The Dirty Secret of Most Prediction Models
Here’s something that doesn’t get talked about enough: a lot of “prediction models” you see floating around aren’t really predicting anything. They’re laundering the betting market’s opinion through a spreadsheet and calling it analysis.
The market opens on Monday morning. Punters pile in. The odds shift. The model ingests those odds as a feature. The model now “predicts” something very close to what the market already said. Everyone goes home feeling clever.
This model doesn’t do that.
What the Markets Know (A Lot)
To be clear: the betting markets are genuinely impressive. They aggregate information from thousands of punters, professional analysts, injury tipsters, and people who probably have a guy inside the club. The line moves fast when a key player gets a late scratch. It adjusts for weather, travel, and the specific psychological state of teams coming off a big win or a demoralising loss.
Markets are efficient in the way that makes economists happy. They’re not perfect, but they’re rarely stupid.
So yes — ignoring them has a cost. A model that uses market odds as an input will almost certainly be more accurate than one that doesn’t, all else being equal. The market knows things this model doesn’t. That’s just true, and pretending otherwise would be dishonest.
So Why Not Just Use Them?
Because that’s not the question I’m trying to answer.
If I wanted to know who was most likely to win a given game, I’d look at the odds. Done. No model required. The markets have already done the hard work, pooled vast amounts of information, and distilled it into a probability. It’s probably pretty close.
What I’m actually interested in is: what does the football tell us? Not what the market thinks, not what the punting public thinks — what do the underlying statistics of how teams have actually been playing suggest about what’s likely to happen next?
That’s a different question, and it requires a model that’s genuinely blind to market opinion.
The Philosophy (Bear With Me)
Think of it this way. You can build a model that predicts temperature using a weather forecast as an input. It will be highly accurate. It will also be completely useless as a scientific instrument, because all it’s doing is repeating someone else’s forecast back at you.
Or you can build a model that predicts temperature using atmospheric pressure, humidity, historical patterns, and satellite imagery — no forecast allowed. It might be less accurate in the short run. But it’s actually doing something. It’s making independent claims about the world based on underlying signals, not just echoing consensus.
This model is the second kind. It looks at how teams have been moving the ball, how their players have been performing, who’s travelling interstate, who’s got an inexperienced lineup. It doesn’t know what Tab.com.au thinks about any of it.
That means when it disagrees with the market, it’s a genuine disagreement — not noise.
The Advantage: Finding the Market’s Blind Spots
Because this model forms its opinions independently, it occasionally sees things the market doesn’t weight heavily enough.
Travel is a good example. The market prices in some interstate disadvantage, but it’s a blunt adjustment. This model calculates actual travel distances and applies a penalty based on historical evidence. Sometimes that surfaces something the odds haven’t fully captured.
Player experience is another. When a team’s lineup is unusually young — a lot of players under 50 games — the model applies an inexperience penalty based on rolling individual game counts. Markets probably notice the obvious cases (a team suddenly fielding four rookies), but the subtler version of this effect tends to get washed out in the noise.
The model’s opinion is formed entirely from what happened on the field, over the last handful of weeks. If the market is pricing something else — reputation, interstate crowd size, the narrative around a particular coach — then the model is ignoring all of that. Which is either a bug or a feature depending on what the market has gotten wrong lately.
The Honest Part
This approach comes with real costs, and it would be silly to pretend otherwise.
Late team changes — particularly the Thursday night selections that get announced after the TAB has already adjusted — mean the model is sometimes working with outdated lineup information. The market has already moved. This model is still thinking about last week’s 22. Run make lineups and make predict after selection Thursday and that narrows the gap, but it doesn’t close it entirely.
There’s also a deeper issue: some things that predict football outcomes aren’t cleanly measurable in statistics. Team morale. Player confidence. A coach who’s about to get sacked. The market absorbs some of that. This model, emphatically, does not.
So when the model and the market disagree, there are two possibilities: either the model has found something the market missed, or the market knows something the model doesn’t. Both happen. The trick is figuring out which.
The Bottom Line
Most tipping models are glorified odds converters. This one is trying to do something different — build an independent view of what the football says, without peeking at the consensus.
That makes it less accurate in absolute terms than a model that just launders market opinion. It also makes it more interesting. When it’s right despite disagreeing with the market, that’s a genuine signal. When it’s wrong, at least you know it failed on its own terms, not because it was following someone else’s homework.
Flying blind has costs. But it also keeps the lights on.