Unfortunately, as I balance University work alongside working as an Analyst at a club with another part-time job, I have found myself with less time to work on my analytical skills in Tableau and R compared to all the free time that I found myself with in the middle of Lockdown. However, I am thoroughly enjoying bringing new ideas to my professional working life, trying to develop the analysis provided to the teams that I cover. Although I am now constantly working to tight deadlines, I am still finding some time to further refine those skills so that I can improve the services I give to the club. Updates to this portfolio may come about slower now, but I think it is more important to ensure that when I do add to this that I am ensuring high quality, accurate visualisations are included.
In this post, I again look into Statsbomb's Messi data, this time I have made Passing Networks for Madrid's home and away game against Barcelona during the 2017/2018 season. Madrid fell 17 points behind Barcelona in the race for La Liga. Clearly this season wasn't lost in these two games, however, these are most definitely two key games in each season in the battle for the title.
Pass Networks are a good way of showing the attacking strategy of a team. The Pass Networks that I have created are designed to show the average position that every pass was played from for each player. For this I include all passes for the starting 11 up to the first substitution, if there was one. Each individual is represented by the blue circle, with the size showing how many passes they played; the larger the circle, the greater number of passes. Key relationships can also be shown by the bridges between players; the thicker the bridge, the more passes are played between the two. I have set these relationships to require a minimum of five passes. This is done so that only the most key relationships are seen, rather than the whole pitch being linked by bridges. These visualisations are completed with the number of passes and completion rate. I made these visualisations in R with the aid of the Soccermatics package.
First, looking at the game played at the Bernabeu where Madrid lost 0-3, we see how key the full backs are to Madrid's offensive play. This is highlighted by Marcelo's average positioning being the third furthest forward player. Madrid also played no natural wide midfielders in this game, with four central midfielders playing instead. Casemiro sat deep and Kroos, Kovacic and Modric in front in a middle three. With this being the case, its only natural to see Madrid dependant on their full backs for their attacking width.
When conducting opposition analysis, a team might be particularly interested in the relationship between Marcelo and Ronaldo. This is possibly the thickest line on the Pass Network, showing that this was the most common link up. Given that these passes are likely to be played within the opposition's half this is something that other teams would like to disrupt. However, like always it is important to refer back to the video to assess this relationship and if this produced high quality chances or not.
Real Madrid Pass Network Home Game vs FCB
Now looking at the game played at the Nou Camp in May 2018, we see several differences. Firstly, Madrid appear to be much more on the backfoot based on average positioning compared to the first game. They have also switched to a 4-3-3 now. This was a much better result for Madrid, coming away with a 2-2 draw. However, Madrid may have been disappointed to not come away with more considering that Barcelona played the second half with ten men.
It is important to ensure that we read the whole visualisation also. The size of each player on this visualisation suggests that they played drastically less passes than the first visualisation, however there were almost 100 more passes played in the second game. The reason for the different node sizes are the number of minutes included in each until that first substitution; 66 versus 46. Madrid also had a much greater pass completion in the second game (90.5% versus 82.3%) which may be a good indicator of their improved performance or possibly a different pressing game from Barcelona as a result of the sending off for Roberto. The pass completion in the away game is much greater than their 85.3% season average (according to FBref).
Real Madrid Pass Network Away Game vs FCB
There is much more that we can read into these visualisations, and each individual might draw their own conclusions from them, however, as usual the context of the game must be included and is best done by matching this with video analysis. We must ensure that these are accurately explained to coaches when sharing these to not be confused by factors like the number of minutes included. Also, the second visualisation highlights why simply doing up until the first substitution is not necessarily always the best thing to do; I highly doubt we would see a similar Pass Network in the second half of this game, once Barcelona go down to ten men and most likely sit a lot deeper, as shown by the number of passes and high pass completion from Madrid.
Overall, I think I have succeeded in my outcomes of producing Passing Networks visualisations and explaining how they can effectively be used in the analysis of your own team or the opposition.
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