MAFL 2013 : Team Dashboard for Round 9

This week I'll be highlighting significant differences between the teams' competition ladder positions and their Scoring statistics: 

• Carlton are 8th on the ladder, but 1st on Opponent Conversion and 16th on Own Conversion
• Essendon are 3rd on the ladder, but 10th on Own Conversion
• Fremantle are 4th on the ladder, but 12th on Own Scoring Shots and 1st on Opponent Scoring Shots
• Geelong are 2nd on the ladder, but 13th on Opponent Conversion
• GWS are 18th on the ladder, but 7th on Own Conversion
• Hawthorn are 1st on the ladder, but 11th on Opponent Conversion
• The Kangaroos are 13th on the ladder, but 6th on Own Scoring Shots, Own and Opponent Conversion
• Melbourne are 17th on the ladder, but 5th on Own Conversion
• Port Adelaide are 9th on the ladder, but 15th on Opponent Conversion
• St Kilda are 15th on the ladder, but 3rd on Opponent Conversion
• Sydney are 5th on the ladder, but 13th on Own Scoring Shots and 12th on Opponent Conversion
• West Coast are 6th on the ladder, but 1st on Own Scoring Shots and 17th on Opponent Conversion
• The Western Bulldogs are 16th on the ladder, but 4th on Own Conversion

One feature from this list is the apparent lack of importance of Opponent Conversion rates - teams high up on the ladder seem to have poor Opponent Conversion statistics and teams lower on the ladder seem to have better Opponent Conversion statistics. Indeed, the rank correlation between ladder position and ranking based on Opponent Conversion is just +0.18. 

What's mattered instead this season has been how many scoring shots opponents a team has allowed its opponents. The correlation between the ranking on that metric and ladder position now stands at +0.86. 

MAFL 2013 : Team Dashboard for Round 8

This week I'll draw your attention to the right-hand portion of the Scoring Shot Data, which provides information about each team's actual performance in terms of competition points earned, relative to what might be expected based on the team's scoring shot metrics and the win production function I derived back in this blog from 2011. (Note that I'm using the first equation from that blog).

According to this analysis, the teams that have achieved significantly more wins than their scoring shot data would predict are: 

• Geelong, who've won 88% of their games when their scoring statistics would predict a 67% record,
• Collingwood, who've won 63% of their games when their scoring statistics would predict a 46% record, and
• The Brisbane Lions, who've won 38% of their games when their scoring statistics would predict a 24% record

Confirmatory evidence for the relative good fortune of these three teams comes from the fact that both the Cats and the Pies have recorded 3 victories by less than 3 goals, and that the Lions have recorded 2 victories by less than 2 goals.

Conversely, the teams that have achieved significantly fewer wins than their scoring shot data would predict are: 

• Adelaide, who've won 50% of their games when their scoring statistics would predict a 72% record,
• The Roos, who've won 38% of their games when their scoring statistics would predict a 59% record, and
• St Kilda, who've won 25% of their games when their scoring statistics would predict a 40% record

Again we can find support for these assessments in relevant game margins: Adelaide have lost 2 games by less than 2 goals, while the Roos have lost 4 games and the Saints 3 games by less than 3 goals.  

MAFL 2013 : Team Dashboard for Round 7

Here's the latest Team Dashboard, about which I'll make a single observation: the Cats, despite being the only undefeated team, have conceded more points than all but two of the top 11 teams on the competition ladder, and more than two other teams below these top 11.

MAFL 2013 : Team Dashboard for Round 6

This week I'm going to discuss some of the rank correlations between various of the metrics on the Team Dashboard and teams' competition ladder positions.

Firstly, looking at the metrics in the Scoring Shot Data section, we find the following correlations with ladder position (the equivalent correlations for the Team Dashboard at the end of the 2012 home-and-away season are in brackets): 

• Correlation with Own Scoring Shots per Game : +0.79 (+0.88)
• Correlation with Opponent Scoring Shots per Game : +0.78 (+0.77)
• Correlation with Difference in Scoring Shots per Game : +0.95 (+0.89)
• Correlation with Own Conversion Rate : +0.27 (+0.70)
• Correlation with Opponent Conversion Rate : +0.23 (+0.29)
• Correlation with Difference in Conversion Rate : +0.39 (+0.69)

The only substantial differences in these correlations from the equivalent figures for last season are those for Own Conversion Rate and the Difference in Conversion Rate. This season then, so far, it's been more important for teams to create lots of scoring shots and prevent their opponents from doing the same than it has been to convert those opportunities or prevent their opponents from doing so. Put another way, it's been a season for quantity of opportunities rather than quality. 

Finally, consider the correlations between quarter-by-quarter performance rankings and competition ladder position: 

• Q1 performance : +0.39 (+0.79)
• Q2 performance : +0.72 (+0.76)
• Q3 performance : +0.84 (+0.62)
• Q4 performance : +0.65 (+0.70)

The obvious conclusion from this is that 1st terms have been far less important and 3rd terms somewhat more important so far this season than they were in the home-and-away season last year. I'll have more to say about this in a blog I'm planning to write this week.