# Performance Analysis in Speed Climbing Among Developing Athletes

Adrien Blay, Isaac Baudinet, Julien Clément

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Adrien Blay, Isaac Baudinet, Julien Clément. Performance Analysis in Speed Climbing Among Developing Athletes. Multidisciplinary Biomechanics Journal, 2025, 50th congress of the., 10.46298/mbj.16168. hal-05191720v2

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# Performance Analysis in Speed Climbing Among Developing Athletes

Adrien Blay<sup>a</sup>, Isaac Baudinet<sup>a</sup>, Julien Clément<sup>a, b*</sup>

<sup>a</sup> École de technologie supérieur, Montréal, Canada

<sup>b</sup> Institut national du sport du Québec, Montréal, Canada

* Corresponding author: julien.clement@etsmtl.ca

Received date: 03/04/2025

Accepted date: 27/06/2025

Publication date: 27/10/2025

**Keywords:** sport climbing, performance, young athletes, IMU, wearable sensors

© 2025 The Authors
Licence CC-BY 4.0
Published by Société de Biomécanique

## 1. Introduction

Speed climbing is a relatively unknown sport, but its popularity has surged since its debut at the Tokyo 2020 Olympics. Consisting of reaching the top of a 15-meter wall with a 5° overhang faster than an opponent, speed climbing has received little research attention despite its growing popularity and the need to optimize performance. Krawzyck et al. (2020) was one of the first to analyse the influence of anthropometry and lower limb power (Wingate test) on the performance of junior elite athletes. Hosseini et al. (2023) conducted a literature review and video analysis of the final round of speed climbing at the Tokyo 2020 Olympic Games, highlighting the critical roles of hand and foot sequences, as well as reaction time. Finally, Winkler et al. (2023) explored performance predictors in sport climbing, with a slight emphasis on speed climbing in senior athletes and underscored the importance of lower limb power (counter movement jumps) and arm strength for speed performance.

Speed climbing combines technique, movement strategy, and physical ability, yet remains under-researched, particularly in younger athletes. This project aims to identify performance predictors, both physical and technical, in young speed climbers.

## 2. Methods

### 2.1 Participants

A total of 21 young athletes participated in this study: 14 men (age 15.4 years, height 174,7 cm, weight 61.6 kg)

and 7 women (age 14.6 years, height 162.7 cm, weight 50.5 kg). Ethical approval was granted by ÉTS, and written consent was obtained.

### 2.2 Data collection

Two categories of factors that may influence athletes' performance were measured: internal factors related to morphology and physical capacities, and execution factors related to climbing technique on the wall.

The internal factors included: height, weight, leg, tibia, trunk, arm, forearm lengths, and arm span measured with a measuring tape; maximum jump height across three countermovement jumps (CMJ) and fastest auditory/visual reaction times during six additional CMJs, both measured with OptoJump (Microgate, Italy); and maximum grip strength of both hands across three 90° elbow flexion tests measured with a dynamometer.

The execution factors were extracted from three runs performed by each athlete on an official international speed climbing wall. Runs were recorded with a Luxov touch speed system (Luxov connect, France, 1000 Hz), and a GoPro HERO12 Black camera (GoPro, Inc, USA, 120 Hz). Climbers were also equipped with a Blue Trident inertial measurement unit at the sacrum (IMU) (Vicon Motion Systems, UK, 1600 Hz). The Luxov system measured ascension time and contact times for each hold, enabling the calculation of the movement frequency, the number of holds touched, and time spent in each zone. IMU measured acceleration
and rotational velocity along the X, Y, and Z axes, used to derive various performance predictors, as described below.

## 2.3 Data analysis

IMU data were analyzed using a Python script and filtered with a 6th-order Butterworth filter with a 20 Hz cutoff. The script automatically detected the start and end moments of each run. The start moment was defined as the last dip before a peak exceeding 10.5 m/s², while the end moment was identified as the last instance where the value reached 0 m/s². Data from the Luxov system were then integrated into the signal. Each hold corresponded to a specific area of the wall, and since the holds touched and their contact times were known, this information could be overlaid with the acceleration data (Fig. 1).

<figure><img src="image_2.png" /><figcaption>Figure 1. Acceleration curve of a run for one athlete.</figcaption></figure>

Various performance predictors were then computed, including: peak, mean and standard deviation of vertical acceleration, mean jerk, and velocity variation (trapezoidal integration of vertical acceleration).

Pearson correlation analysis was conducted to examine the relationships between run times, internal factors, and execution factors derived from Luxov and IMU data. Subsequently, multiple linear regression analysis was performed to identify key performance predictors in speed climbing. Only each climber's best run was included in the analysis.

## 3. Results and discussion

The results are preliminary, with additional athletes and variables to be included in future analyses.

The statistical analysis began with a total of 28 predictors derived from internal and execution factors. Pearson correlation analysis reduced the list to 11 predictors by excluding those uncorrelated with run times and those showing autocorrelation. Table 1 presents the results of the multiple linear regression.

<table><caption>Table 1. Results of the multiple linear regression</caption><thead><tr><td>Performance predictors</td><td>Coeff.</td><td>p-value</td></tr></thead><tbody><tr><td>Frequency of Movements</td><td>-3.006</td><td>&lt; 0.001</td></tr><tr><td>Velocity variation</td><td>0.093</td><td>&lt; 0.001</td></tr><tr><td>Mean Contact Time</td><td>0.030</td><td>0.548</td></tr><tr><td>Number of holds touched</td><td>0.182</td><td>0.557</td></tr><tr><td>Mean Acceleration</td><td>7.915</td><td>&lt; 0.001</td></tr><tr><td>Peak Acceleration</td><td>-0.131</td><td>0.465</td></tr><tr><td>Reaction Time</td><td>7.052</td><td>0.314</td></tr><tr><td>Grip strength dominant hand</td><td>0.104</td><td>0.481</td></tr><tr><td>Grip strength non-dominant hand</td><td>-0.128</td><td>0.423</td></tr><tr><td>CMJ height</td><td>0.117</td><td>0.188</td></tr><tr><td>Forearm size</td><td>0.084</td><td>0.452</td></tr></tbody></table>

Frequency of movements, velocity variation and mean acceleration were associated with a *p-value* less than 0.05, suggesting a significant relationship with the run time. These results are not surprising, as they relate to temporal execution factors. In a future study, it would be valuable to explore these factors in greater detail to identify key sections or critical moments during speed climbing, such as the start or specific movements.

It is worth noting that none of the internal factors—such as grip strength or CMJ height—emerged as significant performance predictors, which appears counterintuitive and contrasts with the findings of Winkler et al. (2023). These results are possibly due to the limited maturity of the athletes, who were not elite climbers—unlike those in the cited study—and had an average age of only 15 years. A follow-up study is planned with a more experienced adult population, in order to compare our results across the two groups.

The resulting execution factors, along with the graph, provide valuable tools and areas of improvement that coaches can use to train athletes and focus on specific aspects of their performance. Nevertheless, the Python
script requires upgrades, particularly for start/end
detection. Additionally, statistical analyses should be
approached with caution. They must be refined to
account for multiple runs per athlete. Currently, only the
best run is saved, but including all runs could provide
more accurate and relevant insights.

4. Conclusions

This preliminary study highlighted key performance
predictors within the initial group of young athletes
and identified some limitations. The next stage involves
collecting new data from additional athletes, refining
our tools, and testing new factors to assess their signif-
icance. Performance predictors and the graphs may
assist coaches in training athletes.

Acknowledgements

The authors would like to thank Horizon Roc (Montréal,
Canada) climbing centre and their athletes for their
availability.

Conflict of Interest Statement

None.

Contributor Roles

AB: Methodology, Data analysis, Writing original draft;
IB: Data curation, Data analysis; JC: Methodology,
Funding acquisition, Supervision, Validation, Writing-
review & editing.

Funding

This work was funded by ÉTS career launch funds of
Julien Clément.

References

Hosseini, A.S., & Peter Wolf. (2023). Performance indicators in speed climbing: Insights from the literature supplemented by a video analysis and expert interviews. *Frontiers in Sports and Active Living, 5*. https://doi.org/10.3389/fspor.2023.1304403

Krawczyk, M., Pociecha, M., Ozimek, M., Stepek, A., & Kozioł, P. (2020). Value of speed capabilities in youth speed climbing at high sports level. *Society, integration, education. Proceedings of the International Scientific Conference, 6*, 264. https://doi.org/10.17770/sie2020vol6.4862

Winkler, M., Künzell, S., & Augste, C. (2023). Competitive performance predictors in speed climbing, bouldering, and lead climbing. *Journal of Sports Sciences*, 1–11. https://doi.org/10.1080/02640414.2023.2239598