It's pretty easy to tell how effective most of the models floating around are, because their performance is documented over several years. They generally go at around 68-74%, depending on the year. If you track your own tipping, you can see whether you beat them or not.
The kind of perfect model you describe, which uses dozens of different inputs, doesn't sound very good to me, because it would inevitably introduce more noise than signal. This is the overfitting problem, where if you give a model a ton of different factors, it will fit the historical data really well, but can't predict anything, because all it does is find a bunch of coincidences. For example, I bet you could find one particular seat at the MCG where almost every game in 2016 was won by the team whose supporter sat there. That fits the data well but has no predictive power.
The smart way to use a model, in my opinion, is as a tool rather than a Magic Eight-Ball. Because while computer models are better than most punters, they're not better than good punters. They're really best at aggregating large amounts of data that humans have trouble remembering all at once. So you don't want to take their predictions as gospel, but if you can understand what they're measuring and why they're reaching the conclusions they are, that's a good input for the model you're running inside your human brain.
Also, you know, when you just haven't been paying attention to the footy recently, then that's a good time to snag their tips.
I heard a guy talk a a big data conference where they did analysis on e-harmony results. They take hundreds on data points that people enter about themselves (height, weight, income, movie likes etc) and tried to make the perfect matching model. They wanted to know which data points were 'signal' and which were 'noise'.
What they found was that it was almost all noise. What they found was that there were really only 2 data points that were better than random picks for a successful matching of a couple - 'time of day of entering the profile' with 'type of camera used to take profile picture'!
So adding more information into a model does not always improve it - it just adds more noise!