- Aug 18, 2009
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- AFL Club
- Richmond
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- #1,851
Models don't take a "bottom-up" approach like you describe - because of the problems that you also describe.Very rough guide, there are too many variables to dissect if you look at a broader picture.
For example, Collingwood last year despite winning the same amount of games never really hit form largely due to a tweak in game plan. So from a fundamental perspective the change was visually so but the outcome very similar.
So how does one measure 'fundamentals' in numbers? Well one could argue disciplined aspects of the game like contested possession, pressure, tackles, DE etc.
I don't know if these things are measured in the squiggle but my point proves it is possible to drop in form for similar outcome, it's also possible to improve for similar or less outcome. To further open the can of worms you'd need to measure things like draw difficulty and opposition form against those 'fundamentals' also.
So how does one correlate those measurements of fundamentals to things like goal kicking accuracy? The can of worms gets deeper doesn't it.
Deeper still, how does one correlate them to other metrics like I50, marks I50, metres gained etc. etc. ?
At some point you have to consider basic fundamentals or 'effort' on how they effect the numbers or the squiggle. But you can't really because good effort doesn't automatically equal good game metrics outcomes.
A very simple AFL model that takes only scores and venue as inputs will have no idea about any of these things like disposals, metres gained, etc. It doesn't even know that it's measuring a football match. But it will still reliably outperform most human tipsters.
That's because the scores are the bottom-line. Regardless of how a team gets there, we can safely assume that everyone is trying their best to tip the scores in their favour.
Once a model starts moving away from scores to track things like I50s and disposals, it becomes more prone to misreading a team because their gameplan doesn't align with everyone else's. A model that adjusts for disposals, for example, will underrate Richmond, because Richmond's gameplan is about forcing turnovers and moving very directly to attack, with as few possessions as possible.
So models don't need to account for exactly how a team produces scores - and, in fact, usually shouldn't.





