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the official betting thread.


Two fairly intuitive points he's made there. Would be interesting to see if the actual EFL closing lines on o/u 2.5 reflected the seasonality effect.

Hadn't seen this book previously. Going for ~£4.50 on amazon. Probably worth a flick through but it's close to 15 years old now, so wouldn't expect there's much actionable insight in it.

 
Two fairly intuitive points he's made there. Would be interesting to see if the actual EFL closing lines on o/u 2.5 reflected the seasonality effect.

Hadn't seen this book previously. Going for ~£4.50 on amazon. Probably worth a flick through but it's close to 15 years old now, so wouldn't expect there's much actionable insight in it.

I remember Cloudy posting about how the xG got ridiculously low at times and thought this might partially explain it.

Never read his book. I'd imagine the research skills might still be relevant. He's meticulous in that from his columns on the RP
 
I played about in python to scrape game-by-game stats from understat and build some basic formulae to work out how much each team was overperforming or underperforming xG on a 3 and 5 game basis (e.g. over the past 5 games Leeds have, on average, a goal difference of 1 goal higher per game than the xG and xGA numbers would suggest).

For the nerdier amongst us, I used Chat GPT to help build the formulae which cycle through the fixtures & compute the rolling 3 and 5 game averages.

The tables are sorted based on their xG over/under performance in the last 3 games

  • The higher a team is on these rankings: the worse their goal difference is relative to xG/xGA
  • The lower a team is on these rankings: the better their goal difference is relative to xG/xGA

Obviously there's no info here that the betting market hasn't already priced in, but it might put some teams on your radar to consider backing or opposing if you think they might return to the norm in terms of being efficient / inefficient at each end .

DXuOs1q.png


NuhhSCO.png


7CvvS3G.png


fGD73X6.png


Xry9YZd.png
 
I played about in python to scrape game-by-game stats from understat and build some basic formulae to work out how much each team was overperforming or underperforming xG on a 3 and 5 game basis (e.g. over the past 5 games Leeds have, on average, a goal difference of 1 goal higher per game than the xG and xGA numbers would suggest).

For the nerdier amongst us, I used Chat GPT to help build the formulae which cycle through the fixtures & compute the rolling 3 and 5 game averages.

The tables are sorted based on their xG over/under performance in the last 3 games

  • The higher a team is on these rankings: the worse their goal difference is relative to xG/xGA
  • The lower a team is on these rankings: the better their goal difference is relative to xG/xGA

Obviously there's no info here that the betting market hasn't already priced in, but it might put some teams on your radar to consider backing or opposing if you think they might return to the norm in terms of being efficient / inefficient at each end .

DXuOs1q.png


NuhhSCO.png


7CvvS3G.png


fGD73X6.png


Xry9YZd.png
I wouldn't mind having the skills to pull this together so easily.

A suggestion from me (though you may already be doing it) would be to also have these tables for the last 10 matches/20 matches/full season worth of matches, so that you can see a bigger picture of what's happening.

Things that are happening in the last 3-5 games could just be a correction to things that were going on before that, and could be just as likely to continue for a while rather than regress.
 
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