The Ranking on Dashboard Metrics data appears below and shows that the number one spot on every metric except Q2 Performances is held by a team currently in the top 8 spots on the ladder (ordered by competition points per game played and then percentage).

Four of the number two spots - those for the two Conversion metrics and those for the two Own Scoring metrics - are held by teams outside the top 8, as are four of the number three spots.

The most reliable predictor of a team’s ladder position continues to be its Expected Win percentage according to the Win Production Function.

The Ranking on Dashboard Metrics data appears below and shows that every team in the current Top 8 on the ladder (which has been ordered based on competition points per game and then percentage) is in the bottom 8 on at least one metric, and in the bottom 6 on at least one metric for all but two of the teams.

Ranking on Scoring Shot Conversion (own and opponent) and Q2 performances remain, relatively speaking, unimportant in terms of teams’ ordering on the ladder.

The Ranking on Dashboard Metrics data appears below and shows that every team in the Top 8 on the ladder is in the bottom 8 on at least one metric, and in the bottom 6 on at least one metric for all but one of the teams.

Ranking on Scoring Shot Conversion and Q2 performances are, relatively speaking, unimportant in terms of teams’ ordering on the ladder.

The Ranking on Dashboard Metrics data appears below and reveals how starkly unimportant Scoring Shot Conversion has been so far this season in that four of the nine lowest-ranked teams on this metric are currently in the Top 8 on the ladder, and the two teams on equal-sixth on the metric sit in 17th and 18th spots.

The Ranking on Dashboard Metrics data appears below, and still sees a number of teams sitting in the Top 8 on the competition ladder despite being ranked 10th or lower on a number of seemingly-important metrics.

We have:

• Melbourne, 10th on Scoring Shot Conversion

• Fremantle, 11th on Scoring Shots, 12th on Scoring Shot Conversion, and 11th on Own Goals and Points per Game

• Carlton, 12th on Opponent Scoring Shot Conversion, 13th on Q3 Performances, and 12th on Q4 Performances

• Sydney, 11th on Q1 Performances

• St Kilda, 14th on Scoring Shot Conversion, 10th on Goals per game, 10th on Q1 Performances, 13th on Q2 Perfomances, and 11th on Q4 Performances

• Geelong, 16th on Scoring Shot Conversion, 17th on Opponent Scoring Shot Conversion, and 10th on Q3 Performances

• Richmond, 14th on Opponent Scoring Shots, 10th on Goals Conceded, 11th on Points Conceded, and 13th in Q4 Performances

The Ranking on Dashboard Metrics data appears below, and are probably most anomalous for Carlton who, despite sitting 4th on the competition ladder, are ranked no higher than 4th on any metric, and ranked 8th or lower on 9 of the metrics, including a number that are most correlated overall with teams’ ladder position. These include:

• The MoS Win Production Function

• Percentage of Quarters Won

• Goals and Points Conceded

Teams’ ranking on Own Scoring Shot Conversion continues to be only slightly correlated with their ladder position.

The Ranking on Dashboard Metrics data appears below, and currently shows that, collectively, the teams in the Top 8 on the competition ladder fill:

• Five of the bottom 10 spots on Own Scoring Shot Conversion

• Three of the bottom 10 spots on Opponent Goal Scoring, Opponent Scoring Shot Conversion, Q2, Q3, and Q4 Performances

• Two of the bottom 10 spots on Own and Opponent Scoring Shot Production, Opponent Point Scoring, and Q1 Performances

The Ranking on Dashboard Metrics data appears below, and currently shows that the teams’ ladder positions are most correlated with:

• The MoS Win Production Function (+0.95)

• Number of Quarters Won (+0.91)

• Goals Scored per Match (+0.88)

• Points Conceded per Match (+0.88)

• Points Scored per Match (+0.87)

• Own Scoring Shots per Match (+0.84)

• Goals Conceded per Match (+0.82)

• Q4 Performances (+0.80)

• Opponent Scoring Shots per Match (+0.79)

Ladder positions are least correlated with:

• Own Scoring Shot Conversion (+0.25)

• Q2 Performances (+0.43)

• Opponent Scoring Shot Conversion (+0.49)

• Q1 Performances (+0.57)

• Q3 Performances (+0.64)

The Ranking on Dashboard Metrics data appears below, and shows that the MoS Win Production Function continues to output expected wins data strongly correlated with reality.

It also shows the following interesting rankings:

• Melbourne 13th on Q2s

• Fremantle 13th on Own Scoring Shot Conversion, 11th on Q2s, and 14th on Q3s

• St Kilda 11th on Opponent Scoring Shot Conversion, and 11th on Q1s

• Carlton 10th to 18th on a range of metrics

• Geelong 12th to 18th on a range of metrics

• Hawthorn 15th on Q2s

• Collingwood 15th on Opponent Scoring Shot Conversion

• Western Bulldogs 16th on Own Scoring Shot Conversion, and 17th on Opponent Scoring Shot Conversion

• Richmond 2nd to 5th on a range of metrics

• Gold Coast 6th on the two Own Scoring metrics, and 6th on Q4s

• Adelaide 4th to 8th on a range of metrics

• GWS 8th on Q2s

• West Coast 2nd on Own Scoring Shot Conversion, and 8th on Opponent Scoring Shot Conversion

• North Melbourne 5th to 9th on a range of metrics

• Port Adelaide 10th on Opponent Scoring Shots per game, and 9th on Q3s

The Ranking on Dashboard Metrics data appears below, and shows that the MoS Win Production Function continues to output expected wins data strongly correlated with reality.

The Ranking on Dashboard Metrics data appears below, and perhaps mostly shows that the better teams tend to mostly do better on most performance metrics, though at this stage moreso on:

• Expected Wins according to the MoS Win Production Function

• Points conceded

• Goals conceded

• Quarters won

• Points scored

• Goals scored

• Scoring Shots conceded

And, less so, at least for now on:

• Opponent Scoring Shot Conversion

• Q3 Performances

• Own Scoring Shot Conversion

• Q1 Performances

If there’s been one consistent message from these metrics in recent seasons, it’s been that scoring shot conversion is far less important than scoring shot creation and concession.

The Ranking on Dashboard Metrics data, which appears below, shows positive correlations between all of the Metrics and teams’ Ladder Position, although not to any extraordinary extent except for Expected Wins based on the MoS Win Production Function that seems to still be relevant despite having been calibrated a dozen years ago.

Here’s the Ranking on Dashboard Metrics data.

It’s Round 1. There’s never much meaningful to say about trends after Round 1 (despite what the papers will tell you).

Below is the final Ranking on Dashboard Metrics data, which suggests that the eight finalists includes:

• The Top 7 teams on Own Scoring Shots (GWS the exception)

• Seven of the Top 8 teams on Opponent Scoring Shots (GWS the exception)

• Six of the Top 8 teams on Own Scoring Shot Conversion (Melbourne and Western Bulldogs the exceptions)

• Six of the Top 8 teams on Opponent Scoring Shot Conversion (Melbourne and Essendon the exceptions)

• The Top 8 teams on Own Goals and Points

• Seven of the Top 8 teams on Opponent Goals and Points (Essendon the exception twice)

• Six of the Top 8 teams on Q1 Performances (Port Adelaide and Geelong the exceptions)

• Six of the Top 8 teams on Q2 Performances (Melbourne and GWS the exceptions)

• Six of the Top 8 teams on Q3 Performances (Western Bulldogs and Essendon the exceptions)

• Seven of the Top 8 teams on Q4 Performances (GWS the exception)

• All of the Top 8 teams on Quarters Won

• All of the Top 8 teams on the MoS Win Production Function

Below is the latest Ranking on Dashboard Metrics data, which suggests that, according to the MoS Win Production Function, all of the Top 8 teams deserve to be Top 8, but in a different order. (And, yikes, that thing is 10 years old now. I should be looking for primary schools for it).

Amongst that Top 8:

• Melbourne stand out for their relatively poor Own and Opponent Scoring Shot Conversion

• Geelong and Port Adelaide stand out for their relatively poor Q1 Performances

• Western Bulldogs stand out for their relatively poor Own Scoring Shot Conversion and Q3 Performances

• GWS stand out for their relatively poor Own and Opponent Scoring Shot Generation, and for their Q2 and Q4 Performances

• Essendon stand out for their relatively poor Opponent Scoring Shot Conversion, and for Goals and Points Conceded

Below is the latest Ranking on Dashboard Metrics data, which shows, as it has done to about the same extent across the entire season, that ladder position is:

Highly related to:

• Percent of Quarters Won

• Expected Wins according to the MoS Win Production Function

• Scoring Shots, Goals, and Points Generated

• Scoring Shots, Goals, and Points Conceded

• Q2 and Q4 Perfomances

Moderately related to:

• Q1 and Q3 Performances

• Opponent Scoring Shot Conversion

Weakly related to:

• Own Scoring Shot Conversion

Below is the latest Ranking on Dashboard Metrics data, which shows that, this year at least, mediocrity on some measures at least doesn’t preclude a team from greatness, as evidenced by the fact that:

• Western Bulldogs, 1st on the Competition Ladder, are 11th on Own Scoring Shot Conversion and Q3 Performances

• Geelong, 2nd on the Competition Ladder, are 7th on Own Scoring Shots and Points, and 8th on Q1 Performances

• Melbourne, 3rd on the Competition Ladder, are 15th on Own Scoring Shot Conversion, 14th on Opponent Scoring Shot Conversion, and 9th on Q1 and Q2 Performances

• Port Adelaide, 4th on the Competition Ladder, are 9th on Own Scoring Shot Production, and 11th on Q1 Performances

• West Coast, 7th on the Competition Ladder, are 11th, 12th, 13th, 14th, and 16th on a range of metrics

• Fremantle, 8th on the Competition Ladder, are 11th, 13th, 16th, 17th, and 18th on a range of metrics

Below is the latest Ranking on Dashboard Metrics data, which shows that the ordering of teams on the competition ladder is very consistent with their ordering based on the MoS Win Production Function.

The teams whose win percentages are most different from what would be expected according to that Function are:

• Collingwood: 1.9 wins fewer than expected

• Essendon: 1.6 wins fewer

• Hawthorn and Brisbane Lions: 0.9 wins fewer

• Melbourne: 1.6 wins more

• St Kilda: 1.3 wins more

• West Coast: 1.0 wins more

• Fremantle 0.9 wins more

In terms of ladder position based on Expected WIns, the largest discrepancies are:

• Collingwood: 3 places lower than expected

• Brisbane Lions, Carlton, and Essendon: 2 places lower

• Melbourne: 3 places higher

• GWS and St Kilda: 2 places higher

Below is the latest Ranking on Dashboard Metrics data, which shows that, on average, the teams doing best have:

• Won more quarters

• Been expected to do well based on the MoS Win Production Function

• Registered more Scoring Shots

• Registered more points and goals

• Conceded fewer goals and points

• Performed better in Q2s

Below is the latest Ranking on Dashboard Metrics data, which continues to show some relatively small rank correlations between ladder position and what would seem like important metrics, in particular:

• Own Scoring Shot Conversion: +0.35

• Q4 Performances: +0.55

• Q1 Performances: +0.60

• Opponent Scoring Shot Conversion: +0.63

• Q3 Performances: +0.64

On average, the teams doing best have:

• Won more quarters (not entirely surprising)

• Conceded fewer goals and points

• Won more Q2s