From Intuition to Insight: Case study ‘Athlete 12' - Part 1

Imagine you are the coach of an ambitious judoka, someone who dreams of winning an Olympic medal. You see him training hard, developing technically, and becoming mentally stronger. But still, the question nags at you: “Are we really on the right track? How do we know that training actually works and that we are making the right choices?” Perhaps you recognize that uncertainty from your own experience, at the club or in elite sports.

These questions and doubts are very common in the judo world. That’s why having a solid benchmark is so valuable: a clear, personal, and concrete picture of where you truly want to go and exactly what that looks like. Benchmarking gives you guidance and structure in the sometimes elusive process of performing and developing.

But then you also often hear: “Judo is too complex to measure with numbers...” Or: “I prefer to trust my experience and intuition rather than data.” That skepticism is understandable, and you are certainly not the only one thinking this. I believe it’s time to show that data can very well complement experience.

Imagine that your feeling and experience are not only leading but are combined with hard facts. Not to replace your expertise, but to make it even stronger. This way, you don’t lose nuance but add a factor to the feeling you already have.

Athlete 12

This case study is about ‘Athlete 12.’ This European male athlete is a senior elite athlete aiming to stand on the Olympic podium in four years. We had conversations with this athlete about the benchmark and gained a clear idea of how we think his judo must look to win an Olympic medal. These conversations are based on our ideas about judo, observations, feelings, past experience, and reflections.

The next step is to analyze the group of athletes who have either won an Olympic medal or whom we considered capable of winning one. We created a benchmark group and gathered data from this group, displayed in a table.

The data

The table below shows in the leftmost column under the heading ‘Athlete’ the best athletes in a certain weight class in the period leading to the Paris 2024 Olympic Games. These athletes are European in this case, except for the top four, who stood on the podium at the Paris Olympics. The top four may therefore come from other continents.

Below these 11 athletes is the ‘Benchmark group average,’ representing the average values achieved on the factors explained below.

Athlete number 12 is the athlete at the bottom of the table, serving as the example athlete in this case to compare with the benchmark group we created.

The columns in the top row describe several calculated values, which I will explain one by one:

Expected scores TW – The number of scores the athlete is expected to make per match in tachi-waza (standing techniques)

Expected scores TW against – The number of scores the athlete is expected to concede per match in tachi-waza

ATWS (Adjusted Tachi-waza Score) – The expected scores made minus the scores conceded in tachi-waza per match

Expected scores NW – The expected scores made per match in ne-waza (ground techniques)

Expected scores NW against – The expected scores conceded per match in ne-waza

ANWS (Adjusted Ne-waza Score) – The expected scores made minus those conceded in ne-waza per match

Expected scores total – Total expected scores per match

Expected scores total against – Total expected scores conceded per match

ATS (Adjusted Total Score) – Total expected scores minus total expected scores conceded per match

In this article, names and weight classes are deliberately omitted because they are not relevant to explaining how data can supplement the benchmark and identify the gap.

The analysis

Looking at the data, I like to start big, with the largest value. In this data, that is the ATS. This value covers all the values in the table and is therefore valuable to compare first.

It immediately stands out that there is a big difference between the value athlete 12 achieves (0.08) and the benchmark group's average ATS value (0.47). As explained earlier, ATS consists of the scores an athlete is expected to make per match minus those expected to be conceded. Notice that athlete 12 is expected to score almost as often (0.54) as to concede (0.46) per match.

In simpler terms, the athlete we coach is expected to score once every two matches but also to concede a score once every two matches. Compared to the benchmark group, the average athlete is expected to score in 4 out of 5 matches and concede a score in 1 out of 3 matches. That is a clear difference with athlete 12. Looking at the Olympic podium athletes, they are expected to score nearly every match. Think of each scored point as a step up a ladder toward the podium and each conceded point as a step down. Athlete 12 balances nearly equally up and down, while the benchmark group steps up far more often than down. That difference determines who climbs faster and higher.

What does this analysis mean in practice? When looking at the Adjusted Total Score (ATS), ask yourself:

• Do I score more than I concede?

• How big exactly is the difference?

• Which parts of my judo give me the most advantage?

Be mindful: data is not just numbers but about your judo. What strategy will you sharpen now?

When we then look at data related to ne-waza, we see athlete 12 scores exactly at the average here, with no big difference from the benchmark at first glance. Possibly, in the third article, we’ll explore this deeper and uncover more insights. Regarding expected scores for and against, there are no large differences with the benchmark. What we do see is that only three of the eleven benchmark group athletes are expected to score more per match in ne-waza. This could indicate athlete 12 has the potential to distinguish himself from competition in this area.

Looking at tachi-waza data, athlete 12 is expected to score 0.39 times per match, roughly two expected scores per five matches. The benchmark group is expected to score almost two times per three matches. Notably, athlete 12 is expected to concede more scores (0.40) than he scores. The benchmark group shows a different balance with only 0.28 scores conceded per match, less than half of their expected scores made, contributing to a positive ATWS.

Overall, there are clear differences in scoring ability and expected scores conceded in tachi-waza; athlete 12 scores well below average and concedes more than average. In ne-waza, differences are smaller, and athlete 12 might already match the benchmark group’s level with his current ne-waza skills. Regarding total expected scores for and against, athlete 12’s figures are too close. He scores less and concedes more compared to the benchmark, showing room for improvement to join the benchmark group.

Next steps

From this first analysis, some cautious assumptions can be made:

• Athlete 12 needs to increase expected scores per match in tachi-waza.

• Athlete 12 needs to reduce expected scores conceded per match in tachi-waza.

• Athlete 12 scores around average but among the best in the group for expected scores made and conceded per match in ne-waza.

• Athlete 12 must ensure total expected scores made clearly exceed total scores conceded per match.

Given these assumptions, it’s important to look beyond the data reviewed so far. Further analysis can determine whether these assumptions hold and, if so, how to work on these factors.

As explained in the first blog, expected scores are calculated as:

Efficiency percentage x Average number of attacks per match = Expected number of scores

For expected scores conceded, the formula is the same, but it applies to the opponent's efficiency and average number of attacks against per match.

By examining underlying data, we can determine if the athlete should focus more on efficiency or attack ratio (number of attacks per match) to increase expected scores. Your attack ratio is like the number of bullets fired in a fight; efficiency is how often those bullets hit. Firing many bullets without hitting doesn’t help; firing fewer but hitting more effectively does. This insight impacts goal setting and training methods.

As noted, ne-waza is an area where athlete 12 competes well with the benchmark group. If this is already a strong quality, it may be worthwhile to explore how to maximize this and create a competitive edge.

I strongly advocate not just looking at how to catch up with the competition but more how to differentiate and excel beyond them.

In the next article, we’ll go deeper into specific interventions based on your data analysis, for example:

How to increase your attack ratio?

Developing new attack patterns, adding techniques, or quicker positional changes after grip fighting (kumi-kata).

How to improve attack efficiency?

Technical refinement, coaching to recognize openings, and mental training to execute techniques confidently.

How to strengthen your defense?

Targeted grip fighting training, defensive drills to minimize opponent’s chances, and video analysis to identify and avoid pitfalls.

Linking your data to these specific objectives makes your plans concrete, measurable, and achievable. This not only makes your training more effective but also boosts motivation by clearly showing what you are working on and how it relates to your competition performance.

At Katalyst Performance Consulting, we believe data analysis can make a difference in athletes' projects. Whether you are an athlete, coach, federation, or club, we are ready to help translate data into action. We can assist with implementing data analysis in your projects, providing insights that can significantly enhance your performance.

Want to know how we can support your training, strategies, or organization with data-driven insights? Contact us at info@katalystperformance.nl. We look forward to working together to help you reach your ultimate goal.

In the next article, we will guide you step-by-step in translating numbers to action, so you know how to get started and what to do to set up a benchmark and grow to the highest podium using data, experience, and expertise.