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Reliability and Sensitivity of the Notio Konect to quantify Coefficient of Drag Area in Elite Track Cyclists

  • Royal Dutch Cycling Federation (KNWU)


Elite level cycling events are performed at speeds in excess of 50 km/h. At these speeds, over 90% of the resistance forces come from aerodynamic resistance (CDA). Recently bicycle-mounted pitot tubes, such as the Notio Konect (NK) have become more commercially available making CDA easier to measure. Its reliability and sensitivity would be useful for riders and coaches to be able to understand what constitutes as a change in CDA. Accordingly, the aim of this study was to establish the intra- and inter-effort reliability and sensitivity of the CDA measures of the NK. Seven elite level track riders were used in this study which was broken into two parts: 1) Reliability and 2) Sensitivity. For both parts of the experiment, riders performed identical efforts, riding at ∼50 km/h for six laps of a 250m indoor velodrome. For reliability, the riders performed six efforts without any changes in position or resistance. For sensitivity, they performed the efforts with a rod with discs of a known diameters attached at each end to vary the CDA by a known amount. For the reliability assessment, low coefficient of variation of intra- (0.2%) and inter-effort (0.9%) reliability were measured. With regards to sensitivity, the smallest changes in resistance (from 5 – 6cm i.e., 1.2% or 0.002m²) was identified by the NK. The data in this experiment suggests that the NK is a highly reliable in measuring CDA can detect changes up to at least 1.2% in an indoor velodrome using elite level track riders.
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European Journal of Sport Science
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Reliability and Sensitivity of the Notio Konect to
quantify Coefficient of Drag Area in Elite Track
Mehdi Kordi , Gert Galis , Teun van Erp & Wouter Terra
To cite this article: Mehdi Kordi , Gert Galis , Teun van Erp & Wouter Terra (2021): Reliability and
Sensitivity of the Notio Konect to quantify Coefficient of Drag Area in Elite Track Cyclists, European
Journal of Sport Science, DOI: 10.1080/17461391.2021.1891296
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Publisher: Taylor & Francis & European College of Sport Science
Journal: European Journal of Sport Science
DOI: 10.1080/17461391.2021.1891296
Reliability and Sensitivity of the Notio Konect to quantify Coefficient of Drag Area in Elite
Track Cyclists
Mehdi Kordi1,2, Gert Galis3, Teun van Erp4 and Wouter Terra3
1Royal Dutch Cycling Federation (KNWU), Papendal, Arnhem, Netherlands
2 Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle,
United Kingdom
3Aerospace Engineering Department, TU Delft, Delft, Netherlands
4Division of Orthopaedic Surgery, Faculty of Medicine and Health Sciences, Stellenbosch
University, Tygerberg, South Africa.
Corresponding Author:
Mehdi Kordi
Royal Dutch Cycling Federation (KNWU)
Papendal 49
Elite level cycling events are performed at speeds in excess of 50 km/h. At these
speeds, over 90% of the resistance forces come from aerodynamic resistance (CDA). Recently
bicycle-mounted pitot tubes, such as the Notio Konect (NK) have become more
commercially available making CDA easier to measure. Its reliability and sensitivity would be
useful for riders and coaches to be able to understand what constitutes as a change in CDA.
Accordingly, the aim of this study was to establish the intra- and inter-effort reliability and
sensitivity of the CDA measures of the NK. Seven elite level track riders were used in this
study which was broken into two parts: 1) Reliability and 2) Sensitivity. For both parts of the
experiment, riders performed identical efforts, riding at ~50 km/h for six laps of a 250m
indoor velodrome. For reliability, the riders performed six efforts without any changes in
position or resistance. For sensitivity, they performed the efforts with a rod with discs of a
known diameters attached at each end to vary the CDA by a known amount. For the reliability
assessment, low coefficient of variation of intra- (0.2%) and inter-effort (0.9%) reliability
were measured. With regards to sensitivity, the smallest changes in resistance (from 5 – 6cm
i.e., 1.2% or 0.002m2) was identified by the NK. The data in this experiment suggests that the
NK is a highly reliable in measuring CDA can detect changes up to at least 1.2% in an indoor
velodrome using elite level track riders.
Keywords: Aerodynamics; Performance; Testing; Velodrome.
The speeds that are reached in elite cycling races, generally range between 50 km/h in
road race time trials (TT) and can reach almost 80 km/h in track sprinters. The mechanical
power to overcome aerodynamic resistance is a third-order polynomial of the velocity and
thus, at these high velocities, it is as very important to reduce or minimise aerodynamic drag
as well as maximising mechanical power output in order to improve performance
(Underwood & Jermy, 2010). At these forementioned velocities, the aerodynamic drag
contributes to more than 90% of the overall resistance riders need to overcome (e.g. Martin et
al., 2006). Hence, reducing the aerodynamic drag is a widely used approach in order to
increase a rider‟s speed and, in turn, improve its performance.
The „gold standard‟ for measuring the aerodynamic drag is by installing a rider and
the bike on a force balance in a wind tunnel, resulting in highly repeatable conditions, testing
with professional cyclists in a wind tunnel the coefficient of variation is generally around 1%
(Juan García-López et al., 2008) and high precision measurement of the aerodynamic forces.
However, the wind tunnel environment generally differs from the conditions a rider
experiences on a track or outdoors (e.g. model supports, turbulence characteristics of the
flow) and, hence, the obtained results may not always be representative for race conditions.
The aerodynamic drag is also evaluated in the field, for example by means of towing
methods, coast-down methods and mathematical models (Debraux et al., 2011). The latter,
calculates the aerodynamic drag or drag area (CDA) of a rider using the measured mechanical
power output and speed (Martin et al., 2007; Underwood & Jermy, 2010). However, these
methods usually make use of a number of assumptions, for example, default rider trajectory
and stagnant air (Lukes et al., 2012), which generally do not match the real conditions. Whilst
the CDA measures of the aforementioned methods have not been compared extensively in the
literature, it is likely that each method will produce different CDA values. As a consequence,
comparing the absolute CDA measures between methods is futile. Understanding the
reliability and sensitivity of each methodology will be more practical and beneficial to riders,
coaches and practitioners.
One of the commercially available products for aerodynamic drag evaluation in the
field using mathematical models is the Notio KonectTM (NK). A particular advantage of the
NK is its capability to measure the speed of the rider relative to the air using a pressure sensor
mounted in front of the handlebars. This allows to use the actual air speed in the
mathematical model, instead of using the measured ground speed and assuming stagnant air
conditions, and so avoid one source of error. A previous study has tried to investigate the
validity, reliability and sensitivity of the NK (Valenzuela et al., 2020). The authors concluded
that the aerodynamic drag measured with the NK is comparable to that obtained by other
mathematical models (e.g. Track Aero System) when riding in an indoor velodrome. In
addition, whilst the NK could detect large (or more obvious) differences in CDA (in this case,
between an upright position and a TT position), it remained unclear if the NK can detect
smaller differences. For example, they could not detect any significant differences between
wearing a training helmet or a more aerodynamic racing helmet. The present authors identify
three main limitations to this study. Firstly, it was unclear what differences to expect when
changing helmets between riders because of a missing benchmark. Secondly, the study did
not systematically vary rider configuration in order to determine the accuracy of the
measured drag area. Note, that this is also not possible without the benchmark. Finally, the
CDA was only measured for one minute rather than a given number of full laps, which could
have influenced the final CDA measures (Lukes et al., 2012) and, related to it, no inter-effort
reliability has been established (i.e. comparing the reliability between laps in the same effort).
Being able to further know the (intra- [within effort] and inter-[between effort]) reliability
and sensitivity of the NK would help riders, coaches and support staff to make informed and
evidenced-based decisions of which positions and/or attire optimise CDA and therefore,
Accordingly, there were two aims of this study. Firstly, to ascertain the intra- and
inter-effort reliability of the NK for measurement of the CDA of elite track cyclists in an
indoor velodrome setting. Secondly, to evaluate its sensitivity against a known CDA
In total 7 (4 women and 3 men) elite level track endurance riders volunteered for this
experiment. All were either currently competing at senior World or European championship
level (including two current World Champions). All riders participated in experiment 1. Of
those, 4 riders participated in experiment 2. They performed the experiment(s) as part of their
training and on their preferred track bike set-up. Three participants performed the efforts on
their TT bike holding a TT position, while four participants held a dropped position on their
bunch bikes with handlebars similar to those of regular road bikes. Before their involvement,
the riders were informed of the purpose and potential risks of the study and provided written
informed consent. All riders were healthy and had no underlying injuries or health conditions.
Study Design
This study sets-out to ascertain the reliability and sensitivity of the NK and the
cyclists as a system rather than the reliability and sensitivity of the NK. The study was split
into two experiments to assess the reliability and sensitivity of the NK. In experiment 1, the
inter- and intra-effort reliability was assessed by comparing the measured CDA of 6 identical
efforts. The goal of experiment 2 was to determine the sensitivity of the NK. Discs of
increasing size (5, 6, 8 and 10cm) were mounted to the bike by means of a one-meter-long
rod (Figure 1). Discs have been selected as a means to add aerodynamic drag to the system,
in contrast to spheres for example, because their drag area remains constant along all the
speeds considered in this study. Considering the relatively large distance between the discs
and the rider (~40 cm), it can be assumed that the interaction of the flow around the discs and
the rider is negligible and, hence, the aerodynamic drag of the discs can be obtained from
wind tunnel measurements in absence of a rider. Both experiments were performed in an
indoor 250m velodrome (Omnisport, Apeldoorn, The Netherlands) and the average
conditions were: 53.1% humidity, 1015kPa and 27oC. In order to minimise any
environmental influences (e.g., wind and turbulence), the environmental conditions of the
velodrome were kept constant (i.e., all doors closed, and the velodrome track was used
exclusively without any other riders). Each experimental session was carried out on separate
days but within 1 – 7 days of each other. Each rider had a statically calibrated portable power
crank (SRM, Jülich, Welldorf, Germany), magnet-based speedometer (SRM, Jülich,
Welldorf, Germany) attached to their bikes which were wireless connected to a NK unit
(Notio Technologies, Montréal Canada) which was mounted on the handlebars of the bike.
Each rider rode the same front 5-spoke and a rear disc wheel which were each inflated to 1.1
MPa. The tyres (Diamant, DuGast, The Netherlands) used had an assumed Crr of 0.0025
which is what has been used before (J. García-López et al., 2014; Martin et al., 2006). Each
rider wore their own race attire (e.g. helmets, skinsuit and socks) throughout.
Before each session, using previous training and race data, a pre-defined speed was
agreed with each rider to perform the calibration and efforts (the group average speed for the
men: 52.2 ± 2.8 km/h [14.5 ± 0.8 m/s]; and the group average for the women: 46.0 ± 1.5
km/h [12.8 ± 0.4 m/s]). The rider performed a 2 x 3000m calibration run (with two laps of
light pedalling in between each 3000m) as per manufactures guidelines. Subsequent to the
calibration efforts, the respective experimental session was performed. Each effort was aimed
to be identical: the system (rider and bicycle) mass was measured then the rider was given 2-
lap build-up to their target speed. Once they had reached their target speed, they were asked
to hold a constant position and average lap speed for 6 laps. Real-time lap splits were
communicated by a coach that was trackside each lap. The riders also had access to their real-
time speed, power and cadence via their cycling computer that was attached to their bike.
In experiment 1, the riders performed six identical efforts (i.e. 2 build-up laps and 6
laps at constant speed) without any changes or modifications to their bicycle. The riders were
given approximately 10 min passive rest between each effort. During experiment 2, an
aerofoil shaped rod was attached to the handlebars of the bicycles with a disc of 5, 6, 8 or 10
cm in diameter attached to both ends of the rod (Figure 1). Prior to the data collection for
experiment 2, the CDA of the rod and each set of discs was measured in a wind tunnel at 14
m/s (which equates to 50.4 km/h). The drag increments between these configurations are used
to benchmark the expected change in riders‟ CDA in comparison to the NK data. When the
changes in CDA measurements of the discs in the wind tunnel were compared to theoretical
calculations, an average difference of 0.0003 ± 0.0001 m2 was observed. The addition of
CDA from the discs was also calculated theoretically showed was 0.0003 m2 from the Similar
to experiment 1, riders performed two separate efforts (i.e. 2 build-up laps and 6 laps at
constant speed) with each disc set attached to their bike. The estimated CDA of the rider was
ascertained (again) from another preliminary calibration run and the additional CDA for each
disc set was added. The relationship between changes (both absolute [m2] and relative [%]) in
CDA measured from the NK and the calculated CDA were compared.
Data Analysis
For both experiments, the NK edition of Golden Cheetah (
was used to process and calculate an average CDA. The principles of the CDA calculations are
based from previously published literature (Martin et al., 1998). Each lap was identified
using the “Velodrome” function. This function differentiates laps by using the inbuilt
gyroscope to identify every other peak roll value (i.e. maximum lean angle) and well as
setting all recorded elevation values (the most variable measure) to zero then performs the
calculations, accordingly.
Absolute reliability was measured using absolute standard error and calculated
relative reliability using coefficient of variation (CV), standardised typical error and
intraclass correlation coefficient (ICC). CV was used to measure inter-effort reliability by
comparing of the individual lap CDA measures from experiment 1. Intra-effort reliability was
measured by taking the average CDA of each 6-lap effort and comparing it between efforts
during experiment 1. For standardised typical error, the results were doubled prior to
interpretation using modified effect size thresholds (trivial, ≤ 0.2; small, > 0.2–0.6; moderate,
> 0.6–1.2; large, > 1.2) as previously advocated (Smith & Hopkins, 2011). ICC was
interpreted according to the following thresholds: high, > 0.90; moderate, 0.80–0.90; low, <
0.80 (Vincent, 2012). Raw and relative typical error, as well as ICC, were determined by
using the MS Excel Reliability spreadsheet developed by Hopkins (Hopkins, 2015). Results
are presented as mean ± SD or mean (± 90% CI)
Experiment 1
As a group average, the reliability trials were performed at 49.1 ± 3.5 km/h and 334 ±
64 W. The individual and average ±SD intra- and inter-effort CV and CDA are summarised in
Table 1. Intra-effort CV average was 0.20% and ranged from 0.16 – 0.69%. Absolute typical
error was 0.0017 (0.0013 – 0.0023) m2 and standardised typical error was 0.13 (0.11 – 0.18).
Both of which are classed as a trivial effect size. ICC was 0.99 (0.98 – 1.00), which
represented high repeatability. Inter-effort CV average was 0.90% and ranging from 0.46 –
1.52%. Absolute typical error was 0.00158 (0.00122 – 0.00232) m2 and standardised typical
error was 0.12 (0.09 – 0.18) which is classed as a trivial effect size. ICC was 0.99 (0.98 –
1.00), which showed high repeatability.
Experiment 2
The mean relationship between changes and measured changes in CDA exhibited a very large
and positive relationship (r2 = 0.996) which is shown in Figure 2. Individually, the changes in
absolute and relative CDA each of the riders all displayed near perfect linear relationships (r2
= 0.95 – 0.99) which are shown in Figure 3.
The aim of this experiment was two-fold. First, to establish the intra- and inter-effort
reliability of CDA when using the NK. Second, to establish the sensitivity of the NK. With
regards to the first aim, a very high level of reliability was observed with this cohort of riders.
The average intra-level reliability of a 6-lap effort 0.20% with the range from as low as 0.16
– 0.69%. The group inter-effort reliability was 0.90% ranging from 0.46 – 1.03%. With
regards to the second aim, the NK was able to distinguish between all levels of changed
resistance with the smallest step change being a change of 0.002m2 (or average of 1.2%) in
The results of experiment 1 suggest that the high levels of (intra- and inter-) effort
reliability. These findings suggest that the NK can be used to assess changes in CDA of a
rider during a training session as well as between training sessions. Making it possible for
riders, and support staff to understand how CDA could change throughout a particular effort
or how a constant riding position is held at different speeds or different levels of fatigue.
Intra-effort reliability of the NK was also assessed by Valenzuela et al. 2020. Intra-effort ICC
values were identical between studies (0.99). However, the typical error presented in this
study was noticeably lower than was is shown by Valenzuela et al. 2020 by a factor of ~10
(0.0015 vs. 0.015 m2) (Valenzuela et al., 2020). This may be due to two reasons: First, the
cohort of riders in this data collection are elite riders who have competed at a number of
World and European championships (which include several World Champion winners and
medallists). As such, they are very experienced in riding the track and even though four of the
seven riders were on their upright/bunch race bike, they are accustomed to holding low and
aggressive positions. In addition, typical error represents absolute values of variation. The
riders in this data collection had much lower average CDA (~26% lower) values than those of
Valenzuela et al., 2020. When put in this context, the typical errors should be closer than it
initially seems. As the relative reliability of Valenzuela et al., 2020 was not reported, this is
no way of being completely certain whether this is true and is largely speculation.
The “velodrome” lap function in the analysis provided by the NK allowed inter-effort
reliability to be also measured. This was very low throughout this experiment (Table 1). To
the best of the author's knowledge, this function has not been used in any other experiments
in the literature as at the time of writing, this is a new feature provided by NK. In any case, it
has shown to be a useful tool which allows coaches and riders to analyse each lap of the
larger effort. This is particularly useful when trying to understand how a rider‟s CDA changes
under fatigue, throughout an effort or at different speeds.
The results from the addition of known resistances suggest that the NK is sensitive to
the full range of drag increments that was presented to it. All of the four riders who
participated in this part of the experiment showed perfect linear relationships with the
changes in CDA. All of which also were able to detect the smallest presented changes, going
from 5 to 6 cm discs which equates to an increase of 0.002 m2 (or an average of 1.2%).
With the NK being able to detect changes (as small as 0.002m2) makes it very
beneficial for its use in the field. The NK can help riders, coach and practitioners to find
small differences in riders‟ position or attire which are extremely valuable at all levels of
performance especially at the elite level track cycling. To put this in perspective, in a simple
model suggests that if a rider can lower their CDA from 0.198 (average of this experiment) to
0.196 m2 and performs a ride at 50 km/h, the difference is ~4 W of aerodynamic drag. Over
an individual pursuit this can equate to an improvement of ~0.8s in an individual pursuit and
over 5s in a 10-mile TT.
The average data of the sensitivity data was closely matched with the line of identity
(Figure 2). However, when as resistance increased (by adding larger discs), the further the
line of regression deviates from the line of identity. This could be as attributed to the increase
in aerodynamic wake. As the size becomes significant (~10% of total drag) there is likely to
be some significant interaction between the wake of the discs and the wake of the rider. This
could be somewhat mitigated by mounting the discs at a wider spacing.
When displayed individually (Figure 3), the strong linear relationships are still seen of
each rider with the addition of resistance and being able to distinguish between small
differences in CDA. However, each rider had their own “off-set” which was not systematic.
There does not seem to be an obvious reason to this. The authors speculate that this “off-set”
may partly be explained by the flow around the rod that interacts with the measurement of the
air speed of the NK, considering that the two were installed relatively close together, in
particular for the TT setup. In addition, it is possible that the orientation of the NK was
altered (by accident and without noticing) when installing the rod and, so, that the air speeds
measured after installing the rod are different from those before installation. The conclusion
of the sensitivity of the Valenzula et al. 2020 study suggested the NK can differentiate
between larger differences in CDA (i.e. between upright and aero positions) but questioned its
sensitivity when trying to differentiate between smaller differences (between training and
racing helmet) (Valenzuela et al., 2020). Unfortunately, the difference between the helmets
were not quantified and as such they were not able to conclude the exact sensitivity of the
NK. This study builds on the work of Valenzuela and colleagues and suggests that the NK
can differentiate in differences as small as 0.002 m2 (or 1.2%).
There are limitations and improvements that could have been made to this study. The
limits of sensitivity of the NK were not established i.e., to what point can the NK no longer
differentiate between changes in CDA, so the exact sensitivity has yet to be determined. so the
exact sensitivity of the NK has been found. Furthermore, the results of this experiment are
only applicable to an indoor velodrome setting. The influence of outdoor environmental
conditions (e.g., wind) and elevation change have not been factored in. Future experiment
evaluating the impact of outdoor environmental conditions and/or outdoor velodrome
settings. Lastly, therefore, the sampling rate is limited by the smallest sampling measure. In
this case it was power and cadence was transmitting data at 1Hz, while the NK samples at
4Hz. Higher sampling frequency could mean higher levels of sensitivity and could limit the
number of laps necessary at these high speeds. Future experiments could ascertain the limits
of sensitivity of the NK with smaller changes in aerodynamic drag.
In conclusion, this study showed a high reliability and sensitivity of the NK, this
means it can be useful for riders to perform aerodynamic testing with the NK at a relatively
constant speed in a velodrome. Furthermore, the new function “velodrome‟ function
introduced by NK is highly valuable for it use in the field.
The authors would like to thank the riders who participated in this study as well as Fulco van
Guilk and Adriaan Helmantel for facilitating the study.
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use of velodrome tests to evaluate aerodynamic drag in professional cyclists.
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Table 1: Individual and group intra- and inter-effort coefficient of variation (CV) as well as
average (±SD) and individual coefficient of drag area (CdA) of all the identical repeat
Intra-effort CV (%)
Inter-effort CV (%)
CdA (m2)
Rider 1
0.195 ± 0.001
Rider 2
0.179 ± 0.001
Rider 3
0.197 ± 0.001
Rider 4
0.213 ± 0.002
Rider 5
0.202 ± 0.003
Rider 6
0.185 ± 0.002
Rider 7
0.212 ± 0.002
0.198 ± 0.013
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... (Strathcona Calgary, Canada), and Velocomp LLC (Stuart, Florida, EE.UU). The main feature of this devices is that it includes a pressure sensor capable of measuring wind speed, which together with other external sensors resolve an energy balance equation to obtain a composite value that includes the aerodynamic drag coefficient multiplied by the frontal area; that is, the so called coefficient of aerodynamic drag (CdA) (Kordi et al., 2021;Valenzuela et al., 2020 ¶ ). In the case of aerostick Notio Aerostick, it uses its own equation that has not been published. ...
Purpose Cyclists need to measure aerodynamic resistance accurately and reliably, as well as economically. Devices such as Notio Aerostick, an equipment device that includes one pitot tube, have appeared for this purpose. The aim of this study is, therefore, to test the reliability and degree of agreement in the evaluation of the CdA (coefficient of aerodynamic drag), assessed by means of the Notio Aerostick compared to the Virtual Elevation (VE) and Martin mathematical models. Method Seventeen professional cyclists rode in a 250-meter-long velodrome covered with a concrete surface with their own time trial bikes. Each cyclist completed 3 rides of 15 laps at constant speed for the evaluation of the CdA, each of them in a different position [Baseline (B), Change 1 (C1) and Change 2 (C2)). Results The differences in CdA between methods were found for Martin in comparison with VE in all positions (p <0.001) and with Notio Aerostick in B and C2 (p> 0.05). About differences of CdA for each method, considering between position changes, the results were the same for VE and Martin, but different for Notio Aerostick. Conclusions Findings suggest that, notwithstanding Notio Aerostick is valid if we compare CdA values with respect to VE, since the direction of their between-positions CdA changes differs, the results of their aerodynamic evaluation could lead us to recommend different final setups. We need studies that evaluate different units of the Notio Aerostick device as well as the reliability and precision of each sensor that includes Notio Aerostick.
... 19 The OpenWeather station provided the barometric pressure (adjusted to the mean sea level). Therefore, a correction was applied through the following formula: (14) where P atm (ELE station ) is the atmospheric pressure at the station level (Pa), P b is the barometric pressure (Pa), T Station is the temperature at a weather station (K), ELE station is the elevation of the station (m), L b is the gradient of temperature (i.e. 0.0065 K/m), M 0 is the molar mass of air, R* is the gas constant and g is the gravitational acceleration (i.e. ...
... Two studies have demonstrated the validity and reliability of the Notio in indoor velodromes to measure C d A and argued in favour of the need to continue research in outdoor conditions to take into account the wind and altitude. 13,14 The mathematical model 1 is often used to measure the C d A in indoor tracks in order to avoid the influence of the wind and changes in potential and kinetic energies. 10,11 However, neglecting F slope , F accel and outdoor meteorological variables amounts to not taking into account the reality of the field when the cyclist should modulate his speed due to the slope of the road, changes in direction or variation of wind force and intensity. ...
Full-text available
The aim of this study was to model the cycling displacement under uncontrolled outdoor conditions with a wearable sensor and different meteorological measurement methods. One participant completed eight courses of a distance of 9.2 ± 2.4 km with varied profiles and directions. Data were recorded every second with a power meter, a GPS and a speed sensor. The aerodynamic drag coefficient, measured by a Notio wearable sensor, and the meteorological variables provided by the Notio, a Kestrel fixed meteorological station and the OpenWeather website were integrated into the Martin mathematical model to calculate the theoretical power output. The power calculated by the model on the basis of data from Notio, Kestrel and OpenWeather were, respectively, 1 ± 4 W higher, 7 ± 15 W lower and 67 ± 111 W higher than the power measured by the sensor. The overall RMSE and R ² , including 7325 data points, were 12.8 W and 0.77 ( p < 0.001), respectively, between the power output measured by the sensor and the power output modelled with the data from Notio. The use of the model with the wearable sensor was more precise mainly due to the relative wind measures at all points of the course. Therefore, the Notio can be useful for coaches to follow the evolution of the C d A of athletes on the field. Moreover, the model has the potential to predict the time of a cyclist just before a time trial in order to optimise his pacing strategy taking into account actual weather conditions.
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Wind tunnel experiments were conducted to assess the validity of the outdoor use two commercially available bicycle-mounted static pitot tubes (BMPT), viz the Notio KonectTM [NK] and AeropodTM [AP]. Three different experiments were conducted by comparing wind tunnel speed to the measured wind speed by both BMPTs. The sensors are tested, firstly, in a wide range of wind speed (8 to 20 m/s); secondly in a range of yaw angles (0° to 20°) and, thirdly, for three riders’ positions. The results show that both sensors require calibration to ensure that the measured wind speed matches that of the wind tunnel. After calibration at 14 m/s, the measured wind speed of the NK is within 0.22% over the velocity range up to 20 m/s. Instead, an error is present in the wind speed measured by the AP, which grows with the velocity offset to that of the calibration and reaches -2.51% at 20 m/s. Further both the NK and AP did not measure the wind speed accurately when yaw angles were introduced, this resulted in an error of 5.8% for the AP and 3.9% with a yaw angle of 20degrees. Besides, rider position influences the measurement of the wind speed for both the NK and AP. We concluded that both the NK and the AP are not suitable for outdoor testing when crosswinds occur. Furthermore, before every position changes, it is necessary to do a calibration run to collect accurate results.
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This investigation sought to determine if cycling power could be accurately modeled. A mathematical model of cycling power was derived, and values for each model parameter were determined. A bicycle-mounted power measurement system was validated by comparison with a laboratory ergometer. Power was measured during road cycling, and the measured values were compared with the values predicted by the model. The measured values for power were highly correlated (R2 = .97) with, and were not different than, the modeled values. The standard error between the modeled and measured power (2.7 W) was very small. The model was also used to estimate the effects of changes in several model parameters on cycling velocity. Over the range of parameter values evaluated, velocity varied linearly (R2 > .99). The results demonstrated that cycling power can be accurately predicted by a mathematical model.
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The purpose of this study was to analyse the validity, reliability and sensitivity of velodrome tests to detect small changes in aerodynamic drag in cycling. 12 professional cyclists were assessed to obtain the drag area (SCx) during wind tunnel and velodrome tests. Incremental and steady-state protocols were performed in the velodrome with a portable power meter, and 6 bicycle positions were analysed and compared that involved lowering the handlebars and advancing the pads between 2-5 cm. A significant relationship (r=0.88, p<0.001) between the SCx in the wind tunnel and velodrome tests was found (0.240±0.007 and 0.237±0.008 m2, respectively). The velodrome tests underestimated the SCx (0.0035±0.0038 m2 and p<0.01), which decreased (p<0.001) when the bicycle speed increased (0.0013 m2 each 1 km · h-1). The SCx values showed high reliability during the steady-state (r=0.99, p<0.001) and incremental protocols (r=0.94, p<0.001). Small changes in the aerodynamic position affected the SCx (p<0.001), which decreased by 0.011±0.007 m2 (4.6±2.9%, 95% CI=2.7-6.4%). In conclusion, the validity, reliability and sensitivity of velodrome tests to detect small changes in aerodynamic drag in cycling were demonstrated. Although SCx values were not interchangeable between different studies, the velodrome tests presented advantages with respect to the wind tunnel tests.
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Mathematical modelling has been used to predict the performance of cyclists for a number of years. The aim of this study was to develop a mathematical model for Individual Pursuit (IP) cyclists on an indoor velodrome, taking into account the effects of leaning in the bends and the actual position of the rider on the track using data collected by the SRM training system. This model uses forward integration to predict the velocity of the centre of mass and the finishing time for IP athletes, accurate to within 3% with currently available input data. (C) 2009 Published by Elsevier Ltd.
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When cycling on level ground at a speed greater than 14 m/s, aerodynamic drag is the most important resistive force. About 90% of the total mechanical power output is necessary to overcome it. Aerodynamic drag is mainly affected by the effective frontal area which is the product of the projected frontal area and the coefficient of drag. The effective frontal area represents the position of the cyclist on the bicycle and the aerodynamics of the cyclist-bicycle system in this position. In order to optimise performance, estimation of these parameters is necessary. The aim of this study is to describe and comment on the methods used during the last 30 years for the evaluation of the effective frontal area and the projected frontal area in cycling, in both laboratory and actual conditions. Most of the field methods are not expensive and can be realised with few materials, providing valid results in comparison with the reference method in aerodynamics, the wind tunnel. Finally, knowledge of these parameters can be useful in practice or to create theoretical models of cycling performance.
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Sprint-cycling performance is paramount to competitive success in over half the world-championship and Olympic races in the sport of cycling. This review examines the current knowledge behind the interaction of propulsive and resistive forces that determine sprint performance. Because of recent innovation in field power-measuring devices, actual data from both elite track- and road-cycling sprint performances provide additional insight into key performance determinants and allow for the construction of complex models of sprint-cycling performance suitable for forward integration. Modeling of various strategic scenarios using a variety of field and laboratory data can highlight the relative value for certain tactically driven choices during competition.
Objectives: To assess the reliability, validity, and sensitivity of a novel device (Notio Konect™) which is purported to provide a real-time analysis of aerodynamic drag area (CdA) during cycling. Design: Observational, cross-sectional study. Methods: Fifteen trained cyclists rode in an indoor velodrome using three different positions (upright, aero [holding aero bars], and optimized aero [similar to aero, but wearing a time-trial helmet]). They completed six 1-min trials in each position. The CdA was measured with Notio and with two other systems (Track Aero System™ [TAS] and a validated mathematical model). Results: The CdA measured with Notio showed good reliability (intra-class correlation coefficient [ICC]=0.92, 90% confidence interval [CI]=0.89-0.95). Notio showed an almost perfect relationship with both TAS (ICC=0.99, 90% CI=0.98-0.99) and the mathematical model (ICC=0.99, 90% CI=0.98-0.99). However, the CdA values provided by the former (0.308±0.051m2) were significantly higher (albeit with a trivial effect size [ES]) compared with TAS (0.300±0.051m2, p<0.001, ES=0.15) and the mathematical model (0.303±0.051m2, p=0.005, ES=0.09). The CdA was higher in the upright than in the aero position with all systems (all p<0.001, ES=1.84-1.89), and higher in the aero than in the optimized aero position when measured with TAS (p=0.033, ES=0.22) or the mathematical model (p=0.024, ES=0.24), but not with Notio (p=0.220, ES=0.19). Conclusions: Notio appears to be reliable, strongly correlated to other established systems, and discerns large (upright vs aero) but not small (aero vs optimized aero) variations in riding position. Further research is needed to confirm its validity in outdoor conditions.
This paper presents the full derivation of an analytical model for track cycling. The model takes into account the unique aspects of track cycling associated with riding around a velodrome. These include, riding upon a banked track and the resulting tyre scrubbing effects, and the tipping motion of a cyclist passing through a corner with the resulting centripetal forces. Validation was provided using SRMTM power crank data and split times obtained for an elite national cyclist in a 4 km pursuit competition. Results have shown the model to over-predict cyclist performance with a discrepancy of 0.7 s in a finals event and 4.3 s, less than 2% error, in a qualifying race. It is believed this may be attributable to discrepancies in atmospheric variables. However the model has proved capable of predicting the velocity increase, specifically associated with track cycling, as a cyclist passes through a bend. The model is useful for analysis of the physics of track cycling, and can be used to quantitatively predict performance dependent upon bicycle efficiencies, tyre type and venue conditions, in a racing scenario.
Little is known about the competitive performance characteristics of elite rowers. We report here analyses of performance times for finalists in world-class regattas from 1999 to 2009. The data were official race times for the 10 men's and 7 women's single and crewed boat classes, each with ∼ 200-300 different boats competing in 1-33 of the 46 regattas at 18 venues. A linear mixed model of race times for each boat class provided estimates of variability as coefficients of variation after adjustment for means of calendar year, level of competition (Olympics, world championship, World Cup), venue, and level of final (A, B, C, …). Mean performance was substantially slower between consecutive levels of competition (1.5%, 2.7%) and consecutive levels of finals (∼ 1%-2%). Differences in the effects of venue and of environmental conditions, estimated as variability in mean race time between venues and finals, were extremely large (∼ 3.0%). Within-boat race-to-race variability for A finalists was 1.1% for single sculls and 0.9% for crewed boats, with little difference between men and women and only a small increase in lower-level finalists. Predictability of performance, expressed as intraclass correlation coefficients, showed considerable differences between boat classes, but the mean was high (∼ 0.63), with little difference between crewed and single boats, between men and women, and between within and between years. The race-to-race variability of boat times of ∼ 1.0% is similar to that in comparable endurance sports performed against water or air resistance. Estimates of the smallest important performance enhancement (∼ 0.3%) and the effects of level of competition, level of final, venue, environment, and boat class will help inform investigations of factors affecting elite competitive rowing performance.
We previously reported that a mathematical model could accurately predict steady-state road-cycling power when all the model parameters were known. Application of that model to competitive cycling has been limited by the need to obtain accurate parameter values, the non-steady-state nature of many cycling events, and because the validity of the model at maximal power has not been established. We determined whether modeling parameters could be accurately determined during field trials and whether the model could accurately predict cycling speed during maximal acceleration using forward integration. First, we quantified aerodynamic drag area of six cyclists using both wind tunnel and field trials allowing for these two techniques to be compared. Next, we determined the aerodynamic drag area of three world-class sprint cyclists using the field-test protocol. Track cyclists also performed maximal standing-start time trials, during which we recorded power and speed. Finally, we used forward integration to predict cycling speed from power-time data recorded during the maximal trials allowing us to compare predicted speed with measured speed. Field-based values of aerodynamic drag area (0.258 +/- 0.006 m) did not differ (P = 0.53) from those measured in a wind tunnel (0.261 +/- 0.006 m2). Forward integration modeling accurately predicted cycling speed (y = x, r2 = 0.989) over the duration of the standing-start sprints. Field-derived values for aerodynamic drag area can be equivalent to values derived from wind tunnel testing, and these values can be used to accurately predict speed even during maximal-power acceleration by world-class sprint cyclists. This model could be useful for assessing aerodynamic issues and for predicting how subtle changes in riding position, mass, or power output will influence cycling speed.