
Mobius Grey posted an interesting link that gives a perspective and insight into odds compilers mindset. (Matthew Trenhaile)
https://tradematesports.medium.com/how- ... 36b4937439
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Useful websites I use for the raw data are;
https://www.soccerstats.com/
https://fbref.com/en/
https://footballxg.com/xg-league-tables/
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Peter has a useful 18 minute you-tube video that shapes the way I crunch the data.
https://www.youtube.com/watch?v=Ihkv3kSWt-Y
Also, this Y-T video about machine learning as well.
https://www.youtube.com/watch?v=00YPWyB5aR4
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I also posted up some thoughts about value bettting on football leagues by creating my own tissue prices and comparing with Betfair. If the market hasn't matured much, I'd compare it with the Betfair sportsbook, and yes, there's still value there too.
The results worked out well overall. (so far)
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My initial approach was posted in the todays football thread. Essentially is follows as;
The Home/Draw.Away data is calculated into %probability and converted into decimal odds for value comparison. The data is a combination of normal data and Xg data that is usually overlooked by some. If a game looks to be value, further checks into team data/injuries, etc needs to be carried out. Most leagues can be evaluated. If value is found in the H/D/A market, then other markets like O/U will too.
It's likely I won't post up lists after lists of games pre-off, and just use it more as a reflective diary on games that went well and others that didn't.
There are big organisations (Starlizard springs to mind) doing all this in some way or with data analytics/analysis and investing for others, so I'm just doing things my way and take a piece of the profit pie where and when I can.

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