Competitive Performance
Prediction of Elite Alpine Skiers
Robert Nilsson
Department of Community Medicine and Rehabilitation
Section of Sports Medicine
Umeå 2019
Responsible publisher under Swedish Law: The Dean of the Medical Faculty This work is protected by the Swedish Copyright Legislation (Act 1960:729) Dissertation for PhD ISBN: 978-91-7855-079-1 ISSN: 0346-6612 Electronic version available at: http://umu.diva-portal.org/ Printed by: CityPrint i Norr AB Umeå, Sweden 2019
I dedicate this dissertation to my parents, who have always
believed in me and through their love and support made the
completion of this dissertation possible.
i
Table of Contents
Abstract ............................................................................................ iii
Abbreviations .................................................................................... v
Sammanfattning på svenska ............................................................ vi
List of publications ......................................................................... viii
Introduction ....................................................................................... 1
Background ....................................................................................... 3 Competitive alpine skiing .................................................................................................3
Slalom .........................................................................................................................3 Giant Slalom ...............................................................................................................3 How competitive performance is quantified ........................................................... 4
The competitive season and structuring of on-snow training ....................................... 4 On-snow training ...................................................................................................... 4
Characteristics of alpine skiers ........................................................................................ 5 The career of elite alpine skiers ................................................................................. 5 Body composition and skiing performance .............................................................. 5
Forces, muscular activity and movement speed during skiing ...................................... 6 Forces ......................................................................................................................... 6 Muscular activity ...................................................................................................... 6 Isometric and eccentric muscle strength .................................................................. 7 Movement speed ......................................................................................................... 7
Aerobic and anaerobic capacity ...................................................................................... 8 Balance ............................................................................................................................. 9 Core strength and core stability ..................................................................................... 10 Flexibility ........................................................................................................................ 10 Physiological testing in alpine skiing ............................................................................. 11 Summary ......................................................................................................................... 16
The rationale for the thesis .............................................................. 17
Aims ................................................................................................. 18 Specific Aims ................................................................................................................... 18
Materials and Methods .................................................................... 19 Study design .................................................................................................................... 19 Participants .................................................................................................................... 20 Testing procedures ......................................................................................................... 21
One Repetition Maximum tests ............................................................................... 21 Hand Grip Strength ................................................................................................ 22 Pull-ups .................................................................................................................... 22 Brutal Bench ............................................................................................................ 23 Change of Direction Speed ...................................................................................... 23 20m Sprint test ........................................................................................................ 23 Jump tests ................................................................................................................ 23 Force-Velocity Bike Test .......................................................................................... 24
ii
Y Balance Test™ ...................................................................................................... 24 Lateral Box Jump 90-s ............................................................................................ 25 500 meters Rowing Ergometer Test ...................................................................... 25 Peak Oxygen Uptake ............................................................................................... 25 Anthropometric tests ............................................................................................... 26
Fédération Internationale de Ski points ....................................................................... 26 Statistics .......................................................................................................................... 27
Bivariate analysis ................................................................................................... 28 Multivariate analysis .............................................................................................. 28
Ethical statement ........................................................................................................... 29
Results ............................................................................................ 30 Paper I ............................................................................................................................ 30 Paper II ........................................................................................................................... 31 Paper III ......................................................................................................................... 33
Discussion ....................................................................................... 34 Why is there a lack of predictive power on a group level? ........................................... 34 How important is the physiological dimension? .......................................................... 36 Is there an assumption of validity? ............................................................................... 38 Validity and reliability of physiological tests in alpine skiing ...................................... 38 Methodological considerations ..................................................................................... 40
Other dimensions .................................................................................................... 40 Menstrual cycle effects ............................................................................................. 41 Other predictive modeling procedures ................................................................... 41 More tests specifically designed for alpine skiers .................................................. 41 Fédération Internationale de Ski points ................................................................. 41
The novel approach of this thesis .................................................................................. 42 Application in sports ..................................................................................................... 42
Conclusion ...................................................................................... 43
Acknowledgments ........................................................................... 44
References ...................................................................................... 46
iii
Abstract
Introduction Competitive alpine skiing is a multifaceted and challenging sport
that places high demands on its practitioners. To achieve competitive success,
alpine skiers must master a variety of physical, technical, mental and social skills.
Among the physiological qualities that have been described as necessary for elite
performance in alpine skiing are qualities such as muscular strength and
endurance, power, aerobic and anaerobic capacity, balance, core strength and
core stability as well as flexibility. Testing these qualities can therefore be
important, not only in order to optimize physiological training but ultimately to
maximize the sport-specific performance of the individual athlete. Although
several individual tests have been shown to correlate with skiing performance,
the combined importance of a selection of tests, referred to as "test batteries", has
rarely been investigated. As a consequence, these test batteries risk misleading
both athletes and coaches when planning and implementing preparatory
training. Also, by using results from these tests, prediction of future performance
is difficult. To generate useful results, a test protocol must be valid and reliable.
Without taking this into account, significant resources seem to be invested in
non-relevant tests each year, wasting important resources for clubs and
federations as well as valuable time for athletes and coaches. Therefore, the
development of valid test protocols is essential for all sports. The overall aim of
this doctoral thesis was to identify physiological and anthropometric variables
valid for prediction of competitive performance in alpine skiing (indicated by FIS
points).
Method Paper I-III in this doctoral thesis followed an experimental, hypothesis-
generating design which included both junior and senior elite alpine skiers. In all
papers, physiological and anthropometric test results (X-variables) were
correlated with FIS points (Y-variables) in order to investigate the predictive
power of physiological and anthropometric variables for competitive
performance in alpine skiing. The significance of the included test results was
examined using bivariate and multivariate data analysis.
Results The results of Paper I show that included aerobic test results, neither
alone nor in combination with anthropometric variables, could predict
competitive performance of junior elite alpine skiers. Principal component
analysis shows that male and female junior alpine skiers could be separated based
on test results but that none of the included tests were important for sport-
specific performance. The best multivariate models reached R2 = 0.51 to 0.86 and
Q2 = -0.73 to 0.18. While several significant regression models could be observed,
none of these met the criteria for valid models. The lack of predictive power of
observed prediction models was confirmed by cross-validation. The results of
iv
Paper II show that included physiological test results from the test battery
Fysprofilen could not predict competitive performance of senior elite female
alpine skiers. Principal component analysis shows that there is a high correlation
between individual physiological test results and their corresponding Fysprofilen
score points, indicating that they can be used interchangeably. The Mann-
Whitney U test was not significant neither for SL nor for GS. This suggests that
Fysprofilen score points (summarized as Fysprofilen Index) and competitive
performance (indicated by FIS points) are independent. The best multivariate
models for SL and GS reached R2 = 0.27 to 0.43 and Q2 = - 0.8 to - 0.17, indicating
low predictive power for competitive performance (as confirmed by cross-
validation). The results of Paper III show that included physiological test results
from a novel test battery could not predict competitive performance of senior elite
female alpine skiers on a group level. When data were analyzed on a group level,
the best models for SL and GS reached R2 = 0.39 to 0.40, Q2 = 0.15 to 0.21,
indicating low predictive power. In contrast, when data were analyzed on an
individual level, valid models with high predictive power (R2 = 0.88 to 0.99 and
Q2 = 0.64 to 0.96) were generated. A comparative analysis between individual
OPLS models shows that the relative importance of different physiological
qualities for athletic performance varies between skiers.
Conclusion When applying tests on alpine skiers, a holistic approach should be
considered. This because competitive performance in alpine skiing is the result of
a number of interacting dimensions. Before applying physiological tests, the
validity and reliability of the test protocols must be determined. Administering
tests that do not meet these criteria will probably waste not only important
resources for clubs and ski federations but also risk misleading coaches and
athletes when planning and implementing preparatory training.
v
Abbreviations
1RM One Repetition Maximum
BMI Body Mass Index
C Combined
CMJ Countermovement Jump
CMJa Countermovement Jump with arm-swing
DXA Dual-Energy X-ray Absorptiometry
DH Downhill
FIS Fédération Internationale de Ski
GS Giant Slalom
GRF Ground Reaction Force
MVDA Multivariate Data Analysis
OPLS Orthogonal Projections to Latent Structures
PCA Principal Component Analysis
SC Super Combined
SIMCA Soft Independent Modeling of Class Analogy
SL Slalom
SG Super Giant Slalom
SOC Swedish Olympic Committee
V̇O2max Maximal Oxygen Uptake
V̇O2peak Peak Oxygen Uptake
vi
Sammanfattning på svenska
Introduktion Tävlingsmässig alpin skidåkning är en mångfacetterad och
utmanande idrott som ställer höga krav på dess utövare. För att uppnå
tävlingsmässig framgång måste alpin skidåkare uppvisa prov på en rad olika
fysiologiska, tekniska, mentala och sociala färdigheter. Bland de fysiologiska
egenskaper som har beskrivits som viktiga för prestationsförmågan i alpin
skidåkning återfinns kvalitéer som exempelvis muskelstyrka och uthållighet,
power, aerob och anaerob kapacitet, balans, bålstyrka och bålstabilitet samt
flexibilitet. Att testa dessa kvalitéer kan därför vara viktigt, inte bara för att
optimera fysiologisk träning utan också för att maximera idrottarens
grenspecifika prestationsförmåga. Även om enskilda fysiologiska tester har visat
sig korrelera med prestationsförmåga på skidor har betydelsen av kombinationer
av tester (ofta benämnda som "testbatterier") sällan undersökts. På grund av
detta riskerar dessa testbatterier att vilseleda både idrottare och tränare vid
planering och tillämpning av förberedande träning. Dessutom är möjligheten att
predicera idrottsspecifik prestationsförmåga med hjälp av testresultaten låg. För
att generera praktiskt användbara resultat bör testprotokoll ha hög validitet och
reliabilitet. Trots detta tycks ändå betydande resurser satsas på icke relevanta
tester varje år, något som slösar med viktiga ekonomiska medel för klubbar och
förbund likväl som värdefull tid för idrottare och tränare. Av denna anledning är
identifieringen av valida och reliabla testprotokoll viktigt för alla idrotter. Det
övergripande syftet med denna doktorsavhandling var att identifiera fysiologiska
och antropometriska variabler valida för prediktion av tävlingsmässig
prestationsförmåga i alpin skidåkning (indikerad av FIS-punkter).
Metod Studie I-III som ingår i denna doktorsavhandling följde en experimentell,
hypotesgenererande design som inkluderade alpin skidåkare på både junior och
senior elitnivå. I samtliga studier korrelerades fysiologiska och antropometriska
testresultat (X-variabler) med FIS-punkter (Y-variabler) i syfte att undersöka
deras prediktiva förmåga för tävlingsmässig prestanda i alpin skidåkning.
Betydelsen av inkluderade testresultat undersöktes med hjälp av bivariat och
multivariat dataanalys.
Resultat Resultaten av Studie I visar att inkluderade aeroba testresultat, varken
enskilt eller i kombination med antropometriska variabler, kunde predicera
tävlingsmässig prestationsförmåga hos vare sig manliga eller kvinnliga alpin
skidåkare på junior elitnivå. Tillämpningen av principalkomponentanalys visade
att manliga och kvinnliga junior alpin skidåkare kan separeras med hjälp av deras
fysiologiska testresultat men att ingen av de undersökta variablerna var
diskriminerande för idrottsspecifik prestationsförmåga. Resultaten av Studie II
visar att de inkluderade testresultaten från testbatteriet Fysprofilen inte kunde
vii
predicera tävlingsmässig prestationsförmåga hos kvinnliga alpin skidåkare på
senior elitnivå. Tillämpningen av principalkomponentanalys visade att det
föreligger en hög korrelation mellan fysiologiska testresultat och
korresponderande Fysprofilen poäng, något som indikerar att de kan användas
som alternativ för varandra. Mann-Whitney U testet var inte signifikant för vare
sig SL eller för GS, något som tyder på att Fysprofilen poäng (summerat som
Fysprofilen Index) och tävlingsmässig prestationsförmåga (indikerat av FIS-
punkter) är oberoende av varandra. Resultaten av Studie III visar att det
applicerade testbatteriet inte genererade multivariata modeller med hög
prediktiv förmåga för vare sig SL eller GS när betydelsen av inkluderade variabler
analyserades på gruppnivå. När data analyserades på individuell nivå kunde
emellertid valida modeller med hög predikativ förmåga för både SL och GS
genereras. En jämförelseanalys mellan individuella OPLS modeller visar att den
relativa betydelsen av olika fysiologiska delkvalitéer varierar mellan olika
utövare.
Slutsats Vid tillämpning av tester på alpin skidåkare bör en holistisk ansats
övervägas. Detta eftersom tävlingsmässig prestationsförmåga i alpin skidåkning
är ett resultat av ett flertal interagerande dimensioner. Innan fysiologiska tester
appliceras bör validiteten och reliabiliteten hos testprotokollen fastställas. Att
administrera tester som inte uppfyller dessa kriterier kommer sannolikt inte bara
slösa med viktiga resurser för klubbar och skidförbund utan riskerar även att
vilseleda tränare och idrottare vid planering och implementering av förberedande
träning.
viii
List of publications
This doctoral thesis is based on the following original articles. They will be referred to by their roman numerals. Paper I Nilsson R, Lindberg A-S, Theos A, Ferguson RA, Malm C (2018).
Aerobic Variables for Prediction of Alpine Skiing Performance – A
Novel Approach. SMIO 2(4): E105–E112
Paper II Nilsson R, Theos A, Lindberg A-S, Ferguson RA, Malm C. Lack
of Predictive Power in Commonly Used Tests for Performance in
Alpine Skiing. Submitted
Paper III Nilsson R, Theos A, Lindberg A-S, Ferguson RA, Malm C.
Individual Profiling for Prediction of Competitive Performance in
Alpine Skiing. In manuscript
The scientific work in this thesis is reprinted with permission from the original
publisher.
1
Introduction
Competitive alpine skiing is a multifaceted and challenging sport that places high
demands on its practitioners [134,149]. To achieve competitive success, alpine
skiers must master a variety of physical, technical, mental and social skills [46].
During skiing, athletes may be subjected to immense physiological strains
[78,113] and must, therefore, be able to display a broad spectrum of physiological
qualities. Elite performance in alpine skiing is suggested as a result of a number
of interacting qualities [7,41] including muscular strength and endurance, power,
aerobic and anaerobic capacity, balance, core strength and core stability as well
as flexibility [4,66,97,108]. Testing these qualities can therefore be important, not
only in order to optimize physiological training but ultimately to maximize the
sport-specific performance of the individual athlete [46,66]. An overview of
influencing dimensions is exemplified in Figure 1.
Figure 1. Overview of dimensions that affect competitive performance in alpine skiing.
An overview of dimensions that potentially affect competitive performance in alpine skiing (including potential
unknown dimensions). The proportion of each dimension has not yet been determined. This doctoral thesis focuses
on the physical dimension (physiological performance and anthropometric variables).
Over the years, several different testing protocols in alpine skiing have been used
[66], many of them with the intention of evaluating the physical status of athletes
before and after the racing season. These protocols usually include general
evaluations of strength, power, mobility, and endurance, but some countries also
use physiological tests specifically designed for alpine skiers [46,66,109].
Although several individual tests have been shown to correlate with skiing
performance (Table 1), the combined importance of a selection of tests, referred
to as "test batteries", has rarely been investigated. As a consequence, these test
batteries risk misleading both athletes and coaches when planning and
Physical
Equipment
?
?
2
implementing preparatory training. Also, by using results from these tests,
prediction of future performance is difficult.
To be useful, a test battery intended for a specific type of athlete must be valid
and reliable [96]. Still, substantial efforts seem to be spent on non-relevant tests
each year, wasting both financial resources as well as valuable time for athletes,
coaches, and training advisors alike. Therefore, the development of valid test
protocols is essential for all sports. This thesis focuses on alpine skiing, with the
overall aim to identify physiological and anthropometric variables valid for
prediction of competitive performance (indicated by FIS points). Through a
multivariate statistical approach and validation of models, the results and
analytical methods presented in this thesis can hopefully contribute to the future
development of more relevant testing procedures for alpine skiing and other
sports as well.
3
Background
Competitive alpine skiing
Competitions for elite alpine skiers are organized by the Fédération
Internationale de Ski (FIS) and include FIS World Championship, FIS World Cup
and Continental Cups. Alpine skiing has been an Olympic sport since its debut in
the Winter Olympic Games 1936 [144] and now consists of six different
disciplines including Slalom (SL), Giant Slalom (GS), Super Giant Slalom (SG),
Downhill (DH), Combined (C) (or Super Combined (SC)) and the Team event
[145]. The disciplines are divided into different categories, of which SL and GS
are referred to as the technical disciplines, and SG and DH are referred to as the
speed disciplines. Each of these disciplines differs from each other, mainly by
different turning radius, speed, the length of the course and the vertical distance
between the gates. FIS regulates the standards of each discipline which differs
based on age and gender [37]. As the focus of this thesis is on SL and GS, other
disciplines are not described in detail.
Slalom
Slalom (and GS) consist of two consecutive runs conducted on the same slope but
with two different courses where the skier with the fastest combined time wins.
Slalom is the shortest event, lasting 45-60 s and are usually conducted in
relatively steep paths with speeds ranging from 20-60 km·h-1 [7,41]. In SL, a race
course is composed of a series of gates by alternating pairs of red and blue poles
[37]. For the Winter Olympic Games, FIS World Championships and FIS World
Cup, an SL course must have a vertical drop of between 140-220 m and be
comprised by approximately 55-75 gates for men and between 40-60 gates for
women. Each gate should be located at a relatively close distance to each other
(4-13 m), forcing skiers to conduct quick and tight, short radius turns. Unlike the
other disciplines, each gate in SL consists of just one pole, which allows skiers to
follow a tight race line by cross-blocking the gates with their hands or repress
them with the shin guards. By using this technique, athletes can ski close to, or
even crossing the gates, in order to limit the intended skiing path [75].
Giant Slalom
Giant Slalom events are often conducted in relatively steep and undulating terrain
in courses covering the entire width of the slope. A GS run usually lasts between
60-90 s [7] with speeds between 60-90 km·h-1 [66]. In GS, SG, and DH, each gate
consists of four slalom poles and two gate panels [37]. As for SL, the course setting
for GS and SG is regulated via the vertical drop distance of the course [44].
According to the FIS regulations [37], a GS course must have a vertical drop of
4
250-450 m (up to 400 m for women) and direction changes equal to 11-15% of
the altitude change for the race course.
How competitive performance is quantified
Competitive performance in alpine skiing is quantified by the FIS point scoring
system and is used to regulate starting positions in all disciplines. Fédération
Internationale de Ski points are calculated according to the alpine formula and
are organized so that the best skiers in the world in each discipline have 0 (zero)
points and that those placed 30 have 6 points [38]. In brief, FIS points is
calculated according to the following formula: P = ((F * Tx) / To) - F where P =
FIS points, Tx = time for qualified competitor in seconds, To = time of the winner
in seconds and F = factor for each discipline (which is announced annually by
FIS) [38]. The FIS points lists are adjusted several times each year and apply to
all athletes participating in FIS alpine skiing events.
The competitive season and structuring of on-snow training
The FIS official competition calendar for alpine skiing starts on July 1 and extends
until June 30 the following year [39]. For most elite skiers, the competitive season
starts in October/November and ends in the middle of March [46]. During a
transitional period lasting from the end of the competition season until mid-
April, many athletes continue their on-snow training before transitioning to
physical preparation training that lasts from May until the end of July [42]. From
August until the competition season starts, the focus is on on-snow training which
is alternated with recovery periods and physical training aimed at maintaining
the general physical fitness and increasing the sport-specific performance
[42,46].
On-snow training
Elite alpine skiers usually train and compete for 130-150 days during a
competitive season [46]. For skiers who specialize in the technical disciplines,
approximately 120-140 of these are training days, and the remaining are
competition days. For speed specialists, the number of training and competition
events amounts to approximately 100-120 days in one year [46]. Usually, training
sessions are conducted in the morning due to favorable snow conditions and low
temperatures. A typical training session lasts between 2-4 hours which include
recovery between training runs and lift transports [66,123].
During ski training, athletes usually perform between 1-5 warm-up runs, one
inspection run of the training course and between 3-12 training runs depending
on the discipline [46,66]. A SL training session typically consists of 2-12 runs and
up to 700 turns in total. During a GS training, skiers usually perform up to 12
5
runs and a total of up to 600 directional changes while a SG training often
consists of 2-8 runs and up to 300 turns depending on the number of runs and
the length of the course. Like SG, a DH training usually consists of up to 8 runs
and, depending on the training venue, between 100-280 directional changes in
total [46].
Characteristics of alpine skiers
The career of elite alpine skiers
The career of professional alpine skiers seems to get longer. Throughout 40 years
(from 1967-71 to 2009-13), the average age of medalists during the major alpine
skiing competitions (FIS World Championships and the Winter Olympic Games)
was extended by about five years (from 20.7 to 25.8 years for women and from
24.3 to 28.7 years for men) [118]. Speculative, the reasons for this development
may be due to, e.g., improved training methods (on-snow training and
preparatory training), development of surgical procedures and rehabilitation
strategies in case of injuries and an increased opportunity to make a living on the
sport (thanks to a larger amount of prize and sponsor money).
Body composition and skiing performance
Body composition is an important determinant of performance in many sports
[89]. However, the requirements in different sports are often unique and require
specific body compositions for them to be advantageous. While endurance
athletes such as triathletes [49] and distance runners [79] usually benefit from
relatively low body mass and low body fat percentage, basketball players
generally have the advantage of being tall [141] and a having a larger amount of
lean body mass. In alpine skiing, anthropometric variables such as body fat
percentage [3,8,55,100], body weight [3,8,19,55,97,152,154], body stature [3,64],
lean body mass [55,97], leg length [127] and somatotype [32,149,152] have all
been suggested to affect the competitive success of athletes. For example, one
study [64] examined the difference in anthropometric variables between skiers,
based on their placing during one so-called Olympic cycle (including the Winter
Olympic Games, FIS World Cup, and FIS World Championships between the
years 2006 and 2010). While the researchers did not find any statistical difference
between skiers ranked top 30 in any of the investigated disciplines, they observed
a significant difference in stature and BW when comparing top 10 between the
disciplines SL, GS and C among those who placed over the top 30. As such, these
results indicate that it may be more beneficial to be shorter and lighter in
disciplines such as SL [55,64], while longer and heavier skiers are likely to have
an advantage in disciplines such as DH [55,64,97]. These results also suggest that
6
differences in body composition [64] or somatotype [32] can potentially
discriminate between athletes at different levels.
During the years, successful alpine skiers have seemingly become taller and
heavier [97,100,154]. In a classical study by Eriksson, Ekholm, Hulten, et al. [33],
the researchers found that skiers who were part of the Swedish national team
between 1965-1975 had an increase in both body weight and body stature, from
64 to 76 kg and 168 to 178 cm, respectively. These results were later supported by
interventions showing that the average body weight among U.S national female
alpine skiers had increased from ~58 [55] to 66 kg [116] and that the body weight
in the Swedish men's national alpine team had increased to 81 kg on average [12].
However, based on more recent studies [52,67,91,154], and despite some
exceptions [97], this development seems to have stagnated.
An explanation for the previous development has been proposed as a result of the
change in ski technique and the introduction of breakaway poles [155]. As the
poles could be passed through instead of being forced around them, this likely
favored skiers with higher body weight [155]. Although changes to alpine skiing
have been made since then, e.g., the introduction of the carving ski [62], none of
which has seemingly meant a fundamental change in the requirements of athletes
body size or body composition. An exception to this is speed discipline specialists,
where a larger amount of upper body muscle mass [97] or of being heavier in
general probably is beneficial [47,136,152].
Forces, muscular activity and movement speed during skiing
Forces
There is a number of different forces acting on the athletes during a run. Among
the most important of these are gravity, ski-snow friction, air drag, centripetal
and centrifugal force [56,130]. Forces generated during alpine skiing vary
depending on discipline [45,47] and are affected by factors such as speed, turning
radius and body weight of the athlete [42,59]. Studies conducted during skiing as
well as mathematical calculations indicate that skiers may be subjected to ground
reaction forces (GRF) of 2-4 times the BW in both SL and GS [78,113,135] and
that peak GRF can reach 3 times of the athletes BW during a regular SG and DH
run [45,48].
Muscular activity
The ability to withstand these forces while maintaining balance and control is
crucial for the competitive success of an alpine skier [66] and requires a
significant contribution and coordination of muscle structures in the trunk and
7
lower extremities [59,66,136]. Monitoring of electromyographic (EMG) activity
shows that muscle structures such as the tibialis anterior, erector spinae, gluteus
maximus, hamstrings, and quadriceps muscle groups are highly activated during
different turning phases [59,74,136] and that several antagonists muscles help to
stabilize the knee and hip joints during the completion of a turn [59]. Studies also
show that the regimen for muscular activity ranges between 50-280% of
maximum voluntary isometric contraction (MVC) in investigated disciplines
[59,60], indicating that large forces occur and that high levels of isometric and
eccentric muscle strength could be essential to maintain performance [42].
Isometric and eccentric muscle strength
Not surprisingly, elite alpine skiers demonstrate significant leg strength during
both isometric [55,73] and eccentric muscle actions [1,12,143]. When compared
to other athletes, alpine skiers tend to have superior strength during low angular
velocities and static muscle contractions [55,73,148]. However, as the angular
velocities increases, this difference diminishes, suggesting a specific
neuromuscular adaptation to loading patterns associated with skiing [148].
Studies conducted on elite and sub-elite skiers also indicate that more successful
skiers tend to have higher maximum torque output and smaller hamstring-to-
quadriceps (HQ) ratio compared to those competing at lower levels [1,19].
However, when scaled for body mass, differences in isometric and concentric
strength between these practitioners seem to be alleviated [1]. As such, eccentric
muscle strength has been proposed as a predictor of performance in alpine skiing
[12,143].
Movement speed
The skiing turn itself can be divided into various phases that typically include the
initiation, turning, completion and the transition phase [66]. Each of these
phases differs from each other in terms of physical and technical requirements
[66] and can, therefore, be categorized based on visual analysis or interpretation
of EMG data [59]. The execution of a complete turning cycle takes about 1.4 – 4.1
s (depending on discipline) [11] and usually requires a shift from isometric-
eccentric muscle work at the beginning of the turn to a concentric muscle action
at the completion and transition to the next [66]. During such transitions, the
average joint angle values of the hip, the inner leg and the outer leg ranges
between 81-129°, 101±16°, and 114±26° respectively [118]. In addition, contrary
to the notion that alpine skiing is an explosive sport, research shows that the joint
angular velocity at SL, GS, and SG usually are below 200° s-1 [59,74], which is
significantly slower compared to those of martial arts athletes and professional
sprinters who can reach 1000° s-1 [13,43].
8
Aerobic and anaerobic capacity
The relative importance of aerobic versus anaerobic capacity for alpine skiing
performance has long been a matter of debate [4,16,55,73,90,97,106,127,132,155].
For many years, high aerobic capacity was considered critical for the competitive
success of skiers [106,132], probably due to the high V̇O2max test results [73]
recorded on dominant skiing star Ingemar Stenmark [42,90]. While some
scientific findings support this assertion [55,97,127,159], others have failed to
establish aerobic capacity as a discriminating physiological variable between
skiers at different performance levels [19,154]. Instead, several researchers have
reported a correlation between anaerobic test results and skiing performance
[5,19,93,131,155,161,162].
Because a majority of alpine skiing racing events last between 45-120 s [42], both
the aerobic and the anaerobic energy systems will be utilized [42,66]. Studies
show that the average energy requirement during high-intensity skiing varies
between 80-200% of maximal oxygen uptake (V̇O2max) depending on discipline
[122,142,151]. When differentiating the total energy supply, calculations indicate
that the aerobic energy system accounts for 30-54% of the energy required during
a SL or GS run [122,151]. Studies also suggest that differences in ski technique
[21,121], mechanical and metabolic stress [41], as well as the overall skill of
athletes, affect the relative contribution of the energy systems [123,151]. As such,
these results further emphasize the intermediary relationship between the
aerobic and anaerobic abilities of alpine skiers compared to pronounced
endurance athletes [42].
Studies on alpine skiers show that the average V̇O2max values range from ~ 53 to
68 ml·min·kg-1 for males and ~ 46 to 57 mL·kg-1·min-1 for female athletes
[4,55,73,97,108,129,151]. Although many of these skiers exhibit relatively high
aerobic test values, even when compared to elite cyclists and distance runners
[54], high V̇O2max values are considered more a result of endurance training
adaptations than a representation of real demands of the sport [72].
The consensus of published studies seems to be that both aerobic and anaerobic
capacity is essential for maximum performance in alpine skiing. Although many
researchers suggest that skiers should focus on training aimed at increasing their
anaerobic capacity, the energy systems relative importance for racing
performance on a group level has not yet been established. While most scientific
findings indicate that aerobic capacity is not a limiting physiological factor for
competitive performance, complementary aerobic endurance training can still be
valuable as aerobic energy metabolism contributes up to ~50% of the energy
demands during a run. In addition, a high aerobic capacity will likely allow
athletes to tolerate higher blood lactate levels [80], faster recovery between
9
training runs and help to maintain competitive performance over an extended
period of time [97].
Balance
Todays' alpine skiing is highly demanding and therefore requires athletes to
display a wide range of spatial and proprioceptive abilities [111]. As skiing events
are often performed in challenging and ever-changing environments, athletes
must continuously compensate for high internal and external forces in order to
maintain balance and postural control [160]. As a result, a good balance has been
proposed as imperative for both competitive and recreational skiers [85,160]. In
sports such as basketball and soccer, an excellent balance can be essential in order
to prevent injuries [18]. However, if this also applies to alpine skiing, or if the
balance is important for the sport-specific performance of alpine skiers, remains
to be proven [65,102].
Biomechanical studies suggest that a poor balance can adversely affect skiing
performance [40,56], primarily by initiating compensatory body movements that
ultimately leads to less than optimal skiing technique and lower force production
[102]. While these results support the general perception of superior balance for
alpine skiers, others have shown that elite skiers often perform comparatively or,
in some cases, worse at both static and dynamic balance tests when compared to
less skilled counterparts [99]. These contradictory results may be due to
unspecific testing protocols (tests conducted in a laboratory environment) or that
the balance tests used were not challenging enough to distinguish between skiers
at different levels [102,140].
When balancing tests on experienced skiers are performed in skiing boots, the
balance focus of these athletes seems to be shifted from the ankles to more hip
involvement [98]. Also, experienced skiers seem to have similar balance
performance with and without skiing boots [98], suggesting sport-specific
balance abilities that cannot be tested using standard balance testing procedures
[102]. When more specific balance tests for alpine skiers were applied, they
proved reliable and sensitive, even for top-level skiers [102].
Collectively, testing the balance of alpine skiers in a laboratory environment is
complicated because of the complex demands of the sport. Skiing usually takes
place in a squatting position, which directs the balance towards the hip, while
common balance tests drive towards testing the ankles. During skiing, athletes
are subject to considerable internal and external forces, requiring constant
adjustment of the balance on the skis, which is hard to replicate in a laboratory
setting. Hence, to distinguish between competitive and recreational skiers,
balance tests must be both specific and highly challenging.
10
Core strength and core stability
Core strength and core stability are common topics in the field of sports
performance research [57,112,157]. Although the terms are often used as
synonyms, they are in reality fundamentally different [36,57]. Whereas core
strength generally refers to the ability of the trunk muscles to generate contractile
force [36], core stability can be defined as the ability of the spinal stabilization
system to maintain the lumbopelvic region in a physiologically neutral position
during static and dynamic conditions [101]. Today, core strength and core
stability training is an integral part of many sports conditioning programs [24,70]
and is generally considered important also for alpine skiers [51,58,66,97].
Studies of core strength and core stability on alpine skiers have mainly included
tests of isometric back flexion and extension strength [105,110]. While some of
these studies have indicated that core training may be necessary for injury
prevention [84,110], no one has yet been able to establish core strength or core
stability as important physiological qualities for competitive performance [58].
However, these results are not surprising since alpine skiing is a highly dynamic
sport [58,66] that does not require a significant contribution from muscle
structures such as external obliques and rectus abdominis [59] (which indirectly
suggests a lack of specific testing procedures) [57]. Furthermore, as for other
sports, the importance of core strength and core stability for alpine skiing
performance can be difficult to determine because core training is practically
never performed in isolation and therefore cannot be established as the
performance-enhancing factor [112].
Despite the lack of substantial scientific evidence supporting core strength and
core stability as essential for competitive performance, alpine skiers (especially
those with limited on-snow training time) may still benefit from specific core
training as the core muscles help to maintain balance and control during skiing
[58,66,160]. However, even though specific core training is also suggested to have
other benefits (such as injury prevention) [84,110], alpine skiers should probably
focus mostly on free-weight exercises such as squats and deadlift as these seem
to entail higher core muscle activation and greater overall improvement of
athletic performance [10].
Flexibility
Flexibility in an anatomical context refers to the range of motion (ROM) in joints
and is reflected by the ability of an individual to move without any
musculoskeletal limitations [76]. In alpine skiing, sufficient flexibility
presumably allows athletes to adopt an efficient skiing position without undue
strain on stabilizing structures [42]. In a study by Song [127], junior skiers were
found to have higher overall flexibility in investigated structures compared to age-
11
matched controls. While some have suggested flexibility as an essential quality
for alpine skiers [42], studies have yet to find a difference between practitioners
across performance levels [4,19].
Physiological testing in alpine skiing
Physiological testing in alpine skiing is common [66,109], yet the practical
usefulness can be questioned, which is discussed throughout this thesis. One
crucial aspect to understand is the complexity of testing physiological capacities.
Often a wide range of different tests are required to evaluate physiological
qualities, potentially affecting performance. In addition, many tests are
correlated (e.g., intercorrelated) and therefore, in an overall assessment of
physiological performance, only one representative test for each capacity may be
needed (Figure 2). This can be referred to as a reduction of dimensions, and in
statistical terms deemed principal component analysis (PCA), which we will
return to later.
Although elite athletes often perform better on certain physiological tests when
compared to their less skilled counterparts, these findings are rarely an adequate
measure of true sport-specific performance.
Figure 2. Correlation between Countermovement Jump and Squat Jump.
A significant positive correlation was observed between Countermovement Jump and Jump Squat (R2 = 0.90, p =
<.001). This correlation indicates that as Countermovement Jump increases, Squat Jump tends to increase. Thus,
only one of the tests are needed, or useful, in an overall evaluation of lower-body power.
A selection of studies that have investigated the correlation of physiological and
anthropometric variables to competitive performance in alpine skiing can be
28
30
32
34
36
38
40
42
44
46
26 28 30 32 34 36 38 40 42 44
CM
J (
cm
)
SJ (cm)
12
found in Table 1-2. As this thesis focuses on variables that affect skiing
performance, only studies that met the following criteria were selected:
• Physiological performance and anthropometric variables correlated to
sport-specific performance (points/ranking or time trials)
• Non-descriptive data (e.g., comparisons between skiers on different
performance levels)
In addition, studies were selected based on the following criteria:
• Subjects ≥ 14 years
• Peer-reviewed
• More than one publication per variable
The importance of the physiological variable was assessed as Yes (Y) and No (N):
• For P rs ’s rr ff (in the following referred to as
P rs ’s rr ); Y if variables were described as important,
correlated and/or significant by the authors
• Correlation values outside the range r = ±0.5 were considered important
• For Multiple linear regression; Y if the variable was included in the model
Table 1. Physiological performance variables correlated to sport-specific performance (points/ranking or time trials).
Reference Sex (n) Level/Age Outcome Statistics Important
Y/N (notes)
Lateral Box Jump 90-s
Andersen, Montgomery and Turcotte [5]
M (33) National+
Club Time trail
P rs ’s correlation
Y
Klika and Malina [77] M (21) Junior +
National USSA
ranking
Multiple linear
regression
Y
F (17) Y
von Duvillard and Knowles [162]
M (12) Junior
USSA ranking
P rs ’s correlation
N (SL) Y (GS)
N (SG) Y (DH)
F (14)
Aerobic Capacity
Haymes and Dickinson [55]
M (12) Elite FIS ranking
P rs ’s correlation
N
F (13) Y (DH)
Neumayr, Hoertnagl, Pfister, et al. [97]
M (28) Elite FIS ranking
Simple linear
regression
Y (Speed group, 1998)
F (20) N
Miura and Miura [94]
Junior elite M&F
(31) Junior + National
SAJ (Japan) Points
P rs ’s correlation
Y (SL) Y (GS)
High School
elite M&F (41)
Y SL) Y (GS)
13
Song [127] M (9) Junior Time trail P rs ’s
correlation Y (DH N (GS)
Hexagonal test
Andersen, Montgomery and Turcotte [5]
M (33) National+
Club Time trail
P rs ’s correlation
Y
Klika and Malina [77] M (21) Junior +
National
USSA ranking
Multiple linear
regression
N
F (17) N
Vertical Jump
Andersen, Montgomery and Turcotte [5]
M (33) National+
Club Time trail
P rs ’s correlation
Y
Emeterio and Gonzalez-Badillo [32]
M (16) Junior
Spanish FEDI
ranking
P rs ’s correlation
Y
F (12) N
Haymes and Dickinson [55]
M (12) Elite
USSA ranking
P rs ’s correlation
Y (GS)
F (13) N
Klika and Malina [77]
M (21) Junior +
National USSA
ranking
Multiple linear
regression
N
F (17)
Y
von Duvillard and Knowles [162]
M&F (26) National USSA
ranking P rs ’s
correlation
N (SL) Y (GS) N (SG) Y (DH)
Repeated Vertical Jumps
Emeterio and Gonzalez-Badillo [32]
M (16) Junior
Spanish FEDI
ranking
P rs ’s correlation
Y (average cm)
F (12) N
Patterson, Platzer and Raschner [103]
F (10) Elite FIS ranking P rs ’s
correlation Y (season 4)
Wingate Bicycle test
Andersen, Montgomery and Turcotte [5]
M (33) National+
Club Time trail
P rs ’s correlation
Y (mean power)
Bacharach and von Duvillard [7]
M (10) National
USSA ranking
P rs ’s correlation
Y (SL, 90-s) Y (GS, 90-s)
F (8) Y (SL, 30 and 90-s) Y (GS, 30 and 90-s)
Emeterio and Gonzalez-Badillo [32]
M (16) Junior
Spanish FEDI
ranking
P rs ’s correlation
Y (mean power)
F (12) N
Song [127] M (9) Junior Time trail P rs ’s
correlation Y (DH, mean power)
N (GS)
von Duvillard and Knowles [162]
M&F (26) Junior USSA
Ranking P rs ’s
correlation
N (SL) Y (GS) N (SG) Y (DH)
M = Male; F = Female; Junior = age 14-17; Club = Skiers at club level; National = Skiers at national level; Elite = Skiers at international level
14
Table 2. Anthropometric variables correlated to sport-specific performance (points/ranking or time trials).
Reference Sex (n) Level/Age Outcome Statistics Important
Y/N (notes)
Body fat percentage
Aerenhouts [3] M (58)
Elite FIS ranking P rs ’s
correlation N
F (26) N
Haymes and Dickinson [55]
M (12) Elite
USSA ranking
Pears ’s correlation
Y (SL and DH)
F (13) Y (DH)
Miura and Miura [94]
Junior elite M&F
(31) Junior + National
SAJ (Japan) Points
P rs ’s correlation
N (SL) N (GS)
High School
elite M&F (41)
N (SL) N (GS)
Miura [93] M (14)
National + Elite
FIS ranking P rs ’s
correlation
Y (SL) Y (GS)
F (10) N (SL) N (GS)
Neumayr, Hoertnagl, Pfister, et al. [97]
M (28) Elite FIS ranking
Simple linear
regression
N
F (20) N
Body weight
Aerenhouts [3] M (58)
Elite FIS ranking P rs ’s
correlation
Y
F (26) N
Neumayr, Hoertnagl, Pfister, et al. [97]
M (28) Elite FIS ranking
Simple linear
regression
N
F (20) N
Haymes and Dickinson [55]
M (12) Elite
USSA ranking
P rs ’s correlation
Y (SL)
F (13) N
Klika and Malina [77] M (21) Junior +
National USSA
ranking
Multiple linear
regression
Y
F (17) Y
Miura and Miura [94]
Junior elite M&F
(31) Junior + National
SAJ (Japan) Points
P rs ’s correlation
N (SL) Y (GS)
High School
elite M&F (41)
Y (SL) Y (GS)
Miura [93] M (14)
National + Elite
FIS ranking P rs ’s
correlation
N (SL) N (GS)
F (10) N (SL) N (GS)
Vermeulen, Clijsen, Fässler, et al. [152]
M (34) Elite FIS ranking
P rs ’s correlation
Y (DH)
F (24) Y (DH)
Body Mass Index
Aerenhouts [3] M (58)
Elite FIS ranking P rs ’s
correlation Y
F (26) N
Emeterio and Gonzalez-Badillo [32]
M (16) Junior
Spanish FEDI
ranking
P rs ’s correlation
-
F (15) -
Neumayr, Hoertnagl, Pfister, et al. [97]
M (28) Elite FIS ranking
Simple linear
regression
N
F (20) N
Vermeulen, Clijsen, Fässler, et al. [152]
M (34) Elite FIS ranking
P rs ’s correlation
N F (24) N
Body stature
15
Aerenhouts [3] M (58)
Elite FIS ranking P rs ’s
correlation
Y (SL) N (DH)
F (26) N (SL and DH)
Neumayr, Hoertnagl, Pfister, et al. [97]
M (28) Elite FIS ranking
Simple linear
regression
N
F (20) N
Klika and Malina [77] M (21) Junior +
National USSA
ranking
Multiple linear
regression
N
F (17) N
Miura and Miura [94]
Junior elite M&F
(31) Junior + National
SAJ (Japan) Points
P rs ’s correlation
Y (SL) Y (GS)
High School
elite M&F (41)
N (SL) N (GS)
Miura [93] M (14)
National + Elite
FIS ranking P rs ’s
correlation
N (SL) N (GS)
F (10) N (SL) Y (GS)
Vermeulen, Clijsen, Fässler, et al. [152]
M (34) Elite FIS ranking
P rs ’s correlation
N F (24) N
Lean body mass
Emeterio and Gonzalez-Badillo [32]
M (16) Junior
Spanish FEDI
ranking
P rs ’s correlation
Y (calculated muscle mass)
F (15)
N
Haymes and Dickinson [55]
M (12) Elite
USSA ranking
P rs ’s correlation
Y (SL)
F (13) N
Miura and Miura [94]
Junior elite M&F
(31) Junior + National
SAJ (Japan) Points
P rs ’s correlation
N (SL) N (GS)
High School
elite M&F (41)
N (SL) N (GS)
Miura [93] M (14)
National + Elite
FIS ranking P rs ’s
correlation
N (SL) N (GS)
F (10) N (SL) N (GS)
Somatotype
Aerenhouts [3] M (58)
Elite FIS ranking P rs ’s
correlation N
F (26) N
Emeterio and Gonzalez-Badillo [32]
M (16) Junior
Spanish FEDI
ranking
P rs ’s correlation
-
F (15) -
Vermeulen, Clijsen, Fässler, et al. [152]
M (34) Elite FIS ranking
P rs ’s correlation
N
F (24) N
Skinfold
Emeterio and Gonzalez-Badillo [32]
M (16) Junior
Spanish FEDI
ranking
Pears ’s correlation
-
F (15) -
Klika and Malina [77] M (21) Junior +
National USSA
ranking
Multiple linear
regression
Y
F (17) N
Vermeulen, Clijsen, Fässler, et al. [152]
M (34) Elite FIS ranking
P rs ’s correlation
N
F (24) Y (DH) M = Male; F = Female; Junior = age 14-17; Club = Skiers at club level; National = Skiers at national level; Elite = Skiers at international level
16
Summary
In summary, competitive events in alpine skiing are organized and regulated by
FIS. Competitions for elite alpine skiers include; FIS World Championships, FIS
World Cups, Continental Cups, and the Winter Olympic Games. Disciplines in
alpine skiing all differ from one another in terms of turning radius, the length of
the course and the vertical distance between the gates. The competitive season
for elite athletes is generally in progress from October/November until mid-
March. Elite alpine skiers usually train and compete for 130-150 days in a year.
On-snow training is often conducted during the morning and often consists of
between 3-12 runs depending on the discipline. During skiing, athletes are
subjected to immense forces, causing significant contribution from, and
coordination of, several interacting muscle structures. Alpine skiing requires a
large number of isometric and eccentric muscle contractions. In contrast to a
common belief, alpine skiing cannot be characterized as an explosive sport.
The career of professional alpine skiers appears to get longer. Alpine skiers also
seem to have become heavier and taller, which may be due to changes in skiing
technique and the induction of breakaway poles. Both the aerobic and the
anaerobic energy systems are utilized during a run, but the relative importance of
each energy system for alpine skiing performance is still a matter of debate. While
balance, core strength, core stability, and flexibility have been proposed as
important physiological qualities for competitive performance, current scientific
findings do not support these assertions. Physiological tests can be important for
governing organizations as well as for individual athletes. However, to be useful
for world-class competitors, a physiological test battery should be validated
against sport-specific performance. To date, only a few studies have investigated
the correlation between a combination of physiological test results and skiing
performance in alpine skiing (indicated by points/ranking or time trials).
17
The rationale for the thesis
The notion that specific physiological qualities are necessary for alpine skiing
performance seems to lack solid scientific evidence. While several studies have
found a correlation between individual physiological test results and
points/ranking or time trails, few have examined the predictive power of a
combination of test results (Table 1-2). Thus, the optimal combination and the
true importance of physiological test results remain to be determined. To our
knowledge, no previous studies have approached this issue using multivariate
data analysis (MVDA). Hence, the scientific rationale for this doctoral thesis was
to identify physiological and anthropometric variables valid for prediction of
competitive performance in alpine skiing using a multivariate statistical
approach.
18
Aims
The overall aim of this doctoral thesis was to identify physiological and
anthropometric variables valid for prediction of competitive performance
(indicated by FIS points).
Specific Aims
Paper I to investigate the predictive power of aerobic test results and
anthropometric variables for competitive performance (indicated
by FIS points) of junior elite male and female alpine skiers.
Paper II to investigate the predictive power of the national test battery of
the Swedish Olympic Committee (Fysprofilen) and
anthropometric variables for competitive performance (indicated
by FIS points) of senior elite female alpine skiers.
Paper III to investigate the predictive power of a novel physiological test
battery, or by individual physiological profiling, for competitive
performance (indicated by FIS points) of senior elite female alpine
skiers.
19
Materials and Methods
Study design
Paper I-III in this doctoral thesis followed an experimental, hypothesis-
generating design which included both junior and senior elite alpine skiers. In all
papers, physiological and anthropometric test results (X-variables) were
correlated with FIS points (Y-variables) in order to investigate the predictive
power of physiological and anthropometric variables for competitive
performance in alpine skiing (Figure 3). The importance of the included tests was
examined using bivariate and multivariate data analysis. Selection of tests was
based on the following:
• Paper I - The importance of aerobic capacity for alpine skiing
performance has long been a matter of debate. Whether disagreements
are due to inadequate predictive power of aerobic capacity for skiing
performance, or the use of less than optimal statistical methods is
unclear. For the prediction of competitive performance of junior elite
alpine skiers, we applied multivariate data analysis on commonly used
aerobic test results.
• Paper II - Lack of predictive power using aerobic test results in Paper I,
led us to the assumption that this was due to the inclusion of results
recorded during only one physiological performance test. Therefore, we
chose to analyze a commonly used test battery in Sweden (Fysprofilen)
for prediction of competitive performance of senior elite female alpine
skiers.
• Paper III - As a more complex test battery (Fysprofilen) could not predict
alpine skiing performance, we chose to apply a novel test battery on
senior elite female alpine skiers. The new test battery was based on
previous research [5,15,32,88,127,162] and physiological reasoning
regarding the performance demands in competitive alpine skiing.
20
Figure 3. Overall study design and project stages. The "X" marked boxes list physiological and anthropometric tests
that were carried out during each study. The "Y" marked boxes list outcome variables that were used as an indication
of competitive performance in each study. The arrow indicates the order in which the studies were conducted.
Participants
Physiological test results from two sample groups were included in this thesis.
The participants included in Paper I consist of a total of twenty-three (n = 23)
junior elite male and female alpine skiers (Table 3), and the participants included
in Paper II-III consist of a total of fourteen (n = 12-14) senior elite female alpine
skiers (all members of the Swedish national alpine ski team, which was the
inclusion criteria) (Table 3). Participating athletes were tested multiple times
each season dependent on training and racing schedule as well as federation
decisions. Prior to each test occasion, all participants underwent a brief medical
examination and completed a health questionnaire. Exclusion criteria for
participation during each occasion were indicated forms of health status (e.g.,
disease or injury) or use of medication that could potentially adversely affect their
health or performance. After being informed of the risks and possible discomforts
that could occur during testing, all participants provided their written consent for
participation, as stated in the corresponding ethical approval form.
Paper I P r P r
r s r s s
Anthropometric testsBody stature Body mass
Anthropometric testsBody stature Body mass
Anthropometric testsBody stature Body mass
Body composition
Physiological performance tests1RM Back squat
1RM Bench press1RM Cleans
Hand Grip StrengthPull-ups
Brutal BenchHarre's test
20m Sprint testSquat Jump
Countermovement JumpCountermovement Jump with arm-swing
Peak Oxygen Uptake
Physiological performance testsHand Grip StrengthIllinois Agility test
Countermovement JumpForce-Velocity Bike Test
Y Balance s ™Lateral Box Jump 90-s
500m Rowing Ergometer TestPeak Oxygen Uptake
s
Project stages
X X
Y
X
Competitive performanceFIS points in SL and GS
Competitive performanceFIS points in SL and GS
21
Table 3. Descriptive values of participants included in Paper I-III
Level Paper Sex (age/n) Body mass (kg) Body stature (cm)
Junior elite Paper I
M (age 17, n = 10) 75.4 ± 5.3 178 ± 5
M (age 16, n = 13) 69.3 ± 5.5 178 ± 5
F (age 17, n = 6) 68.1 ± 3.7 170 ± 6
F (age 16, n = 10) 69.7 ± 3.4 172 ± 4
Senior elite Paper II F (n = 14) 67.4 ± 3.6 171 ± 5
Paper III F (n = 12) 67.9 ± 3.7 172 ± 5
M = Male; F = Female; Junior elite= Skiers at junior elite level; Senior elite = Skiers at senior elite level. Data as Mean ± SD.
Testing procedures
Physiological tests were conducted at the Section of Sports Medicine, Umeå
University, Sweden (Paper I-III) and Bosön (the development center for the
Swedish Sports Confederation), Lidingö, Sweden (Paper II). For Paper I,
physiological testing was conducted during the summer break (June-July) and
the autumn term (October-December). For Paper II-III, all physiological testing
was performed either immediately after the previous (April-May) and prior to the
upcoming competitive season (August-October). All physiological performance
tests included in Paper II were performed according to guidelines described in
the testing procedures for Fysprofilen [138]. In brief, Fysprofilen is a
physiological test battery developed by the Swedish Olympic Committee and
consists (for alpine skiing) of thirteen different performance tests.
Participants were instructed to avoid strenuous physical activities and adhere to
the same routines regarding, e.g., sleep, water, and nutrition intake during the 24
hours before each test occasion. Prior to any physiological performance testing,
participants performed a ~10-15 minutes warm-up protocol consisting of low-
intensity jogging or cycling, lateral strides, jumps, sprints, and dynamic
stretching exercises. Before each specific test, a self-regulated exercise-specific
warm-up was also performed. These warm-ups were carried out with low to
moderate intensity (e.g., before rowing 500m) or at low to moderate load (e.g.,
before back squats). Verbal encouragement was provided during all physiological
performance testing to encourage participants to perform at their maximum
potential. All performance tests were carried out with maximum effort or to
volitional exhaustion. Physiological test results were assigned as X-variables in
the statistical analyses.
One Repetition Maximum tests
The assessment of One Repetition Maximum (1RM) performance (tested with
Back squats, Bench press and Cleans) was conducted using factory calibrated
competition weights and barbells from Eleiko (Eleiko Sport, Halmstad, Sweden)
22
(Paper II). All 1RM performance tests were carried out using an incremental
loading protocol (suggested load increase was (set x reps/percent of 1RM): 1 x
8/50%, 1 x 6/60%, 1 x 5/70%, 1 x 3/80 %, 1 x 2/90%, 1 x 1/100, 1 x 1/102%).
Attempts were conducted until failure or voluntary discontinuation of more
attempts on higher loads. The recovery time between attempts was standardized
≥ 5 m s.
A Back squats attempt was approved when the participants descended, with the
top of their femur parallel to the floor, and back up to the starting position. A
Bench press attempt was approved when the participants descended the barbell,
from fully extended arms, down to the chest and back up to the starting position
without bouncing the barbell on the sternum or by lifting the back of the bench.
A Cleans attempt was approved when participants managed to clean the barbell
in one explosive motion from the initial starting position, up to the anterior
deltoids and catch it in an upright position. The best performance (kg) for each
1RM test was recorded. The 1RM tests for back squats [26,124,150], bench press
[115,124], and cleans [26,35] are considered safe and reliable tests (ICC ≥ 0.91)
for assessing strength in a number of different populations.
Hand Grip Strength
Maximal isometric hand grip strength was tested unilaterally using calibrated
hand grip dynamometers (T.K.K. 5401 GRIP D (Takei Scientific Instruments Co.,
Ltd., Niigata, Japan) (Paper II) and Jamar Hydraulic Hand Grip Dynamometer
(Patterson Medical, Warrenville, IL, USA) (Paper III)). A Hand Grip Strength test
was approved when the participants squeezed the dynamometer with as much
force as possible for 3 seconds. The best performance (kg) out of three attempts
on each hand was recorded. The Hand Grip Strength test is considered a valid
and reliable method (r ≥ 0.9 r r s r s and ICC = 0.86–0.97,
respectively) for monitoring hand grip strength of athletes [30].
Pull-ups
The Pull-ups test was conducted using a regular pull-up bar with a pronated hand
grip position slightly wider than shoulder width (Paper II). A Pull-ups repetition
was approved when the participants pulled themselves up until the chin was
above the bar, without kipping of the body or legs or by changing the hand grip.
The total number (n) of approved repetitions was recorded. The Pull-ups test is
considered a reliable method (ICC = 0.96-0.99) for assessing upper extremity
pulling strength [29].
23
Brutal Bench
The Brutal Bench test was conducted using a regular brutal bench (Paper II). A
Brutal Bench repetition was approved when the participants managed to get up
from the starting position, mark with their elbows against their knees and down
back again without bouncing against the backrest in the bottom position. The
total number (n) of approved repetitions was recorded. To our knowledge, the
Brutal Bench test has not yet been validated.
Change of Direction Speed
The assessment of Change of Direction Speed (CODS) performance (tested with
H rr ’s s s g y s ( )) w s s g v
timing systems (PF MuscleLab MA4020e (Ergotest Innovation AS, Porsgrunn,
Norway) [137] and IVAR Jump and Sprint system (Spin Test, Tallinn, Estonia)
[23]). All CODS tests were performed three times with ≥3 minutes of rest between
each attempt.
H rr ’s s w s f w g r r s s r Hoyek,
Champely, Collet, et al. [63] (Paper II). A Harre's test attempt was approved when
the participants completed the course as fast as possible without touching the
center cone or the hurdles. The IAT test was conducted following the procedures
described in Lacy [82] (Paper III). An IAT test attempt was approved when the
participants completed the course as fast as possible without touching the
markers or by taking a shortcut. The best performance (s) for each CODS test was
recorded. r k w g , H rr ’s s s y been validated. The IAT
is considered a reliable test (ICC = 0.85–0.98) for the assessment of CODS
performance [53].
20m Sprint test
The assessment of 20m Sprint performance was conducted using the same timing
equipment as for the CODS tests (Paper II). A 20m Sprint attempt was approved
when the participants completed 20 meters as fast as possible. The best
performance (s) was recorded. The 20m Sprint test is considered a reliable test
(ICC = 0.91) for the assessment of sprint performance [95].
Jump tests
The assessment of jump performance (tested with Squat Jump (SJ),
Countermovement Jump (CMJ) and Countermovement Jump with arm-swing
(CMJa)) was conducted using validated infrared jump mat systems (PF
MuscleLab MA4020e (Ergotest Innovation AS, Porsgrunn, Norway) and IVAR
Jump and Sprint system (Spin Test, Tallinn, Estonia)). All jump tests were
24
performed as single jumps three times with ≥3 minutes of rest between each
attempt.
The SJ test was conducted from a squatting position (at 90° knee angle) with the
hands firmly placed on the hips (Paper II). A SJ test attempt was approved when
participants, on the test leader's command and without countermovement,
jumped as high as possible and landed with normal knee flexion in the same
position as the take-off. The Countermovement Jump tests were conducted from
an upright position: the CMJ with the hands firmly placed on the hips and with
countermovement (Paper II-III), the CMJa with countermovement and arm-
swing (Paper II). A Countermovement Jump test attempt was approved when
participants, on the test leader's command, jumped as high as possible and
landed with normal knee flexion in the same position as the take-off. The best
performance (cm) for each jump test was recorded. The SJ and the CMJ are both
considered reliable tests (ICC = 0.91-0.93) for the assessment of jumping
performance [95].
Force-Velocity Bike Test
The Force-Velocity Bike Test (FVBT) was conducted from a standing start against
a breaking weight equivalent to 2, 4, 6, 8, 10 and 12% of participants body weight
using a friction-loaded Monark 894E Peak Bike (Monark Exercise, Varberg,
Sweden) (Paper III). s r s w r r m z r r w ≥5
minutes of rest between each attempt. Peak power in absolute measurement (W)
(PP), peak power relative to body weight (W·kg-1) (RPP) and time to peak power
(TTPP) for each sprint were recorded using Monark ATS Software (Monark
Exercise, Varberg, Sweden). The best performance (PP, RPP, and TTPP) for 10
seconds during all sprints were used in the statistical analysis. The FVBT is
considered a reliable test (ICC = 0.94-0.98) for the assessment of power in the
lower extremities [68].
Y Balance Test™
The assessment of balance performance was conducted following the procedures
described in Plisky, Gorman, Butler, et al. [107] using a s K ™
(FunctionalMovement, Danville, VA, USA) (Paper III). A s ™ (YBT)
attempt was approved when the participants, on the test leader's command,
managed to reach out as far as possible and back without losing their balance or
by lifting their heels from the center platform or by releasing their hands from
their hips. The best performance (cm) on each foot in each of the three directions
(anterior, posteromedial and posterolateral) was recorded. The YBT is considered
a reliable test (ICC = 0.85-0.93) for the assessment of balance performance [126].
25
Lateral Box Jump 90-s
The Lateral Box Jump 90-s (LBJ90) test was conducted following the procedures
described in von Duvillard and Knowles [162] using a wood-frame box with the
dimensions of 40 cm high x 60 cm long x 50 cm wide (Paper III). A LBJ90
repetition was approved when the participants, starting from the top of the box,
performed a lateral jump with both feet down on one side and then back up to the
top of the box. The total number (n) of approved repetitions (back and forth over
the box) for 90 seconds was recorded. The LBJ90 is considered a reliable test for
the assessment of jump strength endurance of both male (ICC = 0.90) and female
(ICC = 0.87) athletes [110].
500 meters Rowing Ergometer Test
The 500 meters Rowing Ergometer Test (500mRET) was conducted following the
procedures described in Lindberg, Oksa, Gavhed, et al. [88] using a Concept2
Model D Indoor Rower (Concept2, Morrisville, VT, USA) (Paper III). The test was
performed from a standing start using the highest resistance (spiral damper
setting at 10). The time (s) to complete 500 meters, peak power (W) (PP) and
mean power (W) (MP) was recorded. The 500mRET is considered a reliable test
for the assessment of rowing performance (ICC = 0.99-1.0 to 0.80-0.98 for power
output and overall rowing time, respectively) [128].
Peak Oxygen Uptake
The Peak Oxygen Uptake (V̇O2peak) tests was conducted using either a
programmable cycle ergometer (Monark 839E, Monark Exercise, Varberg,
Sweden) (Paper I-III) or a running treadmill (Rodby RL2500E, Rodby
Innovation, Hagby, Sweden) (Paper II-III). During both protocols, pulmonary
gas exchange was measured continuously using a calibrated Oxycon Jeager Pro
(mixing chamber mode setting) (CareFusion, Hoechberg, Germany). The highest
mean value for VO2 for 30 seconds was considered maximum and recorded as
V̇O2peak in both absolute rate (L·min-1) (Paper I) and the relative rate (mL·kg-
1·min-1) (Paper I-III). Heart rate (HR) was monitored using a Polar Electro S610i
(Polar Electro Oy, Kempele, Finland) (Paper I-III). Fingertip blood lactate
samples were collected during the last 30 seconds on each load (3-minute stages)
and analyzed using a YSI 1500 Sport Lactate Analyzer (YSI Life Sciences, Yellow
Springs, OH, USA) (Paper I).
For Paper III, V̇O2peak was also estimated using the following prediction formula:
V̇O2peak (mL·kg-1·min-1) = 124.165 + (-0.748 * 500mRET (s)).
26
Anthropometric tests
For assessment of anthropometric variables, participants were instructed to wear
only light clothing (e.g., t-shirt, sports bra, shorts, and tights). Body mass was
measured to the nearest 0.1 kilograms using a standard digital weight scale
(Soehle weights, Leifheit AG, Nassau, Germany) (Paper I-III). Body stature was
measured to the nearest 0.1 centimeters using a wall-mounted body length meter
(Fosamax stadiometer, Merck & Co. Inc., New Jersey, USA) (Paper I-III). Body
mass index (BMI) (Paper I) was calculated using the following formula: BMI =
body mass (kg) · stature (m)2. Body composition was measured using a phantom
calibrated Dual-energy x-ray absorptiometry (DXA) Lunar iDXA scanner (GE
Medical Systems Lunar, Madison, WI, USA; Encore Version 14.10.022) (Paper
III). Due to a large number of variables recorded during a DXA scan, PCA was
used to reduce the number of dimensions in the dataset. After visual inspection
of the PCA plot, the following variables were selected as they appeared relevant:
the total amount of fat mass (g), lean body mass (g), tissue mass (g), bone mass
and body fat in percentage (%). Dual-energy x-ray absorptiometry is considered
a valid method for the assessment of body composition in a variety of populations
[2,14,119]. Anthropometric data were assigned as X-variables in the statistical
analyses.
Fédération Internationale de Ski points
Fédération Internationale de Ski points used in this thesis were extracted from
FIS point score lists published in December (6th list) and in April (11th list) each
year (Paper I-III). The lists were selected because (1) they were published during
the ongoing competition season, (2) results from the first competition in both
disciplines were included and, (3) they represent a change in points/ranking data
over time (Table 4). The points lists were obtained from the FIS official website
at https://www.fis-ski.com/DB/alpine-skiing/fis-points-lists.html. Fédération
Internationale de Ski points were assigned as Y-variables in the statistical
analyses.
27
Table 4. Spearman’s correlation matrix (values as Spearman's rs) between FIS points lists in SL and GS for Swedish female skiers published during the season 2013/2014.
Discipline List 1 2 3 4 5 6 7 8 9 10 11 12
SL
2 1.00 -
3 1.00 1.00 -
4 1.00 1.00 1.00 -
5 1.00 1.00 1.00 1.00 -
6 1.00 1.00 1.00 1.00 1.00 -
7 0.98 0.98 0.98 0.98 0.98 0.98 -
8 0.97 0.97 0.97 0.97 0.97 0.97 1.00 -
9 0.97 0.97 0.97 0.97 0.97 0.97 1.00 1.00 -
10 0.94 0.94 0.94 0.94 0.94 0.94 0.99 0.99 0.99 -
11 0.94 0.94 0.94 0.94 0.94 0.94 0.99 0.99 0.99 1.00 -
12 0.94 0.94 0.94 0.94 0.94 0.94 0.99 0.99 0.99 1.00 1.00 -
13 0.94 0.94 0.94 0.94 0.94 0.94 0.99 0.99 0.99 1.00 1.00 1.00
GS
2 1.00 -
3 1.00 1.00 -
4 0.99 0.99 0.99 -
5 0.95 0.95 0.95 0.96 -
6 0.94 0.94 0.94 0.95 0.99 -
7 0.94 0.94 0.94 0.95 0.99 1.00 -
8 0.83 0.83 0.83 0.85 0.95 0.96 0.96 -
9 0.83 0.83 0.83 0.85 0.95 0.96 0.96 1.00 -
10 0.83 0.83 0.83 0.85 0.94 0.95 0.95 0.96 0.96 -
11 0.74 0.74 0.74 0.76 0.85 0.86 0.86 0.85 0.85 0.89 -
12 0.74 0.74 0.74 0.76 0.85 0.86 0.86 0.85 0.85 0.89 1.00 -
13 0.74 0.74 0.74 0.76 0.85 0.86 0.86 0.85 0.85 0.89 1.00 1.00
Correlated lists contained only skiers who were listed as active. The critical values are 0.58, 0.71, and 0.82 for significance levels .05, .01, and .001 respectively. Lists published during other seasons may correlate between each other differently.
Statistics
Statistical calculations were conducted using JMP 13.1-14.0 (SAS Institute Inc,
Cary, NC, USA) and SIMCA 14.0-16.0 (Sartorius Stedim Data Analytics AB,
Umeå, Sweden).
For Paper I-II, Shapiro-Wilk goodness of fit tests investigated data distributions
of physiological performance tests and anthropometric variables. Data were
considered normally distributed if p > 0.05. For Paper III, distribution was
assessed using SIMCA's built-in tests for normality (skewness). The following
variables had skewed distributions: Pull-ups (W = 0.94, p = 0.004) (Paper II),
Stature (cm), Hand Grip Strength R (kg), YBT Posteromedial H (cm) and YBT
Posterolateral L (cm) (Paper III). Variables that were skewed (based on an alpha
of 0.05) were log10 transformed to normality. After transformation, all variables
exhibited a normal distribution.
28
Bivariate analysis
A Spearman’s rank correlation analysis was conducted in order to examine the
relationship between physiological test results and FIS points in both SL and GS
(Paper I). Cohen's standard was used to evaluate the strength of the relationship:
levels of effect size between 0.10 and 0.29 were classified as small, levels of effect
size between 0.30 and 0.49 were classified as moderate, and levels of effect size
above 0.50 was classified as large. Spearman’s rank correlation is a
nonparametric test that estimates how well the relationship between two
variables can be described in the form of a monotonic function [27].
A Mann-Whitney U two-sample rank-sum test was performed in order to
investigate whether there were significant differences in physiological
performance (indicated by Fysprofilen Index) between the levels of ranking
(Lowest and Highest) in both SL and GS (Paper II). The Mann-Whitney U two-
sample rank-sum test is a nonparametric alternative to the independent samples
t-test but does not share the same assumptions of normal distributions [27].
Multivariate analysis
Principal component analysis was conducted in order to identify representative
body composition variables (Paper III) and to detect trends and outliers in the
data (Paper I-III). Principal component analysis is a statistical method used to
identify trends and patterns as well as for visualizing and describing similarities
and differences between variables in a multivariate dataset [34].
Orthogonal projections to latent structures (OPLS) was performed in order to
examine regression and prediction of competitive performance (indicated by FIS
points) from physiological test results (Paper I-III) (Figure 4). R2 (goodness of
fit) and Q2 (goodness of prediction) measures were calculated for the overall
assessment of all models. R2 and Q2 >0.60 (less than ≤0.20 difference between
R2 and Q2) were considered valid [86] (Paper I). DmodX and H g’s 2 range
plots were used to visually access the statistical significance of fitted models
(Paper II-III). Shared and Unique Structures (SUS)-plots were generated in
order to compare individual OPLS SL and GS models (Paper III). Cross-
validation by permutation was used to visually assess the validity of each OPLS
model (Paper I-III). In brief, when conducting a permutation test, class labels are
permuted and assigned in a random order to different observations [153]. With
incorrectly assigned class labels, a new model is then calculated. The assumption
when conducting a permutation test is that permuted models should not be able
to predict data well. Because all new models are based on random assignment of
class labels, there should be no difference between them [153]. Thus, an original
model with lower predictive power than a certain number of permuted models is
probably not valid. All data were scaled according to Univariate (UV) scaling
29
(Paper I-III). Orthogonal projections to latent structures is a robust statistical
method used to generate models that describe the relationship between sets of
variables in a multivariate dataset [34].
Figure 4. A simplified description of the OPLS procedure. R2 is a measurement of goodness of fit. The procedure is
used to estimate the relationship between one dependent variable (Y) and one or more independent variable (X). Q2
is a measurement of goodness of prediction. Q2 is calculated via cross-validation as follows: the dataset is divided
into seven parts, where 1/7th of the data is removed each cycle in a systematic order. The remaining 6/7th of the data
is then used to predict a new model. This procedure is repeated until a complete set of new models has been
predicted. Thereafter, cumulated data is compared with the original model for the calculation of Q2. The arrows
indicate the theoretical order of the procedures.
Ethical statement
The ethical committee for Northern Sweden at Umeå University granted ethical
permissions for Paper I (Dnr 2011-236-31M) and Paper II-III (Dnr 2016-260-
31M) and the studies were conducted according to the guidelines expressed in the
Declaration of Helsinki – Ethical Principles for Medical Research Involving
Human Subjects 2008 [146]. As strenuous exercise and maximal performance
are part of the daily routines of elite athletes, included procedures, and all
competitive results are openly published no ethical issues are visualized.
Regression
X2
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X4
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X5
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6/7th
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Predicted data
f s
30
Results
Paper I
This study investigated the predictive power of aerobic test results and
anthropometric data for competitive performance (indicated by FIS points) of
junior elite alpine skiers. The study included physiological test results from
twenty-three (n = 23) junior elite male and female alpine skiers. While principal
component analysis indicated that male and female skiers could be separated
based on their aerobic test results, none of the included variables were of
significant importance for performance. The best multivariate prediction models
reached R2 = 0.51 to 0.86 and Q2 = −0. 3 0. 8. Although several significant
regression models could be observed, none of these met the criteria for valid
models (R2 >0.8, Q2 >0.5, s g f y “y s” f r 2 cross-validation)
(Table 2 in Paper I). The lack of validity of these models was confirmed via cross-
validation by permutation. Also, an important finding in this study was that FIS
points demonstrate a non-normal distribution when treated as continuous data.
As such, this finding may be an explanation for inconsistent results among
previous studies that have examined the importance of aerobic test results for
alpine skiing performance (Table 1).
The lack of performance predictive power of included physiological test results in
this study is exemplified in Figure 5-6.
Figure 5. Correlations between physiological test results and FIS points in SL and GS for thirteen
junior elite male alpine skiers age 16. A) Orthogonal projections to latent structures visualize correlations
between variables (X = 16, Y = 2, n = 13). Physiological test results and FIS points (labeled as rank) located in the
same or opposite part of the loading plot are correlated. The horizontal axis displays the X- and Y- loadings of the
predictive component, and the vertical axis the X- and Y-loadings for the first Y-orthogonal component. For
example, a high value (max = 1) means that the component is aligned with the original variable, a value close to zero
X-
an
d Y
-lo
ad
ing
sfo
r t
he
fo
r f
irs
tY
co
mp
on
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t
FIS SL ranking
R2VY
Q2VY
A B
31
shows that it has no influence. A low value (min = –1) indicates the opposite influence. B) The X/Y overview plot
shows the cumulated R2 and Q2 values for the model. The plot indicates a low predictive power (R2 = 0.25 to 0.45,
Q2 = − 0.50 to – 0. 27).
Figure 6. Correlations between physiological test results and FIS points in SL and GS for ten junior
elite female alpine skiers age 16. A) Orthogonal projections to latent structures visualize correlations between
variables (X = 16, Y = 2, n = 10). Physiological test results and FIS points (labeled as rank) located in the same or
opposite part of the loading plot are correlated. The horizontal axis displays the X- and Y- loadings of the predictive
component, and the vertical axis the X- and Y-loadings for the first Y-orthogonal component. For example, a high
value (max = 1) means that the component is aligned with the original variable, a value close to zero shows that it
has no influence. A low value (min = –1) indicates the opposite influence. B) The X/Y overview plot shows the
cumulated R2 and Q2 values for the model. The plot indicates a low predictive power (R2 = 0.65 to 0. 71, Q2 = − 0.25
to – 0.24).
Paper II
This study investigated the predictive power of the physiological test battery
Fysprofilen for competitive performance (indicated by FIS points) of senior elite
female alpine skiers. Fysprofilen is the official test battery of the Swedish Olympic
Committee and consists of thirteen different tests divided into four main
categories: strength, power, aerobic and anaerobic. The study included
physiological test results from fourteen (n = 14) senior elite female alpine skiers.
Principal component analysis demonstrated a high correlation between
individual physiological test results and their corresponding Fysprofilen score
points, indicating that they can be used interchangeably. The Mann-Whitney U
test was not significant for either SL (U = 99.5, z = -0.07, p = 0.95) or GS (U = 80,
z = -0.83, p = 0.41), indicating that Fysprofilen score points (summarized as
Fysprofilen Index) and competitive performance (indicated by FIS points) are
independent. Generated OPLS models for SL and GS reached R2 = 0.27 to 0.43
and Q2 =− 0.8 to - 0.17, indicating low predictive power. Cross-validation by
permutation confirmed the lack of validity of these models.
R2VYQ2VY
X
Y
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sfo
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X- and Y-loadings for the predictive components
32
The lack of performance predictive power of included physiological test results in
this study is exemplified in Figure 7-8.
Figure 7. Correlations between physiological test results and FIS points in SL for fourteen senior
elite female alpine skiers between the years 2012 and 2014. A) Orthogonal projections to latent structures
visualize correlations between variables (X = 15, Y = 1, n = 14). Physiological test results and FIS points (labeled as
rank) located in the same or opposite part of the loading plot are correlated. The horizontal axis displays the X- and
Y- loadings of the predictive component, and the vertical axis the X- and Y-loadings for the first Y-orthogonal
component. For example, a high value (max = 1) means that the component is aligned with the original variable, a
value close to zero shows that it has no influence. A low value (min = –1) indicates the opposite influence. B) The
X/Y overview plot shows the cumulated R2 and Q2 value for the model. The plot indicates a low predictive power (R2
= 0.43, Q2 = − 0.17).
Figure 8. Correlations between physiological test results and FIS points in GS for fourteen senior
elite female alpine skiers between the years 2012 and 2014. A) Orthogonal projections to latent structures
visualize correlations between variables (X = 15, Y = 1, n = 14). Physiological test results and FIS points (labeled as
rank) located in the same or opposite part of the loading plot are correlated. The horizontal axis displays the X- and
A B
XY
X-
an
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sfo
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R2/Q
2v
alu
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FIS SL ranking
A BR2VY
Q2VY
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X- and Y-loadings for the predictive components
R2/Q
2v
alu
es
33
Y- loadings of the predictive component, and the vertical axis the X- and Y-loadings for the first Y-orthogonal
component. For example, a high value (max = 1) means that the component is aligned with the original variable, a
value close to zero shows that it has no influence. A low value (min = –1) indicates the opposite influence. B) The
X/Y overview plot shows the cumulated R2 and Q2 value for the model. The plot indicates a low predictive power (R2
= 0.39, Q2 = − 0.17).
Paper III
This study investigated the predictive power of a novel physiological test battery
for competitive performance (indicated by FIS points) of senior elite female
alpine skiers. The test battery was based on previous research and designed to
cover a wide range of physiological and anthropometric qualities (e.g., strength,
endurance, balance, and body composition). The study included physiological
test results from twelve (n = 12) senior elite female alpine skiers. When data were
analyzed on a group level, the best OPLS models for SL and GS reached (R2 = 0.39
to 0.40, Q2 = 0.15 to 0.21), indicating low predictive power. In contrast to Paper
II, however, the included physiological test results in this study generated valid
models (as confirmed by cross-validation by permutation). Importantly, when
data were analyzed on an individual level, valid models with high predictive
power were generated (R2 = 0.88 to 0.99 and Q2 = 0.64 to 0.96). In order to
exemplify the unique individual profiles of equally top-ranked skiers, individual
OPLS models for SL and GS were generated, and subsequently compared with
the SUS plot analysis.
34
Discussion
The overall aim of this doctoral thesis was to identify physiological and
anthropometric variables valid for prediction of competitive performance in
alpine skiing. The main result of Paper I was that included aerobic test results,
neither alone nor in combination with anthropometric variables, could predict
competitive performance of junior elite male and female alpine skiers. The main
result of Paper II was that included physiological test results could not predict
competitive performance of senior elite female alpine skiers. Even when
converted into separate score points or as a summarizing test score (Fysprofilen
Index), the included tests failed to discriminate between skiers on an individual
level. These results were confirmed in Paper III, where included physiological
test results failed to generate OPLS models with high predictive power (R2 and Q2
>0.6) for competitive performance of senior elite female alpine skiers on a group
level. In contrast, when data were analyzed on an individual level, valid models
with high predictive power could be generated. Collectively, findings indicate that
none of the investigated physiological performance tests are predictive for
competitive performance on a group level. Rather, ranking data and physiological
test results must be collected over time and analyzed using individual profiling.
Why is there a lack of predictive power on a group level?
Competitive performance of alpine skiers is affected by a number of interacting
factors [134,149], including the mental, social, technical and physiological
dimensions [46]. As this thesis focus on physiology, other dimensions were not
directly investigated, but are to some extent incorporated in the discussion. The
significance of particular dimension varies between individuals, as exemplified
by the SUS-plots of physiological variables in Paper III. Individual variations, and
the lack of comprehensive testing of all dimensions, partially explain the lack of
predictive power when analyzing data on a group level.
Another explanation for the lack of significant models may be due to the lack of a
reliable outcome variable, as validity and reliability interact [6,31]. As stated by
Currell and Jeukendrup [31], "A protocol can be reliable, but not valid, while a
valid protocol must be reliable". These issues are discussed in more detail below.
The reliability of the outcome variable (Y) affects the validity of the test protocol
(X). In this thesis, FIS points were used as the outcome variable, an indication of
competitive performance. As extensively discussed in Paper II, the ranking
system has been criticized for not being fair (primarily because of how points are
calculated) and encouraging an opportunistic behavior [92].
35
However, the main problem with the use of FIS points as an outcome variable is
that individual differences in ranking points over time (Figure 9) are not
exclusively due to changes in physiological performance (concluded in Paper I-
III and exemplified in Figure 10). Factors such as starting order, participating
competitors and previous race results also affect the ranking position. Hence, FIS
points are probably not at any given time a reliable measure of physical
performance. Consequently, no matter the reliability and validity in
measurements of physical performance tests, prediction of competitive
performance cannot be performed with high accuracy (closeness to the truth) and
precision (closeness between repeated measurements). Thus, when data is
analyzed on a group level, individual changes in terms of ranking will inevitably
result in models with low predictive power. While other performance outcomes
than FIS points could have been used (e.g., time trails during training and
individual competition results), none of these represent true long-term
competitive performance. Despite its apparent shortcomings, FIS points are
currently the most reliable and valid indications of competitive performance over
multiple competition seasons.
Figure 9. Overview of the change of FIS points in GS between the season 2011/2012 until the season
2016/2017 for all participants included in Paper III. Data were extracted from the 11th FIS points list
published each season. An upward trend indicates an improvement in FIS points while a downward trend indicates
a deterioration. For ethical reasons and in order to protect each participants identity, the y-axis scale has been
removed.
Season2011/2012
Season2012/2013
Season2013/2014
Season2014/2015
Season2015/2016
Season2016/2017
Skier A Skier B Skier C Skier D Skier E Skier F
Skider G Skier H Skier I Skier J Skier K Skier L
36
Figure 10. Example of the individual change of FIS points in GS and physiological test results
between the season 2011/2012 until the season 2016-2017. The plot is an example of the change of
physiological performance and FIS points in GS for one of the participants included in Paper II-III. Ranking data
was extracted from the 11th FIS point list published each season. Included physiological test results have been scaled
proportionally to FIS points for an illustrative purpose. An upward trend indicates an improvement in performance
while a downward trend indicates a deterioration. For ethical reasons and in order to protect the participant's
identity, the y-axis scales have been removed.
How important is the physiological dimension?
As shown in Figure 1, the physiological dimension is just one of several interacting
dimensions affecting competitive performance in alpine skiing. As this thesis only
investigated the importance of physiological test variables, the proportion of
physiology in relation to athletic performance cannot be determined. High
physiological performance is important in almost every sport [9]. The ability to
generate high neuromuscular strength and power [54,133], high oxygen uptake
[54], superior coordination and change of direction ability [20] are just some of
the physiological qualities that often characterize top-level athletes. In alpine
skiing, physiological qualities such as muscle strength, aerobic and anaerobic
capacity, balance, core strength and core stability have all been proposed as
prerequisites for elite performance [4,66,97,108]. These arguments have been
supported by numerous studies [3,5,32,55,93,94,97,103,127,152,162], all finding
significant correlations between physiological test variables and skiing
performance.
As indicated by our results, these claims may not be valid. First and foremost, it
can be concluded that competitive alpine skiing does not seem to require
physiological qualities that can be classified as extreme in terms of human
performance. If this were the case, some of the participants included in this thesis
Season2011/2012
Season2012/2013
Season2013/2014
Season2014/2015
Season2015/2016
Season2016/2017
FIS points GS Body mass (kg) Hand grip (kg)
CMJ (cm)
37
would probably not belong to the absolute elite. Rather, our results show that
athletes with significantly different physiological capacities can reach comparable
elite level performance. In other words, one weakness can be compensated for by
another strength. Also, in agreement with previous studies [19,99,102,140,154],
our findings suggest that differences in competitive performance between groups
of skiers cannot be explained by physiological test results alone. For example, in
Paper I junior elite female skiers displayed similar peak V̇O2 values as their
higher-ranked senior counterparts, included in Paper II-III. Some studies have
identified differences in physiological performance between skiers at different
competitive levels [32,114]. However, this is no proof of causality; physiological
characteristics may not be related to, or the reason for, the difference in ranking.
Furthermore, as discussed briefly in Paper II-III, there may be an issue with the
choice of statistical method in the abovementioned correlation studies as almost
all of these have applied P rs ’s rr to ranking data. For example, as
demonstrated in Paper I, FIS points is an ordinal variable based on a sigmoid
function which, if assigned as continuous data, does not exhibit a normal
distribution. However, as P rs ’s rr assumes constant variance and
linearity [25], it is unable to detect monotonic relationships in data. That is,
applying Pears ’s rr to this type of data may not be optimal. Another
concern with published studies is the lack of cross-validation as this technique
could have been used to assess the validity and reliability of observed
correlations. As such, the results in some previous studies may be due to chance,
which to a certain extent could also explain the inconsistent results reported in
different publications.
The question one must ask is then; how important is the physiological dimension
for alpine skiing performance (Figure 1)? As this thesis has shown, the question
will probably never be fully answered as the relative importance of each
influencing dimension may vary on an individual level. What can be stated,
however, is that one or a few physiological test variables will not discriminate
between skiers once they have reached a certain level of performance. Skiers with
similar physiological characteristics can reach different competitive performance
levels, and vice versa. Alternatively, physiological variables not measured by us,
or other research groups, are the ones of importance. Regardless, there is an
imminent risk that the use of inappropriate statistical methods may have misled
research focus and that the importance of certain physiological qualities for skiing
performance may be overemphasized.
Despite extensive physiological testing, included test results could not generate
models valid for prediction of competitive performance. Rather, our results
indicate that the importance of the physiological dimension is likely due to
variations on an individual level. Thus, it can be concluded that the relative
38
importance of all influencing dimensions must be considered when investigating
the sport-specific performance of athletes (Figure 1).
Is there an assumption of validity?
With the previous section in mind, another interesting question is, therefore, why
the physiological dimension seems to get so much attention from coaches and ski
federations alike? One explanation is probably that physiological test results are
often easier for coaches to understand in comparison to results provided by tests
of other essential qualities (e.g., mental "performance"). For example, the results
of a jump test performed on an infrared jump mat are likely easier to interpret
and evaluate compared to the assessment and evaluation of reaction time and
decision making in the virtual reality [117]. Furthermore, assessment of social
skills can be an overwhelming task as this includes measurements of verbal and
non-verbal interactions, as well as communications with others in a specific
context. Most likely more difficult to comprehend than results from a body mass
assessment using a regular body weight scale [81]. Content validity [83] may also
(incorrectly) be assumed [109] due to the fact that people are generally inclined
to accept arguments that support their knowledge and beliefs. To understand the
world around them, people tend to simplify complex problems [50]. Because
performance in alpine skiing can be difficult to quantify, coaches and leaders may
be convinced that selected parts of the physiological dimension are crucial for
competitive performance [109]. It is, therefore, reasonable to believe that some
of these coaches and leaders may seek evidence to support their presumptions.
Another plausible explanation may be that many coaches and leaders assume
scientifically reported results to be valid and reliable, as this is the very core tasks
of science [120]. However, in the field of alpine skiing research, it seems almost
as if the scientific community itself may have contributed to creating some
misconceptions regarding the importance of certain physiological qualities for
skiing performance. This, in turn, seems to have created a form of chain reaction
where coaches and leaders trust what researchers say, and vice versa [109]. The
importance of applying correct scientific methods, study designs and statistical
analyses when reporting results emphasize the responsibility researchers have
towards society [120].
Validity and reliability of physiological tests in alpine skiing
During the last decade, physiological testing of athletes has become an essential
part of high-performance sports [139]. Due to the often dynamic and complex
nature of sport [22,31], a controlled simulation and estimation of athletic
performance can provide competitive advantages [17]. Among the many benefits
of conducting physiological tests is the opportunity to establish baseline values
that provide athletes with an insight into their current physiological status (data
39
useful when conducting comparative follow-up tests) [28]. Physiological tests can
also provide incentives for athletes to try to beat previous test records, providing
motivation in their preparatory training [158]. Testing athletes' physiological
capacity is important also when designing exercise programs for maximized
training efficiency by addressing correct physiological qualities [22]. Finally, test
results are used by coaches and managers as selection criteria for recruitment to
teams and participation in competition.
To generate useful results within a competitive context, a performance test
protocol must be valid and reliable to the performance in question [31]. For the
athletes themselves, the validity and reliability of the testing protocol are critical
as they presumably perform physiological tests with the intention of identifying
strengths and weaknesses in relation to their sport. In sports performance
research, different types of validity and reliability can be applied [31]. In general,
validity refers to which degree a test protocol or instrument measures what they
intend to measure [61] whereas reliability refers to the extent to which they
provide consistent results [6]. For physiological test protocols, validity can be
divided into three main categories: logical validity, construct validity and
criterion validity [31]. Logical validity can be defined as a subjective assessment
of whether the test measures what it intends to measure [147]. Construct validity
refers to the degree to which the test protocol measures the theoretical construct
or the characteristics that are examined (e.g., a comparison between two groups
in terms of physiological performance) [147]. Lastly, criterion validity indicates
to which degree a test protocol correlates with a present or a prospective external
criterion [31,147]. Hence, for alpine skiing, criterion validity refers to the extent
to which of a test protocol can simulate or predict sport-specific performance.
Based on our results, however, it is unlikely that a performance test protocol for
alpine skiers, applied on a group level, will ever achieve high logical, construct or
criterion validity for several reasons. In the case of logical validity, high validity
applies only if the logical argument is true [147]. As discussed above, several
previous studies have found a significant correlation between physiological test
results and points or ranking [3,32,55,77,93,94,97,103,127,152,162]. If these
results had high logical validity, this would have meant that, for example, athletes
with the highest V̇O2 value should also be the fastest skiers. However, as our
results show, it is relatively unlikely that this argument would be true.
To exemplify the issue with construct validity, an example with basketball players
follows. As in other sports, an assessment of the physiological characteristics of
alpine skiers may be necessary in order to recognize trend patterns in a specific
population, establish normative data, and provide educational content to coaches
and others involved in the development of young athletes [22,139]. While this
certainly can be important on an organizational level, our results indicate that
40
descriptive and normative data often are of little use to the very elite athlete. For
example, in a sport like basketball, athletes have a competitive advantage of being
tall [104]. For a professional basketball team, this is probably vital information
when they are about to add new players to their roster. For shorter basketball
players, this can be valuable information because they know that they must "train
themselves longer" to compete on the same terms as taller players. For NBA
players, however, this is probably not important information because they are
already playing at the highest level in their sport, where body size is no longer a
discriminating factor in terms of performance [104]. Although the basketball-
case may be an absurd example, our results indicate that this analogy most likely
also applies to elite alpine skiers, where physiological performance comparisons
with lower ranked athletes do not help top athletes improve their sport-specific
performance. This is especially evident when it comes to V̇O2peak, where the senior
elite athletes in Paper II-III exhibited similar values as the junior elite skiers in
Paper I.
Finally, in terms of criterion validity, skiing performance does not seem to be
predictable on a group level, at least not using physiological test results alone. As
already discussed, this can be due to the fact that several different dimensions
(Figure 1) influence competitive skiing performance [46,134,149] and that the
relative importance of these dimensions may vary on an individual level. Most
likely, this is also due to the lack of a reliable outcome variable [6,31] as FIS points
have a considerable within-group variation between seasons (Figure 9). Because
of these considerations, it is highly unlikely that a physiological test protocol
intended for alpine skiers, applied on a group level, will ever achieve high
criterion validity (i.e., high cross-validated Q2) for true racing performance. As
such, all these arguments also emphasize the need for a new approach when it
comes to assessing and evaluating the physiological performance of elite athletes.
Methodological considerations
Other dimensions
Because this thesis has focused on the physiological dimension, the importance
of other influencing dimensions for alpine skiing performance has not been
investigated. Given the fact that competitive performance in alpine skiing is
affected by dimensions other than just the physical (such as equipment, skiing
technique, and mental performance), the relative importance of these dimensions
could also be important to investigate. By including other dimensions, the
predictive power for competitive performance on a group level would maybe have
been improved.
41
Menstrual cycle effects
The menstrual cycle phases and fluctuations of hormones may affect trainability
[156] and physiological performance in women [69]. This may, in turn, explain
part of the individual variations in physiological performance between different
test occasions (Figure 10). As physical testing occurred in random orders for
several years, systematic errors in data were avoided. Implementation of tests
based on the menstrual cycle may prove difficult, as the cycle does not always
start on the same day and vary in length [69]. Regardless, it was beyond our
control when tests were carried out.
Other predictive modeling procedures
Besides using only PCA and OPLS, it would also be interesting to investigate the
predictive power of physiological test results for competitive performance using
other predictive modeling procedures. While we are confident that the analytical
techniques used in this thesis are useful in this type of context [87], other
predictive methods could have been used to ensure the validity of our results.
More tests specifically designed for alpine skiers
This thesis could also be criticized for not including sufficiently challenging and
specifically designed tests for alpine skiers. Examples of these could be a more
demanding balance test performed in ski boots, a test for the assessment of
isometric and eccentric muscle strength as well as specific core strength and core
stability tests. While this would have been desirable, it could be argued that valid
tests for assessing the sport-specific performance of alpine skiers do not exist.
Instead of examining the validity of numerous individual tests for skiing
performance, we focused on investigating the predictive power of already
validated tests or those previously used in research on alpine skiers. In addition,
as the end goal of this doctoral thesis was to provide practically useful results, we
mainly chose to include physiological performance tests that can be performed
without access to advanced laboratory equipment.
Fédération Internationale de Ski points
As mentioned, one of the limitations of this thesis was also the use of FIS points
as an indication of competitive performance. While other skiing performance
metrics could have been used, FIS points still seem to be the best indication for
actual competitive performance over several competition seasons. Still, a more
reliable measure of performance had been desirable, which can hopefully be
determined in future research.
42
The novel approach of this thesis
Elite performance in any sport is multi-dimensional [71]. Consequently, data
analysis must follow the same trait. Traditionally, variables are analyzed one by
one, using bivariate statistical methods [125]. Finding correlations between
capacities and performance may not correspond to high predictive power. That
is, a correlated variable may or may not be essential for competitive performance.
This thesis explores, for the first time in an elite athletic setting, a novel approach
of multivariate analysis proven useful among firefighters [86]. By doing so, both
correlation and importance of each included physiological testing result are
evaluated. A move from evaluating test results on a group level to individual
profiling is suggested. Also, other dimensions presented in Figure 1 must be
incorporated in future research in order to cover the complex aspects of athletic
performance.
Application in sports
Although no valid physiological tests for competitive performance were
identified, the results of this thesis can hopefully encourage both athletes and
coaches involved in alpine skiing to continue with the implementation of
physiological tests. However, as our results show, these tests should be
implemented over time with the intention of identifying important physiological
qualities for sport-specific performance on an individual level. A test battery
intended for alpine skiers should include valid and reliable tests aimed at
assessing physiological performance in general (e.g., strength, endurance,
mobility and balance) and providing easy-to-understand and practically useful
results to training advisors and athletes alike. This in order to provide with a
comprehensive individual physiological profile that can be correlated with sport-
specific performance (e.g., FIS points) while simultaneously providing with
results that can be implemented (and be useful) in everyday training.
Furthermore, the results and analytical procedures presented in this thesis can
also be useful in sports such as ski cross, snowboard and cross-country skiing
where competitive performance is quantified using a similar ranking system such
as the one used in alpine skiing. Besides the similarities between the ranking
systems, it is also likely that the competitive performance of these athletes, as for
alpine skiers, is affected by several interacting dimensions where, for instance,
the relative importance of physiological performance varies on an individual
level.
43
Conclusion
A more holistic approach to testing of elite athletes must be considered, as elite
performance is multi-dimensional. Before implementing extensive testing of
athletes, validity and reliability of protocols applied must be determined. Failing
to meet these criteria may have extensive consequence, such as a potential waste
of the limited resources for athletes, coaches, clubs, and ski federations as well as
misguided training.
44
Acknowledgments
During my years as a Strength and Conditioning coach at Ski Team Sweden
Alpine and as a PhD student, I have met several fantastic people who have all
inspired me and, in some way, contributed to the completion of this work. I would
like to express my deepest gratitude to the following persons:
My supervisor Christer Malm for everything that you have done for me.
Without you I would not have been where I am today and thanks to you, I have
gained a more profound knowledge, not only in the field of sports medicine but
also about myself. You are an extraordinary researcher but an even better person.
Thank you for sharing your expertise with me. I will be grateful for this forever.
My co-supervisor Apostolos Theos because you have always had time to listen
and because you have always been there for me. You are a kind-hearted person
who always tries to do the best for everyone. Thank you for sharing your
knowledge with me and because you have taught me some of the Greek ways of
handling different situations in life.
My co-supervisor Ann-Sofie Lindberg because you have contributed structure
to this project. You are an incredibly skilled researcher and without you, the
completion of this project would have been so much more difficult. Thank you for
sharing your knowledge with me and for being such a nice and caring person.
My co-supervisor Richard Ferguson for your invaluable contribution to the
completion of this project. Thanks to your involvement and expertise, I have
always felt confident that I am on the right track. Thank you for being a part of
this project and for sharing your knowledge with me.
The former Sports Director at Ski Team Sweden Alpine, Anders Sundqvist, for
believing in me and for the opportunity to start this project. I will always be
grateful for everything that you have done for me. I am also grateful to survive the
colossal ice hockey tackle that you handed out to me during the 2015 World
Championship in Vail, USA.
My mentor and friend Tom Pietilä, who have supported and helped me ever
since I started my studies at the Section for Sports Medicine. Thanks for all the
advice and because you have continued to motivate and encourage me during
periods when it sometimes felt tough.
The personnel at the Section for Sports Medicine: Håkan Alfredson, Kajsa
Gilenstam, Lars Berglund and Lisbeth Wikström-Frisén for your
45
encouraging words and invaluable inputs during this work, Michael Svensson
for sharing your knowledge with me and for your encouraging words, Lars
Göran Fjellborg for your encouraging words and because you have adjusted my
back now and then, Daniel Jansson, for rewarding discussions and because
being an arch-enemy during important games between Sweden and Finland in all
major international ice hockey tournaments, Ewa Johansson, Britt-Marie
Eliasson and Jonas Lorentzon for help with all the administrative and
technical work, Ji-Guo Yu for encouraging words.
The personnel at the Sports Medicine Laboratory at Umeå University: Roger
Andersson, Lennart Burlin, Mikael Therell and Joel Sjölander for
invaluable help during the data collection and all the pleasant conversations in
between. Without your help, this project could not have been completed.
My former PhD student colleagues, Andreas Hult, Jonas Johansson, and
Tommy Henriksson because you have shown me th “ s” and
thereby inspired me to continue to work hard.
My friends and former colleagues at Ski Team Sweden Alpine, Anders Nilsson,
Sara Königsson and Mikael Junglind, for all the pleasant and rewarding
conversations. Thank you for believing in me.
The former head of the Swedish female alpine skiing national team, Fredrik
Steinwall, for giving me the opportunity to work with such an incredible group
of formidable athletes and colleagues.
All the fantastic athletes I have had the privilege to work with. You are the biggest
reason why this project was started. You all have been a huge inspiration during
all these years. Thanks for all the wonderful memories, I will carry them with me
for the rest of my life.
In addition, I would like to express my deepest gratitude to my family:
My sister Josefine Nilsson and her wonderful family for their love and support.
You are all fantastic and I wish that we could meet each other more often.
My fiancé Ida Johansson because you have always been there for me. Without
you, I would never have been able to complete this project. Now I finally have
some more time to help you with all your crazy ideas. I love you.
Finally, my parents Bo Nilsson and Birgitta Nilsson who have always
supported and believed in me. I could not have wished for better parents than
you. I am proud to be your son. I love you.
46
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