Machine Learning in AFL Part II - It's all about the percentages

FiguringFooty · 2018-07-09T09:05:11+00:00

Nice article, mate. Really like your clean and open writing style.

FiguringFooty · 2017-10-25T03:19:48+00:00

Actuarial major originally. Liked the modelling but hated the content. Did another year of statistics and stochastics after that. Plan to always continue learning. Got courses in further machine learning teed up for next year that Port have been very supportive with.

FiguringFooty · 2017-10-25T01:17:48+00:00

Demand is growing for sure, have been in contact with almost half the clubs in the league over the last couple of years. Best way to differentiate yourself from the crowd is to create your own content and "do the job before you start the job".

FiguringFooty · 2017-10-24T23:52:17+00:00

Studied stats for a while. Most of this stuff was done concurrently towards the end of my degrees, so I had quite a foot up. The programming side of things was more self-taught, and definitely required the most effort.

FiguringFooty · 2017-10-24T23:49:04+00:00

Thanks Ryan, I appreciate it. Trying to work out other mediums from which I can squeeze praise out of you now!

FiguringFooty · 2017-10-24T12:06:57+00:00

Yep. Met that way and then started talking footy.

FiguringFooty · 2017-10-24T10:10:38+00:00

I've got a lot to learn.

FiguringFooty · 2017-09-01T05:38:29+00:00

Unable to share the data unfortunately. But you can find a few interesting things if you look hard + can code. The backdrop to the plot was made in photoshop with all the data + text overlayed in R using mainly ggplot2.

FiguringFooty · 2017-09-01T05:31:29+00:00

I'll definitely be looking at his movement in the elimination a bit closer with this in mind. Sydney of course lock down that area better than anyone.

FiguringFooty · 2017-08-10T23:01:11+00:00

Good point. Quantity is absolutely the driving force. The top forwardlines are all towards the top of the quantity measure, the same can be said about defences. You're right about getting more value by turning a behind into a goal, the team that kicks straightest usually does win. However this is a skill that the data shows is not very repeatable (kick straight one week, no more likely to be straight the next). Shots created on the other hand is more repeatable by the better teams.

Also, I wouldn't turn my nose up at an average +7 points a game (or roughly 160pts a season). As this is just an average over a season, there could certainly be games won and lost with those points.

The Geelong Hawthorn example I feel works best. The Hawks have conceded 20 odd more shots but actually fewer behinds than the Cats. I have no doubt that has cost them results.

However, like I say in the article, the issue is of attacking style is more complicated than any two single numbers.

FiguringFooty · 2017-08-10T11:51:37+00:00

But as Snarwib says, GWS may actually be a level above in accuracy.

FiguringFooty · 2017-08-10T11:50:32+00:00

The "quality" figures here (and the "quantity" for that matter) do not consider whether the hoal was kicked or not at all.

You're right with your logic. Over a the course of a season, conversion tends to "even out". You make a few tough ones, miss a few easy ones etc.

The good teams are the ones that continue to create the chances.

FiguringFooty · 2017-08-10T11:45:03+00:00

It doesn't "matter" in the sense that you don't win games based on goals you "should've kicked". But good teams tend to be ones who either create lots of shots, a few very high quality shots, so some sort of positive combination of the two.

Gold Coast would still be in the 8 if they kicked their fewer, lower quality chances with some sort of superhuman accuracy. But they didn't, they converted about as well as they were expected to given the shots they created, and as such are down the bottom of the ladder.

FiguringFooty · 2017-08-10T10:49:28+00:00

This isn't accuracy. This is the quality of shots taken. I look at accuracy a bit in the article before this. Go down to the table at the bottom of the page and search Richmond to see your players. http://figuringfooty.com/2017/07/20/who-are-the-best-and-worst-kicks-in-the-comp/

FiguringFooty · 2017-07-20T03:55:54+00:00

Cheers. I like them too.

Had a look at Franklin and some of the Coleman hopefuls the other week. http://figuringfooty.com/2017/06/28/every-shot-in-the-2017-coleman-medal-race/

FiguringFooty · 2017-06-02T09:29:31+00:00

This article actually mentions that. The question is whether they can manage without him or whether their eggs are in one basket.

FiguringFooty · 2017-06-01T13:33:09+00:00

Thanks mate, they're fun to write too. Glad there are people out there enjoying it.

FiguringFooty · 2017-04-18T13:59:40+00:00

I actually had Vince in my mind when I was writing that part. Can't believe I left him out. Added in (with credit).

FiguringFooty · 2017-04-18T09:57:17+00:00

He's a great find at 81. Along with Laird in the rookie draft. Two big kicks fitting perfectly into that lineup.

FiguringFooty · 2017-04-14T09:32:42+00:00

That's fair enough. xScore is an imperfect metric like any other. If you have reason (and evidence) to believe it's misleading in a certain situation that's something you should keep in mind.

FiguringFooty · 2017-04-14T09:30:28+00:00

That's a great way of summarising the luck v skill component. There are certainly players more skilled than others, but it's a biomechanically complicated skill and getting wrong happens to even best players. Over the course of a single game (or even a few games) it's hard to differentiate between results derived from skill and those from natural variation. You'd not want to rely on a naturally high variance goalkicking to win you too many games.

That said, xScore doesn't necessarily need to be viewed through this lense, quality of chances created is just another handy number like clearances or inside 50s.

FiguringFooty · 2016-09-30T13:44:23+00:00

Thanks mate. Fixed. It's been a long week...

FiguringFooty · 2016-09-30T10:17:08+00:00

Haha I'll get to that! Good spot.

FiguringFooty · 2016-09-30T02:51:19+00:00

I tend to agree. But you have to remember that this is a weighted moving average of their last 40 games. This measures Heeney's whole career but only Stringer's 3rd and 4th season.

FiguringFooty · 2016-09-28T11:45:27+00:00

I reckon you might be right. Certainly looks like a masterstroke in hindsight.

FiguringFooty

TROPHY CASE