Time at #FIFAWWC 2019

FIFA provided Match Facts for each of the games played at the 2019 Women’s World Cup (link). From these Facts is was possible to construct a time profile of the World Cup.

The data available suggest that the median ball in play time was 55 minutes. The median game time was 97 minutes. The ball was not in play for a median time of 43 minutes. Three of the games went to extra time (Norway v Australia, France v Brazil, Netherlands v Sweden).

Ball in Play ranged from 41 minutes (Germany v Nigeria) to 73 minutes (Norway v Australia).

The geom_smooth profile of ball in play was:

I used a smoothing method to look at trends in the time data (link). The grey area visualises confidence levels (95% confidence level interval for predictions from a linear model) . The confidence limits can be varied (link). In this example, I used Loess smoothing as I had less than 1000 data points.

The FIFA data made it possible to calculate ball not in play time.

In the Netherlands v Sweden game there were 63 minutes of time when the ball was not in play. This was an extra time game.

The total game length varied from 93 minutes (Japan v Scotland, Jamaica v Australia) to 135 minutes (France v Brazil). The three extra time games are indicated in red.

The FIFA data were particularly helpful in constructing time profiles of the games.. Data were presented for each half. Extra time data were included in the match report. As well as describing what occurred these data raise important questions about ball in play time.

Photo Credit

France v Brazil (FIFA Live Blog)

#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)

#RWC2019: thinking of games as single numbers

World Rugby provide data about each game at #RWC2019 (link). These data include a record of kicks, passes, scrums and lineouts.

For some time, I have been interested in whether we can describe a game in a single number. This number expresses the relationships between kicks, passes, scrums and lineouts. To date, 28 games have been played. In these games, my numbers vary from 0.52 to 5.34. The median ratio is 2.16.

The 28 games as a single number (a ratio of kicks/passes divided by lineouts/scrums):

I have used ggplot to visualise these data points with geom_point and identified the range of ratios:

I used the glm () function to look at the relationship between the ratios: (“glm is used to fit generalized linear models, specified by giving a symbolic description of the linear predictor and a description of the error distribution” (link)).

The median ratio for the 28 games played is 2.16. I used the geom_hline function to draw this median (link).

Photo Credit

France v Tonga (World Rugby)