Edging Velocity: The Crucial Role of Edge Engagement in Alpine

Skiing

Christoph Thorwartl

1,2,* a

, Thomas Grah

, Harald Rieser

, Günter Amesberger

Stefan Kranzinger

, Thomas Stöggl

2,3 f

, Helmut Holzer

and Thomas Finkenzeller

Human Motion Analytics, Salzburg Research Forschungsgesellschaft m.b.H., Salzburg, Austria

Department of Sport and Exercise Science, University of Salzburg, Hallein/Rif, Austria

Red Bull Athlete Performance Center, Thalgau, Austria

Atomic Austria GmbH, Altenmarkt, Austria

Keywords: Edging, Performance Analysis, Skiing Technique, Sonification, Wearable Sensors.

Abstract: In alpine skiing, the way a ski engages with the snow surface – particularly at the beginning of a turn – plays

a key role in determining performance. This study introduces Edging Velocity (EV) as a novel metric to

quantify how quickly the ski is tipped onto its edge during turn initiation. Building upon sensor-based motion

analysis using the “Connected Boot” system, we investigated three distinct skiing techniques: race carving,

moderate carving, and parallel ski steering. An expert skier performed multiple turns for each technique, and

EV was computed from edge angle progression. Results show that EV was highest during race carving,

followed by moderate carving, and lowest during parallel ski steering. All pairwise differences were

statistically significant (p < 0.001 or p < 0.01). These findings highlight EV’s potential as a performance-

relevant parameter for optimizing edge engagement. Integrated into real-time feedback systems, EV may

support learning and refinement of skiing technique, particularly in the critical early phase of a turn.

1 INTRODUCTION

A skier continually strives to place the ski on its edge,

carefully adjusting edge angles and body positioning

to navigate tight radii without lateral skidding,

allowing gravity to guide them through the turn (Jo,

2020). This effect typically occurs during carving

turns and is primarily utilized by experienced skiers.

It refers to the technique in which the ski's tip forms

a groove in the snow that the entire length of the ski

edge follows, thereby producing a self-steering effect,

where the ski naturally follows a curved path dictated

by its edge angle and deflection characteristics,

minimizing lateral skidding and enhancing dynamic

stability throughout the turn (LeMaster, 2009;

https://orcid.org/0000-0002-5685-9821

https://orcid.org/0000-0002-4588-1249

https://orcid.org/0000-0003-1407-4601

https://orcid.org/0000-0002-3078-5326

https://orcid.org/0000-0002-4014-7846

https://orcid.org/0000-0002-6685-1540

https://orcid.org/0000-0003-2736-2004

Corresponding author

Federolf, Roos, Lüthi, & Dual, 2010). In contrast,

parallel ski steering creates additional braking forces,

which make the turns feel less smooth and

continuous. Most of the literature focuses on parallel

ski steering or carving, but these are not rigidly

separate and can occur simultaneously along different

segments of the ski (Reid, Haugen, Gilgien, Kipp, &

Smith, 2020; Thorwartl et al., 2023).

Attaining competence in the carving technique is

fundamental. Motor learning theory emphasizes the

need for immediate and precise feedback in the form

of knowledge of performance (KP) to support

technical refinement and skill acquisition (Schmidt &

Young, 1991; Oppici, Dix, & Narciss, 2024). Recent

evidence highlights that KP may outperform

Thorwartl, C., Grah, T., Rieser, H., Amesberger, G., Kranzinger, S., Stöggl, T., Holzer, H. and Finkenzeller, T.

Edging Velocity: The Cr ucial Role of Edge Engagement in Alpine Skiing.

DOI: 10.5220/0013665100003988

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 13th International Conference on Sport Sciences Research and Technology Support (icSPORTS 2025), pages 25-28

ISBN: 978-989-758-771-9; ISSN: 2184-3201

knowledge of results (KR) in certain skill-learning

contexts, especially when complex movement

patterns or spatial–temporal precision are involved. In

alpine skiing, where technique relies heavily on

kinematic subtleties, KP-based real-time feedback

may provide greater value than outcome-focused cues

alone (Künzell, Knoblich, & Stippler, 2025).

A key challenge in skiing is identifying sensitive,

learn-efficient parameters that can guide technique

refinement. It has been demonstrated that the edge

angle (EA) is a key metric in determining carving

performance, as a higher EA leads to a smaller turn

radius (Jentschura & Fahrbach, 2004). Furthermore,

it has been shown that the EA is closely related to

both the radial force and the deflection of the ski,

factors which also play a crucial role in

discriminating performance (Thorwartl et al., 2023).

Therefore, proper edging of the ski serves as a

fundamental requirement for achieving the desired

carving experience. To further analyze and enhance

skiing performance, a "Connected Boot" has been

developed alongside the accompanying "Atomic

Connected" app. This system evaluates performance

by generating a "motion quality score" (ranging from

1 to 10) based on key metrics such as EA, EA

symmetry, g-force, and speed (Martínez et al., 2019a;

Martínez et al., 2019b; Snyder, Martínez, Jahnel, Roe,

& Stöggl, 2021; Snyder, Martínez, Strutzenberger, &

Stöggl, 2022). Recently, the system was utilized to (a)

analyze effects of physical stress in alpine skiing

(Finkenzeller et al., 2022), (b) detect big air jumps

and jumps during skiing (Kranzinger, Kranzinger,

Martinez Alvarez, & Stöggl, 2024a) and (c) analyze

skiing quality of recreational skiers (Kranzinger,

Kranzinger, Hollauf, Rieser, & Stöggl, 2024b).

Building on this framework of the “Connected

Boot”, the importance of precise edging techniques

becomes evident, particularly during the initiation

phase of a turn. Based on prior findings, it is

hypothesized that initiating edging earlier in the

initiation phase of a turn positively impacts

performance and the entire movement chain leading

to the self-steering effect. The authors propose early

edging, defined by a high edging velocity (EV), as a

potential additional metric to measure motion quality

during skiing and for providing real-time feedback.

However, no study has yet validated EV as a

performance metric. Thus, the objective of this paper

is to compare race carving, moderate carving, and

parallel ski steering turns to evaluate whether these

techniques differ in terms of EV, assess its potential

integration into the "Atomic Connected" app, and

explore its potential for real-time feedback that could

optimize skiing technique.

2 METHOD

2.1 Experimental Setup

An expert skier performed race carving, moderate

carving, and parallel ski steering turns with long radii

on a uniform slope under consistent soft snow

conditions. The skier wore Atomic Hawx 130 CTD

Ultra ski boots equipped with a strap, on which each

one IMU (Suunto, Vantaa, Finland) was mounted,

and skied using an Atomic Redster G7 with a length

of 1.82 m and a sidecut radius of 19.6 m. The IMU

data was transmitted to the phone and recorded with

the "Atomic Connected" app (Fig. 1).

ure 1: Ex

erimental Setu

3 DATA PROCESSING AND

ANALYSIS

The analysis included 32 turns per technique, and the

data were segmented using an automatic turn

detection algorithm (Martínez et al., 2019a; Martínez

et al., 2019b). Specific features, including EA, EA

symmetry, g-force, and velocity, were calculated

icSPORTS 2025 - 13th International Conference on Sport Sciences Research and Technology Support

based on established methods (Snyder et al., 2021).

Additionally, the EV during the initiation phase was

calculated using (1)

EV =abs(EA

– EA

)/Δt (1)

and incorporated into the app's metrics (see Fig. 1).

represents the point at which EA first reaches

90% of its maximum value, while EA

denotes the

initial EA value at the start of the turn. Δt is the time

from the start of the turn to reaching EA

90.

therefore represents the difference quotient in °/s

(Fig. 2). For each turn, only data from the dominant

outside leg were used for analysis, while data from

the inside leg were excluded. An ANOVA was used

to compare the mean EV across the three situations

race carving, moderate carving, and parallel ski

steering.

Figure 2: Calculation of Edging Velocity (EV) based on the

time required to reach 90% of the maximum edge angle

(EA) from the beginning of a turn. The figure shows EA

progression over time and highlights EA₀, EA₉₀, and the

resultin

time interval Δt.

4 RESULTS

EV differed across the three skiing techniques. The

mean EV was 41.4 ± 16.4 °/s for parallel ski steering,

69.4 ± 16.7 °/s for moderate carving, and 81.5 ± 20.4

°/s for race carving. The differences were statistically

significant, with p < 0.001 for carving vs. parallel ski

steering and p < 0.01 for moderate vs. race carving

(Fig. 3). Similarly, the maximum EA was 34.8 ± 3.8°

for parallel ski steering, 54.1 ± 5.9° for moderate

carving, and 62.4 ± 5.1° for race carving (all p <

0.001).

Figure 3: Comparison of edging velocity (mean +/- SD) fo

different performance levels. ** denotes p < 0.01, ***

denotes p < 0.001.

5 DISCUSSION

This study investigates EV as a novel metric for

skiing performance using the connected boot,

focusing on its significance during the initiation of a

turn. The results indicate that in carving turns, the

edges are engaged significantly faster compared to

parallel skiing, which aligns with the hypothesis that

proper edge engagement plays a crucial role in

optimizing performance. This early edge engagement

likely contributes to the self-steering effect

(LeMaster, 2009; Federolf et al., 2010), enhancing

stability and control during the turn. The maximum

EA is also significantly different; therefore, EV is

explained by a higher EA range of motion. The

findings related to EV highlight differences in

technique; however, larger sample sizes may be

necessary to reach more definitive conclusions. The

aim of this exploratory study of one expert skier was

to investigate whether EV is a potential indicator of

different turn techniques. Further studies should

replicate the analysis with more participants to

confirm robustness and inter-individual applicability.

In the future, EV could provide real-time

feedback to support recreational alpine skiers in

learning and competitive alpine skiers in refining

their carving technique. It is possible to get near real-

time feedback from the EV using the sonification

method. Sonification has recently been identified as

an intuitive and effective method for delivering

continuous knowledge of performance in dynamic

sports (Effenberg & Hwang, 2024). Integrating EV

into auditory feedback may thus facilitate earlier

perception–action coupling and support implicit

learning processes.

Edging Velocity: The Crucial Role of Edge Engagement in Alpine Skiing

6 CONCLUSIONS

This study highlights EV as a meaningful and

performance-relevant metric for optimizing edge

engagement, particularly during the early phase of

alpine ski turns. By integrating EV into real-time

feedback systems – particularly during the early turn

phase – motor learning processes can be supported

and technique refinement accelerated.

Real-time feedback is a key factor for effective

motor learning (Geisen & Klatt, 2021; Baca &

Kornfeind, 2006). However, only one existing system

in alpine skiing currently utilizes lateral skidding as a

feedback parameter (Kirby, 2009). To address this

gap, a novel system has been developed to sonify the

EA in near real-time (latency: 28 ms), using pitch-

modulated audio signals via helmet-integrated

speakers. The system, along with a proof-of-concept

field approach, will be presented at the congress.

ACKNOWLEDGEMENTS

This work was funded by the COMET project DiMo-

NEXT, which is supported by the Federal Ministry

for Climate Action, Environment, Energy, Mobility,

Innovation and Technology (BMK), the Federal

Ministry for Labour and Economy (BMAW), and the

provinces of Salzburg, Upper Austria, and Tyrol

within the framework of COMET – Competence

Centres for Excellent Technologies (Grant No.:

48584933). COMET is managed by the Austrian

Research Promotion Agency (FFG).

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