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.

1 INTRODUCTION

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

https://orcid.org/0000-0002-2887-9139

https://orcid.org/0000-0002-2359-6076

https://orcid.org/0000-0002-7452-120X

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

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.

2 METHODS

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

(RSA).

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

KPI

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

1).

Figure 1: Yo-Yo Intermittent Recovery Test Level 1 (IR1)

scheme.

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

CMJ

, 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

SLJ

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

CMJ

and KPI

SLJ

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,

2018).

Figure 5: 30 m sprint scheme.

2.1.5 KPI Total (KPI

)

The KPI

is the final KPI index that considers the KPI

values coming from the performed tests as follows:

KPI

= (KPI

+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

deviation

Elite

score

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

work;

 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

3 RESULTS

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,

p<0.05.

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

4 DISCUSSIONS

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

technique.

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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