Can Health Status and Lifestyle Indicators Predict Amateur Soccer
Players Performance Level? A Preliminary Study
Beatrice De Lazzari
1,2 a
, Giuseppe Vannozzi
, Federico Caramia
, Filippo Lupi
Paolo Salvatore
and Valentina Camomilla
Department of Movement, Human and Health Sciences, Interuniversity Centre of Bioengineering of the Human
Neuromusculoskeletal System, University of Rome “Foro Italico”, Rome, Italy
GoSport srl, Rome, Italy
Keywords: Anamnesis Sheet, Amateurs, Performance Evaluation, Football.
Abstract: Introduction: Soccer is one of the most popular sports in the world. To determine the performance potential
of an athlete, various tests are typically performed in elite athletes, but not in amateur ones. Aim: To evaluate
if and which health status and lifestyle indicators can be useful predictors of physical performance level in
amateur soccer players. Methods: A group of 32 male subjects (age: 32±12 years, mass: 77±10 kg, stature:
1.78±0.06 m) voluntarily participated in the study. To assess their functional capacities, five in-field tests
were conducted, while as an anamnesis sheet, a questionnaire was developed that investigated: body mass
index (BMI), age, physical activity level, lifestyle, alcohol consumption and smoking habits, sports career,
occurring injuries, and medical history. A stepwise backward regression was then conducted. Results: A
significant R
=0.722 was found between the questionnaire outputs and the physical tests, using only six of
the nine investigated indicators. Conclusions: With a simple questionnaire, an estimate of amateur athletes’
physical performance can be obtained. Prospectively, a wider dataset, including women, will allow for the
definition of a synthetic biometric index.
Soccer is one of the most popular sports in the world,
practiced by elite athletes and amateurs, and it is an
intermittent sport (Tessitore et al, 2005; Bangsbo,
1994). Players Key Performance Indicators (KPI) can
be assessed through in-field tests for different soccer-
related functional capacities (Figueiredo et al, 2011;
Taher and Shahbazi, 2013). While these tests are
usually performed by elite athletes and research is
mainly focused on them, there is a lack of information
about amateurs' functional capacity. This is more
evident for amateurs not belonging to sport clubs or
associations, that are our target, where there is a
reduced accessibility to physical testing facilities and
a limited knowledge of coaches about the players. To
overcome this issue, a technological support is
proposed to provide an accessible way to assess and
predict the level of performance of the athletes.
Biometric quantities, such as BMI and physical
activity level, were proved to be related to functional
capacity of a player (Nikolaidis, 2012; Gil-Rey et al,
2015) and can be collected through a purposely
developed questionnaire, together with players’ in-
field performance tests (Kaplan, 2010). Soccer
research attempted to identify the link between
biometric quantities and in-field test scores (Campa
et al, 2019; Nikolaidis, 2012), but these do not cover
all biometric quantities in relation with many
different performance tests. With this work we aimed
to investigate health status and lifestyle indicators as
possible predictors for the player performance level,
considering different functional capacity tests that
contribute to the overall performance assesment of
the player.
De Lazzari, B., Vannozzi, G., Caramia, F., Lupi, F., Salvatore, P. and Camomilla, V.
Can Health Status and Lifestyle Indicators Predict Amateur Soccer Players Performance Level? A Preliminary Study.
DOI: 10.5220/0011523900003321
In Proceedings of the 10th International Conference on Sport Sciences Research and Technology Support (icSPORTS 2022), pages 86-92
ISBN: 978-989-758-610-1; ISSN: 2184-3201
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
From the questionnaire results, and through a
regression analysis, we aimed at identifying the main
indicators with the potential of being used in the
future to implement a new collective index, related to
biometrics, that could allow to predict the physical
status of an athlete when it is not possible or
appropriate to perform in-field tests.
The great advantage related to this potential index
is that it could be an accessible tool if implemented
into a smartphone app specifically devised to support
soccer users. Through this type of app, big data could
be gathered to provide knowledge about amateurs
soccer players. The app could also make available
physical testing procedures for this underinvestigated
population. As a fallout, personalized training plans
also based on training time and player role could be
promoted for amateur soccer players.
To identify the main biometric indicators with the
potential to predict an athlete’s physical status, a
questionnaire of 20 questions was developed. The
questionnaire evaluates 9 different aspects: body
mass index (BMI), age, physical activity level
(through IPAQ), lifestyle, alcohol consumption and
smoking habits, sports career, occurring injuries, and
medical history. From the multiple responses
available for each question, a single score is obtained
as descriptive of each abovementioned aspect.
Thirty-two male participants (age: 32±12 years,
mass: 77±10 kg, stature: 1.78±0.06 m) completed the
questionnaire and performed the following five tests,
as detailed in section 2.1, to assess their KPIs: 30 m
sprint test, Yo-Yo Intermittent Recovery Test Level 1
(Yo-Yo IR1), countermovement jump (CMJ),
standing long jump (SLJ), repeated sprint ability test
Each test result is compared to the typical score
performed by an elite soccer player (whose scores
come from the literature cited in Section 2.2, Table
1), and a related percentage value is obtained. At the
end of all tests and once the percentage values are
obtained, a KPI is calculated for each test. An overall
value is then obtained as average of these
specific values. A stepwise backward regression
analysis is performed using IBM SPSS 28 by
receiving the questionnaire scores as independent
variables of the regression analysis. A collective
index is then obtained as the result of a mathematical
relation among the independent variables which
minimises the distance to KPI
as value obtained from
the physical tests (see 2.1.5).
2.1 Performed Tests
In this section, the tests performed by the participants
are briefly described.
2.1.1 Yo-Yo Intermittent Recovery Test
Level 1 (Yo-Yo IR1)
This test is typically used for the evaluation of agility
and aerobic capacity in intermittent sports (Bangsbo
et al, 2008; Bangsbo et al, 2006). In untrained
individuals, it evaluates the ability to perform a
repeated and intense test and the capacity of recovery.
It is a maximal test, and it is evaluated as follows: the
participant must run 20 m back and forth across a
marked track keeping time with beeps. After a 20+20
m run, the participant has 10 s of recovery before
starting again. Going over the time, the time interval
in which the 20 m must be performed decreases,
while the recovery time remains the same. In the 10 s
of recovery, the participant can run slower or walk 5
m back and forth before starting again, performing an
active recovery, as shown in Figure 1.
The KPI value for this test, KPI
, is calculated as
the total distance run by the subject in percentage of
the typical score of an elite athlete (reported in Table
Figure 1: Yo-Yo Intermittent Recovery Test Level 1 (IR1)
2.1.2 Repeated Sprint Ability (RSA)
This test is composed of six shuttle runs of 20 m with
change of direction and a time recovery of 20 s among
runs, as shown in Figure 2. This test evaluates the
change of direction ability. This type of test is
particularly influenced by the aerobic capacity
(Rampinini et al, 2009) and reproduces the metabolic
phenomena that characterize the most intense phases
in a match, such as pH reduction and anaerobic
glycolysis activation (Rampinini et al, 2007).
The values of interest are the time intervals
needed by the subject to perform each shuttle. The
Can Health Status and Lifestyle Indicators Predict Amateur Soccer Players Performance Level? A Preliminary Study
minimum time spent in a trial by an elite athlete (see
Table 1) is used to obtain the KPI of interest for this
test, KPI
Figure 2: Repeated Sprint Ability (RSA) scheme.
2.1.3 Lower Limb Strength
The Counter-Movement Jump (CMJ) is a test
repeated three times and the variable of interest is the
jump height, measured by an inertial sensor, Gyko
(Migrogate, Italy, sampling frequency = 1000
samples/s; full scale: ±16g, ±2000deg/s). The
execution of the jump is described in Figure 3. In the
CMJ, the participant starts with their hands on their
hips and, following the experimenter's start, performs
a sudden bend in their legs and, without stopping the
movement, jumps upwards. To calculate a jump
related KPI
, the maximum jump height among
these trials is taken as the reference value and
normalised by the typical jump height of an elite
soccer athlete found in the literature (see Table 1).
Figure 3: Counter-Movement Jump (CMJ) vertical
execution (the frames refer to the same position in space).
The Standing-Long Jump (SLJ) is repeated three
times and performed as shown in Figure 4. In the SLJ,
the participant starts with their arms at their sides, and
following the go-ahead given by the experimenter,
performs a forward jump using their arm swing and
by landing on the ground with their feet close
together. The variable of interest is the jump length,
taken with a tape as the heel-to-heel distance. The
longest jump is taken as the value to compare with the
result performed by an elite athlete (see Table 1) to
obtain the related KPI
Figure 4: Standing-Long Jump (SLJ) execution.
Both SLJ and CMJ describe the lower limb power.
In particular, CMJ gives an estimate of the capacity
to storage and use the elastic energy of extensor
muscles (Tessitore et al, 2005). For this reason, KPI
is calculated as the mean of the two results coming
from KPI
and KPI
2.1.4 The 30 m Sprint Test
The 30 m test is a sprint test in which the participants
must run 30 m at their maximum velocity. The test is
performed three times and the variable of interest is
the time required to run 30 m. The value of the best
attempt is compared to the score of elite athletes to
calculate KPI
(see Table 1).
This was considered as a sport-specific test
because high-intensity sprints are frequent in soccer
matches: in fact, 96% of sprints are shorter than 30m
and their frequency is one every 90 s (Izzo et al,
Figure 5: 30 m sprint scheme.
2.1.5 KPI Total (KPI
is the final KPI index that considers the KPI
values coming from the performed tests as follows:
= (KPI
)/4 (1
2.2 Statistics of the Tests
In Table 1, the results of the tests are reported. For
Yo-Yo IR1, 30 m, RSA, CMJ and SLJ tests, the mean
and standard deviation are reported. These values are
obtained from the best results performed by each
participant. In the last column, the scores of elite
athletes found in literature are reported (Schmitz et al,
2018; Rampinini et al, 2007; Sporis et al, 2009;
Zapartidis et al, 2009; Chaouachi et al, 2010).
icSPORTS 2022 - 10th International Conference on Sport Sciences Research and Technology Support
Table 1: In the second and third columns, there are the mean
and standard deviations of the results of the tests, in the
fourth column, the value of reference of elite athletes as
found in the above-cited literature.
Test Mean Standard
Yo-Yo IR1 [m] 785 473 2800
RSA [s] 7.4 0.5 6.5
CMJ [m] 0.35 0.06 0.46
SLJ [m] 2.1 0.4 >2.8
30 m [s] 4.8 0.4 4.0
2.3 Questionnaire Implementation
The proposed questionnaire is composed of 20
questions and investigates nine different aspects
about health status and lifestyle as possible predictors
of the performance of the athlete. In particular, the
acquired information is about:
anthropometric data, as weight and height of the
subject to calculate the BMI, age and sex;
health information: smoking habits, alcohol
consumption, medical history and occurring
injuries in the last 12 months;
habits information, such as sports career and
physical activity data, evaluated through the
IPAQ -FS questionnaire (Lee, 2011).
Each explored domain pertains to the sphere of
personal health and is widely considered as risk factor
in the literature. Specifically, BMI is one of the
screening factors for subjects’ health (Erickson,
1998), and it is correlated to their quality of life
(Bottcher et al, 2020). Age is one of the main risk
factors for the development of chronic degenerative
diseases, according to the guidelines of WHO (2004),
and for what concerns soccer, it’s one of the factors
for the risk of injury in both elite and amateur players
(Ekstrand et al, 2011; Gebert et al, 2018; Dallinga et
al, 2012; Arnason et al, 2004).
Smoking habits can affect the health of the subject
following the 2002 guidelines of the WHO. For the
aim of this work, the questionnaire evaluates simply
if the participant smokes or not, on the basis of the
study of Jeon (2021) which compared the
performance of smokers and not-smoker populations.
Following this study, it is found that smokers have a
reduced performance about 21% in the test used for
that scope.
Alcohol consumption is an indicator obtained as
the result of two questions of the questionnaire which
evaluates alcohol consumption in a day and with
which frequency. Alcohol consumption is included as
variable in this study since it is one of the main factors
for the health state of the subject, according to the
guidelines of WHO (2004).
Medical history is investigated through a question
in which the subject indicates the presence or not of
chronic pathologies, divided into controlled and
severe ones.
Information on occurring injuries is another
indicator associated to one question in which the
subject has to identify the time spent without doing
physical activity due to the presence of the injury. The
time reference is related to the last 12 months. This
parameter is important in amateurs, who have high
relapse risks, which can affect their health status.
The sports career indicator associates the subjects
to three main groups, depending on their experience
as athlete: not professionals, former elite athletes,
former amateur athletes.
Work indicator is obtained as the result coming
from the answers of two questions which evaluate the
type of work that the subject does, and how they
arrive to their place of work.
The level of physical activity is investigated
through the IPAQ-FS questionnaire, which evaluates
the time spent (in terms of hours and days) in doing
different physical activities.
Each indicator derived from the questionnaire is
associated with a score between 0% and 100%. Once
completed the questionnaire, the nine indicators will
be represented by a percentage value.
2.4 Stepwise Regression Analysis
A stepwise regression analysis is performed,
considering the results coming from the questionnaire
as independent variables.
The regression allows estimating the contribution
of each indicator coming from the questionnaire to a
potential synthetic index related to individual
biometric characteristics. Using this technique, it is
possible to evaluate different models that, step by
step, don’t consider the less significant predictor. A
total of 6 models are thus obtained, with the first one
characterized by the presence of all the 9 variables
above mentioned and the last one which considers
just the four main indicators. For each model, a R
value is obtained.
The choice of the best model is identified by the
evaluation of the R
: as the number of input variables
decreases, the R
decreases, too. As it is possible to
see in Figure 6, an R
value is reported for each
computed model. A model is selected as good if its R
changes less than 3% with respect to the model
including all the 9 variables (MO9), which includes
all the input variables.
Can Health Status and Lifestyle Indicators Predict Amateur Soccer Players Performance Level? A Preliminary Study
The model including 6 variables (MO6) is the last to
have a good R
according to the set criterion (Figure
6). In the following, MO9 and MO6 are compared to
see the impact of removing variables on a collective
biometric index.
Figure 6: R
values of the regressive models are here
reported. MO9 considers all the nine variables, MO4
considers only four main variables.
In MO9, all the variables contribute to the
estimate of the global KPI, with R
=0.734, F(9,22) =
6.744, p<0.05. Considering MO6, the situation
changes only slightly: R
=0.722, F(6,25) = 10.842,
Figure 7: Relation between overall biometric index based
on MO9 and the KPI
value obtained for each subject.
Figure 8: Relation between overall biometric index based
on MO6 and the KPI
value obtained for each subject.
In Table 2 and 3, b and β values are reported for
MO9 and MO6 respectively:
Table 2: MO9 b and β values.
MO9 b β
Constant 40.305 -
Work 0.115 0.193
Physical activity Level 0.113 0.528
Sports career -0.028 -0.061
Alcohol consumption -0.094 -0.132
Occurring injuries -0.059 -0.181
Medical history 0.11 0.033
Age 0.102 0.235
BMI 0.221 0.442
Smoking habits 0.097 0.109
Table 3: MO6 b and β values.
MO6 b β
Constant 48.026 -
Work 0.115 0.193
Physical Activity Level 0.116 0.544
Alcohol consumption -0.096 -0.135
Occurring injuries -0.051 -0.157
Age 0.095 0.219
BMI 0.227 0.455
These preliminary results support the possibility to
determine the performance level of amateur athletes
based on health and daily life indicators. Thus, a
prediction of the KPI
could be obtained without
performing specific in-field tests.
Looking at β values in the previous section, BMI
and physical activity level seem to be the main
predictors. This is in agreement with the literature that
reports a correlation between those two factors and
the performance of the athlete in different in-field
tests (Nikolaidis, 2012; Gil-Rey et al, 2015). In both
MO9 and MO6 models, the polynomial regression
explains more than the 70% of the variability of the
dependent variable, obtaining an adequate predictive
model, with a potential of improvement through
further data sampling and updates in the regression
As shown in the results, not all the aspects seem
to be meaningful based on the population of interest:
comparing MO9 and MO6, the R
decreases less than
3%, thus three out of nine indicators seem to
icSPORTS 2022 - 10th International Conference on Sport Sciences Research and Technology Support
contribute minimally to the prediction of amateur
performance level. Specifically, looking at MO6, the
less important aspects are sports career, smoking
habits, and medical history. For instance, a previous
brilliant sporting career does not guarantee the
preservation of soccer performance: in fact, following
Mujika and Padilla (2000), a body that is not
constantly exposed to training stimuli, can easily
regress. The smoking habit may be a performance-
altering factor, but it is possibly necessary to
investigate the amount of tobacco consumed by the
athlete (Tetelepta et al, 2019). In fact, the proposed
questionnaire analyses simply if the individual is a
smoker or not, so it seems to not be sufficient as
discriminatory factor. Medical history possibly needs
to be more specific to be predictive. For example,
teenagers with diabetes can nowadays find
information on how to prepare themselves to
participate in different forms of physical activity and
sports, both amateur and professional, without
affecting their performance (Krzykała et al, 2021).
Conversely, not all the diseases are well known, or it
is difficult to assess if they can alter sport
performance. One of these cases is COVID-19, which
is still not known and its effects on physical status is
not yet identified (Sarto et al, 2020).
From Table 2 and 3, in both models, BMI, IPAQ
and age are the three main factors that may contribute
to the identification of the performance level of the
athlete, if used as predictors.
As a limitation, while the indices examined by the
questionnaire relate to the population of interest only,
genetic factors are here neglected, which might be
main contributors to sports performance (MacArthur
and North, 2005). Nonetheless, remaining at the in-
field assessment level, this preliminary study was
able to highlight how a subset of health status and
lifestyle indicators can approximate player’s
performance level, even if further investigations and
a wider dataset are needed to confirm the results.
As a future perspective, the implementation of an
app able to give accessibility to these tests and to
acquire data coming from amateurs can be a powerful
tool to collect a larger dataset, potentially including
both male and female players. The inclusion of
female amateurs will bring to adjustments due to
existing gender-related differences (e.g. BMI). Such
larger dataset is expected to lead to more reliable
regressions; thus, the definition of an overall
biometric index could be provided, based on a
minimal set of indicators to obtain a prediction of
functional capacity in amateurs. Prospectively, the
availability of a larger dataset could also open to the
application of other data mining techniques to find
possible interesting factors as the evaluation of a
common trend or the identification of the factors that
can be used to reliably classify players from a
physical performance point of view.
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icSPORTS 2022 - 10th International Conference on Sport Sciences Research and Technology Support