Football Musings

[wearthefoxhat]

Took a look at the Dixon-Coles model, after watching Tom Brownlee's instructional you-tube video. (19.42 minutes)

He also gives a link to a free spreadsheet with all the formulas.


https://www.youtube.com/watch?v=SiBhLYf8YJ4

[Euler]

AI can do a great job of creating models now, as long as you ask it for specifics. It can also be produced it in a self-contained HTML file to make things nice as simple.

[wearthefoxhat]

AI can do a great job of creating models now, as long as you ask it for specifics. It can also be produced it in a self-contained HTML file to make things nice as simple.

Thanks, I'll take a look.

[wearthefoxhat]

Cobbled together the Dixon-Coles model as previously posted, and added it into my existing excel framework. Looking at the Championship games from last night and tonight, this was the output. The data (xG) is my own, based on certain in-play metrics, but they're similar to what's out there already on SofaScore...etc

Week 28.png

The ticks show the tissue to be greater than the live odds, but the bigger value ones >= 10% edge, are brought more to the fore.

The top 3 are highlighted for quick reference.

[Euler]

Football is one of those sports where variance dominates a lot. You’ve got very few scoring events, long periods where nothing happens, and then one incident, a deflection, a refereeing call, an early red card, completely changes the shape of the game.

From a modelling perspective that makes it extremely noisy. You can have the right read on a match but one one low-probability event swings everything.

That doesn’t mean modelling is pointless, but it does mean you have to be realistic about what it can deliver. Models are much better at describing long-term behaviour and edges across many matches than they are at predicting the outcome of a single game.

[wearthefoxhat]

Football is one of those sports where variance dominates a lot. You’ve got very few scoring events, long periods where nothing happens, and then one incident, a deflection, a refereeing call, an early red card, completely changes the shape of the game.

From a modelling perspective that makes it extremely noisy. You can have the right read on a match but one one low-probability event swings everything.

That doesn’t mean modelling is pointless, but it does mean you have to be realistic about what it can deliver. Models are much better at describing long-term behaviour and edges across many matches than they are at predicting the outcome of a single game.

It's been said, all predictive models are wrong. (but some are useful) - Prof George Box. (I suppose I'll find out in the long run)

I built in predictive elements based on Home / Away records and strength of previous opposition, but as you say variance for one game can put the kibosh on a well structured trade, ending up being right, but with the wrong result.

I have noticed, where there's "value" in one market, ie: BTTS, it carries over into the other goal markets too. (common sense really)

[firlandsfarm]

Interesting second goal from Arsenal from a corner but not a scorer you might expect ... The scorer was Arsenal's reputation at scoring from corners! The ball came over and no Arsenal player was within a yard but the Leeds goalkeeper and two defenders all jumped for the ball, all determined to defend the ball but they actually all got in each other's way and scored an own goal!

[Euler]

Interesting second goal from Arsenal from a corner but not a scorer you might expect ... The scorer was Arsenal's reputation at scoring from corners! The ball came over and no Arsenal player was within a yard but e Leeds goalkeeper and two defenders all jumped for the ball, all determined to defend the ball but they actually all got in each other's way and scored an own goal!

Will have to have a look at that.

Had it down as 0-1 to Arsenal

[firlandsfarm]

Interesting second goal from Arsenal from a corner but not a scorer you might expect ... The scorer was Arsenal's reputation at scoring from corners! The ball came over and no Arsenal player was within a yard but the Leeds goalkeeper and two defenders all jumped for the ball, all determined to defend the ball but they actually all got in each other's way and scored an own goal!

Will have to have a look at that.

Had it down as 0-1 to Arsenal

The 'close up' is here https://www.youtube.com/watch?v=LZ84Yfs5JjM at 1:20

[thepressure]

Cobbled together the Dixon-Coles model as previously posted, and added it into my existing excel framework. Looking at the Championship games from last night and tonight, this was the output. The data (xG) is my own, based on certain in-play metrics, but they're similar to what's out there already on SofaScore...etc


Week 28.png


The ticks show the tissue to be greater than the live odds, but the bigger value ones >= 10% edge, are brought more to the fore.

The top 3 are highlighted for quick reference.

So im looking to focus on football really now, how does one even begin to start building their own models like this, and especially something that adapts to inplay / live stats as I think thats where Id find more edge.

Do you have info for the smaller leagues too? What software do you use for stats, i currently pay for soccerscanner.

[wearthefoxhat]

Cobbled together the Dixon-Coles model as previously posted, and added it into my existing excel framework. Looking at the Championship games from last night and tonight, this was the output. The data (xG) is my own, based on certain in-play metrics, but they're similar to what's out there already on SofaScore...etc


Week 28.png


The ticks show the tissue to be greater than the live odds, but the bigger value ones >= 10% edge, are brought more to the fore.

The top 3 are highlighted for quick reference.

So im looking to focus on football really now, how does one even begin to start building their own models like this, and especially something that adapts to inplay / live stats as I think thats where Id find more edge.

Do you have info for the smaller leagues too? What software do you use for stats, i currently pay for soccerscanner.

I started by watching a you-tube video (pre GPT) and pulled together a similar excel sheet. Had to pause it a few times as I went along.

https://www.youtube.com/watch?v=24e_Z4WHR48&t=733s

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Recent one about Dixon Coles model.

https://www.youtube.com/watch?v=SiBhLYf ... WL&index=9

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At varying stages I adapted the sheet with the help of GPT and Gemini (free versions).


I have given out bits of info in this thread too.

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The guy that is developing the soccer scanner is doing a good job. I don't use it, but If you can get the right combination of data to anticipate a goal, then that's an edge right there.

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Another you-tuber is trying to charge £99 for a sheet that I cobbled together based on on my interpretation.

IP Pred.png

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I used the fishy league data as it covers all the small leagues too. (not xG but I can use the base statistics to produce my own)

https://thefishy.co.uk/

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