6 hours ago, Deelightful Dee said:

Hit the nail on the head @At the break of Gawn

Until we can improve our consistency of method and execution in the turnover game, our results will be also inconsistent. This year, in every match we've won the scoring from turnover battle, we've won the game, other than against the Hawks where we only won it by 2 points but got outscored from stoppage by 30 (our 3rd worst result for the year behind -42 in R2 and -33 in R3). We lost the turnover battle by an average of 22 points in the first 5 rounds, won it by an average of 14 in the next 6 rounds (in which won our only 5 games for the year) and lost it by an average of 21 in the next 3. I know where I'd be focusing our opportunity for improvement over the bye period: win the turnover battle to give ourselves the best chance of winning games and in particular how we defend turnover from Opp D50 to Inside 50 which we are 12th over the last 5 weeks and 11th for the season according to Wheelo's premiership metrics tweet this week!

Here is the premiership metrics tweet mentioned:

13 hours ago, WheeloRatings said:

I have done some work replicating the player ratings and I have a reasonable model with pressure being the main factor I don't have access to. The breakdown of the source of ratings helps to understand the individual player ratings even if you still don't agree with the rating system.

The following are estimates only but they give you an idea of what's contributing to a player's rating. Gawn was rated very highly in winning the ball and through his ruck work, but he lost a lot of points through his disposal and in particular his two shots on goal. The rating system is based on how a player impacts both team's scoring chances. So easy missed shots at goal are heavily penalised and kicking difficult goals earn a lot of points.

On the flipside, while Georgiades kicked 7 goals, he missed 4 and pretty much kicked at the expected accuracy for his shots. He gained a lot of rating points through his possessions (marks/free kicks), he didn't gain many through his disposal. He also lost points through giving away free kicks and dropping a mark.

Most rating points from POSSESSIONS

15.3 Ratugolea
14.4 Gawn
11.9 Georgiades
10.9 May

Most rating points gained (and lost) from DISPOSALS

10.1 Langford
9.7 Pickett
7.9 Bergman
7.9 Rozee
---------------
-2.8 Petty
-2.9 Viney
-4.1 Oliver
-7.3 Gawn

Most rating points from HITOUTS

3.9 Gawn
0.8 Sweet

Most rating points lost from FREE KICKS

-2.6 Georgiades
-2.6 Visentini

Most rating points lost from NO PRESSURE ERRORS (incl. dropped marks)

-3.0 van Rooyen
-2.8 Georgiades
-1.9 Gawn

Hey @WheeloRatings. Thank you for this thoughtful response. It's appreciated and improved my understanding -> thinking about the game.

Your modelling is brilliant — and you are right. Not having access to 'pressure' data — and particularly the inputs used to assemble that data point — is a handicap. And that's my point: many football followers observe first-hand what a player does (or doesn’t do) during a game that appears to be neglected in what I would term 'complexity' inputs. That is, the layered decision-making, tactical compliance, off-ball movement, and game-shaping intent that don’t result in a stat — but change the shape of the game. Things like sacrificing a lead to create space, halting a chain with smart positioning, or neutralising a dangerous opponent without ever laying a hand on them.

For example:

1. Not All Actions Are Created Equal — Even When They Look Identical

Two disposals may appear the same on a stat sheet — but one may be:

• Delivered with tactical precision (e.g. breaking a zone)

• Made after scanning multiple options in a high-stakes moment

The Champion Data model attempts to weight difficulty (e.g. a pressured kick may be worth more), but it still relies on observable, event-based outcomes, not the complexity of the decision behind them.

2. Systemic Thinking Goes Unmeasured

Modern footy is defined by:

• Executing roles within highly structured team systems

• Making spatial decisions on the fly (e.g. when to fold back, when to block space)

• Adapting to team tactics in real time

Yet players who do these things well — often without touching the ball — receive little to no recognition in the ratings system.

3. Complexity Isn’t Always Visible in the Data

The system cannot assess:

• Whether the options ignored were better or worse

• The quality of positioning before the disposal

• The degree of difficulty in reading the play or shaping it without possession

In short, a player who makes high-complexity, low-volume decisions (e.g. a defender who sets up a zone intercept or a forward who self-sacrifices to create space) is penalised relative to players with high-volume, low-difficulty contributions.

4. Intelligence and Influence ≠ Quantity

Footy IQ — the ability to read the game, think a kick ahead, and shape space — is a core reason why players like Max Gawn, and notably, Sidey and Pendles, have sustained elite performance late into their careers. Yet these forms of intelligence are not adequately captured, because they are not consistently associated with discrete, countable events. In Max’s case, he’s even penalised in the data for an errant possession — despite that disposal coming at the end of a 100-metre sprint to drop into the hole, absorbing contact, and creating the space for a teammate’s clean exit. The nuance of why an error occurred, what it created, or how it shaped the contest simply isn’t factored in.

The result? A system that reduces football intelligence to raw outcomes — overlooking the structural, leadership, and game-shaping qualities that explain why these players remain indispensable, even when they’re no longer statistically dominant.

5. Summary

Champion Data’s ratings provide a useful baseline — but by failing to account for complexity in decision-making and game understanding, the system risks undervaluing the players who make teams functional. Until complexity is measured, true on-field influence will remain partially hidden behind the numbers.

5 hours ago, Queanbeyan Demon said:

Hey @WheeloRatings. Thank you for this thoughtful response. It's appreciated and improved my understanding -> thinking about the game.

Your modelling is brilliant — and you are right. Not having access to 'pressure' data — and particularly the inputs used to assemble that data point — is a handicap. And that's my point: many football followers observe first-hand what a player does (or doesn’t do) during a game that appears to be neglected in what I would term 'complexity' inputs. That is, the layered decision-making, tactical compliance, off-ball movement, and game-shaping intent that don’t result in a stat — but change the shape of the game. Things like sacrificing a lead to create space, halting a chain with smart positioning, or neutralising a dangerous opponent without ever laying a hand on them.

For example:

1. Not All Actions Are Created Equal — Even When They Look Identical

Two disposals may appear the same on a stat sheet — but one may be:

• Delivered with tactical precision (e.g. breaking a zone)

• Made after scanning multiple options in a high-stakes moment

The Champion Data model attempts to weight difficulty (e.g. a pressured kick may be worth more), but it still relies on observable, event-based outcomes, not the complexity of the decision behind them.

2. Systemic Thinking Goes Unmeasured

Modern footy is defined by:

• Executing roles within highly structured team systems

• Making spatial decisions on the fly (e.g. when to fold back, when to block space)

• Adapting to team tactics in real time

Yet players who do these things well — often without touching the ball — receive little to no recognition in the ratings system.

3. Complexity Isn’t Always Visible in the Data

The system cannot assess:

• Whether the options ignored were better or worse

• The quality of positioning before the disposal

• The degree of difficulty in reading the play or shaping it without possession

In short, a player who makes high-complexity, low-volume decisions (e.g. a defender who sets up a zone intercept or a forward who self-sacrifices to create space) is penalised relative to players with high-volume, low-difficulty contributions.

4. Intelligence and Influence ≠ Quantity

Footy IQ — the ability to read the game, think a kick ahead, and shape space — is a core reason why players like Max Gawn, and notably, Sidey and Pendles, have sustained elite performance late into their careers. Yet these forms of intelligence are not adequately captured, because they are not consistently associated with discrete, countable events. In Max’s case, he’s even penalised in the data for an errant possession — despite that disposal coming at the end of a 100-metre sprint to drop into the hole, absorbing contact, and creating the space for a teammate’s clean exit. The nuance of why an error occurred, what it created, or how it shaped the contest simply isn’t factored in.

The result? A system that reduces football intelligence to raw outcomes — overlooking the structural, leadership, and game-shaping qualities that explain why these players remain indispensable, even when they’re no longer statistically dominant.

5. Summary

Champion Data’s ratings provide a useful baseline — but by failing to account for complexity in decision-making and game understanding, the system risks undervaluing the players who make teams functional. Until complexity is measured, true on-field influence will remain partially hidden behind the numbers.

You raise some very good points and I'm not suggesting that it's a perfect system.

The system was initially developed 15 or so years ago and the game has changed in that time, and it's obviously limited to events which are captured. The events that are captured are largely focussed on the player who wins the ball and what they do with the ball, the nearest player applying pressure to the ball carrier, and certain other defensive actions (spoils/smothers).

It does a much better job than ranking points (now used for Supercoach) and fantasy points at assessing quality and influence over quantity.

For example, a kick is rated based on the location the kick is taken from, the pressure on the kicker, the location it ends up, and the possession "state" of the result (e.g. was it to a contest and therefore reliant on a teammate winning possession, was it a teammate's uncontested mark or gather, or did it go to an opponent or out of bounds).

Using these data points, the model assesses whether your team is more likely to score after the kick than before. It does a pretty good job, but it doesn't account for who you're kicking it to or where all the other players on the field are. E.g:

• Kicking it to a one-on-one marking contest involving Gawn is not the same as your average one-on-one marking contest.

• Kicking a backwards 45 degree kick may be worth negative points, but if that opens up the switch it may be preferable to kicking down the line.

Other specific limitations are:

• A key defender allowing his opponent to take a leading mark is not penalised for not competing, but is penalised for competing and giving away a free kick.

• A ruck is not penalised for not winning a hitout, but is penalised for winning the hitout if it's sharked by the opposition.

Some of the limitations with the system could be improved through better data capture and some adjustments to the system, but I'm not sure that a number of the issues you have raised could actually be rectified (or maybe they could and it's beyond my comprehension).

A model is never complete, but a stripped back representation of the reality. The ‘complexity’ argument is valid, as is the way Wheelo represents the current data sets. The trick would also to understand what’s is valued by the team in their system and how that could be measured in the data.

For example, maybe Collingwood prize the forward of the ball running that is often unrewarded - against us, 4 players sprinted to an imaginary line up/down the field the forward flank to provide 4 options during a d50 transition. Was damned impressive, but only one of those lads would get a stat.

We have probably put maximal weighting on contested ball and insides 50’s/ forward half time. I wonder what we need to value now?

Round 14, 2025 Adelaide Oval - Power vs Demons

Well the question was asked over the last three weeks and the result... we failed miserably.

Our Team Rating vs Port, 6% worse than our average in 2024, which in itself was obviously a poor year.

Our Player Rating was 11% under that of Port's and our Top 6 a massive 18% off that of Port's Top 6 with only two of our boys finishing in the Top Ten Rated Players on the day.

It's fair to say that the club is now likely to finish in the bottom 6 for two seasons running.

The other worrying aspect is the complete failure of our mid-field aside from Max & Kozzy. With senior players unable to impact or execute even the most straight forward aspects of the game. One great example among many, Tracc running in to the 50 under absolutely no pressure and kicking it on top of Melk's head, when he clearly had the jump on his opponent 1 v 1. What should have been an easy kick to his favored side for a straight forward mark and decent shot at goal instead resulted in the oppo getting the resulting spill and running it down the other end for a 12 point turnaround and a massive team deflator. Handing them the momentum, which we could not stop thereafter.

Add to that the constant inability of senior players to do the team discipline things required to be a top line professional team almost every week.

The gap between our best and worse is also just too massive to be competitive at this level.

And the icing on the cake, the worst rated goal kicking team in the AFL for accuracy.

All those issues add up to a team that has now become predictably bad under any sort of pressure and who will more than likely crack early if the oppo turns up the wick. Unable to convert and put a winning score on the board (to defend) we have no way of countering any momentum lost, which in the majority of games happens far too easily and too often.

We are absolutely boring to watch in terms of the way we move the ball, turn it over so often and miss straight forward shots.

We occasionally put in one decent quarter and then go missing for two or more. Very predictable stuff and for mine, we have now become unwatchable.

The turn over king from this match was Clarry with a massive 8 turnovers, the most of any player on the night.

To highlight the gap between our best and worst, Clarry finsihed as the No.1 Rated player vs the Pies to a lowly 14th against Port with the the lowest Rating of all mids.

Again ther gap between our best and worst is too great. In our last win vs the Swans our % of goals from inside 50 was way above the AFL average (approx 24%) at 29.7% Against the Saints the next week, a shocking 12.3%. Vs the Pies 18.4% (the Pies just over the AFL average for the win).

All this horror fest with a huge advantage on Port in terms of game time experience. An average advantage of nearly 30 games per player. No excuses here.

The bottom line is, our next genuine finals team (that seriously threatens; ie making a PF or GF) is unlikely to come from many on the present list. I suspect those that do make it might well be playing under a different coach.

Player & Team Ratings - Demons

> Subbed out TOG %

> Subbed in TOG %

Player & Team Ratings - Power

Combined Player Ratings

Stats courtesy of footywire.com and wheeloratings.com

Edited June 19Jun 19 by Demon Dynasty

Thanks DD we looked cooked and these stats bare that out, Tracc, Claz,JV, Rick, Salo, Ed Langdon, Riv, the Duke, Melky and Petty all below par at the same time. It’s obviously contagious, performance management has taken a real dip. I would have liked to be a fly on the wall at the after game meeting after that game. Very unusual for Ed Langdon and Riv has fallen off a cliff.

5 hours ago, DeeZone said:

Thanks DD we looked cooked and these stats bare that out, Tracc, Claz,JV, Rick, Salo, Ed Langdon, Riv, the Duke, Melky and Petty all below par at the same time. It’s obviously contagious, performance management has taken a real dip. I would have liked to be a fly on the wall at the after game meeting after that game. Very unusual for Ed Langdon and Riv has fallen off a cliff.

Driving standards at training and on and off the field has to be up for question DZ. But so is our decision making and skills / execution once we get forward of centre. All deplorable since 2024 bar the odd match here and there along with some commendable efforts from a few like Kozzy and Melk.

Our levels of skill, fitness and decision makimg worse than VFL sometimes.

We are also seemingly switching off at times as if our heads aren't even in the game.

Again, i wonder if the players are calling the shots here as to when they decide to turn it on. If things get too hard they revert back to CGAFF status. Take the $$ and run.

That's what some past players have done at the MFC for many a decade pre the Roos era, with few exceptions. So many have taken the pizz and just been happy to turn up and choose when they go.

Edited June 19Jun 19 by Demon Dynasty

The 2025 AFL Goals from Free Kic...

The 2025 AFL Goals from Free Kicks Ladder after Round 14

The best and worst differentials.

On 16/06/2025 at 10:04, Queanbeyan Demon said:

Player ratings, like so many stats, are like bikinis — they reveal a lot, but they conceal the most important bits. Perfect for pretending you’ve seen the whole picture while completely missing the context.

you can't say that!

9 CBA attendances for Clarry in that game? That slipped past me.

We're sitting here worried about going forward with Trac, Clarry and Viney in the middle all the time when the stats are clearly showing that Kozzie had around 20 CBAs.

It's annoying because now you have to think about a new complaint to have about our midfield 😛

We are good in the arcs butthe middle is where we struggle. Cant seem to transition between 50s or use our wings to run. I dont think our fitness is good enough

what is our total kms ran compared to afl average? averae speed? it must be well below par

My view is that Ed Langdon is a specialist winger, play maker, taking him off the wing to play a high h/f role is creating a net loss for the team. I would prefer Windsor rotate with Langford wing/hf, not many can emulate Ed Langdon and the Duke is stagnating. Fix it Goody, Chunk, Bassett ASAP.

Team & Player Ratings to Rnd 14, 2025 vs H&A Season 2024

* Played less than two full matches

< Subbed out at least once or more

> Subbed in at least once or more

Stats courtesy of footwire.com & wheeloratings.com

Edited June 20Jun 20 by Demon Dynasty

10 hours ago, Demon Dynasty said:

Team & Player Ratings to Rnd 14, 2025 vs H&A Season 2024

* Played less than two full matches

< Subbed out at least once or more

> Subbed in at least once or more

Stats courtesy of footwire.com & wheeloratings.com

Thanks DD it is quite sobering that Max has gone from No.2 to 1, Clarry has clawed his way back to No.2 and a battered Tracc has worked his way back to No.3 and Bowsa 4, Kozzie leapt to 7th and turner, Lindsay, Langford, Windsor, Howes and Chandler all wedged into our top 23.

Fox Sports

AFL’s newest and most important stat: Speed of Ball expla...

Truth in Pies shock as AFL’s fastest... and slowest teams revealed: Every club ranked 1-18

I'm not quite sure what to make of this.

38 minutes ago, roy11 said:

Fox Sports

AFL’s newest and most important stat: Speed of Ball expla...

Truth in Pies shock as AFL’s fastest... and slowest teams revealed: Every club ranked 1-18

I'm not quite sure what to make of this.

I can.

As ive noted on the pod a few times, we are implementing a new method based on fast transition (ie the modern game plan).

And contrary to the views of some we are implementing that method, or at least the all important ball movement part of it, very effectively.

As montagna notes our issue is how we enter inside 50, and of courseour woeful accuracy. Improve those aspects of our game and the gap between us and the very best teams closes significantly.

Which is worth considering when assessing where we are at vis a vis the conversation on another thread about how woeful we supposedly are.

Edited Thursday at 09:525 days by binman

4 minutes ago, binman said:

I can.

As ive noted on the pod a few times, we are implementing a new method based on fast transition (ie the modern game plan).

And contrary to the views of some we are implementing that method, or at least the all important ball movement part of it, very effectively.

As montagna notes our issue is how we enter inside 50. Improve thst aspect of our game and the gap between us and the very best teams closes significantly.

Thanks Binman I was hoping that you would get across this report, Geelong are the best but the only thing separating Dees from Cats is their finishing skills inside 50, goal kicking accuracy.!!!

Melbourne v Gold Coast (Round 16, 2025)

https://www.wheeloratings.com/afl_match_stats.html?ID=20251604

Key Team Stats

Stats in bold were won by Melbourne.

Stat	For	Against	Diff	AFL
Disposal Efficiency
Disposal Efficiency	72.1	74.9	-2.8	72.4
Kicking Efficiency	66.7	74.0	-7.4	66.0
Territory/Attack
Time In Forward Half	44.3	55.7	-11.5
Inside 50s	45	52	-7
Shots At Goal	30	31	-1
Scores Per Inside 50	55.6	55.8	-0.2	44.5
Goals Per Inside 50	26.7	28.8	-2.2	23.4
Marks Inside 50	12	19	-7
Transition
Chain To Score %	26.0	31.1	-5.1	20.7
Defensive 50 To Forward 50 %	23.7	30.0	-6.3	22.6
Defensive 50 To Score %	15.8	13.3	+2.5	9.4
Defensive Half To Forward 50 %	33.3	33.3	+0.0	31.0
Defensive Half To Score %	22.2	16.7	+5.6	12.9
Contest
Contested Possessions	129	131	-2
Ground Ball Gets	96	84	+12
Post Clearance Contested Poss	73	77	-4
Post Clearance Ground Ball Gets	57	49	+8
Contested Marks	5	16	-11
Clearance
Total Clearances	31	36	-5
Centre Clearances	11	15	-4
Stoppage Clearances	20	21	-1
First Possessions	36	37	-1
First Possession To Clearance %	61.1	67.6	-6.5	75.4
Defense
Intercepts	52	46	+6
Intercept Marks	7	11	-4
Tackles	62	65	-3
Tackles Inside 50	7	9	-2
Def One On One Loss %	33.3	7.7	+25.6	26.1
Ruck
Hitouts	37	40	-3
Hitouts To Advantage	9	5	+4

Transition stats measure how often chains result in a score or an inside 50. Chains include all kick-in chains, all clearances, and intercepts with at least one disposal in the chain.

• Chain To Score %: proportion of all chains that resulted in a score.

• Defensive 50 To Forward 50 %: proportion of all chains starting in the defensive 50 that resulted in an inside 50.

• Defensive 50 To Score %: proportion of all chains starting in the defensive 50 that resulted in a score.

• Defensive Half To Forward 50 %: proportion of all chains starting in the defensive half that resulted in an inside 50.

• Defensive Half To Score %: proportion of all chains starting in the defensive half that resulted in a score.

Player Ratings

	Q1	Q2	Q3	Q4	Match	TOG
Kysaiah Pickett	−0.6	5.8	3.5	16.1	24.9	87%
Jake Melksham	2.0	14.5	−0.9	−2.7	12.7	83%
Christian Petracca	0.4	3.3	1.8	7.2	12.6	89%
Jake Bowey	5.3	−0.4	4.9	1.9	11.8	86%
Koltyn Tholstrup	−0.6	2.8	2.2	6.4	10.8	79%
Jack Viney	1.9	4.4	2.7	0.9	10.0	84%
Kade Chandler	2.0	1.3	2.5	4.1	9.8	99%
Max Gawn	−1.0	5.4	1.3	3.8	9.5	92%
Clayton Oliver	4.1	1.2	−0.7	4.5	9.1	75%
Steven May	4.2	−0.8	0.7	5.0	9.0	94%
Tom Sparrow	0.0	1.5	2.6	4.0	8.1	83%
Bayley Fritsch	−0.9	1.3	3.9	3.6	7.9	89%
Trent Rivers	1.8	2.7	1.5	1.7	7.7	87%
Matthew Jefferson	0.0	−0.2	3.4	3.4	6.6	80%
Judd McVee	4.2	0.1	0.4	1.2	5.9	86%
Daniel Turner	0.7	4.8	0.2	0.1	5.8	96%
Harrison Petty	−0.6	3.8	0.0	0.0	3.2	22%
Harvey Langford	−2.7	1.5	2.5	1.9	3.1	72%
Christian Salem	−1.3	0.6	3.0	0.6	2.9	81%
Xavier Lindsay	−0.2	2.7	0.1	−0.1	2.5	74%
Blake Howes	2.4	0.0	0.0	0.0	2.4	23%
Ed Langdon	−0.8	1.1	1.5	−0.4	1.5	79%
Harry Sharp	0.0	0.8	1.2	−1.6	0.4	60%

Contested Possessions

	For	Against	Diff
Melbourne's Defensive 50
Hard Ball Get	6	8	-2
Loose Ball Get	13	12	+1
Contested Mark	2	5	-3
Contested Knock On	1	2	-1
Free For	1	6	-5
Total	23	33	-10
Melbourne's Forward 50
Hard Ball Get	4	2	+2
Loose Ball Get	13	13	0
Contested Mark	1	2	-1
Ruck Hard Ball Get	0	1	-1
Gather From Hitout	4	0	+4
Free For	3	3	0
Total	25	21	+4
Post clearance
Hard Ball Get	9	13	-4
Loose Ball Get	48	36	+12
Contested Mark	5	16	-11
Contested Knock On	2	2	0
Free For	9	10	-1
Total	73	77	-4
Pre clearance
Hard Ball Get	17	7	+10
Loose Ball Get	22	28	-6
Ruck Hard Ball Get	2	5	-3
Gather From Hitout	8	5	+3
Contested Knock On	0	2	-2
Free For	7	7	0
Total	56	54	+2

• Official data on pre- and post-clearance contested possessions are not available. These have been estimated by Wheelo Ratings and should be indicative.

• Ground ball gets are inclusive of hard ball gets and loose ball gets.

• 'Free For' does not include free kicks to advantage or free kicks while in possession of the ball as these are not counted as contested possessions.

Expected scores

	xScore	Score	xWin %	xMargin	Margin	Swing
Melbourne	90.3	85	10%			+4.1
Gold Coast	113.4	104	90%	+23.1	+19

	Shots	Score	Accuracy	xScore	+/-	xSc. / Shot	Shot Rating
Overall
Melbourne	30	12.13 85	40.0%	90.3	−5.3	3.01	−0.18
Gold Coast	31	15.11 101	48.4%	112.4	−11.4	3.63	−0.37
General Play
Melbourne	14	4.5 29	28.6%	37.2	−8.2	2.66	−0.59
Gold Coast	8	5.3 33	62.5%	25.5	+7.5	3.19	+0.94
Set Position
Melbourne	16	8.8 56	50.0%	53.1	+2.9	3.32	+0.18
Gold Coast	23	10.8 68	43.5%	86.9	−18.9	3.78	−0.82

• xWin %: win probability based on expected scores.

• Swing: difference between expected margin and actual margin.

• xScore: total expected score from all shots taken.

• +/-: total score above or below expected score.

• xSc. / Shot: average expected score per shot. This represents the average shot difficulty.

• Shot Rating: average score above or below expected score per shot at goal.

Notes: Expected scores are calculated by Wheelo Ratings. Each shot at goal is assigned an expected score based on the distance from goal, shot angle, and type of shot (e.g. set shot, general play following contested possession, general play following uncontested possession, ground kick, etc) as a proxy for pressure. The model does not take into account factors like the player, whether the ball was kicked with their preferred or non-preferred foot, and pressure on the player when taking the shot. Rushed behinds are excluded from actual and expected scores.

Territory (time in zones)

Region	Q1	Q2	Q3	Q4	Match	Season
Half
Forward	23%	57%	45%	52%	44%	52%
Defensive	77%	43%	55%	48%	56%	48%
Region
Forward 50	8%	26%	21%	33%	22%	25%
Attacking Midfield	16%	31%	24%	19%	22%	27%
Defensive Midfield	32%	23%	25%	23%	26%	25%
Defensive 50	45%	20%	29%	25%	30%	23%

Source: Calculated by Wheelo Ratings.

Score Sources

Summary

Score Source	Score	Against	Diff
Kick-in	0.1 1	1.2 8	-7
Centre Bounce	2.1 13	4.1 25	-12
Stoppage (Other)	4.5 29	4.5 29	+0
Turnover	6.6 42	6.6 42	+0

Score Source	For		Against
	Match	Season	Match	Season *
Kick-in	1	3.4	8	4.9
Centre Bounce	13	12.5	25	14.2
Stoppage (Other)	29	19.3	29	22.1
Turnover	42	43.3	42	49.1

* Against season average represents average points conceded by Melbourne across the season, not average points scored by Gold Coast.

Chain start region

Note: region is from the scoring team's perspective.

Region	For		Against
	Match	Season	Match	Season *
Defensive 50	21	11.1	9	17.6
Defensive midfield	23	15.7	13	22.1
Centre bounce	13	12.5	25	14.2
Attacking midfield	25	22.9	53	24.1
Forward 50	3	16.3	4	12.4

* Against season average represents average points conceded by Melbourne across the season, not average points scored by Gold Coast.

Points from defensive half

For		Against
Match	Season	Match	Season *
44	26.8	22	39.7

* Against season average represents average points conceded by Melbourne across the season, not average points scored by Gold Coast.

Centre Bounce Attendances

	CBAs	CBA %	2025 %	2024 %
Max Gawn	28	90%	85.5%	85.0%
Kysaiah Pickett	27	87%	70.4%	33.0%
Christian Petracca	26	84%	73.9%	55.8%
Clayton Oliver	21	68%	70.8%	70.7%
Jack Viney	17	55%	70.0%	69.1%
Harvey Langford	2	6%	15.4%
Matthew Jefferson	2	6%	1.8%
Harrison Petty	1	3%	0.5%	7.5%
Trent Rivers	0	0%	25.4%	29.9%
Tom Sparrow	0	0%	9.2%	37.7%
Ed Langdon	0	0%	4.8%	0.7%
Christian Salem	0	0%	1.9%	12.3%
Kade Chandler	0	0%	1.7%	0.0%
Judd McVee	0	0%	0.8%	6.3%
Daniel Turner	0	0%	0.3%	0.3%
Jake Melksham	0	0%	0.3%	0.0%
Koltyn Tholstrup	0	0%	0.0%	5.7%
Tom Fullarton			17.2%
Aidan Johnson			13.9%
Jacob van Rooyen			12.2%	17.8%
Bailey Laurie			0.0%	11.0%
Charlie Spargo			0.0%	4.2%

Ruck Contests and Hitouts

Ruck Contests

	Ruck Contests	RC %	2025 %	2024 %
Max Gawn	83	89%	83.0%	81.1%
Matthew Jefferson	7	8%	2.0%
Harrison Petty	3	3%	0.9%	7.9%
Daniel Turner	0	0%	0.4%	3.1%
Clayton Oliver	0	0%	0.1%	0.0%
Tom Fullarton			20.5%
Aidan Johnson			15.6%
Jacob van Rooyen			13.9%	17.6%

Hitouts

	Ruck Contests	Hitouts	To Adv.	To Adv. % (2025)	To Adv. % (2024)
Melbourne
Max Gawn	83	35	9	26.4%	27.9%
Harrison Petty	3	1	0	50.0%	24.4%
Matthew Jefferson	7	1	0	0.0%
Daniel Turner	0	0	0		50.4%
Tom Fullarton				43.8%
Jacob van Rooyen				33.3%	24.7%
Aidan Johnson				21.1%
Opposition
Jarrod Witts	87	37	5
Ethan Read	6	3	0

Melbourne v Gold Coast (Round 16, 2025)

https://www.wheeloratings.com/afl_match_stats.html?ID=20251604

Pressure

Team pressure

Quarter	For	Agn	Diff
1	160	209	-49
2	213	198	+15
3	174	189	-15
4	170	166	+4
Match	180	190	-10

Source: Herald Sun

Most Pressure Points

Note: pressure points are the 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/ )

Player	Pressure Acts	Pressure Points	Season Average
Christian Petracca	19	45	37.7
Clayton Oliver	16	42	52.1
Jack Viney	15	41	45.7
Tom Sparrow	17	39	37.7
Kade Chandler	14	36	32.7
Kysaiah Pickett	14	35	37.8
Ed Langdon	13	34	30.8
Koltyn Tholstrup	12	30	26.7
Xavier Lindsay	13	28	25.8
Jake Bowey	10	26	28.7
Harvey Langford	13	26	21.8
Christian Salem	12	26	21.1
Bayley Fritsch	9	23	19.4
Matthew Jefferson	10	22	14.5
Harry Sharp	8	20	19.8
Jake Melksham	6	17	18.8
Max Gawn	5	15	19.3
Judd McVee	4	12	16.4
Steven May	4	11	10.0
Trent Rivers	3	10	29.2
Daniel Turner	4	9	16.8
Harrison Petty	3	7	17.1
Blake Howes	0	0	15.6

Source: Herald Sun