Under the Microscope: Identifying the defensive underperformers in UEFA’s top leagues – data analysis

The 2023/24 campaign has come to an end for most of Europe, including each of the Big Five leagues.

With a full season of data, this is the perfect time to look at each teams body of work and look for tactical trends. In this data analysis, we will use statistics to evaluate defensive performances. More specifically, we will use the data to identify which clubs put in the worst defensive performances.

Performance-based statistics will help identify our focus group. Next, we will look at how teams manage tempo in their matches or perhaps how they struggled to control the tempo. Finally, we will look at more general defensive metrics and look for trends relative to how teams finished in their respective tables.

Though defensive performances can be difficult to measure since the statistics need the context of a teams defensive tactics, this broader approach will help us move from a data analysis towards a more nuanced look at defensive underperformers in a couple of weeks.

Lets start with the data.

Finding the defensive underperformers

As we begin our data analysis, the first step is to identify which teams rated poorly with respect to the field. We want to get a sense of which teams were not only conceding the most goals but also allowing the highest xGA per game. That gives us a sense of both the highly concrete feedback tied to the final scoreline and indicators that are more predictive of performance.

Before moving to our first graph, one note is that were not only looking for a teams defensive performances but also how poor defensive showings impact their spot on the table. There is some missing context, given that were not looking at the other side of the coin, attacking production. Teams that struggle to keep the balls out of the net are likely to also struggle at the end other end of the pitch. In an era of bloated team salaries and staggering player values, the “haves” should outperform the “have nots” across the board.

Were going to focus on just the defensive angle today. As you look at these charts, the brighter the yellow plot point, the higher a team has finished in the table. As it blends to a faint yellow or pale pink, those are the mid-table teams. The brighter the pink a club has, the lower it finished on the table. This gives us some feedback on the impact of a teams defensive quality on their final spot on the league standings.

Our first graph plots goals conceded per game against xGA per game. The top right quadrant represents the best defensive performances across UEFAs top five leagues, whereas the bottom left gives us the teams conceding the most goals and the highest xGA per game. Poor defensive performances didnt stop West Ham, Hoffenheim and Manchester United from securing spots in the top half of the table. While strong performances in these two categories arent exclusive to the top half of the table, as we see with a very strong defensive season from Mallorca in La Liga, there is a general trend that the best teams defend well and the worst teams struggle to keep the ball out of the net.

That point is further backed up when comparing xGA to expected points. While every manager and player will take three real points on the league table over 1.8 expected points, we once again see a strong correlation between better defending and more points secured on average. The three defensive underperformers listed above remain in the bottom left quadrant, registering a below-average expected points per week match. Werder Bremen, Montpellier and Eintracht Frankfurt join the list of defensive underperformers, as do high-status clubs like Sevilla and Borussia Mönchengladbach, which endured poor seasons.

This is perhaps where we can get a sense of how a team’s att