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2 hours ago, WheeloRatings said:

I found out that the time in forward half stats are published in the Herald Sun, so I have subscribed to that and I'll see if it's worth keeping the subscription. I have added time in forward half and pressure for last night below. Also, I am able to calculate the score sources from one of my datasets so I have included that information below.

Melbourne v Richmond

https://www.wheeloratings.com/afl_match_stats.html?ID=20230608

Note: first number below is Melbourne, higher value is bold.

Pressure

Q1: 173 - 187
Q2: 159 - 162
Q3: 154 - 171
Q4: 188 - 157
Tot: 168 - 169

Time in forward half

47% - 53%

Score sources

Centre bounce
2.0.12 - 1.3.9

Ball up
0.0.0 - 3.0.18

Throw in
2.0.12 - 1.0.6

Turnover
11.6.72 - 6.9.45

Kick-in
0.0.0 - 0.0.0

Expected scores

77 - 85

 

 

Score sources, Rounds 1 - 6

Centre bounce
v WB: 2.0.12 - 0.1.1
v Bris: 1.0.6 - 2.1.13
v Syd: 3.3.21 - 2.4.16
v WC: 1.1.7 - 1.1.7
v Ess: 2.3.15 - 4.2.26
v Rich: 2.0.12 - 1.3.9

Ball up
v WB: 0.3.3 - 1.1.7
v Bris: 1.0.6 - 3.0.18
v Syd: 2.1.13 - 2.1.13
v WC: 4.4.28 - 1.1.7
v Ess: 1.1.7 - 1.1.7
v Rich: 0.0.0 - 3.0.18

Throw in
v WB: 2.1.13 - 2.0.12
v Bris: 3.0.18 - 4.1.25
v Syd: 3.0.18 - 2.1.13
v WC: 4.1.25 - 2.1.13
v Ess: 1.1.7 - 0.2.2
v Rich: 2.0.12 - 1.0.6

Turnover
v WB: 11.9.75 - 5.9.39
v Bris: 7.4.46 - 5.7.37
v Syd: 13.4.82 - 5.6.36
v WC: 10.6.66 - 4.5.29
v Ess: 5.6.36 - 10.9.69
v Rich: 11.6.72 - 6.9.45

Kick-in
v WB: 2.0.12 - 1.0.6
v Bris: 1.0.6 - 0.0.0
v Syd: 0.0.0 - 1.0.6
v WC: 0.0.0 - 1.1.7
v Ess: 2.0.12 - 0.0.0
v Rich: 0.0.0 - 0.0.0

Nice work.

So we capitalised more on turnovers but seem to have lost the around ground ball up battle by the looks of that

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Weighted Average Ratings up to & including Rnd 6, 2023 vs H&A Season 2022

Just a quick look at Viney's MVP from last night...

25 effectives @ 83% DE, 1 mark inside 50, 5 clearances, 2 rebounds, 4 inside 50s, 2 tackles, 7 score involvements, 4 intercepts, 318 meters gained, 1 goal and 4 turnovers.

Statistically May's best game for the year...

18 effectives @ 82%, 3 contested marks, 6 one percenters, 8 rebounds, 2 score involvements, 11 intercepts, 347 meters gained & 5 turnovers.

Rivers our best rated defender so far this season with a massive leap up the tables jumping 15 places from his 2022 ranking.

#24  T Rivers 2022 2023 % Change 
CP 4.19 5.92 41.5
UP 9.88 14.98 51.7
ED* 10.88 16.72 53.8
DE% 78.19 83.47 5.3
CM* 0.31 0.35 11.5
Mi50* 0.00 0.17 -
1%* 1.63 2.61 60.8
CL* 0.56 1.05 85.8
R50* 2.63 4.36 65.9
i50* 1.81 2.79 53.8
T* 1.50 2.96 97.4
Si* 2.44 4.53 85.8
MG 250.38 387.46 54.8
TO* 2.56 2.61 2.0
ITC* 4.44 5.57 25.6
T5* 0.00 0.17 -
TOG% 71.00 82.58 11.6
G* 0.13 0.00 -
2022 Rating
2.423
2023 Rating
4.029
% Change 

66.24

 

Player 2023 Rating 2023 Rank 2022 Rating % Change vs 2022 2022 Rank Change in Rank vs 2022
C Oliver 5.396 1 5.320 1.43 1 0
C Petracca 4.638 2 4.456 4.08 2 0
T Rivers 4.029 3 2.423 66.28 18 15
J Jordon > 3.979 4 3.164 25.76 9 5
Jack Viney 3.845 5 3.971 -3.17 3 -2
A Brayshaw 3.471 6 3.839 -9.59 5 -1
Steven May 3.363 7 3.971 -15.31 3 -4
Jake Lever 3.320 8 2.703 22.83 14 6
Ed Langdon 3.304 9 3.109 6.27 11 2
B Grundy # 3.292 10 - - - -
A Tomlinson 3.233 11 2.079 55.51 22 11
L Hunter 3.117 12 - - - -
T Sparrow 2.858 13 2.665 7.24 16 3
Max Gawn # < 2.813 14 3.215 -12.50 8 -6
K Pickett 2.788 15 2.118 31.63 21 6
Alex N-Bullen 2.671 16 2.688 -0.63 15 -1
J V Rooyen # 2.644 17 - - - -
M Hibberd 2.619 18 2.613 0.23 17 -1
K Chandler 2.563 19 3.128 -18.06 10 -9
H Petty # 2.483 20 2.392 3.80 19 -1
B Fritsch 2.435 21 1.936 25.77 27 6
T McDonald # 2.344 22 1.939 20.89 26 4
Jake Bowey 2.308 23 2.856 -19.19 13 -10
Judd McVee 2.246 24 - - - -
J Harmes 2.225 25 3.082 -27.81 12 -13
Ben Brown # < 2.033 26 1.759 15.58 29 3
C Spargo 1.935 27 1.981 -2.32 24 -3
J Melksham > 1.437 28 1.947 -26.19 25 -3
B Laurie < * 1.300 29 - - - -
Team Rating 71.21   68.89 3.36    

* Played less than two full matches (in total) - player rating compromised

# Hit outs to advantage not counted

< Subbed Out at least once or more

> Subbed In at least once or more

Stats courtesy of footwire.com

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9 hours ago, Demon Dynasty said:

Nice work.

So we capitalised more on turnovers but seem to have lost the around ground ball up battle by the looks of that

Ground ball is where teams like Coll, Syd, Ess try to beat us. If you kick high to our defence you will lose so the simple plan is to do what Rich did last night for 2 qtrs. Overlap h/ball, running and short kicks or dirty kicks to the Fwds.

 

We need to get better at recognising when teams are trying that. And then combatting that tactic. North did it last year against us but most teams cant keep up the pressure for 4 qtrs.

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14 hours ago, Demon Dynasty said:

Weighted Average Ratings up to & including Rnd 6, 2023 vs H&A Season 2022

Just a quick look at Viney's MVP from last night...

25 effectives @ 83% DE, 1 mark inside 50, 5 clearances, 2 rebounds, 4 inside 50s, 2 tackles, 7 score involvements, 4 intercepts, 318 meters gained, 1 goal and 4 turnovers.

Statistically May's best game for the year...

18 effectives @ 82%, 3 contested marks, 6 one percenters, 8 rebounds, 2 score involvements, 11 intercepts, 347 meters gained & 5 turnovers.

Rivers our best rated defender so far this season with a massive leap up the tables jumping 15 places from his 2022 ranking.

#24  T Rivers 2022 2023 % Change 
CP 4.19 5.92 41.5
UP 9.88 14.98 51.7
ED* 10.88 16.72 53.8
DE% 78.19 83.47 5.3
CM* 0.31 0.35 11.5
Mi50* 0.00 0.17 -
1%* 1.63 2.61 60.8
CL* 0.56 1.05 85.8
R50* 2.63 4.36 65.9
i50* 1.81 2.79 53.8
T* 1.50 2.96 97.4
Si* 2.44 4.53 85.8
MG 250.38 387.46 54.8
TO* 2.56 2.61 2.0
ITC* 4.44 5.57 25.6
T5* 0.00 0.17 -
TOG% 71.00 82.58 11.6
G* 0.13 0.00 -
2022 Rating
2.423
2023 Rating
4.029
% Change 

66.24

 

Player 2023 Rating 2023 Rank 2022 Rating % Change vs 2022 2022 Rank Change in Rank vs 2022
C Oliver 5.396 1 5.320 1.43 1 0
C Petracca 4.638 2 4.456 4.08 2 0
T Rivers 4.029 3 2.423 66.28 18 15
J Jordon > 3.979 4 3.164 25.76 9 5
Jack Viney 3.845 5 3.971 -3.17 3 -2
A Brayshaw 3.471 6 3.839 -9.59 5 -1
Steven May 3.363 7 3.971 -15.31 3 -4
Jake Lever 3.320 8 2.703 22.83 14 6
Ed Langdon 3.304 9 3.109 6.27 11 2
B Grundy # 3.292 10 - - - -
A Tomlinson 3.233 11 2.079 55.51 22 11
L Hunter 3.117 12 - - - -
T Sparrow 2.858 13 2.665 7.24 16 3
Max Gawn # < 2.813 14 3.215 -12.50 8 -6
K Pickett 2.788 15 2.118 31.63 21 6
Alex N-Bullen 2.671 16 2.688 -0.63 15 -1
J V Rooyen # 2.644 17 - - - -
M Hibberd 2.619 18 2.613 0.23 17 -1
K Chandler 2.563 19 3.128 -18.06 10 -9
H Petty # 2.483 20 2.392 3.80 19 -1
B Fritsch 2.435 21 1.936 25.77 27 6
T McDonald # 2.344 22 1.939 20.89 26 4
Jake Bowey 2.308 23 2.856 -19.19 13 -10
Judd McVee 2.246 24 - - - -
J Harmes 2.225 25 3.082 -27.81 12 -13
Ben Brown # < 2.033 26 1.759 15.58 29 3
C Spargo 1.935 27 1.981 -2.32 24 -3
J Melksham > 1.437 28 1.947 -26.19 25 -3
B Laurie < * 1.300 29 - - - -
Team Rating 71.21   68.89 3.36    

* Played less than two full matches (in total) - player rating compromised

# Hit outs to advantage not counted

< Subbed Out at least once or more

> Subbed In at least once or more

Stats courtesy of footwire.com

Thanks DD so Trent Rivers and JJ the big improvers and welcome back Jake Lever and hopefully May and Gawn on the way back to their best.

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4 hours ago, DeeZone said:

Thanks DD so Trent Rivers and JJ the big improvers and welcome back Jake Lever and hopefully May and Gawn on the way back to their best.

Correct DZ.

JJ off a small sample size though, but he's showing early solid form that suggests he may be tracking ahead of where he was in 2022.  Lets hope he continues on this path eh.

Sparrow had a decent result againt the Tiges.  Starting to look ok through the middle also when he's getting time there.

May & Gawn ...is it possible to rest Max this week?  Its a long time to be out and couldn't hurt to manage him away from the 5 day break.

I guess that depends on how he pulls up & what the HP team are recommending etc.

May ...my gut feel is he's being eased back into the normal training routine & FD / HP are happy to have him regain match fitness / hardness in game vs going for that on the track (in a normal sense).

Edited by Demon Dynasty
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On 4/25/2023 at 9:09 AM, WheeloRatings said:

I found out that the time in forward half stats are published in the Herald Sun, so I have subscribed to that and I'll see if it's worth keeping the subscription. I have added time in forward half and pressure for last night below. Also, I am able to calculate the score sources from one of my datasets so I have included that information below.

Melbourne v Richmond

https://www.wheeloratings.com/afl_match_stats.html?ID=20230608

Note: first number below is Melbourne, higher value is bold.

Pressure

Q1: 173 - 187
Q2: 159 - 162
Q3: 154 - 171
Q4: 188 - 157
Tot: 168 - 169

Time in forward half

47% - 53%

Score sources

Centre bounce
2.0.12 - 1.3.9

Ball up
0.0.0 - 3.0.18

Throw in
2.0.12 - 1.0.6

Turnover
11.6.72 - 6.9.45

Kick-in
0.0.0 - 0.0.0

Expected scores

77 - 85

 

 

Score sources, Rounds 1 - 6

Centre bounce
v WB: 2.0.12 - 0.1.1
v Bris: 1.0.6 - 2.1.13
v Syd: 3.3.21 - 2.4.16
v WC: 1.1.7 - 1.1.7
v Ess: 2.3.15 - 4.2.26
v Rich: 2.0.12 - 1.3.9

Ball up
v WB: 0.3.3 - 1.1.7
v Bris: 1.0.6 - 3.0.18
v Syd: 2.1.13 - 2.1.13
v WC: 4.4.28 - 1.1.7
v Ess: 1.1.7 - 1.1.7
v Rich: 0.0.0 - 3.0.18

Throw in
v WB: 2.1.13 - 2.0.12
v Bris: 3.0.18 - 4.1.25
v Syd: 3.0.18 - 2.1.13
v WC: 4.1.25 - 2.1.13
v Ess: 1.1.7 - 0.2.2
v Rich: 2.0.12 - 1.0.6

Turnover
v WB: 11.9.75 - 5.9.39
v Bris: 7.4.46 - 5.7.37
v Syd: 13.4.82 - 5.6.36
v WC: 10.6.66 - 4.5.29
v Ess: 5.6.36 - 10.9.69
v Rich: 11.6.72 - 6.9.45

Kick-in
v WB: 2.0.12 - 1.0.6
v Bris: 1.0.6 - 0.0.0
v Syd: 0.0.0 - 1.0.6
v WC: 0.0.0 - 1.1.7
v Ess: 2.0.12 - 0.0.0
v Rich: 0.0.0 - 0.0.0

@binman not sure if you saw the above stats before the podcast, but I'll just draw your attention to them now.

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Round 7, 2023 MCG - Demons vs Kangaroos

Statistically the games played of both teams was very similar.  North a slightly older team (on averages)

Melbourne  Attribute  Nth Melbourne
187.3cm  Height  189.7cm
87.4kg  Weight  86.3kg
25yr 11mth  Age  26yr 2mth
114 Games  112

Tracc's maiden No.1 rating for 2023 and the highest rating so far as well...

25 effectives @ 71%, 3 marks inside 50, 4 clearances, 1 rebound, 9 inside 50s, 3 tackles, 14 score involvements (that's a massive 6 ahead of the next best, Clarry / Kozzy & Zurhaar all on 8), 5 intercepts, 552 meters gained (best on the night from either team), 3 goals and 5 turnovers.

Player Rating Rank
C Petracca 6.725 1
C Oliver 6.000 2
A Brayshaw 5.625 3
T Sparrow 4.425 4
J Bowey 4.325 5
K Pickett 4.275 6
K Chandler 4.175 7
Jack Viney 3.950 8
T Rivers 3.825 9
A N-Bullen 3.750 10
Max Gawn # 3.550 11
B Grundy # 3.525 12
J Jordon 3.500 13
S May 3.400 14
L Hunter 3.250 15
Jake Lever 2.600 16
M Hibberd < 72% 2.475 17
Ed Langdon 2.475 17
B Fritsch 1.950 19
J V Rooyen 1.500 20
J McVee 1.450 21
J Schache 1.375 22
J Harmes > 16% 0.750 23
Team Score 78.13  
Top 6 31.38  
Bottom 6 11.23  

# Hit outs to advantage not counted

< Subbed out / TOG %

> Subbed in / TOG %

Stats courtesy of footwire.com

Edited by Demon Dynasty
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Melbourne v North Melbourne

https://www.wheeloratings.com/afl_match_stats.html?ID=20230705

Note: first number below is Melbourne, higher value is bold.

Pressure

Q1: 189 - 185
Q2: 169 - 161
Q3: 168 - 160
Q4: 153 - 139
Tot: 169 - 163

Most Pressure Points

Sparrow: 62
Chandler: 48
Viney: 41
Oliver: 39
Pickett: 38
Neal-Bullen: 31
Hunter: 31
Langdon: 30

Time in forward half

60% - 40%

Score sources

Centre bounce
4.1.25 - 0.0.0

Ball up
3.2.20 - 1.2.8

Throw in
3.1.19 - 0.1.1

Turnover
10.3.63 - 6.4.40

Kick-in
2.0.12 - 0.0.0

Shots at goal

Set position
15.1.91 - 6.3.39

General play
7.2.44 - 1.2.8

Expected scores

111 - 53

 

 

 

 

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Weighted Average Ratings up to & including Rnd 7, 2023 vs H&A Season 2022

Player 2023 Rating 2023 Rank 2022 Rating % Change vs 2022 2022 Rank Change in Rank vs 2022
C Oliver 5.482 1 5.320 3.05 1 0
C Petracca 4.936 2 4.456 10.77 2 0
T Rivers 3.999 3 2.423 65.04 18 15
Jack Viney 3.863 4 3.971 -2.72 3 -1
J Jordon > 3.845 5 3.164 21.52 9 4
A Brayshaw 3.779 6 3.839 -1.56 5 -1
Steven May 3.370 7 3.971 -15.13 3 -4
B Grundy # 3.325 8 - - - -
Ed Langdon 3.304 9 3.109 6.27 11 2
A Tomlinson 3.233 10 2.079 55.51 22 12
Jake Lever 3.200 11 2.703 18.39 14 3
L Hunter 3.136 12 - - - -
K Pickett 3.085 13 2.118 45.66 21 8
T Sparrow 3.082 14 2.665 15.65 16 2
Max Gawn # < 3.058 15 3.215 -4.88 8 -7
Alex N-Bullen 2.825 16 2.688 5.10 15 -1
K Chandler 2.793 17 3.128 -10.71 10 -7
M Hibberd 2.791 18 2.613 6.81 17 -1
Jake Bowey 2.596 19 2.856 -9.10 13 -6
H Petty # 2.483 20 2.392 3.80 19 -1
J V Rooyen # 2.415 21 - - - -
J Harmes 2.407 22 3.082 -21.90 12 -10
B Fritsch 2.354 23 1.936 21.59 27 4
T McDonald # 2.344 24 1.939 20.89 26 2
Judd McVee 2.132 25 - - - -
Ben Brown # < 2.033 26 1.759 15.58 29 3
C Spargo 1.935 27 1.981 -2.32 24 -3
J Melksham > 1.437 28 1.947 -26.19 25 -3
B Laurie < * 1.300 29 - - - -
Team Rating 73.01   68.89 5.98    

* Played less than two full matches (in total) - player rating compromised

# Hit outs to advantage not counted

< Subbed Out at least once or more

> Subbed In at least once or more

Stats courtesy of footwire.com

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Thanks DD and Wheelo love these stats a lot of our players had significant games last night. DD when I watched the replay today I noticed Gus and Sparrow more than last night and this is borne out by the stats, very satisfying.!!!

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3 hours ago, Demon Dynasty said:

Weighted Average Ratings up to & including Rnd 7, 2023 vs H&A Season 2022

Player 2023 Rating 2023 Rank 2022 Rating % Change vs 2022 2022 Rank Change in Rank vs 2022
C Oliver 5.482 1 5.320 3.05 1 0
C Petracca 4.936 2 4.456 10.77 2 0
T Rivers 3.999 3 2.423 65.04 18 15
Jack Viney 3.863 4 3.971 -2.72 3 -1
J Jordon > 3.845 5 3.164 21.52 9 4
A Brayshaw 3.779 6 3.839 -1.56 5 -1
Steven May 3.370 7 3.971 -15.13 3 -4
B Grundy # 3.325 8 - - - -
Ed Langdon 3.304 9 3.109 6.27 11 2
A Tomlinson 3.233 10 2.079 55.51 22 12
Jake Lever 3.200 11 2.703 18.39 14 3
L Hunter 3.136 12 - - - -
K Pickett 3.085 13 2.118 45.66 21 8
T Sparrow 3.082 14 2.665 15.65 16 2
Max Gawn # < 3.058 15 3.215 -4.88 8 -7
Alex N-Bullen 2.825 16 2.688 5.10 15 -1
K Chandler 2.793 17 3.128 -10.71 10 -7
M Hibberd 2.791 18 2.613 6.81 17 -1
Jake Bowey 2.596 19 2.856 -9.10 13 -6
H Petty # 2.483 20 2.392 3.80 19 -1
J V Rooyen # 2.415 21 - - - -
J Harmes 2.407 22 3.082 -21.90 12 -10
B Fritsch 2.354 23 1.936 21.59 27 4
T McDonald # 2.344 24 1.939 20.89 26 2
Judd McVee 2.132 25 - - - -
Ben Brown # < 2.033 26 1.759 15.58 29 3
C Spargo 1.935 27 1.981 -2.32 24 -3
J Melksham > 1.437 28 1.947 -26.19 25 -3
B Laurie < * 1.300 29 - - - -
Team Rating 73.01   68.89 5.98    

* Played less than two full matches (in total) - player rating compromised

# Hit outs to advantage not counted

< Subbed Out at least once or more

> Subbed In at least once or more

Stats courtesy of footwire.com

So Mays dismal year (comp 2022) so far clearly coming through the stats. Bowey as well. Chandlers a bit strange I thought it would be a huge positive.

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10 hours ago, DeeZone said:

Thanks DD and Wheelo love these stats a lot of our players had significant games last night. DD when I watched the replay today I noticed Gus and Sparrow more than last night and this is borne out by the stats, very satisfying.!!!

Cheers DZ.  Yes sometimes it works like that.  There is the reverse scanario of course where you watch a game and wonder how the hell a particular rating was achieved (ie the stats don't tell the tale much at all).

Gus & Sparrow have certainly had two very good weeks now.

Watched the Tiger's match after going to that and have to say that was one of the best Gussy matches for mine.  We got his usual great read of the ball and positioning for intercepts but there was also some lovely field kicks in there.

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10 hours ago, Gawndy the Great said:

So Mays dismal year (comp 2022) so far clearly coming through the stats. Bowey as well. Chandlers a bit strange I thought it would be a huge positive.

Keep in mind May is coming off two stellar years GtG.  Compared to his usual standards over this period, yes he's a bit off.  More so prior to the Tiger's match.  Since then he's started to peg things back a little!  He was off his 2022 season by 22.5% after the game against the bombers.  Coming back from a calf niggle.  Might be still hanging about and bothering.  Doesn't seem to have the same leap / zip (for him) in the contests just yet.  Confidence in the contest might be off a little?  Fingers crossed he will get his bod right as we move through the season.

Bowey's probably the one that's struggled a bit so far vs his 2022 form (Harmes more so but possibly for personal reasons?).  Having said that there's been signs in the last week or two of Bowey getting back to his best in patches.  10% off is very gettable so lets hope he is on the road to finding his mojo again.

Thanks for pointing out Chandler.  When i set this year's ratings up i used last year's table as a template.  Of course when you do that you have to make sure everything's 100%.  And guess what....i used LJ's slot for Chandler and forgot to remove LJ's rating haha!  So you are looking at LJ's 2022 rating vs 2023 Chandler lol.  Now corrected!

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On 4/26/2023 at 6:45 PM, WheeloRatings said:

@binman not sure if you saw the above stats before the podcast, but I'll just draw your attention to them now.

No i didn't, that is so brilliant.

Thanks wheelo (have also now just seen the stats from the roos game)

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@Demon Dynasty

G'day DD - i know you have put up the def etc for the player rankings you post in this thread (whcih ius fanttic by th ewya) prvioulsy. But i cant recall the specifics

Am i right in assuing the ranbkings are the official AFL player rankings (which i presume is a champion dta thing) not a system you have designed?

And if yes, is the def as per this table;

AFL Player Rating Explanation.png

 

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On 4/30/2023 at 9:00 AM, WheeloRatings said:

Melbourne v North Melbourne

https://www.wheeloratings.com/afl_match_stats.html?ID=20230705

Note: first number below is Melbourne, higher value is bold.

Pressure

Q1: 189 - 185
Q2: 169 - 161
Q3: 168 - 160
Q4: 153 - 139
Tot: 169 - 163

Most Pressure Points

Sparrow: 62
Chandler: 48
Viney: 41
Oliver: 39
Pickett: 38
Neal-Bullen: 31
Hunter: 31
Langdon: 30

Time in forward half

60% - 40%

Score sources

Centre bounce
4.1.25 - 0.0.0

Ball up
3.2.20 - 1.2.8

Throw in
3.1.19 - 0.1.1

Turnover
10.3.63 - 6.4.40

Kick-in
2.0.12 - 0.0.0

Shots at goal

Set position
15.1.91 - 6.3.39

General play
7.2.44 - 1.2.8

Expected scores

111 - 53

 

 

 

 

@WheeloRatings

That is brilliant.

I actually checked the the stats for the roos game re time in forward half on your site on Sunday. And couldnt see it.

And checked then and still can't. 

Am i missing something or is this data yet to be added to the site and you have just (super helpfully) put them in this thread?

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1 hour ago, binman said:

@Demon Dynasty

G'day DD - i know you have put up the def etc for the player rankings you post in this thread (whcih ius fanttic by th ewya) prvioulsy. But i cant recall the specifics

Am i right in assuing the ranbkings are the official AFL player rankings (which i presume is a champion dta thing) not a system you have designed?

And if yes, is the def as per this table;

AFL Player Rating Explanation.png

 

Howdy Bin

I've managed to fish out my response to same question from 2020.  Apologies for the length of that.  I do suffer from writer's dihorea at times.

In short (for me haha), these ratings  have nothing to do with Champions' or other ratings.  I would always disclose that if it were the case.

Merely an amature proprietary attempt to (very roughly) try and capture some of what we witness on game day using 10 wasted years of Fund Manager analysis experience back in the 90s as the idea/method behind it.

To cut a boring story short i just wanted a rating/score that roughly gave some resemblence of what i was witnessing when watching the game.

I felt at the time some of the Super Coach ratings etc were, in some instances, a fair way off what i had seen at the game / on TV and on occasions a long way off.

So i went back over 6 matches in 2017 and for want of a better term (and probably a very poor term to describe things), backward engineered a set of data to try to match, as closely as possible, the impact and performances of as many players as possible on the day.

The idea was to try and have the data mimic what happened on the field as best as possible, rather than just have the data spit something out the other end that may or may not be close to what i was seeing.

Of course like all things in life, nothing is perfect (with the exception of my kids, the 2021 premiership year, beating the Tigers on Anzac eve, a top notch pint etc) and the ratings i put up are no exception.  I would however argue they are a reasonable rough guide as to how a player is tracking if taken over a period of approx 4 to 5 matches of data or more.

The methodology is also very subjective as it was only my view of which players impacted / performed at a high / medium / sub par level in the 6 replays i covered.  Ideally you would want a panel of experts to give their feedback etc then backward engineer the data based on their ratings.

But who has the time, access and connections for such a venture!?  And who's to say that method is even a  sound one regardless.

Ideally you would also need to run a separate rating system for Backs, Forwards,  Mids & Followers but who has that time and besides, nowadays players rarely play in set positions. Champion might be capable but not possible for a solo amature.

As i always recommend, do yourself a favour and watch the match, like seriously watch it.  These tables are merely a rough guide and only include publicly available stats.  And whether publicly available or exclusive to champion data, some of them can be pretty diabolical and majorly flawed at their attempt to capture what's going on.

I'm sure most would agree, the eyes are often (but not in every case) your better judge of a player's / team's impact / performance and alot of the time stats fail to pick up the subtle nuances of what takes place on the field.  There's only so much you can capture with stats.

Hope this helps!

  On 8/8/2020 at 2:51 PM, Nascent said:

Apologies Rusty if this has already been explained but where are these numbers derived from? I could be a bit daft but I can't make any sense of these random numbers without context.

What stats combine to create a weighted score? Is it a combination of effective disposal, metres gained, score involvements etc...?

Yes correct Nascent.  Just a series of select weighted stats that are averaged as the weeks go by so that you can theoretically compare one player to another across a series of weeks,  months or even an entire season, regardless of how many games each player has played.  The score is therefore pretty relevant, regardless of whether the player has played say only 5 matches vs a fellow player who may have been lucky enough to play an entire season.

The first thing i will say is.... there is a huge danger in looking at any stats (these included) without having watched the game you are deriving the stats from.... intently and in conjunction with the weights and the overall outcome / Score.  So was there any testing done to ensure the scores were somewhat robust vs what was happening on the field?  Yes, albeit limited.  I watched replays of 6 matches in the second half of 2017 to ensure the player scores were, at the very least, "somewhat" reflective (to the best of my limited ability and the limited compromised data available) of how each of the players performed on match day.  Fair to say the rewind and fast forward buttons received a pummelling.  Obviously there are a myriad of inputs, flaws and subjective bias that goes into the end product and if a serious analyst got hold of this it would probably end up either in the bin or be given a major overhaul but there was some small but subjective back testing carried out over about 3 weeks.  Do i think the scores somewhat reflect the outcome on game day in most cases?  Not always for every player, but yes in most cases they would appear to (aside from the Maxy / ruckman hit outs to advantage issue / players given a lock down or forward pressure role etc)

What stats are being weighted?  Your assumption is accurate and certainly no rocket science involved.  Each stat is subjectively weighted and includes...

Effective disposals (the receiver actually receives the ball ie., there is some robustness in this one), Contested Marks (Maxy, Casboult, Kennedy et al), Marks inside 50 (forwards and mids pushing forward), 1%ers (a pretty dodgy stat but does cover all players, especially critical for defenders spoiling etc), Rebound 50s (covers defenders and mids running both ways to help out.  Even forwards on the odd occasion), Clearances (mostly mids, but also covers forwards, rucks and possibly the odd defender.  Basically anyone hitting in and getting the pill out), Inside 50s (very low weighting as anyone can bomb the ball in), tackles, score involvements, intercepts and goals.  Then i deduct a significant factor off the score of each player for any turnovers.  This last part is extremely arbitrary and something i have thought over for a while but my view is it is no good racking up 30 disposals if you just as easily gave back 10 of those straight to the opp.  And often after game day we will hear those complaints from fans "So and so got plenty but much of it was junk and went straight to the opp" etc etc.  This discount or deduction ensures that poor users of the ball are punished accordingly and the score is reflective of their poor use.  Meters gained is not presently covered but given it's supposed importance in the finals/GF success metrics i am considering adding this in 2021.

Is there a bench mark figure for what players should be attaining?

No but it could be done if you had the time and wherewithal.  If one could be bothered carving out the players that play a similar role or position and compare their relevant scores across the entire AFL then it could be possible but that's already covered and in a much more robust accurate way by AFL ratings.  The only issue i have with their ratings is it is very laggy as it is based on a player's last 40 matches and therefore doesn't cover players who have been out for quite a while injured or rookies etc.  Could i be bothered / do i have the time?  No lol.

Do i presently have any idea what a benchmark pass is by position or at all?  Not really and anything on this topic is purely subjective.  Also, in many ways you are only as good as your opponent and sometimes a benchmark or what i might consider a "good" score one week, might be off 20% the next but that same player might have played just as well on the day and/or have played a very good game, but, he did so playing on a better quality opponent / team and hence his score is off vs the previous week (usually the pressure factor).  Does that make sense?  This is why an average score over a minimum number of games (my guess is at least approx 5 or more) is obviously more robust than just one, two or three week scores.

Is it affected by position, i.e will mids will have a higher number due to overall possession numbers but dour defenders and pressure forwards score lower. For example would that mean a "good" score for a mid might be >3 where as a small forward >1?

A small pressure forward might be there to do just that and maybe score the odd goal.  His stats and therefore his weighted score will generally not look great vs say a mid racking up plenty of effective disposals by handball / boot or a defender chipping plenty of short kicks around the back half or even backwards on occasions (transferring pressure).  So then it comes down to comparing apples to apples while or after watching the game in question.  Unfortunately we don't have access to Champion data's best data.  Things like their 'kick rating' would be very handy as well as hit outs to advantage for ruckmen.  Maxy presently suffers in these tables with this aspect not being captured, albeit the rest of his game still sees him mostly always at or very near the top of the tables.  This would likely improve the robustness of the input data.  The old saying rubbish in rubbish out is a great starting point and in that instance these tables should only ever be considered as a very rough guide and only taken into consideration after having watched the match in its entirety.

What are the poor, average, good benchmarks for players and positions across the league and across and how do our players compare?

Having said all the above my gut feel generally is.... a 1.80 to 2.0 or better is a bare minimum pass mark in almost any role / position unless said player is playing a lock down type role eg; shoulder to shoulder defender a la an Oscar or May type role, a mid or even as a small pressure forward who might be tasked to shut down a dangerous play making rebounding defender etc.  In this instance a 1.30 (ish) to about 1.70 may well classify as a good pass if his opponent has had limited impact vs his usual impact.  I would also apply that lowish approx score to a rookie for a rough pass mark.

Looking at particular areas of the ground.  As a rough guide... Score pass mark i would expect to be generally lower up forward....roughly 1.80 ish.  Mids roughly a 2.5 to 3.0.  And defenders roughly 1.80ish and above.  Any player getting near a 3.0 or higher should (in theory) have had a good to quite good match or at least held his own on the day.  Over 3 to 5.0 is a very good game. Over 5 to 6.9 outstanding.  7.0 and above is off the charts.

Apologies again and I'm certainly don't expect you to do the leg work in some of those questions I asked, just trying to wrap my head around the theory behind the numbers.

 

 

Edited by Demon Dynasty
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3 minutes ago, Demon Dynasty said:

Howdy Bin

I've managed to fish out my response to same question from 2020.  Apologies for the length of that.  I do suffer from writer's dihorea at times.

In short (for me haha), these ratings  have nothing to do with Champions' or other ratings.  I would always disclose that if it were the case.

Merely an amature proprietary attempt to (very roughly) try and capture some of what we witness on game day using 10 wasted years of Fund Manager analysis experience back in the 90s as the idea/method behind it.

To cut a boring story short i just wanted a rating/score that roughly gave some resemblence of what i was witnessing when watching the game.

I felt at the time some of the Super Coach ratings etc were, in some instances, a fair way off what i had seen at the game / on TV and on occasions a long way off.

So i went back over 6 matches in 2017 and for want of a better term (and probably a very poor term to describe things), backward engineered a set of data to try to match, as closely as possible, the impact and performances of as many players as possible on the day.

The idea was to try and have the data mimic what happened on the field as best as possible, rather than just have the data spit something out the other end that may or may not be close to what i was seeing.

Of course like all things in life, nothing is perfect (with the exception of my kids, the 2021 premiership year, beating the Tigers on Anzac eve, a top notch pint etc) and the ratings i put up are no exception.  I would however argue they are a reasonable rough guide as to how a player is tracking if taken over a period of approx 4 to 5 matches of data or more.

The methodology is also very subjective as it was only my view of which players impacted / performed at a high / medium / sub par level in the 6 replays i covered.  Ideally you would want a panel of experts to give their feedback etc then backward engineer the data based on their ratings.

But who has the time, access and connections for such a venture!?  And who's to say that method is even a  sound one regardless.

Ideally you would also need to run a separate rating system for Backs, Forwards,  Mids & Followers but who has that time and besides, nowadays players rarely play in set positions. Champion might be capable but not possible for a solo amature.

As i always recommend, do yourself a favour and watch the match, like seriously watch it.  These tables are merely a rough guide and only include publicly available stats.  And whether publicly available or exclusive to champion data, some of them can be pretty diabolical and majorly flawed at their attempt to capture what's going on.

I'm sure most would agree, the eyes are often (but not in every case) your better judge of a player's impact / performance and alot of the time stats fail to pick up the subtle nuances of what takes place on the field.  There's only so much you can capture with stats.

Hope this helps!

  On 8/8/2020 at 2:51 PM, Nascent said:

Apologies Rusty if this has already been explained but where are these numbers derived from? I could be a bit daft but I can't make any sense of these random numbers without context.

What stats combine to create a weighted score? Is it a combination of effective disposal, metres gained, score involvements etc...?

Yes correct Nascent.  Just a series of select weighted stats that are averaged as the weeks go by so that you can theoretically compare one player to another across a series of weeks,  months or even an entire season, regardless of how many games each player has played.  The score is therefore pretty relevant, regardless of whether the player has played say only 5 matches vs a fellow player who may have been lucky enough to play an entire season.

The first thing i will say is.... there is a huge danger in looking at any stats (these included) without having watched the game you are deriving the stats from.... intently and in conjunction with the weights and the overall outcome / Score.  So was there any testing done to ensure the scores were somewhat robust vs what was happening on the field?  Yes, albeit limited.  I watched replays of 6 matches in the second half of 2017 to ensure the player scores were, at the very least, "somewhat" reflective (to the best of my limited ability and the limited compromised data available) of how each of the players performed on match day.  Fair to say the rewind and fast forward buttons received a pummelling.  Obviously there are a myriad of inputs, flaws and subjective bias that goes into the end product and if a serious analyst got hold of this it would probably end up either in the bin or be given a major overhaul but there was some small but subjective back testing carried out over about 3 weeks.  Do i think the scores somewhat reflect the outcome on game day in most cases?  Not always for every player, but yes in most cases they would appear to (aside from the Maxy / ruckman hit outs to advantage issue / players given a lock down or forward pressure role etc)

What stats are being weighted?  Your assumption is accurate and certainly no rocket science involved.  Each stat is subjectively weighted and includes...

Effective disposals (the receiver actually receives the ball ie., there is some robustness in this one), Contested Marks (Maxy, Casboult, Kennedy et al), Marks inside 50 (forwards and mids pushing forward), 1%ers (a pretty dodgy stat but does cover all players, especially critical for defenders spoiling etc), Rebound 50s (covers defenders and mids running both ways to help out.  Even forwards on the odd occasion), Clearances (mostly mids, but also covers forwards, rucks and possibly the odd defender.  Basically anyone hitting in and getting the pill out), Inside 50s (very low weighting as anyone can bomb the ball in), tackles, score involvements, intercepts and goals.  Then i deduct a significant factor off the score of each player for any turnovers.  This last part is extremely arbitrary and something i have thought over for a while but my view is it is no good racking up 30 disposals if you just as easily gave back 10 of those straight to the opp.  And often after game day we will hear those complaints from fans "So and so got plenty but much of it was junk and went straight to the opp" etc etc.  This discount or deduction ensures that poor users of the ball are punished accordingly and the score is reflective of their poor use.  Meters gained is not presently covered but given it's supposed importance in the finals/GF success metrics i am considering adding this in 2021.

Is there a bench mark figure for what players should be attaining?

No but it could be done if you had the time and wherewithal.  If one could be bothered carving out the players that play a similar role or position and compare their relevant scores across the entire AFL then it could be possible but that's already covered and in a much more robust accurate way by AFL ratings.  The only issue i have with their ratings is it is very laggy as it is based on a player's last 40 matches and therefore doesn't cover players who have been out for quite a while injured or rookies etc.  Could i be bothered / do i have the time?  No lol.

Do i presently have any idea what a benchmark pass is by position or at all?  Not really and anything on this topic is purely subjective.  Also, in many ways you are only as good as your opponent and sometimes a benchmark or what i might consider a "good" score one week, might be off 20% the next but that same player might have played just as well on the day and/or have played a very good game, but, he did so playing on a better quality opponent / team and hence his score is off vs the previous week (usually the pressure factor).  Does that make sense?  This is why an average score over a minimum number of games (my guess is at least approx 5 or more) is obviously more robust than just one, two or three week scores.

Is it affected by position, i.e will mids will have a higher number due to overall possession numbers but dour defenders and pressure forwards score lower. For example would that mean a "good" score for a mid might be >3 where as a small forward >1?

A small pressure forward might be there to do just that and maybe score the odd goal.  His stats and therefore his weighted score will generally not look great vs say a mid racking up plenty of effective disposals by handball / boot or a defender chipping plenty of short kicks around the back half or even backwards on occasions (transferring pressure).  So then it comes down to comparing apples to apples while or after watching the game in question.  Unfortunately we don't have access to Champion data's best data.  Things like their 'kick rating' would be very handy as well as hit outs to advantage for ruckmen.  Maxy presently suffers in these tables with this aspect not being captured, albeit the rest of his game still sees him mostly always at or very near the top of the tables.  This would likely improve the robustness of the input data.  The old saying rubbish in rubbish out is a great starting point and in that instance these tables should only ever be considered as a very rough guide and only taken into consideration after having watched the match in its entirety.

What are the poor, average, good benchmarks for players and positions across the league and across and how do our players compare?

Having said all the above my gut feel generally is.... a 1.80 to 2.0 or better is a bare minimum pass mark in almost any role / position unless said player is playing a lock down type role eg; shoulder to shoulder defender a la an Oscar or May type role, a mid or even as a small pressure forward who might be tasked to shut down a dangerous play making rebounding defender etc.  In this instance a 1.30 (ish) to about 1.70 may well classify as a good pass if his opponent has had limited impact vs his usual impact.  I would also apply that lowish approx score to a rookie for a rough pass mark.

Looking at particular areas of the ground.  As a rough guide... Score pass mark i would expect to be generally lower up forward....roughly 1.80 ish.  Mids roughly a 2.5 to 3.0.  And defenders roughly 1.80ish and above.  Any player getting near a 3.0 or higher should (in theory) have had a good to quite good match or at least held his own on the day.  Over 3 to 5.0 is a very good game. Over 5 to 6.9 outstanding.  7.0 and above is off the charts.

Apologies again and I'm certainly don't expect you to do the leg work in some of those questions I asked, just trying to wrap my head around the theory behind the numbers.

 

 

Thanks so much DD for your thoughtful reply. 

I wonder it it's worth pinning this to top of this thread (i recall now that this info was on the first such thread)

 

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  • Injury List  


      PLAYER INJURY LENGTH
    Shane McAdam Hamstring 1-2 Weeks
    Marty Hore Thumb 1-2 Weeks
    Charlie Spargo Achilles 3 Weeks
    Christian Salem Hamstring 3-4 Weeks
    Jake Bowey Shoulder 4-5 Weeks
    Jake Melksham ACL 9-11 Weeks
    Joel Smith Suspension TBA

  • Player of the Year  


        PLAYER VOTES
    1 Max Gawn 67
    2 Christian Petracca 55
    3 Steven May 35
    4 Jack Viney 28
    5 Alex Neal-Bullen 27
    6 Clayton Oliver 22
    7 Bayley Fritsch 19
    8 Trent Rivers 16
    9 Judd McVee 15
    10 Kade Chandler 14

        FULL TABLE
  • Demonland Interviews 



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