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In 2015, one of Champion Data’s simple tools that caught on both on Twitter and then on the Fox Footy coverage was their ‘Hot Plot’. This was a simple scatter plot of clubs, by average season score conceded against average season score generated.

Although this is a very basic tool, it does serve a purpose, very quickly giving an indication of the offensive and defensive abilities of all clubs in a season.

Both Champion Data and Fox Footy commentators continued to stress throughout the 2015 season that between 2000-2014, 14 of the 15 premier teams averaged more than 100 points per and conceded on average less than 86 points per game throughout the season.

It is more accurate to improve this tool by:

  1. Controlling for competition scoring trends
  2. Controlling for strength of opponent

In the past three decades, scoring has in general dropped relatively consistently. The most prolific scoring season was in 1982, when teams averaged 112.2 points per game (across the home and away season). In 2015 the average score had fallen to 86.5 points per game – the lowest in any season since 1968.

Because of this trend, looking at raw thresholds of averages scored and conceded since 2000 isn’t quite appropriate. In 2000, 53.8% of team game scores were 100 points or over but by 2015 that proportion had fallen to 30.8%. In short, it was a lost easier for anyone to kick 100 points 15 years ago than it is today – making the ‘100 points for’ metric pretty meaningless.

So instead, I came up with my own plot, which now I am calling FourSquare.

FourSquare handles the first of two complications listed above by controlling for competition scoring trends. Instead of using raw average points for and against, it uses a standard score to show how many standard deviations a team’s average points for and against are compared to the average league score.