This set of predictions was meant to go live on Saturday morning, but a glitch in Blogger's post scheduler meant it didn't happen, sorry about that. I had a very kind trail from @OptaPro on Twitter this week too, so it was an even bigger disappointment. On the plus side, I couldn't post manually because I'd gone paragliding and I love paragliding, it's even better than statistics and football matches.
Back to football and the model's new and improved so I thought it would be worth reposting these predictions and explaining what I've been up to.
If you'd like a bit of history, try my past posts. I'm using an agent-based model of football matches to try to predict results and as usual, predicted starting line-ups for the teams are from Fantasy Football Scout. At some point, I'll build an engine to scrape the actual announced line-ups half an hour before kick off and re-run the model automatically, but one step at a time...
The big improvement I've been working on, which has turned out to make a small overall improvement in prediction accuracy, is to allow players to have a good or bad game. Previously, each player always performed at their average level - so for example if their passing accuracy averages 80% they'll always pass at 80% accuracy - but now I use the standard deviation of each player's passing accuracy and sample from a normal distribution, to decide how a player will perform. What this means is (to pick a couple of random examples) a very consistent player like Paul Scholes will always pass well in the model. A player like Darren Bent will have passing accuracy that's all over the place, with some very good games and some very bad ones.
This "form" feature is random for the minute, although I have spotted some interesting relationships in the data and I think to an extent it's predictable when players will have a bad game. Check out this tweet for an example. I've promised EPL Index (where all the data comes from) an article on this though so it will have to wait for a minute.
Onto the predictions! They were predictions, honest.
And how did we do this week?
I actually had a small bet on these and am up for the weekend already, with the Spurs game still to play, so it didn't go too badly. From here, I picked:
Fulham to win (won)
Newcastle to win (won)
Wigan to win (won)
Sunderland to win (lost)
Norwich and Everton to draw (lost)
Spurs to win (playing tonight)
Of the remaining games, I don't trust Arsenal in the model at the moment. They pass well and it's largely a passing-based sim, so it seems to overestimate their chances, although it called this result correctly (just about). Who trusts Arsenal to reliably get a result anyway? The model called Man City and Man United's results correctly, but the odds were rubbish so I left those two.
If Norwich hadn't been allowed to take that last corner, I'd have had an even better weekend! This model's not doing so badly, if I do say so myself. Definitely worth persevering with.
I promise faithfully, on my honour, to have predictions up before the next set of matches this weekend.
Back to football and the model's new and improved so I thought it would be worth reposting these predictions and explaining what I've been up to.
If you'd like a bit of history, try my past posts. I'm using an agent-based model of football matches to try to predict results and as usual, predicted starting line-ups for the teams are from Fantasy Football Scout. At some point, I'll build an engine to scrape the actual announced line-ups half an hour before kick off and re-run the model automatically, but one step at a time...
The big improvement I've been working on, which has turned out to make a small overall improvement in prediction accuracy, is to allow players to have a good or bad game. Previously, each player always performed at their average level - so for example if their passing accuracy averages 80% they'll always pass at 80% accuracy - but now I use the standard deviation of each player's passing accuracy and sample from a normal distribution, to decide how a player will perform. What this means is (to pick a couple of random examples) a very consistent player like Paul Scholes will always pass well in the model. A player like Darren Bent will have passing accuracy that's all over the place, with some very good games and some very bad ones.
This "form" feature is random for the minute, although I have spotted some interesting relationships in the data and I think to an extent it's predictable when players will have a bad game. Check out this tweet for an example. I've promised EPL Index (where all the data comes from) an article on this though so it will have to wait for a minute.
Onto the predictions! They were predictions, honest.
And how did we do this week?
I actually had a small bet on these and am up for the weekend already, with the Spurs game still to play, so it didn't go too badly. From here, I picked:
Fulham to win (won)
Newcastle to win (won)
Wigan to win (won)
Sunderland to win (lost)
Norwich and Everton to draw (lost)
Spurs to win (playing tonight)
Of the remaining games, I don't trust Arsenal in the model at the moment. They pass well and it's largely a passing-based sim, so it seems to overestimate their chances, although it called this result correctly (just about). Who trusts Arsenal to reliably get a result anyway? The model called Man City and Man United's results correctly, but the odds were rubbish so I left those two.
If Norwich hadn't been allowed to take that last corner, I'd have had an even better weekend! This model's not doing so badly, if I do say so myself. Definitely worth persevering with.
I promise faithfully, on my honour, to have predictions up before the next set of matches this weekend.
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