions (made without any knowledge beyond last season’s performances) were surprisingly accurate, so I thought I’d try to work out how good they were. I didn’t
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Today I’m updating one of my more off-the-wall pieces, looking at how different the league table would be if matches finished earlier. These usually tend to generate a few comments which begin with “If my Auntie had…” but I think they’re an interesting way to identify teams who tend to start or end matches unusually well (or badly). What I’ve done
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With the League 1 play-off semi-finals kicking off this evening, I thought I’d see what the E Ratings model made of them. If you’ve not seen these graphics before, they’re explained fully here, but in summary they’re intended as a simple pre-match comparison of two teams using high-level performance data. The graphic has three sections: The current ratings (with ranks) of
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After last week’s onslaught of post-season League 1 graphics I realised there were still a lot of useful – but less obviously visual – stats that I hadn’t managed to work into anything I’d produced. I’m working on a couple of template designs to house some of the simpler stats next season so I thought I’d road-test one of them here by
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With the regular League 1 season over, I thought that I should update the graphics which track my model predictions to see how each team performed relative to its predictions. Explanation The way it works is pretty simple: for pre-season and after every round of games it shows the results of my simulations for a specific club in terms of both:
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