A look at attacking and defensive effectiveness

In this post, I decided to take a look back at the FA Women’s Super Leagure season of 2018-19 to examine the attacking and defensive effectiveness of the teams involved. A nice way of doing this is using scatter plots comparing related data to eachother. For example, the following table shows the shots per match and shots per goal summary for the teams in the FA WSL last season.

Team Matches Shots Goals Shots Per Match Shots Per Goal xG Home Color Code Away Color Code
Arsenal 20 373 68 18.6 5.5 48.1 #EF0107 #d8b126
Birmingham City 19 254 25 13.4 10.2 23.0 #103bce #33333d
Brighton & Hove Albion 19 207 16 10.9 12.9 19.6 #0057B8 #2c2c2c
Bristol City 20 153 15 7.7 10.2 12.1 #ec2128 #1e2327
Chelsea FCW 19 392 38 20.6 10.3 39.1 #034694 #1a1d26
Everton 19 230 14 12.1 16.4 21.0 #003399 #ff584e
Liverpool 19 177 17 9.3 10.4 18.2 #C8102E #7ff7ef
Manchester City 20 389 51 19.4 7.6 44.6 #6CABDD #e1f1aa
Reading 20 295 31 14.8 9.5 27.6 #004494 #b5253e
West Ham United 20 217 25 10.8 8.7 22.2 #7A263A #9fc1f3
Yeovil Town 19 129 10 6.8 12.9 11.9 #4dac26 #9d0117
a Table 1: A summary of shots and goals from the 2018-19 FA WSL

For this table, we used a summarise function, together with group_by of our team names, to sum the shots and goals of each team. We can then count the number of matches they played to provide us with a shots and goals per match value. We can then use this to plot the performance of our teams. A table is a nice way to look at the raw data, but To see who was the best performer out of these teams, it might be easier to view them on a scatter plot. So let’s do that now:

This image gives us a good view of the teams that were able to convert their chances last season. Arsenal managed best shots per goal conversion, managing 5.5 shots per goal and 18.6 shots per match. Manchester City and Chelsea, who ended up 2nd and 3rd respectively, were also in the top 3 performing teams in this scatter, both hovering around 20 shots per match and 10 shots per goal. This is an impressive output which showed how dominant these 3 teams were throughout the season.

When it comes to xG and Goals we again see the top 3 teams from the table coming out towards the top. Arsenal outperformed their xG quite considerably throughout the season, with 68 goals scored compared to an xG of 44.5, they were on top by 17 goals. Manchester City and Chelsea followed closely behind, but the rest of the league were well behind the top 3 in these metrics.

But defensively, how did these teams perform in terms of shots and goals against them.

## `summarise()` ungrouping output (override with `.groups` argument)

To look at goals and shots against an opposition, we need to create a team against column in our dataset. For this we can mutate a column using ‘if_else’ to examine if the team name is the same as the home team, if so put the away team name in the column. We can then use this column to summarise our data using ‘group_by’. Let’s take a look at what this provided us:

Team Matches Shots Against Goals Against Shots Per Match Against Shots Per Goal Against xG Against Home Color Code Away Color Code
Arsenal 35 278 26 7.9 10.7 24.8 #EF0107 #d8b126
Birmingham City 32 351 38 11.0 9.2 40.4 #103bce #33333d
Brighton & Hove Albion 35 471 64 13.5 7.4 52.3 #0057B8 #2c2c2c
Bristol City 34 599 72 17.6 8.3 65.0 #ec2128 #1e2327
Chelsea FCW 34 258 25 7.6 10.3 23.0 #034694 #1a1d26
Everton 33 459 55 13.9 8.3 47.2 #003399 #ff584e
Liverpool 33 478 54 14.5 8.9 45.4 #C8102E #7ff7ef
Manchester City 36 397 24 11.0 16.5 36.0 #6CABDD #e1f1aa
Manchester United 14 134 11 9.6 12.2 12.5 #DA291C #decab1
Reading 34 393 54 11.6 7.3 48.1 #004494 #b5253e
Tottenham Hotspur Women 15 257 22 17.1 11.7 25.3 #132257 #28a6cc
West Ham United 34 504 69 14.8 7.3 60.6 #7A263A #9fc1f3
Yeovil Town 19 460 48 24.2 9.6 45.1 #4dac26 #9d0117
a Table 2: A summary of shots and goals against from the 2018-19 FA WSL

This figure again shows that some of the stronger teams were at the bottom end of this comparison. However, what it does show is that there is a similar number of shots per goal against for all teams. The main difference being the number of shots against was a differentiator between the more successful teams and less successful teams. The top four teams in the league had the least number of shots per match against compared to those who finished below them on the table.

## Warning: Removed 3 rows containing missing values (geom_image).
## Warning: Removed 3 rows containing missing values (geom_text_repel).

Again it is no surprise to see the better teams at the bottom left of this image. With the lowest number of goals and xG against them, these teams were defensively sound and very good in attack.

From this quick post, we can see that scatter plots are a nice way of viewing where teams fit in relation to their opposition. As long as our two metrics have some form of relationship with one another, they will often provide a lot of information.

Give it a go and thanks again to StatsBomb for providing the data for this blog post.

Josh Trewin
Josh Trewin
Data Scientist

I’m a data scientist, learning my way through R / Python and applying to football data from StatsBomb, provided for free through GitHub. Follow my journey on here or Twitter to find out when I add new content.

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