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Individual acceleration-speed profile in-situ: A proof of concept in professional football players

Authors:

Abstract

Assessing football players' sprint mechanical outputs is key to the performance management process (e.g. talent identification, training, monitoring, return-to-sport). This is possible using linear sprint testing to derive force-velocity-power outputs (in laboratory or field settings), but testing requires specific efforts and the movement assessed is not specific to the football playing tasks. This proof-of-concept short communication presents a method to derive the players' individual acceleration-speed (AS) profile in-situ, i.e. from global positioning system data collected over several football sessions (without running specific tests). Briefly, raw speed data collected in 16 professional male football players over several training sessions were plotted, and for each 0.2 m/s increment in speed from 3 m/s up to the individual top-speed reached, maximal acceleration output was retained to generate a linear AS profile. Results showed highly linear AS profiles for all players (all R 2 >0.984) which allowed to extrapolate the theoretical maximal speed and accelerations as the individual's sprint maximal capacities. Good reliability was observed between AS profiles determined 2 weeks apart for the players tested, and further research should focus on deepening our understanding of these methodological features. Despite the need for further explorations (e.g. comparison with conceptually close force-velocity assessments that require, isolated and not football-specific linear sprint tests), this in-situ approach is promising and allows direct assessment of football players within their specific acceleration-speed tasks. This opens several perspectives in the performance and injury prevention fields, in football and likely other sprint-based team sports, and the possibility to "test players without testing them".
Morin et al. 2021 – Journal of Biomechanics – Individual acceleration-speed profile in-situ
1
Individual acceleration-speed profile in-situ: a proof of concept in professional football
players.
Jean-Benoit Morin
1
a, Yann Le Mata, Cristian Osgnachb, Andrea Barnabòb, Alessandro Pilatic, Pierre
Samozinod, Pietro E di Pramperob,e
a Univ Lyon, UJM-Saint-Etienne, Laboratoire Interuniversitaire de Biologie de la Motricité, EA 7424, F-42023, Saint-Etienne, France.
b Department of Sport Science, Exelio SRL, Udine, Italy; c Performance Department, Genoa CFC, Genova, Italy
d Univ Savoie Mont Blanc, Laboratoire Interuniversitaire de Biologie de la Motricité, EA 7424, F-73000 Chambéry, France
e Emeritus Professor of Physiology, University of Udine, Italy
This article is an accepted version for authors’ homepage of this work published in the
Journal of Biomechanics: DOI: https://doi.org/10.1016/j.jbiomech.2021.110524
ABSTRACT
Assessing football players’ sprint mechanical outputs is key to the performance management process
(e.g. talent identification, training, monitoring, return-to-sport). This is possible using linear sprint
testing to derive force-velocity-power outputs (in laboratory or field settings), but testing requires
specific efforts and the movement assessed is not specific to the football playing tasks. This proof-of-
concept short communication presents a method to derive the players’ individual acceleration-speed
(AS) profile in-situ, i.e. from global positioning system data collected over several football sessions
(without running specific tests). Briefly, raw speed data collected in 16 professional male football
players over several training sessions were plotted, and for each 0.2 m/s increment in speed from 3
m/s up to the individual top-speed reached, maximal acceleration output was retained to generate a
linear AS profile. Results showed highly linear AS profiles for all players (all R2>0.984) which allowed
to extrapolate the theoretical maximal speed and accelerations as the individual’s sprint maximal
capacities. Good reliability was observed between AS profiles determined 2 weeks apart for the
players tested, and further research should focus on deepening our understanding of these
methodological features. Despite the need for further explorations (e.g. comparison with
conceptually close force-velocity assessments that require, isolated and not football-specific linear
sprint tests), this in-situ approach is promising and allows direct assessment of football players within
their specific acceleration-speed tasks. This opens several perspectives in the performance and injury
prevention fields, in football and likely other sprint-based team sports, and the possibility to “test
players without testing them”.
KEYWORDS: soccer; testing; running; sprint; GPS
1
Corresponding author: Pr Jean-Benoit Morin, LIBM, Campus Santé Innovations Batiment IRMIS
10 rue de la Marandière 42270 Saint-Priest-en-Jarez, France, jean.benoit.morin@univ-st-etienne.fr
Morin et al. 2021 – Journal of Biomechanics – Individual acceleration-speed profile in-situ
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1. Introduction
Sprinting is a key physical determinant of
performance in team sports (Faude et al., 2012;
Haugen et al., 2014). Thus, assessing and
monitoring team sport players individual sprint
acceleration- or force-velocity profile is
important to improve the training and injury
management process (e.g. Jiménez-Reyes et al.,
2020). Gold standard methods for ground
reaction force measurement during sprint
acceleration require instrumented treadmills or
track-embedded multiple force plate systems,
which is inaccessible to most athletes. For this
reason, a simple field method based on
position-time data and Newtonian laws of
motion applied to the athlete’s center of mass
has been recently presented (Morin et al., 2019;
Samozino et al., 2016). Due to a good ratio
between overall validity, reliability and
simplicity of the model inputs (i.e. mainly
athlete’s body mass and center of mass position
or speed over time), this method has been used
in both training and medical practice and
research (e.g. Mendiguchia et al., 2014).
However, it is based on a single linear test of
acceleration effort that requires preparation and
organization, and that is not specific to team
sports actions that include large amounts of
linear accelerations and sprints of various
durations, and starting from various levels of
initial speed, and sometimes after a change of
direction. Knowing the real “in-situsprint force-
velocity profile of team sport players could be of
interest to (i) assess their acceleration sport-
specific capabilities based on data collected
“passively” (i.e. without specific intervention)
during practice, (ii) potentially save some sprint
testing time and the associated physical/mental
load and (iii) bring continuous information to
sport and medical staffs about players physical
fitness without requiring specific testing and the
associated reluctance. Assessing athletes
physical profile and capacity directly from real
practice data (i.e. not isolated, laboratory-based
or non-specific testing) has been done with race
and training power output data “passive”
collection in cycling with the “power record
profile” concept (Pinot and Grappe, 2015, 2011)
from which the method we propose here is
inspired. In team sports (football for example in
this study) beyond the mechanical power
output, the force-velocity profile informs on
players’ acceleration capacities at different
velocities, which can be also characterized by the
acceleration-speed (AS) profile. In other words,
the AS profile represents the maximal forward
acceleration capability of a player (resulting
from propulsive force in the direction of
running, according to Newtonian laws of
motion) over the range of their running velocity
spectrum. Conceptually, the information
provided by the in-situ AS profile is close to the
sprint force-velocity profile explored during
specific testing (e.g. Morin et al., 2019;
Samozino et al., 2016). The aim of this proof-of-
concept study is to present a simple method to
derive football players individual acceleration-
speed profile from global positioning system
(GPS) data collected over several training
sessions.
Morin et al. 2021 – Journal of Biomechanics – Individual acceleration-speed profile in-situ
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2. Method
2.1. Participants, procedures and equipment
Sixteen male professional football players
(mean ± SD age of 25.3 ± 3.8 yr; body mass
78.9 ± 4.3 kg; height 1.80 ± 0.1 m) gave their
written informed consent to participate in this
study. These football players were on the first
team of an Italian first league professional club.
Their training program during the data
collection period (August 20, 2018 to
September 23, 2018) included 5 football
training sessions, 1 physical training gym
session and 1 official football game per week.
Official game data were not included in the
analysis because the players did not carry their
GPS units. In order to assess the reliability of the
AS profile between collection periods, data were
compared over two 2-week phases: August 20 to
September 2 (Phase1) and September 10 to
September 23 (Phase2).
During each football training session, players
were constantly monitored with the same GPS
unit (GPEXE Pro2, Exelio SRL, Italy, firmware
version 0.13) inserted into tightly fit vests,
between the upper sides of scapula blades.
Speed data were collected at a sampling rate of
18 Hz. The data of 13 players who completed at
least 5 football training sessions (~90 min)
during both Phase1 and Phase2 were retained
for further analyses. This minimal amount of 5
training sessions represent a cumulative total of
~500.000 raw data points, which allowed to
cover the 0-to-maximal running speed range for
each player.
Data collection for this retrospective analysis was
performed within the typical training of the
professional club, under usual technical and
medical supervision. No specific intervention
was therefore required for this study, which was
performed according to Declaration of Helsinki.
2.2. Data analysis
The endpoint of the in-situ AS profile is to identify
the linear relationship between a given running
speed and the corresponding maximal
acceleration generated in cumulated football
practice data collected over a given period. More
specifically, the GPS speed data collected over a
defined time window yielded a cloud of
acceleration-speed points (depending on the
sampling frequency of the GPS devices used)
(Malone et al. 2017). A typical example of this
scatter plot is presented in Fig. 1 with the dataset
of a single player (only positive acceleration
values were displayed and considered). The
initial large set of raw speed data was first filtered
with a custom Gaussian type procedure
accounting for the time-averaged running
acceleration, before forming the scatter plot
shown in Fig. 1 with filtered speed (and derived
acceleration) data. The individual AS profile was
plotted based on the maximal acceleration the
player could generate for every possible running
speed over the measurement period, as follows.
Within a running speed interval ranging
between 3 m/s and the individual maximal
speed, the two maximal values of acceleration
Morin et al. 2021 – Journal of Biomechanics – Individual acceleration-speed profile in-situ
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performed for each 0.2 m/s subintervals (i.e. 3,
3.2, 3.4, 3.6 m/s and so on) were selected for
further analysis. This 3 m/s threshold was chosen
since maximal values of accelerations were rarely
observed below this point (Fig. 1), which is
consistent with the fact that even at the very first
steps of a standing or starting-block start, the
center of mass velocity raises quickly above 3 m/s
within the first step (e.g. Morin et al., 2019;
Nagahara et al., 2014). A first linear regression
was fitted to these speed-acceleration points
(~70 data points depending on the individuals).
Then, after fitting, the residuals were analyzed
and outlier points were removed when out of a
95% confidence interval upper and lower limits
around the linear function in order to improve
the linear regression fitting and the overall
accuracy of the model variables. The remaining
points were then fitted once again with a linear
regression model. For the readers interested, this
procedure is explained and possible at
https://libm-lab.univ-st-etienne.fr/as-
profile/#/home based on data files (.csv
extension) containing time, acceleration and
speed columns.
2
For each individual and phase tested, this
procedure eventually provided ~50 data points
(52±5, range 43-63) from which the AS profile
was derived using the updated linear regression
model (for description of the linear modeling of
sprint force-velocity profile, see (Morin et al.,
2019; Rabita et al., 2015; Samozino et al.,
2016)).
Finally, three main variables were derived to
characterize the players AS profile: A0 is the
theoretical maximal acceleration (y-intercept of
the AS linear relationship); S0 is the theoretical
maximal running speed (x-intercept of the AS
relationship); ASslope is the slope, i.e. overall
orientation of the AS profile (computed as ASslope
= - A0/ S0).
2.3. Statistics
All data are presented as mean ± standard
deviation (SD). The quality of the linear fitting of
the AS relationships was assessed with R2 values.
The inter-Phase reliability for each variable was
quantified through the change in the mean
(systematic error) and the standard error of
measurement (SEM, random error), both
expressed in raw units and in percentage of
mean values, between Phase1 and Phase2 data
(Hopkins, 2000).
3. Results
All acceleration-speed individual profiles
showed nearly perfect linear trends (all
R2>0.984, typical example in Fig. 1). The main
variables computed and between-Phase
reliability are presented in Table 1.
2
A video tutorial is available here:
https://www.youtube.com/watch?v=cOl1jv_7_iw&t=15s
Morin et al. 2021 – Journal of Biomechanics – Individual acceleration-speed profile in-situ
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Figure 1. Typical example of an individual acceleration-speed profile obtained from the data of 8 training sessions
spanned over 2 consecutive weeks in a professional football player. From the total >700.000 raw points, 51 points
were selected (see methods) to compute the acceleration-speed linear profile. The dashed line shows the linear profile,
from which theoretical maximal acceleration (A0 = 7.88 m/s2 in this example) and speed (S0 = 9.19 m/s) were
determined. Raw data below the 3 m/s speed threshold were partially masked for clarity.
Table 1. Main variables of the individual acceleration-speed profile for the two training phases analyzed. Data in
parenthesis indicate the range of values observed in this study (minimal; maximal). S0: maximal theoretical running
speed; A0: maximal theoretical acceleration; ASslope: orientation (slope) of the acceleration-speed linear relationship.
Variable
Phase1
Phase2
Raw
difference
(Phase2 -
Phase1)
Raw
difference
(%from
Phase1)
Standard Error
of
Measurement
(%)
S0 (m/s)
9.21±0.43
(8.41; 9.92)
9.47±0.52
(8.68; 10.3)
0.26±0.43
2.86
0.30
3.31
A0 (m/s2)
7.70±0.53
(6.55; 8.43)
7.20±0.40
(6.72; 7.84)
-0.50±0.59
-6.43
0.41
5.38
ASslope (1/s)
-0.84±0.09
(-0.98; -0.66)
-0.76±0.07
(-0.98; -0.66)
0.08±0.09
-9.03
0.06
7.64
4. Discussion
The main result of this study is that an almost
perfectly linear acceleration-speed relationship
was observed for all the professional football
players, which confirms that an in-situ individual
AS profile may be computed from football GPS
data passively collected and processed as
described above. The range of A0 and S0 values
show that even in a highly trained population,
who followed the same football training
sessions, major inter-player differences were
observed in acceleration-speed capacities.
Morin et al. 2021 – Journal of Biomechanics – Individual acceleration-speed profile in-situ
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This in-situ AS concept, which is easy to
implement (no specific testing required, only
GPS data passively collected throughout several
training sessions), might lead to more specific
and advanced assessment and monitoring of
football and maybe other team sport players
sprinting mechanical outputs. The potential
applications are numerous, within the training,
talent identification or injury management
fields, as observed in recent studies using sprint
acceleration force-velocity profiling (e.g.
Haugen et al., 2020b; Jiménez-Reyes et al.,
2018, 2020; Mendiguchia et al., 2014, 2016).
The very good reliability observed between
Phase1 and Phase2 (random error <8%, Table
1) is in line with standardized sprint testing
(Haugen et al., 2020a; Samozino et al., 2016).
However, the systematic differences in the
individual AS profile between phases 1 and 2
was not negligible. These differences require
further investigation and may be partly due to
the reproducibility of the method and
procedures proposed, the devices used
(although similar units were used by each
player), but also the actual changes in maximal
running acceleration and speed induced by the
several weeks of football training and games.
Since sprint acceleration force and velocity
outputs change over a professional football
season (Jiménez-Reyes et al., 2020), the inter-
Phase systematic differences observed here
could have been influenced by actual changes in
players AS profile.
Although direct comparison with existing
literature is not possible, there is a conceptual
proximity between the in-situ AS profile
presented here and the force-velocity profile
obtained during single, isolated, linear sprint
tests. Indeed, S0 is the same mechanical concept
as “V0”, and by Newtonian laws of motion, A0 and
F0 are two expressions of the same capacity
(maximal acceleration in the forward direction of
motion expressed in m/s2 or the corresponding
ground reaction force antero-posterior
component per unit body mass expressed in
N/kg). The typical values found in this study are
remarkably close to those previously reported in
professional male football players: S0 of 9-10
m/s in the present study versus V0 of 9.3 on
average for (Jiménez-Reyes et al., 2020), 9.25
for (Jiménez-Reyes et al., 2018), or 9.2 for
(Haugen et al., 2020b, 2019); A0 of 7.2-7.7 m/s2
in the present study versus F0 of 7.11 N/kg on
average for (Jiménez-Reyes et al., 2020), 7.35
for (Jiménez-Reyes et al., 2018), or 8.4-8.5 for
(Haugen et al., 2020b, 2019). Thus, FV and AS
profiles both represent the maximal acceleration
(thus indirectly force) capability in the antero-
posterior direction of running at all possible
running speeds. In the FV approach, this is
obtained within a single linear all-out sprint
effort. In the AS approach presented here, it is
obtained within several hours of recorded
football specific efforts.
One limitation of this work is that official game
data could not be pooled with training GPS data
due to different systems used, thus questioning
the extent to which individual AS profiles would
differ by adding official game data. It is
important to note that the quality of the linear
fitting used to determine the AS profile highly
depends on the quality of the sampled GPS
Morin et al. 2021 – Journal of Biomechanics – Individual acceleration-speed profile in-situ
7
signal, which depends on environmental,
hardware and software characteristics, and may
influence the application of the proposed
method to different contexts. This is a track for
future research on the topic, that could include
(i) a comparison with “classical” isolated sprint
testing AS or force-velocity profile, or other types
of football players’ effort assessment, (ii) the
changes in AS profile over a full season or
before-after injury, (iii) the effects of tactical
coaching choices and associated training
content orientation (e.g. small-sided games,
acceleration or speed-oriented training, tactical
periodization), (iv) the effects of strength and
conditioning blocks (e.g. do the effects of heavy
resistance training on F0 (Morin et al., 2017)
transfer to football-specific A0?).
More broadly, the main topic that should be
addressed is the direct comparison of linear test
force-velocity outcomes with those derived from
the in-situ method proposed here, and the
extent to which these pieces of information are
interchangeable and relate to players’ physical
capabilities. Also, in order to narrow the time-
focus down, research should clarify the minimal
amount of data (i.e. training sessions and
days/weeks of data collection) needed to provide
reliable AS profiles. Our most recent
(unpublished) observations show that AS
profiles may be obtained (i) from only a football
half-time or full-game dataset and (ii) with other
video-based time-motion or GPS systems than
the one used in the present study, provided
speed and acceleration data are reliable. Finally,
future works should test the possible use of this
approach in other team sports, provided
accurate individual acceleration and speed data
are collected during practice. Indeed, the
approach may be applied to any type of intense
running and sprint-based team sport if maximal
accelerations are produced during a running or
skating motion, over a large range of speeds.
This is very likely the case in large field games
like football, rugby codes, hurling, or ice hockey.
In conclusion, keeping in mind the limitations
and need for future works, the in-situ
acceleration-speed profiling presented here
allows to individually characterize the limits of
football players acceleration and running speed
capacities, in real football context (with or
without ball, opponents, teammates,
accelerations in different directions), from
football practice GPS data: “testing the players
without testing them”.
Conflict of interest statement
Authors Cristian Osgnach and Pietro E di Prampero are scientific consultants for Exelio SRL, the
company which provided the GPS units used in this study. Other authors have no conflict of interest
to declare.
Related blog post and online conference (to be updated):
https://jbmorin.net/2020/08/02/the-in-situ-sprint-profile-for-team-sports-testing-players-without-testing-them/
Morin et al. 2021 – Journal of Biomechanics – Individual acceleration-speed profile in-situ
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... Based on all these limitations, the individual acceleration-speed profile in-situ has been recently presented as a new method for assessing sport-specific acceleration capabilities based on data collected "passively" (i.e. without specific intervention) during practice (Morin et al. 2021). This method is inspired from the "power record profile" concept (Pinot and Grappe 2011) where athletes' physical profile and capacities were directly assessed through race and training power output data "passively" collected. ...
... Conceptually, the information provided by the AS profile is close to the classical linear sprint force-velocity profile as presented by Morin et al. (2021). They differ in that one is derived from soccer situations and actions (speed) while the other is based on a linear sprint task (velocity). ...
... It is important to add that these invalid clouds of raw data are not due to the reliability and precision of GPS input data as presented previously, but to the lack of high intensity actions during the time window data. Indeed, the number of acceleration points necessary to have a good confidence to the linear regression (and thus the corresponding mechanical outputs A0 and S0) must be higher (~50 data points, Morin et al. 2021) and include more maximal accelerations occurred at high speed (>90% S0). ...
Thesis
Full-text available
Sprinting is a key determinant of performance in soccer. The overall mechanical capabilities of this ability are well described by the macroscopic linear force-velocity (FV) and acceleration-speed (AS) relationships. This study aimed to compare FV and AS profiles computation methods in order to assess athletes’ sprint acceleration mechanical capabilities based on classic single sprint test data, and compare the linear sprint FV profile to the AS profile in-situ, based on soccer data. Ten recreational athletes were equipped with GPS units and performed two 30-m sprints followed by a 45-min soccer period. For the linear sprint test, we observed good agreement between the two methods for kinetic variables (all mean absolute bias <6.7 ± 6.51%), and a similar low inter-trial reliability (mean coefficients of variation <4.6 ± 4.36%). The AS in-situ profile in comparison with the linear FV profile showed an overall underestimation of maximal theoretical acceleration capacities (mean absolute bias of 13.65 ± 7.7%), but contrastingly allows to have a good idea of maximal theoretical running velocity capabilities of the athletes (mean absolute bias of 4.14 ± 3.68%). This pilot-study clearly showed that AS profile can be used confidently for orient training and rehabilitation, but not actually with soccer data.
... It comes with challenging issues due to time and investigation costs required, injury exposure, psychological state disruptions, and adjustments of training plans. Nevertheless, Morin et al. 22 recently proposed a timely method for assessing a player's athletic performance while practicing football without performing any specific tests. In brief, the method consists of determining individual acceleration-velocity profiles (A-V) from continuous GNSS measurements for in-game and post hoc analysis. ...
... They could be used for optimising training plans or to proceed to in-games tactical adjustments in case of significant profile impairments. However, determining in situ A-V profiles for monitoring athletic performances remain at a proof-of-concept stage 22 and should be further validated, in particular for athletic performance modeling and injury explanation purposes. ...
... In order to investigate the effect of training on athletic performances, we relied on A-V profiles such as provided by 22 but in a slightly different way. Individual A-V profiles were modeled for each training session. ...
Preprint
Full-text available
This study aims to predict individual Acceleration-Velocity profiles (A-V) from Global Navigation Satellite System (GNSS) measurements in real-world situations. Data were collected from professional players in the Superleague division during a 1.5 season period (2019-2021). A baseline modeling performance was provided by time-series forecasting methods and compared with two multivariate modeling approaches using ridge regularisation and long short term memory neural networks. The multivariate models considered commercial features and new features extracted from GNSS raw data as predictor variables. A control condition in which profiles were predicted from predictors of the same session outlined the predictability of A-V profiles. Multivariate models were fitted either per player or over the group of players. Predictor variables were pooled according to the mean or an exponential weighting function. As expected, the control condition provided lower error rates than other models on average (p = 0.001). Reference and multivariate models did not show significant differences in error rates (p = 0.124), regardless of the nature of predictors (commercial features or extracted from signal processing methods) or the pooling method used. In addition, models built over a larger population did not provide significantly more accurate predictions. In conclusion, GNSS features seemed to be of limited relevance for predicting individual A-V profiles. However, new signal processing features open up new perspectives in athletic performance or injury occurrence modeling, mainly if higher sampling rate tracking systems are considered.
... Recently, a highly interesting development has been made to allow for higher testing frequency for F0. Morin et al., (2021) showed that calculating F0 from global positioning system data during football training to be reliable, which can be considered to be an "in-situ" measurement (Morin et al., 2021). This could be considered a crucial development, as players can be tested on a microcycle basis without technically "testing them". ...
... Recently, a highly interesting development has been made to allow for higher testing frequency for F0. Morin et al., (2021) showed that calculating F0 from global positioning system data during football training to be reliable, which can be considered to be an "in-situ" measurement (Morin et al., 2021). This could be considered a crucial development, as players can be tested on a microcycle basis without technically "testing them". ...
... In terms of testing methods, exciting developments have taken place during this thesis. For example, F0 has been recently proved reliable to test in-situ (i.e., during football practice via a specific processing method of position data provided by modern GPS units) (Morin et al., 2021). This shifts these types of testing from screening to monitoring, or even "testing without testing". ...
Thesis
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Despite efforts to intervene, hamstring muscle injuries (HMI) continue to be one of the largest epidemiological burdens in professional football. The injury mechanism takes place dominantly during sprinting, but also other scenarios have been observed, such as overstretching actions, jumps, and change of directions. The main biomechanical roles of the hamstring muscles are functioning as an accelerator of center-of-mass (i.e., contributing to horizontal force production), and stabilizing the pelvis and knee joint. Multiple extrinsic and intrinsic risk factors have been identified, portraying the multifactorial nature of the HMI. Furthermore, these risk factors can vary substantially between players, portraying the importance of individualized approaches. However, there is a lack of multifactorial and individualized approaches assessed for validity in literature. Thus, the overarching aim of this doctoral thesis was to explore if a specific multifactorial and individualized approach can improve upon the ongoing HMI risk reduction protocols, and thus, further reduce the HMI risk in professional football players. This was done following the Team-sport Injury Prevention model (TIP model), where the target is to evaluate the current injury burden, identify possible solutions, and intervene. The thesis comprised of three themes within professional football, I) evaluating and identifying HMI risk (completed via assessing the current epidemiological HMI situation and the association between HMI injuries and a novel hamstring screening protocol), II) improving horizontal force capacity (completed via testing if maximal theoretical horizontal force (F0) can be improved via heavy resisted sprint training), and III) developing and conducting a multifactorial and individualized training for HMI risk reduction (completed via introducing and conducting a training intervention). The conclusions from theme I were that the HMI burden continues to be high (14.1 days absent per 1000 hours of football exposure), no tests from the screening protocol were associated with an increased HMI risk when including all injuries from the season (n = 17, p > 0.05), and that lower F0 was significantly associated with increased HMI risk when including injuries between test rounds one and two (~90 days, n =14, hazard ratio: 4.02 (CI95% 1.08 to 15.0), p = 0.04). For theme II, the players initial pre-season level of F0 was significantly associated with adaptation potential after 11 weeks of heavy resisted sprint training during the pre-season (r = -0.59, p < 0.05). The heavy resisted sprint load leading to a ~50% velocity loss induced the largest improvements in sprint mechanical output and sprint performance variables. For theme III, no intervention results could be presented within this document due to the Covid-19 pandemic leading to the intervention being postponed. However, a protocol paper was published, describing in detail the intervention approach that will be used outside the scope of the thesis. In future studies, larger sample size studies are needed to support the development of more advanced HMI risk reduction models. Such models may allow practitioners to identify risk on an individual level instead of a group level. Furthermore, constant development of more specific, reliable, and accessible risk assessment tests should be promoted that can be frequently tested throughout the football season. Finally, based on the results of theme II, individualization of a specific training stimulus should be promoted in team settings.
... The resulting output values in Table 1 are in close agreement with previously published data. Values for a 0 or normalized F 0 (N/kg) are similar to those from several recent experimental investigations (Cross et al., 2015;Rabita et al., 2015;Slawinski et al., 2017a;Haugen et al., 2019;Morin et al., 2019Morin et al., , 2021Watkins et al., 2021). Values for τ are slightly greater than onesecond, agreeing with experimentally determined values (Healy et al., 2019;Morin et al., 2019), or values calculated from previously published maximum velocity and maximum force data (Cross et al., 2015;Rabita et al., 2015;Slawinski et al., 2017a;Haugen et al., 2019;Watkins et al., 2021;Edwards et al., 2022). ...
Article
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Sprinting performance is critical for a variety of sports and competitive activities. Prior research has demonstrated correlations between the limits of initial acceleration and maximum velocity for athletes of different sprinting abilities. Our perspective is that hip torque is a mechanistic link between these performance limits. A theoretical framework is presented here that provides estimates of sprint acceleration capability based on thigh angular acceleration and hip torque during the swing phase while running at maximum velocity. Performance limits were calculated using basic anthropometric values (body mass and leg length) and maximum velocity kinematic values (contact time, thigh range of motion, and stride frequency) from previously published sprint data. The proposed framework provides a mechanistic link between maximum acceleration and maximum velocity, and also explains why time constant values (τ, ratio of the velocity limit to acceleration limit) for sprint performance curves are generally close to one-second even for athletes with vastly different sprinting abilities. This perspective suggests that specific training protocols targeted to improve thigh angular acceleration and hip torque capability will benefit both acceleration and maximum velocity phases of a sprint.
... Multiparametric longitudinal assessments are needed for an indepth analysis of elite players, to manage training plans, support coaches, and prevent injuries. Current development of technologies and data analyses allow practitioners to obtain direct measures of physical performances within sport-specific tasks (Morin et al., 2021). The authors support the creation of a huge database of sports data for improving load management and facilitating talent identification. ...
Article
Inherent physical and anthropometric traits of elite soccer players, influenced by nature and nurture, account for the emergence of performances across time. Purpose: The present study aimed to evaluate inter- and intraseasonal differences and the influence of playing position on training and fitness metrics in talented young soccer players. Methods: A total of 74 male players from U20 teams of a single elite club were tested both at beginning, during, and at the end of three consecutive competitive seasons. Players under went anthropometric measurement and were tested for aerobic, jumping, and sprinting performances; the GPS-derived measures of metabolic power (MP) and equivalent distance index (ED) of every athlete were analyzed. Results: Difference between teams emerged in Mognoni's test, while it did not in countermovement jump and anthropometrics. ED was different across seasons. The model selection criteria revealed that the Bosco-Vittori test achieved the best fit. BMI and countermovement jump (CMJ) increased, and fat mass decreased, during season; different intraseasonal trends for CMJ. MP was slightly greater in midfielder. Conclusion: Network approaches in modeling performance metrics in sports team could unveil original interconnections between performance factors. In addition, the authors support multiparametric longitudinal assessments and a huge database of sports data for facilitating talent identification.
... This method can be implemented accurately and reliably using photocell timing gates, a radar gun, or high-speed video using the My-Sprint iPad application (41,77). The use of GNSS data to calculate sprint forcevelocity power profiling has recently been explored (66), but more work is needed to validate this process. Sprint forcevelocity-power profiling provides a detailed assessment of sprint capabilities and can facilitate an individualized approach to speed development (48). ...
Article
Soccer match play dictates that players possess well-rounded physical capacities. Therefore, player physical development plans must consider developing several fitness components simultaneously. Effective individualization of training is likely facilitated with appropriate player profiling; therefore, developing a time-efficient and informative testing battery is highly relevant for practitioners. Advances in knowledge and technology over the past decade have resulted in refinements of the testing practices used by practitioners working in professional male and female soccer. Consequently, a contemporary approach to test selection and data analysis has progressively been adopted. Furthermore, the traditional approach of using a testing battery in a single day may now be outdated for full-time players, with a flexible approach to the scheduling of testing perhaps more suitable and time efficient. Here, guidance on testing for maximal aerobic, sub-maximal aerobic, linear and change of direction speed, and stretch-shortening cycle performance (i.e., jump testing) are presented for male and female players, with emphasis on time efficient tests, while facilitating effective individualized training prescription. Normative and meaningful change data are presented to aid decision making and provide a reference point for practitioners. Finally, a time-efficient approach to scheduling fitness testing is presented, which complements daily training outcomes of a weekly periodization approach.
... However, the practicality of measuring F0 and its wider use also as a performance measure may outweigh its limitations when used in a multifactorial testing environment. Furthermore, recent developments in technology allows for reliable in-situ quantification of F0 from football training using global positioning devices [37]. This is a promising development from a testing frequency standpoint and should be further explored as it allows screening practices to evolve into a monitoring context. ...
... Gender differences in football have been part of research as well (Bradley et al., 2014) as well as research regarding transfer systems (Hoey et al., 2021). Other sporting abilities have been researched in precise detail by Morin et al. (2021). Hackinger (2019) investigated if football coaches let players with high transfer fees play longer despite bad performance and came to the conclusion that professional sports teams in Germany act rationally. ...
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Research background: Professional football is becoming more and more commercialized. The most recent attempt to establish a “Super League” failed, but the big football clubs are nevertheless trying to generate success in sports through increasingly high player transfers. Purpose of the article: The aim of this paper is to empirically test the above statements and assumptions. On the one hand, the question arises whether the placement in a ranking table of a competition depends on the investment volume. At the same time, it is analyzed whether this relationship exhibits stability over time. Table placement was chosen because it has a direct influence on the distribution of funds in a competition. In addition, individual matches are analyzed to determine whether the investment volume has a statistically significant influence on winning a match. Methods: The years 2014 to 2020 of the competitions of one of the top five European leagues, the German Bundesliga, are prepared in a database. In addition to the player results and table positions, the market values of the players in the season are used. All data is taken from the website Transfermarkt.de. In the context of the table rankings, a regression analysis is performed to explain the place in the table by the market value of the team. When analyzing individual matches, the team value on the field in each case is determined and the differences between the values of the teams playing are established. These differences are explained as a dependent variable in a regression line with three dummy variables: won, lost, and draw. Findings & Value added: The results enable the management of football clubs to make an investment decision for a successful future. They show, on the one hand, whether the team value has an influence on the league position and, on the other hand, on the match result.
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Athletes often require a mix of physical, physiological, psychological, and skill-based attributes that can be conflicting when competing at the highest level within their sport. When considering multiple variables in tandem, Pareto frontiers is a technique that can identify the observations that possess an optimal balance of the desired attributes, especially when these attributes are negatively correlated. This study presents Pareto frontiers as a tool to identify athletes who possess an optimal ranking when considering multiple metrics simultaneously. This study explores the trade-off relationship between batting average and strike rate as well as bowling strike rate, economy, and average in Twenty 20 cricket. Eight hundred ninety-one matches of Twenty 20 cricket from the men's (MBBL) and women's (WBBL) Australian Big Bash Leagues were compiled to determine the best batting and bowling performances, both within a single innings and across each player's Big Bash career. Pareto frontiers identified 12 and seven optimal batting innings performances in the MBBL and WBBL respectively, with nine and six optimal batting careers respectively. Pareto frontiers also identified three optimal bowling innings in both the MBBL and WBBL and five and six optimal bowling careers in MBBL and WBBL, respectively. Each frontier identified players that were not the highest ranked athlete in any metric when analyzed univariately. Pareto frontiers can be used when assessing talent across multiple metrics, especially when these metrics may be conflicting or uncorrelated. Using Pareto frontiers can identify athletes that may not have the highest ranking on a given metric but have an optimal balance across multiple metrics that are associated with success in a given sport.
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Clear decreases in horizontal force production capacity during sprint acceleration have been reported after hamstring injuries (HI) in football players. We hypothesized that lower FH0 is associated with a higher HI occurrence in football players. We aimed to analyze the association between sprint running horizontal force production capacities at low (FH0) and high (V0) velocities, and HI occurrence in football. This prospective cohort study included 284 football players over one season. All players performed 30 m field sprints at the beginning and different times during the season. Sprint velocity data were used to compute sprint mechanical properties. Players’ injury data were prospectively collected during the entire season. Cox regression analyses were performed using new HI as the outcome, and horizontal force production capacity (FH0 and V0) was used at the start of the season (model 1) and at each measurement time point within the season (model 2) as explanatory variables, adjusted for individual players’ (model 2) age, geographical group of players, height, body mass, and previous HI, with cumulative hours of football practice as the time scale. A total of 47 new HI (20% of all injuries) were observed in 38 out of 284 players (13%). There were no associations between FH0 and/or V0 values at the start of the season and new HI occurrence during the season (model 1). During the season, a total of 801 measurements were performed, from one to six per player. Lower measured FH0 values were significantly associated with a higher risk of sustaining HI within the weeks following sprint measurement (HR = 2.67 (95% CI: 1.51 to 4.73), p < 0.001) (model 2). In conclusion, low horizontal force production capacities at low velocity during early sprint acceleration (FH0) may be considered as a potential additional factor associated with HI risk in a comprehensive, multifactorial, and individualized approach.
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This study aimed to describe the seasonal changes in the sprint force-velocity (Fv) profile of professional soccer players. The sprint Fv profile of 21 male soccer players competing in the first division of the Spanish soccer league was evaluated 6 times: preseason 1 (September 2015), in-season 1 (November 2015), in-season 2 (January 2016), in-season 3 (March 2016), in-season 4 (May 2016), and preseason 2 (August 2016). No specific sprint capabilities stimuli other than those induced by soccer training were applied. The following variables were calculated from the velocity-time data recorded with a radar device during an unloaded sprint: maximal force (F0), maximal velocity (v0), Fv slope, maximal power (Pmax), decrease in the ratio of horizontal-to-resultant force (DRF), and maximal ratio of horizontal-to-resultant force (RFpeak). F0 (effect size [ES] range = 0.83–0.93), Pmax (ES range = 0.97–1.05), and RFpeak (ES range = 0.56–1.13) were higher at the in-seasons 2 and 3 compared with both preseasons (p ≤ 0.006). No significant differences were observed for v0, Fv slope, and DRF (p ≥ 0.287). These results suggest that relevant Fv profile variables may be compromised (F0 more compromised than v0) toward the end of the competitive season when specific sprint stimuli are not systematically applied.
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The purpose of this study was to quantify possible differences in sprint mechanical outputs in soccer according to soccer playing standard, position, age and sex. Sprint tests of 674 male and female players were analysed. Theoretical maximal velocity (v0), horizontal force (F0), horizontal power (Pmax), force-velocity slope (SFV), ratio of force (RFmax) and index of force application technique (DRF) were calculated from anthropometric and spatiotemporal data using an inverse dynamic approach applied to the centre-of-mass movement. Players of higher standard exhibited superior F0, v0, Pmax, RFmax and DRF scores (small to large effects) than those of lower standard. Forwards displayed clearly superior values for most outputs, ahead of defenders, midfielders and goalkeepers, respectively. Male >28 y players achieved poorer v0, Pmax and RFmax than <20, 20-24 and 24-28 y players (small to moderate), while female <20 y players showed poorer values than 20-24 and >24 y players for the same measures (small). The sex differences in sprint mechanical properties ranged from small to very large. These results provide a holistic picture of the force-velocity-power profile continuum in sprinting soccer players and serve as useful background information for practitioners when diagnosing individual players and prescribing training programs.
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Measuring the ground reaction forces (GRF) underlying sprint acceleration is important to understanding the performance of such a common task. Until recently direct measurements of GRF during sprinting were limited to a few steps per trial, but a simple method (SM) was developed to estimate GRF across an entire acceleration. The SM utilizes displacement- or velocity-time data and basic computations applied to the runner's center of mass and was validated against compiled force plate (FP) measurements; however, this validation used multiple-trials to generate a single acceleration profile, and consequently fatigue and error may have introduced noise into the analyses. In this study, we replicated the original validation by comparing the main sprint kinetics and force-velocity-power variables (e.g. GRF and its horizontal and vertical components, mechanical power output, ratio of horizontal component to resultant GRF) between synchronized FP data from a single sprinting acceleration and SM data derived from running velocity measured with a 100 Hz laser. These analyses were made possible thanks to a newly developed 50-m FP system providing seamless GRF data during a single sprint acceleration. Sixteen trained male sprinters performed two all-out 60-m sprints. We observed good agreement between the two methods for kinetic variables (e.g. grand average bias of 4.71%, range 0.696 ± 0.540-8.26 ± 5.51%), and high inter-trial reliability (grand average standard error of measurement of 2.50% for FP and 2.36% for the SM). This replication study clearly shows that when implemented correctly, this method accurately estimates sprint acceleration kinetics.
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Purpose: The main aim of this investigation was to quantify differences in sprint mechanical variables across sports and within each sport. Secondary aims were to quantify sex differences and relationships among the variables. Methods: In this cross-sectional study of elite athletes, 235 women (23 ± 5 y and 65 ± 7 kg) and 431 men (23 ± 4 y and 80 ± 12 kg) from 23 different sports (including 128 medalists from World Championships and/or Olympic Games) were tested in a 40-m sprint at the Norwegian Olympic Training Center between 1995 and 2018. These were pre-existing data from quarterly or semi-annual testing that the athletes performed for training purposes. Anthropometric and speed-time sprint data were used to calculate the theoretical maximal velocity, horizontal force, horizontal power, slope of the force-velocity relationship, maximal ratio of force, and index of force application technique. Results: Substantial differences in mechanical profiles were observed across sports. Athletes in sports in which sprinting ability is an important predictor of success (e.g., athletics sprinting, jumping and bobsleigh) produced the highest values for most variables, whereas athletes in sports in which sprinting ability is not as important tended to produce substantially lower values. The sex differences ranged from small to large, depending on variable of interest. Although most of the variables were strongly associated with 10- and 40-m sprint time, considerable individual differences in sprint mechanical variables were observed among equally performing athletes. Conclusions: Our data from a large sample of elite athletes tested under identical conditions provides a holistic picture of the force-velocity-power profile continuum in athletes. The data indicate that sprint mechanical variables are more individual than sport specific. The values presented in this study could be used by coaches to develop interventions that optimize the training stimulus to the individual athlete.
Article
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Purpose: The main aim of this investigation was to quantify differences in sprint mechanical variables across sports and within each sport. Secondary aims were to quantify sex differences and relationships among the variables. Methods: In this cross-sectional study of elite athletes, 235 women (23 ± 5 y and 65 ± 7 kg) and 431 men (23 ± 4 y and 80 ± 12 kg) from 23 different sports (including 128 medalists from World Championships and/or Olympic Games) were tested in a 40-m sprint at the Norwegian Olympic Training Center between 1995 and 2018. These were pre-existing data from quarterly or semi-annual testing that the athletes performed for training purposes. Anthropomet-ric and speed-time sprint data were used to calculate the theoretical maximal velocity, horizontal force, horizontal power, slope of the force-velocity relationship, maximal ratio of force, and index of force application technique. Results: Substantial differences in mechanical profiles were observed across sports. Athletes in sports in which sprinting ability is an important predictor of success (e.g., athletics sprinting, jumping and bobsleigh) produced the highest values for most variables, whereas athletes in sports in which sprinting ability is not as important tended to produce substantially lower values. The sex differences ranged from small to large, depending on variable of interest. Although most of the variables were strongly associated with 10-and 40-m sprint time, considerable individual differences in sprint mechanical variables were observed among equally performing athletes. Conclusions: Our data from a large sample of elite athletes tested under identical conditions provides a holistic picture of the force-velocity-power profile continuum in athletes. The data indicate that sprint mechanical variables are more individual than sport specific. The values presented in this study could be used by coaches to develop interventions that optimize the training stimulus to the individual athlete.
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This study aimed (i) to explore the relationship between vertical (jumping) and horizontal (sprinting) force–velocity–power (FVP) mechanical profiles in a large range of sports and levels of practice, and (ii) to provide a large database to serve as a reference of the FVP profile for all sports and levels tested. A total of 553 participants (333 men, 220 women) from 14 sport disciplines and all levels of practice participated in this study. Participants performed squat jumps (SJ) against multiple external loads (vertical) and linear 30–40 m sprints (horizontal). The vertical and horizontal FVP profile (i.e., theoretical maximal values of force ( F0 ), velocity ( v0 ), and power ( Pmax )) as well as main performance variables (unloaded SJ height in jumping and 20-m sprint time) were measured. Correlations coefficient between the same mechanical variables obtained from the vertical and horizontal modalities ranged from −0.12 to 0.58 for F0 , −0.31 to 0.71 for v0 , −0.10 to 0.67 for Pmax , and −0.92 to −0.23 for the performance variables (i.e, SJ height and sprint time). Overall, results showed a decrease in the magnitude of the correlations for higher-level athletes. The low correlations generally observed between jumping and sprinting mechanical outputs suggest that both tasks provide distinctive information regarding the FVP profile of lower-body muscles. Therefore, we recommend the assessment of the FVP profile both in jumping and sprinting to gain a deeper insight into the maximal mechanical capacities of lower-body muscles, especially at high and elite levels.
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The aim of this study was to investigate the impact of timing gate setup on mechanical outputs in sprinting athletes. Twenty-five male and female team sport athletes (mean ± SD: 23 ± 4 y, 185 ± 11 cm, 85 ± 13 kg) performed two 40-m sprints with maximal effort. Dual-beamed timing gates covered the entire running course with 5-m intervals. Maximal horizontal force (F0), theoretical maximal velocity (v0), maximal horizontal power (Pmax), force-velocity slope (SFV), maximal ratio of force (RFmax) and index of force application technique (DRF) were computed using a validated biomechanical model and based on twelve varying split time combinations, ranging from three to eight timing checkpoints. When no timing gates were located after the 20-m mark, F0 was overestimated (mean difference, ±90%CL: 0.16, ±0.25 to 0.33, ±0.28 N•kg-1 ; possibly to likely; small), in turn affecting SFV and DRF by small to moderate effects. Timing setups covering only the first 15 m displayed lower v0 than setups covering the first 30-40 m of the sprints (0.21 ±0.34 to 0.25 ±0.34 m•s-1 ; likely; small). Moreover, poorer reliability values were observed for timing setups covering the first 15-20 m vs. the first 25-40 m of the sprints. In conclusion, the present findings showed that the entire acceleration phase should be covered by timing gates to ensure acceptably valid and reliable sprint mechanical outputs. However, only three timing checkpoints (i.e., 10, 20 and 30 m) are required to ensure valid and reliable outputs for team sport athletes.
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Background: Sprint running acceleration is a key feature of physical performance in team sports, and recent literature shows that the ability to generate large magnitudes of horizontal ground-reaction force and mechanical effectiveness of force application are paramount. The authors tested the hypothesis that very-heavy loaded sled sprint training would induce an improvement in horizontal-force production, via an increased effectiveness of application. Methods: Training-induced changes in sprint performance and mechanical outputs were computed using a field method based on velocity-time data, before and after an 8-wk protocol (16 sessions of 10- × 20-m sprints). Sixteen male amateur soccer players were assigned to either a very-heavy sled (80% body mass sled load) or a control group (unresisted sprints). Results: The main outcome of this pilot study is that very-heavy sled-resisted sprint training, using much greater loads than traditionally recommended, clearly increased maximal horizontal-force production compared with standard unloaded sprint training (effect size of 0.80 vs 0.20 for controls, unclear between-groups difference) and mechanical effectiveness (ie, more horizontally applied force; effect size of 0.95 vs -0.11, moderate between-groups difference). In addition, 5-m and 20-m sprint performance improvements were moderate and small for the very-heavy sled group and small and trivial for the control group, respectively. Practical Applications: This brief report highlights the usefulness of very-heavy sled (80% body mass) training, which may suggest value for practical improvement of mechanical effectiveness and maximal horizontal-force capabilities in soccer players and other team-sport athletes. Results: This study may encourage further research to confirm the usefulness of very-heavy sled in this context.
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Athlete tracking devices that include global positioning system (GPS) and micro electrical mechanical system (MEMS) components are now commonplace in sport research and practice. These devices provide large amounts of data that are used to inform decision-making on athlete training and performance. However, the data obtained from these devices are often provided without clear explanation of how these metrics are obtained. At present, there is no clear consensus regarding how these data should be handled and reported in a sport context. Therefore, the aim of this review was to examine the factors that affect the data produced by these athlete tracking devices to provide guidelines for collecting, processing, and reporting of data. Many factors including device sampling rate, positioning and fitting of devices, satellite signal and data filtering methods can affect the measures obtained from GPS and MEMS devices. Therefore researchers are encouraged to report device brand/model, sampling frequency, number of satellites, horizontal dilution of precision (HDOP) and software/firmware versions in any published research. Additionally, details of data inclusion/exclusion criteria for data obtained from these devices are also recommended. Considerations for the application of speed zones to evaluate the magnitude and distribution of different locomotor activities recorded by GPS are also presented, alongside recommendations for both industry practice and future research directions. Through a standard approach to data collection and procedure reporting, researchers and practitioners will be able to make more confident comparisons from their data, which will improve the understanding and impact these devices can have on athlete performance.
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Very little is currently known about the effects of acute hamstring injury on over-ground sprinting mechanics. The aim of this research was to describe changes in power-force-velocity properties of sprinting in two injury case studies related to hamstring strain management: Case 1: during a repeated sprint task (10 sprints of 40 m) when an injury occurred (5th sprint) in a professional rugby player; and Case 2: prior to (8 days) and after (33 days) an acute hamstring injury in a professional soccer player. A sports radar system was used to measure instantaneous velocity-time data, from which individual mechanical profiles were derived using a recently validated method based on a macroscopic biomechanical model. Variables of interest included: maximum theoretical velocity (V0) and horizontal force (FH0), slope of the force-velocity (F-v) relationship, maximal power, and split times over 5 and 20 m. For Case 1, during the injury sprint (sprint 5), there was a clear change in the F-v profile with a 14% greater value of FH0 (7.6-8.7 N/kg) and a 6% decrease in V0 (10.1 to 9.5 m/s). For Case 2, at return to sport, the F-v profile clearly changed with a 20.5% lower value of FH0 (8.3 vs. 6.6 N/kg) and no change in V0. The results suggest that the capability to produce horizontal force at low speed (FH0) (i.e. first metres of the acceleration phase) is altered both before and after return to sport from a hamstring injury in these two elite athletes with little or no change of maximal velocity capabilities (V0), as evidenced in on-field conditions. Practitioners should consider regularly monitoring horizontal force production during sprint running both from a performance and injury prevention perspective.