Below is the latest Ranking on Dashboard Metrics data, which is ordered based firstly on each team’s competition points per game played, and then based on percentage:

The most reliable metrics for determining a team’s ladder position ordered in this way are the MoS Win Production Function, Quarters Won, and then Goals and Points Conceded.

Below is the latest Ranking on Dashboard Metrics data, which now shows that, amongst the teams in the Top 8 on the ladder, we have:

• The team ranked 12th on Scoring Shot Concession (Sydney)

• The team ranked 15th on Opponent Scoring Shot Conversion (Melbourne)

• The team ranked 13th on Q3 Performances (Western Bulldogs)

• The team ranked 18th on Q4 Performances (West Coast)

• The team ranked 14th on Q2 Performances and 12th on Q3 Performances (Richmond)

Also, amongst teams outside the Top 8 we have:

• The team ranked 4th on Scoring Shot Production, 2nd on Own Scoring Shot Conversion, 2nd on Own Scoring, and 3rd on Opponent Scoring (Essendon)

• The team ranked 4th on Q1 Performances (GWS)

• The team ranked equal 5th on Scoring Shot Production, and 2nd on Q3 Performances (Fremantle)

• The team ranked equal 5th on Scoring Shot Production (Carlton)

• The team ranked equal 4th on Opponent Scoring Shot Conversion, and 6th on Q3 Performances (Gold Coast)

• The team ranked 5th on Opponent Scoring Shot Production, and 3rd on Q1 Performances (Collingwood)

• The team ranked 6th on Own Scoring Shot Conversion (Hawthorn)

The most reliable metrics for determining a team’s ladder position are the MoS Win Production Function, Quarters Won, Points Scored, and Goals and Points Conceded.

Below is the latest Ranking on Dashboard Metrics data, which now shows that the highest rank correlations with competition ladder position belong to:

• MoS Win Production Function (+0.96)

• % of Quarters Won (+0.92)

• Goals Conceded per Game (+0.85)

• Points Conceded per Game (+0.80)

• Points Scored per Game (+0.80)

• Goals Generated per Game (+0.80)

• Scoring Shots Generated per Game (+0.79)

• Scoring Shots Conceded per Game (+0.76)

• Q2 Performances (+0.73)

• Q4 Performances (+0.70)

Below is the latest Ranking on Dashboard Metrics data, which shows that the highest rank correlations with competition ladder position belong to:

• MoS Win Production Function (+0.96)

• % of Quarters Won (+0.91)

• Goals Conceded per Game (+0.90)

• Points Conceded per Game (+0.90)

• Scoring Shots Conceded per Game (+0.82)

• Points Scored per Game (+0.81)

• Scoring Shots Generated per Game (+0.80)

• Goals Generated per Game (+0.77)

• Q2 Performances (+0.77)

• Q4 Performances (+0.75)

Below is the latest Ranking on Dashboard Metrics data, which still shows mostly the same relationships between competition ladder position and ranking on ket metrics as it did last week.

Melbourne, amazingly, sit 9th on Own Scoring Shot Conversion, 13th on Opponent Scoring Shot Conversion, 13th on Q1 performances, and 8th on Q3 performances, while three of the top 5 teams on Own Scoring Shot Conversion lie 15th through 17th on the competition ladder.

Below is the latest Ranking on Dashboard Metrics data, which still shows some surprisingly low correlations between the teams’ ranking on key dashboard metrics and their placement on the competition ladder.

The most highly correlated metrics with competition ladder ordering are:

• MoS Win Production Function Expected Wins (+0.96)

• % of Quarters Win (+0.93)

• Goals Conceded Per Game (+0.88)

• Points Conceded Per Game (+0.86)

• Q2 Performances (+0.82)

The least highly correlated metrics with competition ladder ordering are:

• Own Scoring Shot Conversion (-0.02)

• Q4 Performances (+0.30)

• Q1 Performances (+0.32)

• Opponent Scoring Shot Conversion (+0.50)

Below is the latest Ranking on Dashboard Metrics data, which shows mostly the same relationships between overall rankings on the metrics and ordering on the competition ladder.

On a team-by-team analysis, it’s interesting to note that:

• Western Bulldogs sit first on the ladder despite being only 11th on Own Scoring Shot Conversion, and 12th on Q3 performances

• Melbourne sit second on the ladder despite being 15th on both Own and Opponent Scoring Shot Conversion

• Geelong sit fourth on the ladder despite being 10th on Own Scoring Shot Generation, 9th on Own Scoring Shot Conversion, 15th on Q1 performance, and 11th on Q4 performances

• Sydney sit fifth on the ladder despite being 15th on Opponent Scoring Shot Generation, and 15th on Q4 performances

• Hawthorn sit 15th on the ladder despite being 4th on Own Scoring Shot Conversion, and 6th on Q4 performances

• Collingwood sit 17th on the ladder despite being 5th on Opponent Scoring Shot Generation, 3rd on Own Scoring Shot Conversion, 5th on Q1 performances, and 5th on Q4 performances

Below is the latest Ranking on Dashboard Metrics data, which still shows some sizeable differences in the rankings of particular teams for particular metrics, when compared with their positions on the competition ladder.

St Kilda are a particularly interesting case in that they, despite sitting 13th on the ladder, are ranked 15th or lower on 10 of the metrics, and last or second-last on seven.

Overall, it’s still the case that Scoring Shot Generation is proving far more important than Scoring Shot Conversion, and Quarters 1 and 2 far more important than Quarters 3 and 4.

Below is the Ranking on Dashboard Metrics data, which this week reveals that Scoring Shot Conversion - a team’s own and its opponents’ - remains relatively unimportant. Six of the teams currently in the Top 8 on the competition ladder are ranked 10th or lower on Own Scoring Shot Conversion, and six of the teams currently in the Bottom 8 on the competition ladder are ranked 9th or higher on Own Scoring Shot Conversion. That is, truly, quite odd.

Here’s the Ranking on Dashboard Metrics data, which this week reveals that Scoring Shot Conversion - a team’s own and its opponents’ - has been relatively unimportant. Melbourne, for example, are in the bottom 3 on both metrics, but sit 3rd on the competition ladder.

Here’s the Ranking on Dashboard Metrics data.

I think, so far, the most interesting aspect of this table is the high correlation between the teams’ actual and expected wins, according to the MoS Win Production Function that was calibrated over a decade ago.

Here’s the Ranking on Dashboard Metrics data.

The final Ranking on Team Dashboard Metrics table appears below, and shows that teams’ final competition ladder positions differ most from their rankings on individual metrics as follows:

• Port Adelaide (1st on the ladder): 16th on Own Scoring Shot Conversion, 13th on Opponent Scoring Shot Conversion, 6th on Q1 and Q3 performances, and 7th on Q4 performances

• Brisbane Lions (2nd on the ladder): 18th on Own Scoring Shot Conversion, 7th on Goals and Points Conceded, and 11th on Q4 performances

• Richmond (3rd on the ladder): 9th on Own Scoring Shot Conversion, and 9th on Q4 performances

• Geelong (4th on the ladder): 10th on Q2 performances

• West Coast (5th on the ladder): 10th on Own Scoring Shots, 11th on Q2 performances, 15th on Q4 performances, and 11th on Percent of Quarters Won

• St Kilda (6th on the ladder): 12th on Q3 performances

• Western Bulldogs (7th on the ladder): (Between 4th and 11th on every metric)

• Collingwood (8th on the ladder): 1st on Scoring Shots Conceded, 18th on Opponent Scoring Shot Conversion, 13th on Own Goals and Points, 3rd on Opponent Points, 3rd on Q1 performances, and 14th on Q2 performances

• Melbourne (9th on the ladder): 17th on Q3 performances, and 2nd on Q4 performances

• GWS (10th on the ladder): 15th on Q1 performances, and 4th on Q3 performances

• Carlton (11th on the ladder): 3rd on Q4 performances

• Fremantle (12th on the ladder): 18th on Scoring Shots Generated, 6th on Scoring Shots Conceded, 4th on Own Scoring Shot Conversion, 2nd on Opponent Scoring Shot Conversion, 4th on Goals Conceded, and 5th on Points Conceded

• Essendon (13th on the ladder): 7th on Own Scoring Shot Conversion, and 18th on Q1 performances

• Gold Coast (14th on the ladder): 9th on Own Scoring Shot Generation, and 2nd on Q2 performances

• Hawthorn (15th on the ladder): 5th on Own Scoring Shot Conversion, and 9th on Own Goals Scored

• Sydney (16th on the ladder): 4th on Opponent Scoring Shot Conversion, and 6th on Q2 performances

• North Melbourne (17th on the ladder): 8th on Own Scoring Shot Conversion, 1st on Opponent Scoring Shot Conversion, and 12th on Q4 performances

• Adelaide (18th on the ladder): (Between 14th and 18th on every metric)

The latest Ranking on Team Dashboard Metrics table appears below, and suggests that success in 2020 has been most associated with:

• Expected Wins (according to the MoS Win Prediction Function)

• Points Conceded

• Scoring Shots Conceded

• Points Scored

• Goals Scored

• Quarters Won

• Q1 Performance

• Scoring Shots Generated

• Goals Conceded

The latest Ranking on Team Dashboard Metrics table appears below, and, given the time, and the fact that I have a truckload of stuff to do before the next round starts tomorrow, will need to tell its own story.

The latest Ranking on Team Dashboard Metrics table appears below, and still shows a surprising number of bottom-half performances by teams in the top-half of the Competition Ladder.

It’s pretty extraordinary, this far into a season, to have three of the bottom five teams on Own Scoring Shot Conversion sitting somewhere in the Top 6 spots on the ladder and, even more so, for the eight best teams on Opponent Scoring Shot Conversion to be sitting outside the Top 8.

Based on the latest Team Dashboard statistics, those most correlated with teams’ competition ladder position (adjusted for games played) are:

• Teams’ ranking on the MoS Win Prediction Function (+0.89)

• Teams’ ranking on Points Scored per Game (+0.87)

• Teams’ ranking on Own Scoring Shots per Game and % of Quarters Won (+0.86)

• Teams’ ranking on Goals Scored per Game (+0.83)

• Teams’ ranking on Q1 Performances (+0.80)

• Teams’ ranking on Opponent Scoring Shots per Game (+0.76)

Based on the latest Team Dashboard statistics, those most correlated with teams’ competition ladder position (adjusted for games played) are:

• Teams’ ranking on the MoS Win Prediction Function (+0.89)

• Teams’ ranking on Points Scored per Game (+0.87)

• Teams’ ranking on Own Scoring Shots per Game and % of Quarters Won (+0.86)

• Teams’ ranking on Goals Scored per Game (+0.83)

• Teams’ ranking on Q1 Performances (+0.80)

• Teams’ ranking on Opponent Scoring Shots per Game (+0.76)

So, we still have half of the teams in the Top 8 ranked in the bottom 6 for Own Scoring Shot Conversion, and five of them ranked in the bottom 9 for Opponent Scoring Shot Conversion. What a strange year …

Given all that, though, the 10-year-old MoS Win Prediction Function continues to do a good job of predicting team winning percentages as a function of scoring statistics. The rank correlation between its ordering of the teams and that based on the ladder ordering (adjusted for games played) is now at +0.92.