I have been looking at the #AFLW 2019 data. I took the opportunity to include some CRAN packages I have not used before.
The data (two csv files) and my code are in a GitHub repository (link). My code is very basic and reflects my own thinking out loud as a I learn more about R.
In the past, I have tended to bookmark R suggestions and yet never manage to return to them as the list gets longer. My new practice is to create an R file to explore packages or code that strike me as interesting.
I used patchwork (link) and ggforce (link) in addition to ggrepel (link) to look at the data in the context of ggplot2 (link).
I was particularly interested in how patchwork helped me combine a range of images.
These work really well as PDF A4 pages and I thought they would be helpful summaries to stimulate conversation.
Centre lines show the medians. Box limits indicate the 25th and 75th percentiles. Whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles. Outliers are represented by dots.
This is my third season of collecting performance information from the official AFLW website. I have been struck by the increasing amount of coverage given to the tournament and the quality of the media images being shared.
This post is headed by Michael Wilson‘s photograph of Tayla Harris. I think it is a wonderful picture and speaks to the athleticism of AFLW.
The 2019 AFLW season starts on Saturday with the opening game between Geelong and Collingwood (link to fixtures).
I have some data from last year’s regular season (link) curated as secondary data from the official AFLW web site (link).
A Violin Plot created with BoxPlotR (link). (W1Q is the winning team, L1Q is the losing team).
These data have given me an opportunity to postulate some naive priors about when points will be scored in the 2019 season. The probabilities per quarter are based upon game outcome so that the labels ‘winning’ and ‘losing’ relate to the game not the quarter.