I’ve updated my World Cup probability graphics (explained here) following today’s matches – basically recalculating each team’s FIFA ranking points and then re-running my model to work out their chances of reaching each stage and which opponents they’re likely to be drawn against. How the knockout stages could pan out This has been split into two graphics to stop if
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I’ve created specific World Cup probability graphics (explained here) for England v Scotland today, as it’s probably one of the most pivotal games in the first round of group matches. Basically I ran the model with a 2-1 England win baked in, then again with a 2-1 Scotland win, and created the team-specific graphics to see how they differed. If
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I’ve updated my World Cup probability graphics (explained here) following today’s matches – basically recalculating each team’s FIFA ranking points and then re-running my model to work out their chances of reaching each stage and which opponents they’re likely to be drawn against. How the knockout stages could pan out This has been split into two graphics to stop if
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The plan During the 2018 World Cup I was messing about with a simple prediction model based on the Elo ratings and came up with graphics like this and this which people quite liked, so I thought I’d try it again for the Women’s World Cup this year. What I’ll be aiming to do throughout the tournament is create updated
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Now that we’ve reached the end of the season, I thought it was worth updating 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
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Now that we’re at the end of the season, I thought that it was about time I updated the long-term trend graphics to see how each club fared throughout their last two-and-a-bit seasons. These are explained in full here and briefly below. Explanation These are adapted from a very similar design by the excellent Swedish blogger Zorba138 intended to track a club’s
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Here’s another quick end-of-season round-up that I always find quite interesting. 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 added time, which means that every club will have a slightly different total
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