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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Continuing today’s onslaught of end-of-season graphics for League 1, here’s an updated version of the squad age profile graphic. These 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
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A quick explanation You can skip this if you’ve seen these before. As usual, here are timeline graphics for the latest round of matches. There’s a full explanation of these here, but in a nutshell they track how each club’s expected goals tally (the number of goals that the average team would have scored from the chances that they created) increased over the course of
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