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MEASURING SPORTS PERFORMANCE
OLA ERIKSRUD
JAN CABRI
Disclosure: Ola Eriksrud works with
1080 Motion in the development and
implementation of robotic technology
OUTLINE
Physical factors of performance –force, speed and power
Test in g fundament al s
Isokinetic dynamometers
Hand held dynamometers
Free weights
Pneumatic technology
Robotic technology
Performance
Mobility
Stability
ForceSpeed
Endurance
Performance
Mobility
Stability
ForceSpeed
Endurance
PHYSICAL FACTORS OF PERFORMANCE
Force, speed and power fundamental to testing, training and performance in the
following sports:
Football (Paul et al, 2014)
Ice hockey (Potteiger et al, 2010)
Rugby (Baker et al, 2008)
American Football (Pryor et al, 2014)
Alpine skiing (Neumayr, 2003)
Golf (Hellstrom, 2010)
Basketball (Simenz, 2005)
Volleyball (Schaal, 2013)
Handball (Debanne, 2011)
=The interaction of force, speed and power in different movement patterns is at the
biomechanical basis of athletic performance
FUNDAMENTALS OF TESTING
Equipment and methods:
1. Validity
2. Reliability
3. Sensitivity
4. Specificity
Performance
Mobility
Stability
ForceSpeed
Endurance
TESTING FUNDAMENTALS –FORCE
Equipment and methods
1. Validity
Construct (divergent or convergent)
Content/logical
2. Reliability
Instrumental
Rater (intra and inter)
3. Sensitivity
4. Specificity
Performance
Force
Concentric
Eccentric
Isometric
TESTING FUNDAMENTALS –SPEED
Equipment and methods
1. Validity
Construct (divergent or convergent)
Content/logical
2. Reliability
Instrumental
Rater (intra and inter)
3. Sensitivity
4. Specificity
Performance
Speed
TimePosition
TESTING FUNDAMENTALS –POWER
Power=Force!speed
Forcevelocity relationship (Hill curve)
Performance
Force
Speed
the fastest and slowest subjects presented in Fig. 1show
their relatively higher difference in V0 than in F
H
0 values.
These two individuals also strongly differed in terms of
peak power and optimal velocity, as shown by the com
parison of their power–velocity relationships (Fig. 1).
Concerning GRF production and orientation onto the
ground, Table 3shows that D
RF
index was signiﬁcantly
correlated to all the performance variables considered,
contrary to F
Tot
, which was only signiﬁcantly correlated to
Smax (P=0.034). For the components of this resultant
GRF, F
H
was signiﬁcantly correlated to 100m perfor
mance (P\0.05), whereas F
V
was only correlated to
Smax (P=0.039), and not to S
100
or d
4
.
These correlations between sprint performance (mean
100m speed) and F
Tot
and D
RF
are shown in Fig. 2.
The ability to orient the resultant GRF vector effectively
(i.e. forward) during the acceleration phase on the treadmill
(analyzed through the D
RF
value) strongly differed between
the fastest and slowest individuals tested (Fig. 3). In
addition to being the individuals presenting the extreme
values of 100m time, they had the highest (0.042) and
second lowest (0.091) values of D
RF
of the group.
Contact time and step frequency showed signiﬁcant and
high correlations (P\0.01) with 100m performance
(Table 4), which was also the case of the swing ti me
(P\0.05). However, neither aerial time (P[0.88) nor
step length (P[0.21) was related to sprint performance.
Last, BMI (23.2 ±2.2 kg m
2
) and L/H ratio
(0.522 ±0.014) were not correlated to any of the perfor
mance variables studied (all P[0.29).
Discussion
To our knowledge, since the work of Weyand et al. (2000),
this is the only study to speciﬁcally report experimental
data obtained in a group of subjects ranging from non
specialists to nationallevel sprinters, and to a sub10s
athlete. Since pioneering works about human sprint per
formance published in the late 1920s (Best and Partridge
1928; Furusawa et al. 1927) involving very fast runners
(estimated 100m time of *10.8 s for subject H.A.R.,
probably 1928 Olympian sprinter Henry Argue Russel, in
the study of Furusawa et al. (1927), many studies involved
highlevel athletes (e.g. Karamanidis et al. 2011; Mero and
Table 2 Correlations between mechanical variables (rows) of the force–velocity relationship and power output and 100m performance
variables (columns)
Maximal speed (m s
1
) Mean 100m speed (m s
1
) 4s distance (m)
Maximal power output 0.863 (<0.01)0.850 (<0.01)0.892 (<0.001)
Average power output 0.810 (<0.01)0.839 (<0.01)0.903 (<0.001)
Theoretical maximal horizontal force F
H
0 0.560 (0.052) 0.447 (0.128) 0.432 (0.14)
Theoretical maximal horizontal velocity V00.819 (<0.01)0.735 (0.011)0.841 (<0.01)
Signiﬁcant correlations are reported in bold. Values are presented as Pearson’s correlation coefﬁcient (Pvalues in italics)
Fig. 1 Typical linear force–velocity and 2nd degree polynomial
power–velocity relationships obtained from instrumented treadmill
sprint data for the fastest (100m best time: 9.92 s, 100m time of
10.35 s during the study: black and dark grey circles) and slowest
(100m time of 15.03 s during the study: white and light grey circles)
subjects of this study. All linear and 2nd degree polynomial
regressions were signiﬁcant (r
2
[0.878; all P\0.001)
Table 3 Correlations between mechanical variables of sprint kinetics
measured during treadmill sprints (rows) and 100m performance
variables (columns)
Maximal
speed (m s
1
)
Mean 100m
speed (m s
1
)
4s distance
(m)
Index of force
application
technique D
RF
0.875 (\0.01)0.729 (\0.05)0.683 (\0.05)
Horizontal GRF 0.773 (\0.01)0.834 (\0.01)0.773 (\0.05)
Vertical GRF 0.593 (\0.05) 0.385 (0.18) 0.404 (0.16)
Resultant GRF 0.611 (\0.05) 0.402 (0.16) 0.408 (0.16)
Signiﬁcant correlations are reported in bold. Horizontal, vertical and
resultant GRF data are averaged values for the entire acceleration
phase. Values are presented as Pearson’s correlation coefﬁcient
(Pvalues in italics)
3926 Eur J Appl Physiol (2012) 112:3921–3930
123
Morin (2012) Eur J Appl Physiol
TESTING FUNDAMENTALS –PRINCIPLE OF SPECIFICITY
Equipment and methods
1. Validity
Construct (divergent or convergent)
Content/logical
2. Reliability
Instrumental
Rater (intra and inter)
3. Sensitivity
4. Specificity
True negative
Principle of specificity (exercise science) = logical validity
TESTING FUNDAMENTALS –PRINCIPLE OF SPECIFICITY
Performance/movement –change of direction (horizontal)
Seated knee extension
Olympic lifts
Squat
≠ Performance (Brughelli, 2008; Markovic, 2007)
=Performance (Nimphius, 2010)
www.biodex.com www.iwf.com
TESTING FUNDAMENTALS –PRINCIPLE OF SPECIFICITY
Starting position, action and ending position
GRF
G
1. Direction of force
2. Position, movement and
joint moments of joints
and regions of the
kinematic chain
= kinematic and kinetic
specificity
TESTING FUNDAMENTALS –PRINCIPLE OF SPECIFICITY
LOW HIGH
DEGREE OF KINETIC AND KINEMATIC SPECIFICITY
TESTING FUNDAMENTALS –PRINCIPLE OF SPECIFICITY
www.biodex.com
Direction of force
TESTING FUNDAMENTALS –PRINCIPLE OF SPECIFICITY
that it was very close to the top speed reached in field sprint
(5). Using this kind of design, Weyand et al. (35) showed
that the support vertical force produced onto the ground per
unit body weight (BW) at top speed was a key determinant
of this speed. Knowing the determinants of human top speed
and its biological limits is of interest. However, in many
sports, if not all, athletes almost never reach their actual in
dividual top speed, yet their forward acceleration capabilities
are often necessary for successful performance (e.g., in
soccer or rugby).
Accelerating body mass and producing forward speed
logically requires production of high amounts of F
H
. How
ever, during forward acceleration, the human body is in a
mechanical situation in which gravitational constraints are
such that the total force is predominantly produced in a di
rection that is not that of their displacement. Therefore, only
the horizontal component of the total force is directed for
ward, and the other component (vertical) can be considered
as ineffective in producing forward acceleration, although
necessary to keep moving forward (Fig. 1). In pedaling
mechanics, where the total force applied onto the pedal has
also been analyzed through its components (e.g., Davis and
Hull [7]), only one component of the total force (that ori
ented perpendicular to the crank arm) is propulsive and
necessary to the rotation of the drive. Thus, effectiveness of
force application in pedaling has been defined as the ratio
of this effective force to the total force applied onto the pedal
(7,11,28,30). Since then, effectiveness has been related to
subjects’ pedaling technique (e.g., Dorel et al. [10]) and
cycling mechanical efficiency (e.g., Zameziati et al. [36]).
Applying this to sprint running for a given support phase,
the mean ratio of forces applied onto the ground (RF) could
objectively represent runners’ force application technique.
This could also be independent from the amount of total
force applied, i.e., their physical capabilities. Therefore, we
hereby propose to study RF in sprint running, which we
define as the ratio of F
H
to the corresponding total GRF
averaged over the support phase (F
Tot
). Thus, theoretically,
for the same F
Tot
applied onto the ground over a given
stance phase, different strategies of force application (hence,
different RF values) may be used and result in different
amounts of F
H
and, in turn, different net forward accel
erations (Fig. 1).
However, contrary to cycling where the aim of a good
pedaling technique is to reach maximal RF (i.e., effective
ness of 100%), the necessary vertical component of the total
force in running makes it mechanically counterproductive to
maximize RF. Indeed, an RF of 100% would mean that the
total force is applied horizontally, with no vertical compo
nent, making the running motion impossible. Between this
implausible maximal RF and RF equal to zero (which the
oretically means no net forward acceleration is produced and
the total force over the stance is applied vertically), it is not
known whether or how RF could be optimized or whether
it is related to field sprint performance. Thus, we thought it
would be interesting and novel to analyze RF values during
sprint acceleration.
To our knowledge, no such approach has been undertaken
that would allow calculation of RF over a sprint acceleration
phase (and consequently test its potential relationship to field
sprint performance). This could be explained by the fact that
computing RF requires measurements of the total GRF and
both its vertical and horizontal components. To date, some
studies performed these measurements using force plates
(2,13,16,20,22,23,27), but none reported RF values. That
said, some studies reported incline angles of the total GRF
vector relative to vertical (16,20), which is mathematically
close to the expression of RF proposed here (Fig. 1). These
studies suggest that the forward incline angle of the total force
vector relative to vertical (and thus RF; Fig. 1) decreases with
increasing running speed over a typical sprint acceleration.
The characteristics of this decrease in RF and whether
limiting it may enhance field sprint performance are un
known. We hypothesized that the technical ability to limit
the decrease in RF with increasing speed would be of in
terest to sprint performance and used a practical index to
characterize subjects’ technique of force application mea
sured during maximally accelerated runs. Computing such
an index requires GRF measurements during the entire ac
celeration phase of a sprint, which has never been done to
our knowledge, for obvious technical and material reasons.
FIGURE 1—Schematic representation of the ratio of forces (RF) and
mathematical expression as a function of the total (F
Tot
) and net positive
horizontal (F
H
) (i.e., contactaveraged) ground reaction forces. The for
ward orientation of the total GRF vector is represented by the angle >.
FORCE APPLICATION TECHNIQUE IN SPRINT RUNNING Medicine & Science in Sports & Exercise
d
1681
APPLIED SCIENCES
Copyright © 2011 by the American College of Sports Medicine. Unauthorized reproduction of this article is prohibited.
Morin, 2014
330
TOTF, but he was also able to maintain
higher values of HF with increasing speed
during acceleration on the treadmill. This
is illustrated by the DRF index, which was
42.9% higher than the average value for
nationallevel sprinters and 95.2% higher
than the average value of nonspecialists.
Individual RFvelocity linear relationships
(from which DRF is the slope) are detailed
in Figure 5. One interesting observation was
the overall steeper RFvelocity relationship
(i.e. faster decrease in RF with increasing
velocity) as 100 m performance decreased.
These results, obtained in high and toplevel
specialists, clearly conﬁrm those obtained
in the previous study. The better ability to
produce and apply high HF onto the ground
in skilled sprinters comes mostly from a
greater ability to orient the resultant force
vector forward during the entire acceleration
phase, despite increasing velocity and not
from their ability to generate high amounts
of TOTF. Furthermore, the only performance
parameter signiﬁcantly related to the
vertical or resultant force production was
top speed, as previously observed3,4.
It seems that the mechanical explanation
of the 100 m performances of the worldclass
sprinter tested was that on average, during a
6 second sprint on the treadmill, he was only
able to produce the same amount of TOTF as
nationallevel athletes (or even some of the
nonspecialists). However, his outstanding
ability to orient the resultant force with
a forward incline led him to produce a HF
that was 12% higher than his nationallevel
counterparts (one of them is a member of
the national 4 × 100 m relay team) and 22%
higher than for nonspecialists.
Overall, the main and very novel results
of these two studies show that the way
sprinters apply force onto the ground
(technical ability) seems to be more
important to ﬁeld sprint performance than
the amount of total force they are able to
produce (physical capability). In addition,
these two mechanical features of the
acceleration kinetics were not correlated,
which means they represent two distinct
skills. The next and last section of this article
widens the interest of using the IST to
monitor sprint mechanics and performance
in order to potentially help prevent injuries
or handle their rehabilitation process.
2. Three French nationallevel sprinters
with personal best times in 100 m
ranging from 10.31 to 10.61 seconds.
3. A worldclass sprinter whose oﬃcial
best performances are: 9.92 seconds
in the 100 m and 19.80 seconds in the
200 m.
The results clearly showed that the world
class sprinter produced remarkably higher
values of H F than the other ind ividuals,
whereas his vertical and resultant force
production relative to bodyweight were in
the range of those displayed by his national
level counterparts (yet much higher than
for the nonspecialists group). Not only
did the worldclass sprinter produce
higher amounts of HF versus vertical or
CL
Nationallevel
Nonspecialists
0
0 2 4 6 8 10 12 14
20
40
60
80
Ratio of forces (%)
Running velocity (m/s 1)

A betterbalanced
training should consider
how force is transmitted
to the supporting ground,
not only how much force
is produced
Figure 5: Individual RFvelocity linear
relationships during the acceleration phase of
the treadmill sprint for the three populations
compared. At high velocities (>6 m/second),
the best athletes are able to produce a higher
RF at each step: nationallevel athletes more
than nonspecialists (the latter reached top
running velocities around 7 m/second on the
treadmill) and the worldclass sprinter (CL)
more than his nationallevel peers.
SPORTS SCIENCE
Test in g met hod s an d equipment f or forc e, s pe ed and powe r:
Test in g of fo rce and speed in move me nt p at terns specif ic to a th le ti c an d phy si cal
performance
different loads and speeds
TESTING FUNDAMENTALS –PRINCIPLE OF SPECIFICITY
the fastest and slowest subjects presented in Fig. 1show
their relatively higher difference in V0 than in F
H
0 values.
These two individuals also strongly differed in terms of
peak power and optimal velocity, as shown by the com
parison of their power–velocity relationships (Fig. 1).
Concerning GRF production and orientation onto the
ground, Table 3shows that D
RF
index was signiﬁcantly
correlated to all the performance variables considered,
contrary to F
Tot
, which was only signiﬁcantly correlated to
Smax (P=0.034). For the components of this resultant
GRF, F
H
was signiﬁcantly correlated to 100m perfor
mance (P\0.05), whereas F
V
was only correlated to
Smax (P=0.039), and not to S
100
or d
4
.
These correlations between sprint performance (mean
100m speed) and F
Tot
and D
RF
are shown in Fig. 2.
The ability to orient the resultant GRF vector effectively
(i.e. forward) during the acceleration phase on the treadmill
(analyzed through the D
RF
value) strongly differed between
the fastest and slowest individuals tested (Fig. 3). In
addition to being the individuals presenting the extreme
values of 100m time, they had the highest (0.042) and
second lowest (0.091) values of D
RF
of the group.
Contact time and step frequency showed signiﬁcant and
high correlations (P\0.01) with 100m performance
(Table 4), which was also the case of the swing ti me
(P\0.05). However, neither aerial time (P[0.88) nor
step length (P[0.21) was related to sprint performance.
Last, BMI (23.2 ±2.2 kg m
2
) and L/H ratio
(0.522 ±0.014) were not correlated to any of the perfor
mance variables studied (all P[0.29).
Discussion
To our knowledge, since the work of Weyand et al. (2000),
this is the only study to speciﬁcally report experimental
data obtained in a group of subjects ranging from non
specialists to nationallevel sprinters, and to a sub10s
athlete. Since pioneering works about human sprint per
formance published in the late 1920s (Best and Partridge
1928; Furusawa et al. 1927) involving very fast runners
(estimated 100m time of *10.8 s for subject H.A.R.,
probably 1928 Olympian sprinter Henry Argue Russel, in
the study of Furusawa et al. (1927), many studies involved
highlevel athletes (e.g. Karamanidis et al. 2011; Mero and
Table 2 Correlations between mechanical variables (rows) of the force–velocity relationship and power output and 100m performance
variables (columns)
Maximal speed (m s
1
) Mean 100m speed (m s
1
) 4s distance (m)
Maximal power output 0.863 (<0.01)0.850 (<0.01)0.892 (<0.001)
Average power output 0.810 (<0.01)0.839 (<0.01)0.903 (<0.001)
Theoretical maximal horizontal force F
H
0 0.560 (0.052) 0.447 (0.128) 0.432 (0.14)
Theoretical maximal horizontal velocity V00.819 (<0.01)0.735 (0.011)0.841 (<0.01)
Signiﬁcant correlations are reported in bold. Values are presented as Pearson’s correlation coefﬁcient (Pvalues in italics)
Fig. 1 Typical linear force–velocity and 2nd degree polynomial
power–velocity relationships obtained from instrumented treadmill
sprint data for the fastest (100m best time: 9.92 s, 100m time of
10.35 s during the study: black and dark grey circles) and slowest
(100m time of 15.03 s during the study: white and light grey circles)
subjects of this study. All linear and 2nd degree polynomial
regressions were signiﬁcant (r
2
[0.878; all P\0.001)
Table 3 Correlations between mechanical variables of sprint kinetics
measured during treadmill sprints (rows) and 100m performance
variables (columns)
Maximal
speed (m s
1
)
Mean 100m
speed (m s
1
)
4s distance
(m)
Index of force
application
technique D
RF
0.875 (\0.01)0.729 (\0.05)0.683 (\0.05)
Horizontal GRF 0.773 (\0.01)0.834 (\0.01)0.773 (\0.05)
Vertical GRF 0.593 (\0.05) 0.385 (0.18) 0.404 (0.16)
Resultant GRF 0.611 (\0.05) 0.402 (0.16) 0.408 (0.16)
Signiﬁcant correlations are reported in bold. Horizontal, vertical and
resultant GRF data are averaged values for the entire acceleration
phase. Values are presented as Pearson’s correlation coefﬁcient
(Pvalues in italics)
3926 Eur J Appl Physiol (2012) 112:3921–3930
123
Morin, 2012
Biodex System 4 Pro Specifications:
Concentric speed up to 500 deg/sec
Eccentric speed up to 300 deg/sec
Concentric torque up to 680 Nm
Eccentric torque up to 544 Nm
Passive speed as low as .25 deg/sec
oPassive torque as low as .7 Nm
oIsotonic torque as low as .7 Nm
Frequency: 100 Hz (Holmback, 1999)
Source: www.biodex.com
DYNAMOMETRY  ISOKINETIC
www.biodex.com
Biodex System 4 Pro output:
Percent deficit –side comparisons
Peak torque: time to, angle, normalized to body weight
Torq ue : at g iv en a ng le (30 °), time (0,2s)
Work: max rep work, work normalized body weight, work total
set, work first (W1/3)and last 1/3 (W3/3) of reps, work fatigue
(W1/3/W3/3)
Power: average,
Acceleration: time to isokinetic speed
Deceleration: time from isokinetic speed to 0 deg/sec
Agonist to anatagonist ratio (eg HS/QS)
= Highly detailed information about movement of one joint in a
single plane of motion
DYNAMOMETRY  ISOKINETIC
www.biodex.com
Output and performance:
Good validity and reliability (Drouin, 2004; McMaster, 2014)
≠ sprinting performance American football (Kin Isler, 2008)
≠ sprinting performance football (Requena, 2009)
≠ injury risk factors in football (McCall, 2015)
= High validity and reliability, however ability to describe performance is variable
DYNAMOMETRY  ISOKINETIC
LOW HIGH
DEGREE OF KINETIC AND KINEMATIC SPECIFICITY
Lafayette Manual Muscle Testing system
Methodology based upon that of Manual Muscle Testing
that has been used since early 1900´s
Reliability and accuracy is questionable (Cuthbert, 2007)
Load range: 01335N
Accuracy: ±1% over full scale
Resolution: (0,1N (0999N)/ 1N (10001335N))
Frequency: ? (100 Hz have been described)
DYNAMOMETRY – HAND HELD
www.uwlax.edu
www.aspetar.com
Output:
Peak Force
Time to reach peak force
Tota l te st t im e
Time within selected ranges
Average force
DYNAMOMETRY – HAND HELD
www.uwlax.edu
www.aspetar.com
Output and performance:
Reliability: very good for different joint motions
(Bohannon, 1985)
Hip external rotation strength and back and lower
extremity injury risk (Leetun, 2004)
Greater shoulder strength in pitchers without pain (Trakis,
2008)
Injury risk recreational runners (Niemuth, 2005)
Limited information on performance
DYNAMOMETRY – HAND HELD
LOW HIGH
DEGREE OF KINETIC AND KINEMATIC SPECIFICITY
Test in g re pe ti ti on m ax im um
Direct or indirect measurement
Based upon Olympic and strength and power lifting
Measurement = mass
FREE WEIGHTS
www.iwf.com
Output and performance:
≠Change of direction/agility (Brughelli, 2008)
= Sprint running and vertical jump height (Wisløff, 2004)
Can measurement be enhanced?
FREE WEIGHTS
www.iwf.com
Technology to e nh ance meas ure me nt :
Forceplate (AMTI, frequency: 1002000 Hz)
Linear encoder (MuscleLab, frequency: 200Hz)
Accelerometer/IMU (Beast, frequency 50Hz)
Motion capture (Elite Form, frequency 30 Hz)
Measurement:
Force, rate of force development, speed, power, distance, work
FREE WEIGHTS
LOW HIGH
DEGREE OF KINETIC AND KINEMATIC SPECIFICITY
www.amti.biz www.thisisbeast.com
www.musclelab.com
www.eleiko.com
Keiser (www.keiser.com)
Hydraulic brake
Load range: 0400 kg (dependent compressor)
Frequency: none given (400 Hz with modifications Earles, 2001)
PNEUMATIC TECHNOLOGY
www.keiser.com
Output and performance:
Good reliability strength (1RM) (Lebrasseur, 2008) and power (Thomas, 1996)
Duration for each rep start and stop, and total duration
Peak velocity, acceleration, force and power for each rep
Workout averages and peak velocity, acceleration, force and power
Position, velocity, force and time for peak power
Tota l wo rk a nd w or k pe r li mb
User reaction time
PNEUMATIC TECHNOLOGY
Shortcomings output and performance:
Isotonic resistance
Concentric phase only
Load and not velocity manipulated
Only vertical movement
PNEUMATIC TECHNOLOGY
LOW HIGH
DEGREE OF KINETIC AND KINEMATIC SPECIFICITY
www.keiser.com
ROBOTIC TECHNOLOGY
1080 Motion
Electric motor (Omron Servo), controlled by patented PLC algorithms
Drum and line (5 110m)
Models: 1080 Quantum and Sprint
Load and speed can be set independently in the concentric and eccentric
phases of movement
Horizontal and vertical movement patterns
1080 Quantum Single 1080 Quantum Syncro 1080 Sprint
ROBOTIC TECHNOLOGY
Quantum single Quantum syncroaSprint
Gear 1 2 1 2
Concentric Load 1 – 25 kg (255lbs) 1 – 50 kg (2110lbs) 27 –75 kg
(59 175 lbs)
27 – 125 kg
(59 386 lbs)
015 kg (033 lbs)
Eccentric Load 1 – 37 kg (282 lbs)1 – 74 kg (2163lbs) 27 –100 kg
(59 220 lbs)
27 –175 kg
(59 386 lbs)
015 kg (033 lbs)
Max con/ecc 3 sec 75 kg (165 lbs) 150 kg (331 lbs) 175 kg (386 lbs) 325 kg (717 lbs) 30 kg (66 lbs) (10 sec)
45 kg (99 lbs) (3 sec)
Concentric/eccentric
speed (m/s)
0.1 –8.0 / 0,16,0 0.1 –4.0 / 0,13,0 0.1 –8.0 / 0,1 6,0 0.1 –4.0 / 0,1 3,0 0,114 /0,114
Frequency
(recorded/output) (Hz)
333/111 333/111 333/111 333/111 333/111
aExternal load (bar) included.Additional load possible as well
ROBOTIC TECHNOLOGY SETTINGS
Gear
Gear 1 – lower load, greater speed
Gear 2 –greater load, lower speed
Normal Weight –mimics normal load and inertia
Isotonic –constant load
No Flying Weight rapid decrease in momentum with a
decrease in force application by user. Ideal for high
velocity power training
Isokinetic –with any setting
Advanced –eccentric boost
1080 Quantum Single
1080 Quantum Syncro
ROBOTIC TECHNOLOGY –ACCURACY OF MEASUREMENT
Position
1080 sprint: error 4,6 mm/m
1080 Quantum: error 4,0 mm/m
Time
1 sec drift internal clock over 1 hr
Speed
Max error: 5 mm/s
Force
1080 Sprint: ±4,8N (95% CI)
1080 Quantum: ±8,0N (95% CI)
Repeatbility
±0,7% (Omron ±1,0%)
ROBOTIC TECHNOLOGY OUTPUT
Output and performance
Force: peak, average
Speed: peak, average
Power: peak, average
Distance
Time: total and intervals
(Acceleration: peak, average)
(Work: repetition, sets)
1080 QUANTUM OUTPUT
Quantification (Hz): force,speed and
power
Squat (1RM) Normal
Isotonic
Isokinetic
1080 QUANTUM OUTPUT
Quantification: force, speed and power
B arm rotation pull (10% BW)
Force
Speed
Power
1080 QUANTUM OUTPUT
Quantification: force, speed and power
Anterior jump (10% BW)
1080 SPRINT APPLICATION
Repeated cyclical motions:
Running
Swimming
Skiing
Skating
Agility (tennis, football, basketball, American football etc)
2
There are a multitude of applications where 1080 Sprint enables a more effective training and a
unique ability to measure true sports performance. Tests conducted on elite level athletes
confirm the usefulness and applications of the system in modern day training.
Visit us online or contact us for more information.
www.1080motion.com
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1080 SPRINT
Power symmetrical
Power asymmetrical
Speed symmetrical
Speed asymmetrical
1080 SPRINT
Speed 1080 Sprint
Speed Usain Bolt
1080 SPRINT
17.08.2009, 20:00
Race distribution: LAVEG measurement curve (blue) and average speed (red)
Split times [s]
Average velocities at 10m, 20m, ... 100m [m/s]
Vmx
at m
V99%
at m
12,27
65,03
12,15
48,18
Vmax is the maximual velocity of 12,27m/s, reached at 65m
V99 is 99% of the maximal velocity, reached at 48,18m
Reaction time t10 t20 t30 t40 t50 t60 t70 t80 t90 t100
Bolt 0,146 1,89 2,88 3,78 4,64 5,47 6,29 7,10 7,92 8,75 9,58
Powell 0,134 1,87 2,90 3,82 4,70 5,55 6,39 7,23 8,08 8,94 9,84
V10 V20 V30 V40 V50 V60 V70 V80 V90 V100
Bolt 5,29 10,10 11,11 11,63 12,05 12,20 12,35 12,20 12,05 12,05
Powell 5,35 9,71 10,87 11,36 11,76 11,90 11,90 11,76 11,63 11,11
1080 SPRINT APPLICATION
Testi ng protocol
•5, 7, 9 kg resistance
variables. In a similar population, the typical error
has been reported to be of 2.2% (90% conﬁdence
limits, 1.9; 2.5) and 1.4% (1.2; 1.6) for Acc and
MSS, respectively (Buchheit & MendezVillanueva,
2013).
Horizontal mechanical proﬁle
Instantaneous speed was measured continuously
throughout the entire sprint via a 100Hz laser
(Laveg 300 C, Jenoptik, Germany) and used to
derive, for each individual, linear horizontal force–
velocity relationships, from which horizontal force–
velocity proﬁle (F–Vproﬁle, slope of the F–Vrela
tionship), theoretical maximal velocity (V
0
), horizon
tal force (F
0
) and horizontal power (P
max
) were
calculated (Mendiguchia et al., 2014; Samozino,
Morin et al., 2013)(Figure 1).
Statistical analyses
Data in the text and ﬁgures are presented as means
with 90% conﬁdence limits (CL) and intervals (CI),
respectively. All data were ﬁrst logtransformed to
reduce bias arising from nonuniformity error. The
reliability of each mechanical variable was assessed
while calculating both the typical error of measure
ment, expressed as a coefﬁcient of variation (CV,
90% CL), and the intraclass correlation coefﬁcient
(ICC, 90% CL) (Hopkins, Marshall, Batterham, &
Hanin, 2009). To allow the analysis of the relation
ships between the different variables for all players
pooled together, despite the agerelated differences in
absolute performances, data were expressed as a per
centage of each age group mean (Buchheit, 2012).
First, the respective horizontal mechanical determi
nants of Acc and MSS were assessed using multiple
linear regression models (stepwise backward elimina
tion procedure), with Acc and MSS as the dependent
variables and V
0
,F
0
,P
max
and F–Vproﬁle as the
independent variables. In the backward procedure,
variables with Fvalue <4 were removed from the
model. The following criteria were adopted to inter
pret the magnitude of the correlation (r, 90% CI):
≤0.1, trivial; >0.1–0.3, small; >0.3–0.5, moderate;
>0.5–0.7, large; >0.7–0.9, very large; and >0.9–1.0,
almost perfect. If the 90% CI overlapped small posi
tive and negative values, the magnitude was deemed
unclear; otherwise that magnitude was deemed to be
the observed magnitude (Hopkins et al., 2009).
Within each agegroup, players were also classiﬁed
as High Acc/Fast MSS (>2% faster than group
mean), medium (between −2% and +2% of group
mean), and Low/Slow (>2% slower than group
mean). The 2% threshold was based both on
withinplayer CV for Acc and MSS (Buchheit &
MendezVillanueva, 2013) and was within the range
of a ﬁfth of the betweenplayer SD within each age
group (i.e., the socalled smallest worthwhile change
(Hopkins et al., 2009)). Between speed group, stan
dardised differences in horizontal mechanical deter
minants were calculated using pooled standard
deviations. Threshold values were >0.2 (small),
>0.6 (moderate), >1.2 (large) and very large (>2)
(Hopkins et al., 2009). Uncertainty in each effect
was expressed as 90% CL and as probabilities that
the true effect was substantially positive and negative.
These probabilities were used to make a qualitative
probabilistic mechanistic inference about the true
effect: if the probabilities of the effect being substan
tially positive and negative were both >5%, the effect
was reported as unclear; the effect was otherwise
clear and reported as the magnitude of the observed
value. The scale was as follows: 25–75%, possible;
75–95%, likely; 95–99%, very likely; >99%, almost
certain. Data for groups with limited sample size
(High/Slow, n=2andMed/Fast,n=4)arepre
sented in the results, but were not included in the
group comparison analysis.
Results
For the reliability analysis, we observed a CV of
1.6% (90% CL, 1.4;1.9) and an ICC 0.97
0
1
2
3
4
5
6
7
8
9
10
0123456
Velocity (m · s–1)
Time (s)
Player A
Player B
0
2
4
6
8
10
12
14
16
0246810
Horizontal Force (N · kg–1)
pr Power output (W · kg–1)
Velocit
y
(
m · s–1
)
Power Player A
Power Player B
Force/Velocity Player A
Force/Velocity Player B
Figure 1. Actual and modelled velocity proﬁles of two representa
tive players throughout the 40m sprint (upper panel) and their
associated mechanical horizontal proﬁles (lower panel).
1908 M. Buchheit et al.
Downloaded by [Norges Idrettshoegskole] at 11:11 15 January 2015
Bucheit, 2014
1080 SPRINT APPLICATION
Variable resistance:
•Set initial load
•Set load at given speed
•Iinear function
8
Velo city p e r st ep o f Ne w to nia n Mod e l
y = 2E05x4 + 0.0018x3  0.0637x2 +
1.1073x + 3.5238
R2 = 0.9998
0
2
4
6
8
10
12
14
0481216202428
Number of steps
Velocity (m/s)
Contact time per step of Newtonian
Mod el
y = 0.0146x3 + 0.8411x2  16.317x +
192.64
R2 = 0.9991
0
20
40
60
80
100
120
140
160
180
200
0481216202428
Number of steps
Contact time (ms)
Graph 1: Graph of velocity per stride showing the
formula that allows calculation of velocity for each
individual step (see Appendix 2)
Graph 2: Graph of contact time per stride showing
the formula that allows calculation of contact time
for each individual step (see Appendix 3)
Power per step of Newtonian Model
500
1000
1500
2000
2500
3000
3500
0481216202428
Number of steps
Power (Watts)
Force pe r step of New tonian Model
0
10
20
30
40
50
60
70
80
90
0481216202428
Numbe r of step s
Force (kg)
Graph 3: Graph of power per step. Graph 4: Graph of horizontal force per step.
The software provides a facility to determine the equation of the line of best fit to the data
which resulted in the following equations for this particular model:
Velocity per step = 0.00002x4 + 0.0018x3 – 0.0637x2 + 1.1073x + 3.5238 (R2 = 0.9998)
Contact time per step = 0.0146x3 + 0.8411x2 – 16.317x + 192.64 (R2 = 0.9991)
Power per step = 0.00005x6  0.0052x5 + 0.1642x4  0.7955x3  40.83x2 + 498.17x + 1735.1
(R2 = 0.9999) from the first step onwards.
Force per step = 0.000005x5 + 0.0001x4 + 0.0073x3 + 0.1983x2  0.6454x + 49.623
(R2 = 1) from the first step onwards
Discussion
For the calculated forces of the Newtonian model to be similar to real measurements the
estimation of drag force also needs to be real. Using the formula developed by Hill (1927),
the drag force due to air resistance was calculated to be 28N at a velocity of 10.10m/s which
is similar to that found by Pritchard & Pritchard (1994) of 27N at the same velocity. Davies
8
Velo city p e r st ep o f Ne w to nia n Mod e l
y = 2E05x4 + 0.0018x3  0.0637x2 +
1.1073x + 3.5238
R2 = 0.9998
0
2
4
6
8
10
12
14
0481216202428
Number of steps
Velocity (m/s)
Contact time per step of Newtonian
Mod el
y = 0.0146x3 + 0.8411x2  16.317x +
192.64
R2 = 0.9991
0
20
40
60
80
100
120
140
160
180
200
0481216202428
Number of steps
Contact time (ms)
Graph 1: Graph of velocity per stride showing the
formula that allows calculation of velocity for each
individual step (see Appendix 2)
Graph 2: Graph of contact time per stride showing
the formula that allows calculation of contact time
for each individual step (see Appendix 3)
Power per step of Newtonian Model
500
1000
1500
2000
2500
3000
3500
0481216202428
Number of steps
Power (Watts)
Force pe r step of New tonian Model
0
10
20
30
40
50
60
70
80
90
0481216202428
Numbe r of step s
Force (kg)
Graph 3: Graph of power per step. Graph 4: Graph of horizontal force per step.
The software provides a facility to determine the equation of the line of best fit to the data
which resulted in the following equations for this particular model:
Velocity per step = 0.00002x4 + 0.0018x3 – 0.0637x2 + 1.1073x + 3.5238 (R2 = 0.9998)
Contact time per step = 0.0146x3 + 0.8411x2 – 16.317x + 192.64 (R2 = 0.9991)
Power per step = 0.00005x6  0.0052x5 + 0.1642x4  0.7955x3  40.83x2 + 498.17x + 1735.1
(R2 = 0.9999) from the first step onwards.
Force per step = 0.000005x5 + 0.0001x4 + 0.0073x3 + 0.1983x2  0.6454x + 49.623
(R2 = 1) from the first step onwards
Discussion
For the calculated forces of the Newtonian model to be similar to real measurements the
estimation of drag force also needs to be real. Using the formula developed by Hill (1927),
the drag force due to air resistance was calculated to be 28N at a velocity of 10.10m/s which
is similar to that found by Pritchard & Pritchard (1994) of 27N at the same velocity. Davies
V0=7kg
V1=7m/s, load 4kg
ROBOT TECHNOLOGY APPLICATION
Bucheit, 2014
variables. In a similar population, the typical error
has been reported to be of 2.2% (90% conﬁdence
limits, 1.9; 2.5) and 1.4% (1.2; 1.6) for Acc and
MSS, respectively (Buchheit & MendezVillanueva,
2013).
Horizontal mechanical proﬁle
Instantaneous speed was measured continuously
throughout the entire sprint via a 100Hz laser
(Laveg 300 C, Jenoptik, Germany) and used to
derive, for each individual, linear horizontal force–
velocity relationships, from which horizontal force–
velocity proﬁle (F–Vproﬁle, slope of the F–Vrela
tionship), theoretical maximal velocity (V
0
), horizon
tal force (F
0
) and horizontal power (P
max
) were
calculated (Mendiguchia et al., 2014; Samozino,
Morin et al., 2013)(Figure 1).
Statistical analyses
Data in the text and ﬁgures are presented as means
with 90% conﬁdence limits (CL) and intervals (CI),
respectively. All data were ﬁrst logtransformed to
reduce bias arising from nonuniformity error. The
reliability of each mechanical variable was assessed
while calculating both the typical error of measure
ment, expressed as a coefﬁcient of variation (CV,
90% CL), and the intraclass correlation coefﬁcient
(ICC, 90% CL) (Hopkins, Marshall, Batterham, &
Hanin, 2009). To allow the analysis of the relation
ships between the different variables for all players
pooled together, despite the agerelated differences in
absolute performances, data were expressed as a per
centage of each age group mean (Buchheit, 2012).
First, the respective horizontal mechanical determi
nants of Acc and MSS were assessed using multiple
linear regression models (stepwise backward elimina
tion procedure), with Acc and MSS as the dependent
variables and V
0
,F
0
,P
max
and F–Vproﬁle as the
independent variables. In the backward procedure,
variables with Fvalue <4 were removed from the
model. The following criteria were adopted to inter
pret the magnitude of the correlation (r, 90% CI):
≤0.1, trivial; >0.1–0.3, small; >0.3–0.5, moderate;
>0.5–0.7, large; >0.7–0.9, very large; and >0.9–1.0,
almost perfect. If the 90% CI overlapped small posi
tive and negative values, the magnitude was deemed
unclear; otherwise that magnitude was deemed to be
the observed magnitude (Hopkins et al., 2009).
Within each agegroup, players were also classiﬁed
as High Acc/Fast MSS (>2% faster than group
mean), medium (between −2% and +2% of group
mean), and Low/Slow (>2% slower than group
mean). The 2% threshold was based both on
withinplayer CV for Acc and MSS (Buchheit &
MendezVillanueva, 2013) and was within the range
of a ﬁfth of the betweenplayer SD within each age
group (i.e., the socalled smallest worthwhile change
(Hopkins et al., 2009)). Between speed group, stan
dardised differences in horizontal mechanical deter
minants were calculated using pooled standard
deviations. Threshold values were >0.2 (small),
>0.6 (moderate), >1.2 (large) and very large (>2)
(Hopkins et al., 2009). Uncertainty in each effect
was expressed as 90% CL and as probabilities that
the true effect was substantially positive and negative.
These probabilities were used to make a qualitative
probabilistic mechanistic inference about the true
effect: if the probabilities of the effect being substan
tially positive and negative were both >5%, the effect
was reported as unclear; the effect was otherwise
clear and reported as the magnitude of the observed
value. The scale was as follows: 25–75%, possible;
75–95%, likely; 95–99%, very likely; >99%, almost
certain. Data for groups with limited sample size
(High/Slow, n=2andMed/Fast,n=4)arepre
sented in the results, but were not included in the
group comparison analysis.
Results
For the reliability analysis, we observed a CV of
1.6% (90% CL, 1.4;1.9) and an ICC 0.97
0
1
2
3
4
5
6
7
8
9
10
0123456
Velocity (m · s–1)
Time (s)
Player A
Player B
0
2
4
6
8
10
12
14
16
0246810
Horizontal Force (N · kg–1)
pr Power output (W · kg–1)
Velocit
y
(
m · s–1
)
Power Player A
Power Player B
Force/Velocity Player A
Force/Velocity Player B
Figure 1. Actual and modelled velocity proﬁles of two representa
tive players throughout the 40m sprint (upper panel) and their
associated mechanical horizontal proﬁles (lower panel).
1908 M. Buchheit et al.
Downloaded by [Norges Idrettshoegskole] at 11:11 15 January 2015
1080 SPRINT OUTPUT
Test re su lt s fr om t wo ol ym pi c sw im me rs –can you spot the difference and
asymmetry in the force measures below?
CONCLUSION
There are many different technologies available to test the physical factors
force, speed and power in isolated joints movements to global movement
patterns. Based on the specificity of the task, function or performance it seems
that pneumatic and robotic technology offers some interesting possibilities to
test these physical factors in movement patterns with a high degree of
specificity to different tasks, functions or performances
Thank you for your attention
Ola.eriksrud@nih.no
REFERENCE CASES
1080 QUANTUM RESULTS
Horizontal to vertical power comparisons in English Premiere League
(soccer) (n=26)
y"="$0,0532x"+"7,4886"
R²"="0,00336"
0,00"
2,00"
4,00"
6,00"
8,00"
10,00"
12,00"
0,00" 5,00" 10,00" 15,00" 20,00"
!"#$%&'(#&'()&'*%(+*'&&,.(+&/012#3&
!"#$%&'(#&45,(+"+&,.(+&/012#3&
Series1"
Linear"(Series1)"
y"="0,1407x"+"5,1828"
R²"="0,03634"
0,00"
2,00"
4,00"
6,00"
8,00"
10,00"
12,00"
14,00"
0,00" 5,00" 10,00" 15,00" 20,00"
Le#$leg$right$lateral$$power$(W/kg)$
Le#$leg$superior$power$(W/kg)$
Series1"
Linear"(Series1)"
Conclusion: There is a need to consider the development of horizontal
power in both testing and training especially considering that research has
shown that vertical power has variable carryover on horizontal power such
as agility and change of direction
1080 QUANTUM RESULTS
Rotational power and club head speed (n=18)
y"="0,039x"+"63,561"
R²"="0,66466"
0"
20"
40"
60"
80"
100"
120"
140"
0" 500" 1000" 1500" 2000"
Club%head%speed%(mph)%
Right%leg%le3%rota6onal%power%(W)%
Series1"
Linear"(Series1)"
y"="0,0451x"+"53,983"
R²"="0,69246"
0"
20"
40"
60"
80"
100"
120"
140"
0" 500" 1000" 1500"
Club%head%speed%(mph)%
Le0%leg%right%rota6onal%power%(W)%
Series1"
Linear"(Series1)"
Conclusion: Rotational power is fundamental to performance in many
sports such as golf. 1080 Quantum allows for accurate measurement and
carefully documented traning of rotational power rotational power is
highly correlated with performance in golf
1080 QUANTUM RESULTS
Case Study: training effect (6 weeks) of silver medalist in classic power
lifting in South Africa in 2014 (Squat)
1080 QUANTUM RESULTS
Case Study: classic power lifting with force curves from 1080 Quantum pre
and post 6 week training period
Pre
Post
1080 QUANTUM RESULTS
Case Study: Power training baseball
October 2013: Ball speed from peg 85.1 mph
Feb 2014: Ball speed from peg 90.1 mph
Conclusion: specificity of force and power development (rotational)
created large increase in horizontal power performance.