Final standings for 2025 after 207 games.
Tips | Bits | MAE | Correct |
---|
"Tips" is the number of correct tips. Draws are counted as correct.
"Bits" from Monash University Probabilistic Footy Tipping rewards tipsters for saying a win was more likely and punishes them for saying it was unlikely. Higher is better.
"MAE" is Mean Absolute Error, which is the average difference between predicted and actual margins. Lower is better.
"Correct" is the percentage of correct tips, i.e. "Tips" divided by the total number of tips provided (which is normally also the number of games played, assuming the model tips all games).
"†number" indicates missing tips: This source did not provide a tip for the specified number of games, which can distort all three metrics. Although missing a game is usually bad for the tipster, it can cause an undeserved boost to Bits and MAE when the missed game is an upset.
[+] Bits, MAE, Tips... which is best?
Tips is what most people care about: How many winners did you pick? So it's the primary variable for the Squiggle leaderboard. But there's a fair bit of luck involved in tipping 50/50 games, so it's hard to know whether a high Tips score is due to skill or good fortune.
Bits are earned by confident correct tips, lost by confident incorrect tips, and not much happens either way for fence-sitting 50/50 tips. The added dimension of "confidence" means Bits are less influenced by luck, so high scores are likely to indicate models that are good performers over the long-term.
MAE (or Mean Absolute Error) measures how far, on average, margin tips are from the actual margin. (Lower MAE scores are better.) It's hard to fluke a good MAE, because your tips have to be consistently close to the real margins, and therefore it's probably the best of the three metrics at measuring a model's underlying forecasting skill. However, it doesn't measure whether anyone is getting the winners right, which means it's not directly tracking the most important factor.