Football Radars: Using Charlotte FC's Back Line to Understand What They Mean and How They're Read

In Mint City Analytic’s inaugural article, we started at the base of the team by introducing goalkeeping analysis and looking into the performances of CLTFC’s number one, Krisitjan Kahlina. The reception and feedback we received were exactly what you’d expect from the friendly and passionate Queen City fanbase. Thank you all so much, and I hope you continue to enjoy our work throughout the season!

Based on the feedback we received from our first article, readers seemed to appreciate the explanation of metrics and interpretation of data in a way that was accessible for people that are newer to these concepts. Considering this feedback and acknowledging that the sample sizes are still too small for this season, this next article will introduce concepts and prepare for more serious analyses once the minutes accumulate. In this new article, we’ll take an opportunity to introduce radar plots, an important data visualization tool, and move up the formation with some early analysis of the backline.

As advanced metrics continue to enter mainstream soccer discussions, countless attempts have been made by data scientists to create easy-to-interpret data visualizations for clubs, players, and fans. Producing figures that tell a story, reveal a pattern, or highlight an outlier to evaluate players in a variety of contexts has become a mainstay of the industry. To understand why let’s start with a simple scatter plot:

This is a scatter plot displaying the number of tackles a player makes against the number of tackles they make that win possession for their team. It includes all players categorized as defenders in Football Reference’s database that have played a minimum of 180 minutes this season in the MLS.

While graphs like this can display interesting information (as evidenced by our first article that exclusively used scatter plots!), they can be fairly limiting and easily misconstrued. For example, this scatter plot shows that Joseph Mora is the most successful tackler for Charlotte FC this season. While this is an interesting observation, making any conclusions about him as a player, especially considering the importance of fullbacks contributing to CLTFC’s attacking movements in Miguel Angel Ramirez’s system, would be nothing more than misguided. How does he compare defensively to his peers at his position? What about his attacking output? How is he in possession?

We could keep churning out scatter plots that assess 1-2 variables and answer each question, but even then it would be difficult to stitch everything together into a single conclusion or player profile. Enter player radars, a way of visualizing a large number of statistics all at one time.

So how do we interpret radars? First, it’s important to know the data source for our figure. The data used for the above radar is pulled directly from Joseph Mora’s FBRef.com page and includes performance data from his last 365 days as a player in the MLS. While this means his time with DC United is included and might limit how this information relates to his performances with Charlotte, the benefits of a larger dataset (1951 minutes of player data) outweigh this potential drawback. The source of the data, the number of minutes included, and the template used (more on this in a moment) are the three things to identify before we interpret these plots.

Now that we know the data we’re working with, what is this radar displaying?  Each radar slice assesses a player’s performance in a particular statistic and compares the given statistic relative to other players in the same position group. The closer to the outside of the circle the colored area is for a statistic, the better that player stacks up in that category relative to his peers. Working from a variety of examples we’ve seen in the past (primarily from StatsBomb), we created a fullback template with four distinct categories that are separated by color. The categories are: Defending (blue), Progression (green), Final 3rd Production (red), and Set Pieces/Other (yellow).

This first radar plot depicts Joseph Mora’s percentile performance in comparison to other fullbacks in the MLS. This means, in comparison to other fullbacks in the MLS over the last 365 days, he’s in the 84th percentile for pressures(18.47 per 90 minutes) and the 82nd percentile for tackles–that’s really good! He also struggles in other areas like final 3rd production and progressing the ball down the flanks. While the additional data supports our initial scatter plot that suggests he performs remarkably well as a defender, it also indicates he struggles in buildup in comparison to his peers and adds to our understanding of his strengths and weaknesses.  By no means does this plot determine whether he’s a good or bad player, rather it informs how Mora contributes to the side and helps us analyze his larger skillset.

So in what ways are these radars most useful? While radars can be used to assess a player’s quality, the ability to view multiple statistics at once and categorize them makes it incredibly useful at profiling how players fit and perform in particular systems and positions. When we build a radar template meant to display the desirable attributes of an attacking fullback, we can look at how they perform in the statistics that make a fullback effective and consider how the overall profile might contribute to a particular system or complement other players in a squad. Let’s take a look at the Charlotte native with a brand new contract, Charlotte FC right back Jaylin Lindsey:

One of the reasons radars have become so popular is how simple they are to interpret at first glance and Jaylin’s radar is no exception. For those that are new to radar plots, this is an impressive one. While maintaining a similar defensive output to Mora, Lindsey excels going forward. He progresses the ball well with his carries and the number of progressive passes he receives, while also completing movements with his relatively high pass completion percentage and passes into the penalty area. It’s clear Jaylin is important to Miguel Angel Ramirez’s attacking system, and the output we see here is all the more impressive when you consider he’s only just turned 22 years old. Jaylin is not only a player with a bright future in the Queen City–his output and performances show that he’s ready to succeed at this level.

What about the center backs along the Charlotte FC back line? What do their radars show and what can we learn from them about our defense? Unfortunately, Adam Armour, Christian Fuchs, and Guzmán Corujo all lack minutes in the MLS and the data necessary to learn anything from their radar plots. We’ve included the radars for all players with scouting reports on Football Reference at the end of the article, just keep in mind we’ll come back to them in a couple months or so for real analysis.

On the other hand, Anton Walkes and Christian Makoun have plenty of minutes under their belts. Similar to Mora and Lindsey, the majority of their minutes come from different clubs. But, as long as we take this context into consideration, taking a look at them gives us a good opportunity to see what Charlotte FC saw in them when they decided to bring them to the club. 

For these center back templates, the blue is Defending, green is Aggressive Ball-Winning, red is Passing, and yellow is Set Pieces/Other. Let’s start with the former Atlanta United defender Anton Walkes:

The passing statistics immediately jump off the page. Given the importance of building from the back in Miguel Angel Ramirez’s system, it’s clear that a major focus in roster construction was finding defenders that were comfortable moving the ball forward and doing so efficiently. I’m sure, much like Anton was, the club were surprised to see such a quality player unprotected for the expansion draft. It’ll be exciting to see how Anton is going to fit in Charlotte FC’s system now that he’s come back from his early season hamstring injury, and we anticipate he’ll become a vital player as the grueling MLS season will require depth and rotation across the starting XI. Below you’ll see a similar profile from his teammate Christian Makoun:

We’ll preface Christian’s radar with the important context that almost all of his minutes were on a pretty mediocre Inter Miami team. Even then, the 22 year old Venezuelan’s solid distribution numbers combined with numbers that show he isn’t easily dispossessed suggest he’s going to fit well with the possession-first club mentality. As more matches are played and the data regarding their playing time with Charlotte FC increases, we’ll begin to use these radars to better understand how these players fit with the clubs tactics and watch them develop along with the club’s performances. 

I’m looking forward to posting more of these radars as the season progresses and learning more through these interesting data visualizations. I hope this article helped introduce these radar plots or improve your understanding of them and how we attempt to understand the beautiful game! Below you’ll find the radars for Christian Fuchs and Guzman Corujo, but keep in mind they only have 630 minutes of MLS play which makes it difficult to make any conclusions from what’s available.

 I’d like to acknowledge and credit the work of Dom Samangy on radar plot R code, Nathan Clark for inspiring the position templates and answering a couple of questions via Twitter, and Jase Ziv for the creation of worldfootballR package that made this analysis possible.

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