#RWC2019: Group Games

The Group Games at the 2019 Rugby World Cup concluded with the Japan v Scotland fixture (link).

World Rugby provided data about each game. From these data, I recorded: penalties and free kicks conceded; kicks; passes; scrums: lineouts. My median profiles for the Group Games were:

  • Penalties and free kicks conceded: 16
  • Kicks: 58
  • Passes: 264
  • Scrums: 14
  • Lineouts: 25

I was also interested in ratio of passes to kicks and lineouts to scrums as a dynamic measure of each game (link). My median ratios for Group Games were: 4.55 passes to kicks and 1.79 lineouts to scrums. I used these ratios to derive a single number for each game to describe what kind of game it was. My median ratio for the Group Games was 2.55.

My ratios for the Group Games were:

Blue horizontal line is the median ratio of 2.55

Below the median:

Above the median:

Other data from the official website included officiating:

Passes and kicks in each of the Group Games:

Games with 300 or more passes:

Lineouts and scrums per Group Game:

Games with 30 or more lineouts:

Photo Credit

Japan v Scotland (World Rugby)

#RWC2019: patterns after 29 games

29 games have been completed at #RWC2019 (link). I have continued with my idea of characterising the games played with a single number (link). This number is the median ratio of kicks and passes divided by lineouts and scrums. My hope is that this expresses the mobility of each game.

I am using data form the official World Rugby website (link) to curate my data for the tournament. I have noted that these numbers do change after the games have played (particularly the number of passes). I have used data twenty four hours after the completion of the game as a record of that game. I am collecting the data as a Google Sheet (link) with tabs for each game played.

I have used ggplot to visualise data and I am using the data to help me improve my use of R. These visualistions include:

  • ggplot
  • geom_point
  • geom_vline
  • geom_smooth
  • labs
  • annotate
  • size
  • theme_minimal
  • a colour blind palette (link)

My visualisations of the 29 games are including identified outliers are:

I have a Ratio for each game. The tournament median is 2.31 and is expressed by a geom_hline default size and shape in black.

The Ratio is expressed with a geom-smooth function in order to see what the trends in the data look like. The confidence limits are set by default at 95%. I have used the loess method with my small number of data points. The grey area expresses the confidence band for the regression line drawn with the method. The confidence interval can be turned of with se = FALSE or set at a level you specify:

Passes with a geom_hline set at the median number of passes (258):

Passes with a geom-smooth:

Kicks during the game with geom_hline set at a median set at 58 kicks per game:

Kicks with geom_smooth:

Penalties and Free Kicks Conceded with geom_point with a geom_hline set at a median of 16 penalties and free kicks conceded per game:

Penalties and free kicks conceded presented with a geom_smooth function:

Lineouts and scrums have medians of 25 and 13 respectively per game:

Lineouts and scrums with a geom_smooth:

Photo Credit

Lineout Win (World Rugby)

#RWC2019: using geom_hline

In my investigation of single numbers to characterise performance in #RWC2019, I have been using ggplot to visualise the data from World Rugby (link).

In the visualisation below, I was keen to look at outliers <1 and >4. I found four games. A fifth game, Australia v Fiji is a 1.20 game.

I used geom_hline() with a yintercept to draw lines at 1 and 4. For these lines I used the geom_hline() function, and specified a range for the lines, their colour and their size (link):

geom_hline(yintercept = range(1, 4), color=’coral’, size=1)

I included the original geom_hline() for the median ratio. My code for this was:

geom_hline(yintercept = 2.16)

I checked the accuracy of this median with median(df$Ratio) (the result was a median of 2.155 which I rounded up to 2.16.)

Photo Credit

Reaching to score (World Rugby)