Using quantiles to explore AFL performance

I have been exploring the quantile function in R as a way to explore AFL performance.

The generic function quantile produces sample quantiles corresponding to the given probabilities. The smallest observation corresponds to a probability of 0 and the largest to a probability of 1.

I have data for 12 rounds of the 2018 AFL competition.

I wondered if the quantile measures might give me a game signature.

My data are:

For the teams that have won games

For teams that have lost games

For game outcomes

In Round 12, I looked at Geelong and Sydney in terms of winning quantile profiles (how good a winner were they?) and North Melbourne and St Kilda in terms of losing quantile profiles (what kind of losing team were they?).

My quantile signatures for these games are:

(Note quantiles are expressed in terms of winning performance for Geelong and losing performance for North Melbourne. Geelong’s final quarter is rated highly in winning terms (top 20%). North Melbourne were in the top 20% of losing performances in the third quarter.)

(Sydney’s first quarter performance was in the top 5% of winning performances. St Kilda elevated their losing performance in the second and fourth quarters to top 25% of losing performances.)


Exploring quantiles in R has helped me think about how to visualise games and express the competitive nature of these games.

I am mindful that I am using a macro indicator for this visualisation but the ability to specify quantiles in a growing database of AFL performance seems promising.

They might also help with early prediction of game outcome.

Photo Credit

Footy (Beau Lebens, CC BY-NC 2.0)

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