WheeloRatings

Votes Matches Polled 1 2 3 4 5 6 7 8 9 10 11 12 Christian Petracca 61 9 5 • 7 9 1 1 10 • 10 • 8 10 Clayton Oliver 40 9 2 1 9 8 • 2 7 7 2 2 Jake Lever 16 3 9 2 • • • • • • • • 5 Jack Viney 13 2 • • • • 6 • • 7 • • • Max Gawn 13 4 5 • 3 • 3 • • 2 • Brodie Grundy 12 2 • • 6 6 • • • • • • • • Kysaiah Pickett 12 2 9 • • • 3 • • • • • Ed Langdon 10 2 • • 6 • • • • • • • • 4 Steven May 9 2 • • • • • • 1 • • 8 Angus Brayshaw 7 2 • • • • • • 5 • • • 2 • Kade Chandler 6 2 • • 1 • • • 5 • • • • • Trent Rivers 6 1 • • • • • • • • 6 • • • Michael Hibberd 4 1 • • 4 • • • • Lachie Hunter 3 2 • • 1 • • • • • • 2 • Tom McDonald 2 1 • • 2 • • •

The aggregate of several Squiggle models is very similar, at least in terms of the gap between Melbourne (4th) and Western Bulldogs (5th). https://squiggle.com.au/ladder/

Here are the round-by-round hitouts to advantage for 2022. Average 1 2 3 4 5 6 7 8 9 10 11 12 13 15 16 17 18 19 20 21 22 23 24 25 Max Gawn 9.1 8 6 3 13 5 11 12 7 7 10 6 11 8 11 3 15 10 6 13 6 14 15 Luke Jackson 3.2 3 5 2 1 0 2 4 1 2 0 2 3 4 4 4 3 3 4 7 12 2 2 Sam Weideman 1.3 0 0 0 0 4 0 4 4 1 0 Tom McDonald 0.6 0 1 0 0 2 0 0 1 1 Ben Brown 0.1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1

I can provide the game-by-game hit outs to advantage for 2022 if you would like to incorporate them into your 2022 ratings.

The home advantage is probably the biggest factor overall actually, but mainly for matches played between teams from different states. That sounds like a reasonable theory. I haven't analysed the different paths through the finals in the simulation results, but one thing it won't take into consideration is 1st playing a battle-hardened 2nd/3rd in the prelim. The simulations just see it as 1st being the home team against a likely lower-rated opponent so would win more often than not. Brisbane and Port's premiership chances are definitely negatively affected by having to play the GF at the MCG, but Brisbane is affected more.

Firstly, a team's rating is the sum of the its attacking rating and its defensive rating. The attacking rating is based on whether teams score above or below average and the defensive metric is based on whether teams concede above or below average. However, instead of using a team's actual score, I calculate an "adjusted" score using a weighted average of their actual score and the score they would have kicked had they kicked at an expected accuracy. This is to account for the luck factor in goalkicking. The attacking and defensive ratings are updated following each match based on these "adjusted" scores compared to the expected scores. If a team's adjusted score is higher than expected, their attack rating increases (and vice versa). If their opponent's adjusted score is higher than expected, their defensive rating decreases (and vice versa). The expected scores take into account the teams pre-game ratings and any venue advantage. I have a page on my site which provides some more detail, noting that I have never actually got around to finalising it: https://www.wheeloratings.com/afl_methodology.html In addition, teams carry over ~65% of their rating from the previous season as there's a general regression to the mean from one season to the next.

Yes, absolutely, I'm more than happy for you to use it!

It's largely due to Melbourne being ranked #1 on team ratings by my model. Believe it or not, I actually have Melbourne favourites against Collingwood on King's Birthday, so a Collingwood v Melbourne final would be close to 50/50, or even Melbourne favourites. That's all based on the fact my model currently rates Melbourne 6.2 points higher than Collingwood, which I don't personally agree with, just to be clear. Collingwood's rating actually decreased from rounds 7 to 10 as their opponents were particular inaccurate (Adelaide 7.16, Sydney 6.12, GWS 7.13, and Carlton 7.15) and then didn't beat North by as much as expected. Even though I have Melbourne ranked fourth on the aggregate ladder, they're still more likely to finish second or third than fourth if you look at the ladder distribution. Melbourne's strong percentage definitely helps. I should note that my model doesn't look at the historical record of teams finishing fourth, it simply looks at the individual finals match up for the given simulation. FYI, here are the premiership probabilities for all teams finishing in a particular ladder position this year, with fourth being much higher than historical results with the current final 8 system: 1: 31% 2: 24% 3: 17% 4: 14% 5: 5% 6: 4% 7: 3% 8: 2%

I'll respond to your other post. @old55 is correct that the simulations use the Team Ratings as a basis, and my model has Melbourne currently ranked #1. I agree that it doesn't necessarily seem correct, but in my defence (or my model's defence), the aggregate of a number of models on https://squiggle.com.au/power-rankings/ also sees Melbourne ranked #1 and Collingwood #4. The model simulates the remainder of the season 50,000 times, including simulating the finals based on the outcomes for a particular simulated season. Basically, for a given simulation, I simulate the remainder of the home and away season and then simulate the final series based on the final ladder positions of that particular simulation. I do that 50,000 times and aggregate the results. Melbourne's premiership favouritism is largely due to them being ranked #1.

Yes definitely, I can add that into the standard stats that I include each week - it's not too much work. If I forget, just remind me! 😊

@Demon Dynasty @binman A large factor in the drop off in our goals / inside 50 is our accuracy. Shots / inside 50 has been quite consistent between rounds 5 and 12 but our accuracy has really dropped off. Round Inside 50s Shots Shots / Inside 50 Goals Goals / Inside 50 Accuracy 1 60 30 50.0 17 28.3 56.7 2 54 21 38.9 13 24.1 61.9 3 60 33 55.0 21 35.0 63.6 4 58 33 56.9 19 32.8 57.6 5 55 24 43.6 11 20.0 45.8 6 53 25 47.2 15 28.3 60.0 7 64 30 46.9 22 34.4 73.3 8 61 26 42.6 13 21.3 50.0 9 66 29 43.9 15 22.7 51.7 10 48 23 47.9 11 22.9 47.8 11 58 26 44.8 10 17.2 38.5 12 59 28 47.5 8 13.6 28.6

Yes I do have the hitouts to advantage by player. Hitouts, Melbourne v Carlton Ruck Contests Hitouts To Adv. To Adv. % (2023) To Adv. % (2022) Max Gawn 38 20 9 30.6 33.6 Brodie Grundy 34 11 7 34.6 30.2 Jacob van Rooyen 7 2 1 18.2 Tom McDonald 25.0 33.3 Harrison Petty 22.2 Ben Brown 0.0 14.3 Josh Schache 33.3 Hitouts to advantage by round Average 1 2 3 4 5 6 7 8 9 10 11 12 Brodie Grundy 7.5 4 5 10 13 12 5 9 7 8 4 6 7 Max Gawn 6.1 7 0 5 6 6 4 9 9 9 Jacob van Rooyen 0.4 1 1 1 0 0 0 0 0 0 1 Tom McDonald 0.3 1 0 1 0 0 0 Harrison Petty 0.2 0 0 1 1 0 0 0 0 Ben Brown 0.0 0 0 0 Christian Petracca 0.0 0 0 0 0 0 0 0 0 0 0 0 0 Josh Schache 0.0 0

The pressure rating is the average pressure applied to the opponent's disposals only, so when Melbourne hold onto the ball it doesn't actually affect Melbourne's pressure rating. But I do agree that the pressure rating can be misleading.

Melbourne v Carlton https://www.wheeloratings.com/afl_match_stats.html?ID=20231201 Note: first number below is Melbourne, higher value is bold. Pressure Q1: 179 - 195 Q2: 172 - 161 Q3: 179 - 166 Q4: 148 - 153 Tot: 170 - 170 Most Pressure Points (Weighed sum of pressure acts. Physical pressure acts are worth 3.75 points, closing acts are worth 2.25 points, chasing acts are 1.5 points and corralling are 1.2. https://www.championdata.com/glossary/afl/) Petracca: 53 Neal-Bullen: 52 Viney: 46 Sparrow: 45 Chandler: 33 Pickett: 33 Brayshaw: 28 Hunter: 26 Time in forward half 58% - 42% Here are my calculations (not official) for time in forward half for each quarter: Q1: 52% - 48% Q2: 56% - 44% Q3: 65% - 35% Q4: 57% - 43% Score sources Centre bounce 0.2.2 - 2.1.13 Ball up 2.3.15 - 2.1.13 Throw in 2.0.12 - 0.1.1 Turnover 4.7.31 - 2.5.17 Kick-in 0.1.1 - 0.0.0 Shots at goal Set position 5.10.40 - 6.5.41 General play 3.3.21 - 0.1.1 Expected Scores 81 - 45 Centre Bounce Attendances @old55 CBAs CBA % 2023 % 2022 % Jack Viney 15 83 66.6 74.6 Tom Sparrow 15 83 46.8 32.2 Christian Petracca 10 56 63.5 74.6 Brodie Grundy 9 50 59.3 83.7 Max Gawn 9 50 44.8 65.5 Angus Brayshaw 7 39 10.2 16.0 James Harmes 6 33 28.7 14.6 Trent Rivers 1 6 4.2 0.0 Kysaiah Pickett 0 0 15.0 1.3 Jacob van Rooyen 0 0 6.2 Alex Neal-Bullen 0 0 4.2 3.5 Clayton Oliver 82.8 86.5 James Jordon 20.3 0.2 Tom McDonald 5.4 0.0 Harrison Petty 1.3 0.0 Ruck Contests Ruck Contests RC % 2023 % 2022 % Max Gawn 38 48 42.7 57.8 Brodie Grundy 34 43 51.6 77.4 Jacob van Rooyen 7 9 9.3 Christian Petracca 0 0 0.1 0.1 Tom McDonald 8.9 7.0 Josh Schache 6.7 13.4 Ben Brown 3.8 3.6 Harrison Petty 3.1 0.0 https://www.wheeloratings.com/afl_stats.html

I obtain the pressure ratings, pressure points, and official time in forward half stats from the Herald Sun with a subscription. It's a manual transcription process so I just do that for DL. The unofficial time in forward half is calculated from a separate data set that I have access to. I haven't loaded these onto the site either, but I can more easily run these for all teams as it is at least in a database (which goes back to 2021). I'm looking at using this data set for other things, like clearances by zone.

Votes Matches Polled Christian Petracca 51 8 Clayton Oliver 40 9 Jack Viney 13 2 Max Gawn 13 4 Brodie Grundy 12 2 Kysaiah Pickett 12 2 Jake Lever 11 2 Angus Brayshaw 7 2 Ed Langdon 6 1 Kade Chandler 6 2 Trent Rivers 6 1 Michael Hibberd 4 1 Lachie Hunter 3 2 Tom McDonald 2 1 Steven May 1 1 https://www.wheeloratings.com/afl_stats.html

Melbourne v Fremantle https://www.wheeloratings.com/afl_match_stats.html?ID=20231103 Note: first number below is Melbourne, higher value is bold. Pressure Q1: 200 - 177 Q2: 164 - 198 Q3: 178 - 200 Q4: 197 - 215 Tot: 185 - 198 Most Pressure Points (Weighed sum of pressure acts. Physical pressure acts are worth 3.75 points, closing acts are worth 2.25 points, chasing acts are 1.5 points and corralling are 1.2. https://www.championdata.com/glossary/afl/) Harmes: 61 Viney: 59 Sparrow: 58 Petracca: 53 Neal-Bullen: 45 Salem: 41 May: 40 Rivers: 34 McVee: 32 Chandler: 32 Time in forward half 56% - 44% Here are my calculations (not official) for time in forward half for each quarter: Q1: 60% - 40% Q2: 48% - 52% Q3: 46% - 54% Q4: 67% - 33% Score sources Centre bounce 3.1.19 - 2.1.13 Ball up 2.1.13 - 3.1.19 Throw in 0.2.2 - 2.1.13 Turnover 5.7.37 - 4.4.28 Kick-in 0.1.1 - 1.0.6 Shots at goal Set position 4.7.31 - 9.3.57 General play 6.3.39 - 3.3.21 Expected Scores 84 - 66

Melbourne v Port Adelaide https://www.wheeloratings.com/afl_match_stats.html?ID=20231001 Note: first number below is Melbourne, higher value is bold. Pressure Q1: 182 - 203 Q2: 200 - 203 Q3: 189 - 198 Q4: 205 - 219 Tot: 194 - 205 Most Pressure Points (Weighed sum of pressure acts. Physical pressure acts are worth 3.75 points, closing acts are worth 2.25 points, chasing acts are 1.5 points and corralling are 1.2. https://www.championdata.com/glossary/afl/) Oliver: 85 Petracca: 65 Viney: 63 Neal-Bullen: 55 Pickett: 50 Brayshaw: 49 Chandler: 40 Salem: 36 Rivers: 32 McVee: 32 Spargo: 31 McDonald: 30 Time in forward half 39% - 61% Here are my calculations for time in forward half for each quarter, which were not too far off the figures Channel 7 put up on the screen during the third quarter. Q1: 45% - 55% Q2: 33% - 67% Q3: 51% - 49% Q4: 32% - 68% Based on my calculations, the second and fourth quarters ranked as two of Melbourne's three worst quarters of the year for time in forward half: 31.6%, Q4 v Port Adelaide 31.8%, Q1 v Brisbane 32.7%, Q2 v Port Adelaide 34.4%, Q1 v West Coast 39.9%, Q1 v Essendon 40.4%, Q2 v Essendon Score sources Centre bounce 2.1.13 - 0.0.0 Ball up 0.1.1 - 3.4.22 Throw in 3.0.18 - 2.1.13 Turnover 6.8.44 - 6.8.44 Kick-in 0.0.0 - 0.1.1 Shots at goal Set position 8.2.50 - 7.9.51 General play 3.5.23 - 4.4.28 Expected Scores 74 - 97

Melbourne v Hawthorn https://www.wheeloratings.com/afl_match_stats.html?ID=20230905 Note: first number below is Melbourne, higher value is bold. Pressure Q1: 210 - 188 Q2: 158 - 153 Q3: 183 - 194 Q4: 185 - 167 Tot: 184 - 175 Most Pressure Points (Weighed sum of pressure acts. Physical pressure acts are worth 3.75 points, closing acts are worth 2.25 points, chasing acts are 1.5 points and corralling are 1.2. https://www.championdata.com/glossary/afl/) Sparrow: 70 Pickett: 65 Viney: 59 Neal-Bullen: 50 Petracca: 47 Oliver: 43 Grundy: 34 Chandler: 33 Lever: 30 Time in forward half 56% - 44% Score sources Centre bounce 0.1.1 - 1.0.6 Ball up 1.2.8 - 1.2.8 Throw in 2.3.15 - 1.0.6 Turnover 12.7.79 - 3.4.22 Kick-in 0.0.0 - 1.1.7 Shots at goal Set position 9.5.59 - 5.2.32 General play 6.3.39 - 2.2.14

The probabilities are for any team with that number of wins making the Top 8/4. However, percentage definitely comes into that. Some of the 27% of teams missing the top 4 with 16 wins in the simulations would miss out on percentage only. Teams with 16 wins and a percentage of 105 are about a 56% chance of top 4, whereas teams with 16 wins and a percentage of 140 are about an 86% chance of top 4.

For what they're worth, my simulations show the following, based on 50,000 simulations of the remainder of the AFL season. Probability of Top 8 12 wins: 23% 12.5 wins: 44% 13 wins: 70% 13.5 wins: 88% 14 wins: 97% Probability of Top 4 15 wins: 27% 15.5 wins: 50% 16 wins: 73% 16.5 wins: 90% 17 wins: 97%

No problem @old55. It's calculated from an AFL API that I have access to, so it's not really in the public domain. @binman in relation to the GPS data that you referred to on the podcast this week, my understanding is that it is actually owned by Telstra, not Champion Data, so there are greater restrictions on what CD can actually do with it.

That chart is simply plotting teams points scored ranking versus points against ranking. Melbourne 2nd in points scored (107.4 per game), Geelong 1st (107.8). Melbourne 4th in points conceded (77.6), Geelong 7th (79.4).

Scores from Turnovers Team For Against Diff St Kilda 438 245 193 Melbourne 512 333 179 Brisbane 483 382 101 Essendon 454 357 97 Geelong 481 408 73 Adelaide 399 354 45 Sydney 440 398 42 Collingwood 362 347 15 Carlton 381 369 12 Western Bulldogs 363 353 10 Fremantle 392 405 -13 Port Adelaide 380 395 -15 Greater Western Sydney 404 435 -31 Richmond 344 376 -32 Gold Coast 336 450 -114 North Melbourne 314 468 -154 West Coast 321 498 -177 Hawthorn 294 525 -231 Scores from Stoppages Team For Against Diff Geelong 353 207 146 Collingwood 319 198 121 Port Adelaide 310 234 76 Melbourne 305 263 42 Brisbane 290 252 38 Gold Coast 245 215 30 Carlton 256 234 22 Richmond 247 243 4 Western Bulldogs 218 215 3 Adelaide 280 281 -1 Greater Western Sydney 237 244 -7 St Kilda 192 204 -12 Fremantle 235 252 -17 Essendon 266 308 -42 Sydney 222 277 -55 Hawthorn 166 231 -65 North Melbourne 173 272 -99 West Coast 209 393 -184 Scores from Kick-Ins Team For Against Diff West Coast 42 12 30 Gold Coast 37 12 25 Melbourne 42 25 17 Sydney 27 15 12 Carlton 40 29 11 Adelaide 42 32 10 Brisbane 36 28 8 Essendon 46 38 8 Geelong 28 20 8 Richmond 16 15 1 Fremantle 29 31 -2 St Kilda 20 25 -5 Hawthorn 31 41 -10 Collingwood 21 34 -13 Western Bulldogs 35 51 -16 Greater Western Sydney 27 48 -21 North Melbourne 31 53 -22 Port Adelaide 6 47 -41

Just on scores from turnovers, Melbourne ranks 1st in points from turnovers, 3rd in scores (goals + behinds) from turnovers, and 2nd in points differential from turnovers. Melbourne doesn't score as often as Brisbane and Geelong from turnovers, but Melbourne's accuracy puts them on top for total points. Points from turnovers 512: Melbourne 483: Brisbane 481: Geelong 454: Essendon 440: Sydney 438: St Kilda 404: Greater Western Sydney 399: Adelaide 392: Fremantle 381: Carlton 380: Port Adelaide 363: Western Bulldogs 362: Collingwood 344: Richmond 336: Gold Coast 321: West Coast 314: North Melbourne 294: Hawthorn Scores from turnovers 128: Brisbane 126: Geelong 122: Melbourne 120: Sydney 119: Essendon 114: Adelaide 113: St Kilda 111: Carlton 109: Greater Western Sydney 107: Collingwood 104: Richmond 103: Western Bulldogs 102: Fremantle 100: Port Adelaide 91: West Coast 91: Gold Coast 84: North Melbourne 84: Hawthorn Points from turnovers differential +193: St Kilda +179: Melbourne +101: Brisbane +97: Essendon +73: Geelong +45: Adelaide +42: Sydney +15: Collingwood +12: Carlton +10: Western Bulldogs -13: Fremantle -15: Port Adelaide -31: Greater Western Sydney -32: Richmond -114: Gold Coast -154: North Melbourne -177: West Coast -231: Hawthorn Points conceded from turnovers 245: St Kilda 333: Melbourne 347: Collingwood 353: Western Bulldogs 354: Adelaide 357: Essendon 369: Carlton 376: Richmond 382: Brisbane 395: Port Adelaide 398: Sydney 405: Fremantle 408: Geelong 435: Greater Western Sydney 450: Gold Coast 468: North Melbourne 498: West Coast 525: Hawthorn