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Visualizing Combos in Judo in R
Posted on May 27, 2025 by Vinicius Bastazini in R bloggers | 0 Comments
As the Judo World Championship draws near this June in Budapest, it feels like the perfect time to bring together my passion for Judo (and Brazilian Jiu-Jitsu) with my gusto for complex network analyses — a fusion that’s been a long time in the making! While my posts typically focus on biodiversity-related topics and statistical modeling, I’ve long considered sharing some thoughts on one of my most cherished interests: Judo. This martial art, with its rich history and intricate techniques, has fascinated me since my childhood. Judo, which means the gentle way in Japanese, is at its competitive heart a dynamic “chess match” of throws, holds, submission techniques, and strategic combinations. While individual techniques (called waza) are foundational, the real artistry lies in how they are chained together — through renraku-waza (combination techniques) and renzoku-waza (continuous combination techniques). Thus, in a Judo match, these individual techniques usually unfold as sequences of moves, often building toward a decisive action that is likely to result in a score.
But how can we objectively analyze which attack combinations work best together? Which techniques serve as crucial setups, and which are reliable “killer moves”? This is where network analysis can offer us some insights.
In this post, we’ll explore how to model combinations of Judo throwing techniques as a network using R, trying to uncover hidden patterns in attacking strategies. So, what we will be doing is treating each individual throwing technique as a node in a network, with an edge (or link) connecting two nodes when one technique naturally sets up or transitions into another as part of an attack sequence. In our Judo attack combination network we should be able to detect: i) which techniques are most frequently used to initiate successful combinations; ii) which techniques are common finishers, “killer moves”; iii) which techniques are most “influential” —or important— in the overall strategic attacking system?
It is important to note that, this post is not intended as a comprehensive review of judo attack combinations; rather, it draws from some classic literature (Kashiwazaki and Nakanishi 1995, Kawaishi 1963, van Haesendonck 1968). I will focus exclusively on two-move attack sequences using only techniques currently recognized by the Kodokan, the temple of Judo. A more exhaustive analysis—particularly relevant for high-performance athletes—would require empirical data from competitions, a broader inclusion of technical variations, etc.
In any case, I believe this post offers insights that will resonate with both martial arts enthusiasts and scientists interested in network analysis. For our ecologist readers, do not worry! I will provide links to some examples of ecological applications (EA) of these network analyses. You can also find more info in older network-related pots here.
Let’s start by loading the necessary packages, building the network from the compiled data, and visualizing it as an interactive network, where you can choose one node, i.e., technique, and see all its relationships.
We will start our analyses by looking at degree centrality (EA1, EA2), a node-level metric that quantifies a technique’s direct importance within attack combinations by counting how many other techniques it either sets up (out-degree) or is preceded by (in-degree) (EA3, EA4). Specifically, in-degree quantifies how many different techniques commonly lead into a particular attack, marking it as a frequent follow-up move, a “finisher” attack. On the other hand, out-degree measures how many subsequent techniques a technique typically sets up, highlighting its versatility as an initial move.
To facilitate the visualization of each technique, we can plot the data on in- and out-degree as a barplot:
So we see that Ouchi-gari has the highest total degree and in-degree, indicating it’s both