Certified Legendary Thread The Squiggle is back in 2023 (and other analytics)

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It's worth noting that a really good model (or the betting line) gets the predicted margin wrong by 27 points per game on average. That's four and a half goals! And upsets are very common, occurring once every three or four games.

But correctly picking upsets is super difficult. At least in betting, when you tip an upset you get an oversized reward, which makes it worth doing more often. In a tipping comp, you get 1 tip no matter how unlikely it was. So you need to be smart or lucky, or both.

Of course, the person who wins the comp is always a contrarian, because that's what distinguishes them from the pack, but most contrarians will do worse than average.


So I'm tipping an upset every time the line is <13.5 points after this post.
 

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I’m not interested in overall, just winning a round or two (or more) in a 50-80 person comp.

I’ve decided on the following approach, based on differences between odds and tipping proportion.

This is an example using round 1 2019 data. Games in order eg tiges first. Proportion is % tipped divided by % chance per odds.

0A620AAE-81F6-45EB-9594-70732921FFEE.png

Interested in any feedback.
 
I’m not interested in overall, just winning a round or two (or more) in a 50-80 person comp.

I’ve decided on the following approach, based on differences between odds and tipping proportion.

This is an example using round 1 2019 data. Games in order eg tiges first. Proportion is % tipped divided by % chance per odds.

View attachment 633604

Interested in any feedback.

I just do it last second using Squiggle, the odds and my gut. I always end up top 10-20%. Only own the office tipping once when I was out of the country for the last 9 rounds and so did them all before I left :thumbsu::cool:
 
I’m not interested in overall, just winning a round or two (or more) in a 50-80 person comp.

I’ve decided on the following approach, based on differences between odds and tipping proportion.

This is an example using round 1 2019 data. Games in order eg tiges first. Proportion is % tipped divided by % chance per odds.

View attachment 633604

Interested in any feedback.

You're not in my tipping comp are you? It's a beauty for trying to pull off what you are..
 
I’m not interested in overall, just winning a round or two (or more) in a 50-80 person comp.

I’ve decided on the following approach, based on differences between odds and tipping proportion.

This is an example using round 1 2019 data. Games in order eg tiges first. Proportion is % tipped divided by % chance per odds.

View attachment 633604

Interested in any feedback.
I think that's a great way to do it. Whether the values you've chosen (e.g. "80%", "less than 10%") are optimal will depend on the makeup of your comp, so you could adjust as you go based on whether you would be winning the week if all your tips had come up -- if you'd have gotten 9 and the next closest person would only have gotten 6 or 7, you're tipping too many upsets; if several other people also would have gotten 8 or 9, you're not tipping enough upsets.
 
I normally use similar logic once I get behind in a tipping comp - tip 6 or 7 faves and look for 2-3 upsets each week - if you nail the upsets you shoot up the rankings really quick. But if you miss the upsets and a couple of the fave tips lose you end up with horrific weekly scores. You have to be pretty high risk to win the big comps though.
 
Hey! I'm on a podcast, talking about why Richmond fans are lunatics, BigFooty is a stew, and stats are awesome.

Find it here:

https://player.whooshkaa.com/episode?id=347991

It's a new podcast called "Chilling With Charlie," running interviews with various sports stats people: https://player.whooshkaa.com/shows/chilling-with-charlie

You can find it on the iPhone Podcast app too.

Very good. You've got an interesting background. And the clubs could learn a fair bit from the data based approach. And old players / commentators could learn a lot
 
Now this thread has come back to life, I'm shifting my algorithm thread here. (Originally posted on Thursday morning, before the Richmond game).

Ratings from the end of last season

1. West Coast 26.4
2. Collingwood 22.7
3. Melbourne 19.4
4. Richmond 12.4
5. Geelong 11.7
6. GWS 9.9
7. Adelaide 8.5
8. Essendon 7.2
9. Hawthorn 2.9
10. North Melbourne 2.5
11. Western Bulldogs -1.9
12. Brisbane -2.2
13. Port Adelaide -2.3
14. Sydney -2.4
15. St Kilda -8.0
16. Fremantle -11.3
17. Gold Coast -16.2
18. Carlton -18.7

Carlton v Richmond +31
Collingwood +11v Geelong
Melbourne +27 v Port Adelaide
Adelaide +12 v Hawthorn
Western Bulldogs +7 v Sydney
Brisbane v West Coast +16
St Kilda +14 v Gold Coast
GWS +8 v Essendon
Fremantle v North Melbourne +5

I'll worry about season predictions once the football has actually started.
 
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I’m not interested in overall, just winning a round or two (or more) in a 50-80 person comp.

I’ve decided on the following approach, based on differences between odds and tipping proportion.

This is an example using round 1 2019 data. Games in order eg tiges first. Proportion is % tipped divided by % chance per odds.

View attachment 633604

Interested in any feedback.

Interesting approach so I'm wondering where you get the % tipped figures from? There's the whotippedwhat website but those differ a fair bit from yours above.
 

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Interesting approach so I'm wondering where you get the % tipped figures from? There's the whotippedwhat website but those differ a fair bit from yours above.

Just from the espn footytips site. They are now out of date.

I picked the margin thurs night and got cats last night. Tipping all upsets from now on except north and crows.
 
Squiggle is very reactive in the early rounds, so these big upsets are causing chaos!

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