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

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".

Full text

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-situ” sprint 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
4
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
(raw units)
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
8
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