Journal of Physical Education and Sport ® (JPES), Vol. 24 (issue 8), Art 220, pp. 1984 - 1996, August 2024
online ISSN: 2247 - 806X; p-ISSN: 2247 – 8051; ISSN - L = 2247 - 8051 © JPES

Original Article
Anthropometric and electromyographic characteristics of the free throw shooting
gesture in university basketball players
NATALI RENDÓN-GALVEZ1, KAROL B. GARCIA-SOLANO2, JULIALBA CASTELLANOS-RUIZ3, JOSÉ
A. VIDARTE-CLAROS4, , CARLOS A. CASTILLO-DAZA5, CARLOS F. MESA-ORJUELA6, LINA M.
VALENCIA-BERMUDEZ7, LUIS A. CARDOZO8
1,2,3,4
Body-Movement Research Group, School of Health, Universidad Autónoma de Manizales, Manizales,
COLOMBIA
5,6,7,8
Research and Measurement Group in Sports Training (IMED), Faculty of Health Sciences and Sports,
Fundación Universitaria del Área Andina, Bogotá, COLOMBIA
Published online: August 31, 2024
Accepted for publication : August 15, 2024
DOI:10.7752/jpes.2024.08220
Abstract:
The free throw in basketball is a shot at the basket awarded after a foul, it depends solely on who makes the shot.
Analyzing muscle contractions during a free throw allows the design of sports training programs with an injury
prevention component. Objectives: were to determine the anthropometric and electromyographic characteristics
in the free throw throwing gesture along with possible associations between the study variables and explore
profiles of the athletes using unsupervised machine learning algorithms. Method: 48 university basketballathletes from Manizales-Caldas were included (24 men and 24 women, age = 20,979±4,128 years). The surface
electromyographic signal of several muscles involved in the technical gesture was recorded. After measuring themuscular electrical activity in an isometric and dynamic manner, the muscle contraction was estimated throughthe maximum voluntary contraction, the average of the signal and the maximum values obtained during thethrowing gesture were estimated, and the with the endorsement of the institutional bioethics committee throughminutes of April 27, 2022. Results: Regarding the maximum voluntary contraction, it was found that the muscles
with the greatest contraction in the gesture are the middle deltoid, longissimus dorsi, triceps and flexor radialis
longus. . Additionally, a higher fatigue index in dynamic tests than in isometric tests. Regarding the Cluster
analysis, three profiles clearly differentiated by anthropometric variables were identified. Conclusions: Most of
the participants are overweight, Cluster 3 was characterized by being made up only of men, having
anthropometric characteristics superior to the other clusters specifically in height, length and body perimeterssuch as grip strength, greater isometric fatigue in the muscles dorsal and triceps for taller athletes), compared tothe other profiles. On the contrary, greater dynamic fatigue was observed in the dorsal and trapezius muscles in
the athletes of profile 2. Regarding the MCV, greater neuromuscular activation was evident in the athletes of
profile 1 (athletes of shorter height and record in the anthropometric variables evaluated) Likewise, a higher
fatigue index was established in the dynamic tests than in the isometric tests. These results make it possible to
make decisions about the free throw gesture and include recommendations in training programs, thus improving
the performance of athletes and preventing upper extremity injuries.
Keywords: Body Mass Index, Muscle Fatigue¸ Exercise, Athletes
Introduction
In basketball, various types of shots, including the free throw – a shooting gesture with consistentcharacteristics and a clear and reproducible pattern – allow for accurate biomechanical study. A free throw is ashot awarded after a foul. The number of free throws granted depends on the exact action during the shot on
which the foul occurred; similarly, according to the National Basketball Association (NBA), every fifth foulcommitted by a team during the same period, whether technical or personal, results in two free throws for the
opposing team (Phatak et al., 2020). The free throw is taken from the line of the same name, located 4.6 metersfrom the backboard and 5.8 meters from the baseline. Each successful free throw scores one point. This technicalgesture is as essential as other shots made from different points on the court, as it can make a difference in theoutcomes of a game. Therefore, studying its biomechanical variables has interested various researchers (Button
et al., 2003; Huston & Grau, 2003).
Several studies (Echevarría et al., 2011; Díaz-Aroca & Arias-Estero, 2020; Grenha et al., 2021) have
employed an observational methodology and provided feedback to athletes, aiming to monitor them throughrecording instruments that detail the step-by-step execution of the correct free throw technique. However, thismethodology is not specialized and does not allow for evaluating variables such as strength, response time,
fatigue, and muscle coactivation.
1984---------------------------------------------------------------------------------------------------------------------------------Corresponding autor: JULIALBA CASTELLANOS RUIZ, E-mail: jcastellanos@autonoma.edu.co

NATALI RENDÓN-GALVEZ, KAROL B. GARCIA-SOLANO, JULIALBA CASTELLANOS-RUIZ, JOSÉ A.
VIDARTE-CLAROS, , CARLOS A. CASTILLO-DAZA, CARLOS F. MESA-ORJUELA, LINA M.
VALENCIA-BERMUDEZ, LUIS A. CARDOZO--------------------------------------------------------------------------------------------------------------------------------------Currently, advanced technology is being used in sports practice to monitor and control abilities andbiomechanical variables important for the performance and execution of sports gestures, although there are very
few studies in this regard and this is becoming even more generalized at the regional level. Electromyography(EMG), a system that allows the detection and analysis of the electrical signal related to muscle contraction;
contributes to understanding neuromuscular patterns and consequences of muscle dysfunctions. (Guzmán &Méndez, 2018). According to the interest of each study, it is possible to carry out different types of analysis
thanks to the signal that is displayed as the frequency of muscle contraction, it can be observed from the MVC or
maximum voluntary contraction, to muscle fatigue. The MVC helps determine the level of contraction andmuscle strength expressed in percentage, thanks to a maximum isometric activity test, and together with thebasal contraction test (at rest), they are the reference signals for other tests that involve work. active, isotonic,
among others (Román-Liu & Bartuzi, 2018).
Muscle fatigue understood as the loss of a muscle's ability to produce force, is generally the result of
prolonged and/or repetitive work, which can generate injuries over the long term. EMG has many advantages foranalyzing muscle fatigue in real-time with minimal risks (Phinyomark et al., 2012). It plays a vital role in sports.
Many investigations include electromyographic analysis in their study variables; for example, in 2008, themuscular activity in elbow extension was studied according to sports practice, comparing volleyball vs karate
athletes, during ballistic movements (Moreno et al., 2008). An electromyographic study was conducted in waterpolo, where upper limb muscles were selected during different throws. Throwing speed was concluded to be
improved by emphasizing strength, endurance, and flexibility training of the pectoralis major and anteriordeltoid. Additionally, the study highlighted the stabilizing role of the biceps and triceps brachii muscles in thesethrows (Yaghoubi et al., 2014).
According to Cerqueira et al. (2013), surface electromyographic activity for biceps brachii isreproducible in protocols involving both isometric (such as maximal strength testing) and dynamic contractions(in this case, during a free throw), which expands the use of surface EMG, making using it in muscle fatigue andeven neuromuscular disorders possible. In the case of basketball, although the application of EMG is still
relatively scarce, there are studies like that of Lv and Dong (2021), in which surface electromyography was used
to analyze muscle discharge time, the rate of contribution, and the integral value of EMG during rotational
movements. The goal was to explore the mechanism for preventing various types of injuries. Among the results,
a high incidence of knee joint injuries was found, including patellar dislocation, meniscus injury, and cruciate
ligament injury, with no difference between genders.
Analyzing the muscle contractions of a high-performance player during a free throw allows for thedesign and execution of preventive programs to avoid injury mechanisms and improve athletic performance.
Therefore, the objective of this study was to determine the anthropometric and electromyographic characteristics
in the gesture of the free throw, along with possible associations between the study variables, and to explore
profiles of athletes using unsupervised machine learning algorithms in collegiate basketball players.
Materials and Methods
Participants
The study included 48 basketball athletes affiliated with sports clubs from four universities in
Manizales-Caldas. The participants comprised 24 men and 24 women, aged between 17 and 45 years (Mean =20.98 ± 4.13 years).
Procedure
The BTS FreeEmg 1000 8-channel device recorded the surface electromyography signal. This device isa wireless surface electromyograph complying with IEEE 802.15.4 standards. It features a 16-bit resolution, asampling rate of 1 kHz, and a proprietary processing platform operated through the EMG Analyzer software. For
the placement of the electromyography sensors, the guidelines and recommendations provided by SENIAM(Surface ElectroMyoGraphy for the Non-Invasive Assessment of Muscles) were followed for individual muscle
locations on the upper trapezius, middle deltoid, long head of the triceps brachii, and both the long and shortheads of the biceps brachii. General recommendations were followed for the pectoralis major, latissimus dorsi,
and the radial extensors and flexors of the wrist. Muscle electrical activity was acquired over 15 seconds ofcontraction followed by one minute of recovery between tests, as per the methodology described by Stegeman,D., and Hermens, H. (2007).
A Seca brand stadiometer and measuring tape were used to assess height and measure lengths andcircumferences of the upper limb. Body weight was measured using a Tanita HD314 digital scale. The Jamar
hydraulic dynamometer was used to evaluate the prehensile or gripping force. For calculating the Body Mass
Index (BMI), the 2023 World Health Organization (WHO) classification was used. The anthropometricmeasurements evaluated for the present study consisted of: Shoulder Perimeter Measurement (PAA): This measurement was taken from the right medial deltoid tothe same point on the left side, using the measuring tape to form the ellipse corresponding to this path.
----------------------------------------------------------------------------------------------------------------------------1985
JPES ® www.efsupit.roNATALI RENDÓN-GALVEZ, KAROL B. GARCIA-SOLANO, JULIALBA CASTELLANOS-RUIZ, JOSÉ A.
VIDARTE-CLAROS, , CARLOS A. CASTILLO-DAZA, CARLOS F. MESA-ORJUELA, LINA M.
VALENCIA-BERMUDEZ, LUIS A. CARDOZO-------------------------------------------------------------------------------------------------------------------------------------- Arm Length (LADD/LADI): This measurement was taken from the acromion to the tip of the index
finger, with the elbow fully extended.
 Horizontal Grip (AHD/AHI): This measurement corresponds to the distance from the thumb's tip to thelittle finger's tip.
 Vertical Grip (AVD/AVI): This measurement corresponds to the distance from the base of the wrist to
the tip of the middle finger.
The electromyographic activity during the free throw was evaluated on the court with each athlete's sports
equipment and consisted of 15 shots from the regulation free throw line. These were performed continuously: an
assistant collected the ball after each shot and passed it back to the athlete for the next throw, avoiding any
external electromyographic recordings unrelated to the free throw.
After measuring the muscle's electrical activity isometrically and dynamically, the process continued
with estimating muscle contraction through maximum voluntary contraction. For this purpose, the obtained
electromyography signal was rectified, and a Root Mean Square (RMS) analysis was performed using a 200 ms
moving window. Subsequently, the maximum value of the RMS signal was determined, and theelectromyographic signal of the dynamic gesture, after RMS processing, was normalized to the signal obtained
during the isometric test. Using the obtained and normalized signal according to maximum voluntary
contraction, the mean of the signal and the maximum values obtained during the throwing gesture were
estimated.
Ethical Considerations
The technical, scientific and administrative standards for health research were established in accordancewith the provisions of Colombian law. this study was considered to have minimal risk. This classification meansthat the anticipated risks to participants are not more significant (considering both probability and magnitude)
than those encountered daily or during routine physical or psychological examinations. When conducting thetests, participants were informed of the process to be undertaken, and authorized their participation by signing
the informed consent form. This research was endorsed by the ethics committee of the Fundación Universitariadel Área Andina, as evidenced by the approval record dated April 27, 2022.
Statistical Analysis
The data collected was analyzed using descriptive statistics through JASP software version 0.16,
developed by the University of Amsterdam. The values presented correspond to muscle activation datanormalized using the Maximum Voluntary Contraction (MVC) technique. Muscle fatigue analysis was
conducted using the analysis of the mean and median of the frequency spectrum using the Fast Fourier
Transform (FFT). The normal distribution of the data was verified using the Shapiro-Wilk test. Parametric andnon-parametric tests for related samples and effect size measurements were used to compare the fatigue index
obtained within the isometric and dynamic tests. Due to the non-fulfillment of the assumptions of normality,
Spearman's rank correlation was used for the analysis of associations between variables, with correlation
strengths classified as follows: < 0.2 very low, < 0.4 low, < 0.6 moderate, < 0.8 good, and > 0.8 high (Salkind,1999). Athletes were divided into clusters to identify characteristics in similar data (natural groupings) andsubsequently to extract patterns that characterize one or more profiles.
For this purpose, the programming software R (R Statistics, version 4.0.5, Inc, Colorado, USA) wasused with the package “FactoMineR.” The Hopkins statistic was used to identify the existence of grouping
tendency of the subjects; using unsupervised machine learning algorithms, the athletes were divided into groups
or clusters according to the similarity in some characteristics. The data from the clusters were analyzed using the
ANOVA test to show the equality of variances with Levene's test to determine the differences between the
profiles generated. Omega squared (ω2) was used to report the magnitude of differences, assuming < 0.01, 0.01,
0.06, and 0.14 as irrelevant, small, medium, and large effect sizes, respectively (Goss-Sampson, 2018). A
significance level of p < 0.05 was considered. In addition, a multivariate analysis was carried out throughPrincipal Component Analysis (PCA). PCA allows the classification of phenotypic variation into independent
systems of correlated traits. This technique makes identifying the main directions of variation in the data possible
and reducing its complexity without losing much relevant information. For this analysis, the variables weregrouped into four groups: anthropometry, average muscle activation normalized by MVC, maximum muscle
contraction normalized by MVC, isometric muscle fatigue, and dynamic muscle fatigue.
Results
Anthropometric Characteristics
Concerning the population evaluated and their anthropometric characteristics, three of the athletes were
found to be left-handed. Concerning BMI, 29.17% were found to be overweight, 6.25% were found to be type I
obese, and 2.08% were found to be underweight. Other anthropometric characteristics can be seen in Table 1.

1986---------------------------------------------------------------------------------------------------------------------------JPES ® www.efsupit.roNATALI RENDÓN-GALVEZ, KAROL B. GARCIA-SOLANO, JULIALBA CASTELLANOS-RUIZ, JOSÉ A.
VIDARTE-CLAROS, , CARLOS A. CASTILLO-DAZA, CARLOS F. MESA-ORJUELA, LINA M.
VALENCIA-BERMUDEZ, LUIS A. CARDOZO--------------------------------------------------------------------------------------------------------------------------------------Table 1. Anthropometric Characteristics and Dynamometry of Athletes.
Variable

Group

X (S)

Min - Maxp (df/46)dSize (m)

M
H
M
H
M
H
M
H
M
H
M
H
M
H
M
H
M
H
M
H
M
H
M
H

1.64 ± 0.06
1.79 ± 0.08
62.15 Me
77.75 Me
23.47 ± 3.45
24.59 ± 3.21
74.31 ± 3.76
81.75 ± 4.66
73.98 ± 3.40
81.52 ± 4.75
33.25 ± 1.69
37.50 ± 1.61
17.66 ± 1.03
19.95 ± 1.17
17.65 ± 0.85
19.93 ± 1.21
19.52 ± 1.28
22.20 ± 1.55
19.52 ± 1.23
22.22 ± 1.61
26.38 ± 5.13
39.90 ± 8.10
24.29 ± 5.24
37.97 ± 7.52

1.54 - 1.80
1.65 - 1.98
47.20 - 79.70
57.80 - 124.70
18.40 - 31.20
19.80 - 34.70
68.00 - 82.50
73.50 - 90.00
69.00 - 81.50
73.00 - 90.00
29.50 - 36.00
34.00 - 40.00
15.00 - 19.50
17.50 - 22.00
16.00 - 19.00
18.00 - 22.00
17.00 - 22.00
19.00 - 26.00
17.00 - 22.50
19.00 - 26.00
18.60 - 39.30
24.00 - 55.00
16.00 - 33.30
22.00 - 53.30<0.001-2.285<0.001-0.7470.004-0.869<0.001-1.758<0.001-1.826<0.001-2.568<0.001-2.062<0.001-2.180<0.001-1.883<0.001-1.882<0.001-1.993<0.001-2.110Body Mass (kg)IMC (kg/m2)
L_AD_D
L_AD_I
P_H
P_MDM_D
P_MDM_I
P_DIDP_D
APDMPI
DIN_M_DER
DIN_M_IZQ

Note: M: Male; F: Female; BMI: Body Mass Index; L_AD_D: Acromion to right middle finger length; L_AD_I:
Acromion to left middle finger length; P_H: Shoulder circumference; P_MDM_D: Carpal tunnel to proper
middle finger circumference grip; P_MDM_I: Carpal tunnel to left middle finger circumference grip; P_P_ID:
Little finger to right thumb circumference grip; P_DIDP_I: Little finger to right thumb circumference grip;
P_DIDP_I: Little finger to left thumb circumference grip; P_DIDP_I: Little finger to left thumb circumference
grip: Little finger to left thumb perimeter grip; DIN_M_DER: Right upper limb dynamometry; DIN_M_IZQ:
Left upper limb dynamometry; Me: Median (does not meet the assumption of normality); †: Mann-Whitney U;
df: Degrees of freedom; d: Effect size by Cohen's D and nonparametric by biserial correlation by ranks.
Muscle Activation Characteristics during Free-throw Shooting
The maximum voluntary contraction (MVC) performed by the athletes had a heterogeneous behavior in
the different muscles, which can be observed in Table 2.
Table 2. Electromyographic Behavior during Launching.
MVC MAX
Muscle
X (S)
CV
Min
Max

X (S)

CV

Min

Max

FCR

63 ± 25

0.4

19

99

7±4

0.549

2

26

Tri

60 ± 20

0.331

28

97

6±3

0.46

0

12

Bic

41 ± 21

0.501

11

97

5±3

0.64

1

15

MVC MEAN

TraS

52 ± 18

0.344

18

92

9±5

0.617

3

38

PecM

43 ± 24

0.562

6

98

6±4

0.672

1

20

DelM

66 ± 14

0.22

36

100

10 ± 4

0.466

5

33

DorA

61 ± 24

0.393

21

99

11 ± 8

0.758

0

39

ECR

41 ± 18

0.446

10

93

6±3

0.501

0

16

Note: FCR: Flexor Carpo Radialis; Tri: Triceps; Bic: Biceps; TrasS: Upper Trapezius; PecM: Pectoralis Major;
DelM: Medial Deltoid; DorA: Llatissimus Dorsi; and ECR: Extensor Carpo Radialis.
Muscle Fatigue Analysis during Isometric and Dynamic Tests
According to the fatigue component analysis, a higher fatigue index was observed in dynamic tests
compared to isometric tests. Additionally, the medial deltoid was the only muscle that did not show significant
changes between tests (see Table 3).
----------------------------------------------------------------------------------------------------------------------------1987
JPES ® www.efsupit.roNATALI RENDÓN-GALVEZ, KAROL B. GARCIA-SOLANO, JULIALBA CASTELLANOS-RUIZ, JOSÉ A.
VIDARTE-CLAROS, , CARLOS A. CASTILLO-DAZA, CARLOS F. MESA-ORJUELA, LINA M.
VALENCIA-BERMUDEZ, LUIS A. CARDOZO--------------------------------------------------------------------------------------------------------------------------------------Table 3. Fatigue Index in Dynamic and Static Tests Performed on Basketball Athletes.
Muscle
Bic
DelM
DorA
ECR
FCR
PecM
TraS
Tri

Fatigue

X(S)

CV

Min

Max

Dynamic
Isometric
Dynamic
Isometric
Dynamic
Isometric
Dynamic
Isometric

12.25 ± 5.59
7.37 ± 3.01
10.82 ± 2.05
10.15 ± 2.77
17.48 ± 7.83
10.35 ± 2.44
21.96 ± 7.23
14.21 ± 3.26

0.46
0.41
0.19
0.27
0.45
0.24
0.33
0.23

5.67
1.42
7.53
5.61
6.81
6.08
11.23
6.87

43.84
16.49
16.50
16.15
50.07
16.04
48.15
21.28

Dynamic
Isometric
Dynamic
Isometric
Dynamic
Isometric
Dynamic

20.85 ± 5.74
13.72 ± 5.18
11.86 ± 5.54
7.71 ± 3.19
7.99 ± 1.91
6.29 ± 2.49
17.86 ± 4.87

0.28
0.38
0.47
0.41
0.24
0.40
0.27

12.43
1.30
3.26
1.35
4.00
0.81
10.02

34.67
24.98
40.57
18.55
15.05
11.27
29.48

Isometric

9.68 ± 3.47

0.36

2.93

19.33

p-value< 0.001
0.176< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001

Cohen's d
1.087
0.278
1.229
1.382
1.304
0.919
0.765
1.935

Association between Study Variables
Analyses of the association between anthropometric variables and EMG recordings show somesignificant inverse associations. As can be seen in Figure 1, the significant findings are detailed. Associations
that were not statistically significant (p < 0.05) and variables that obtained low and very low correlations (rho <0.4) were intentionally excluded. The inverse associations between anthropometric measurements and body massand the EMG recordings of specific muscles stand out, i.e., the greater the weight or height of the subjects, thelower the EMG recordings.

Figure 1. Association between Anthropometric Variables and EMG.
Note: The blue shaded area indicates positive correlations, and the pink area indicates inverse correlations. The
higher the intensity of the color, the higher the value of the association. X_B: Mean biceps; XMax_B: Mean
maximum biceps; X_T: Mean triceps; XMax_T: Mean maximum triceps; PH: Shoulder circumference; LADMI:
Length acromion to left middle finger; LADMD: Length acromion to right middle finger; DMSI: Dynamometryof left upper limb; APDMPI: Grip perimeter from tip of little finger to left thumb; Mass C: Body mass.
Exploring Profiles Using Hierarchical Clustering
The Hopkins statistic value of 0.33 confirmed the presence of a clustering tendency among the subjects.
The internal validation using the clValid tool suggested hierarchical clustering methods, and the number ofclusters determined by the NbClust algorithm was three, using the Ward.D2 method. Three profiles wereidentified as clusters, as shown in the dendrogram illustrated in Figure 2. Cluster 1: Comprises 22 athletes,
Cluster 2: Contains 16 athletes, and Cluster 3: Consists of 10 athletes.
1988---------------------------------------------------------------------------------------------------------------------------JPES ® www.efsupit.roNATALI RENDÓN-GALVEZ, KAROL B. GARCIA-SOLANO, JULIALBA CASTELLANOS-RUIZ, JOSÉ A.
VIDARTE-CLAROS, , CARLOS A. CASTILLO-DAZA, CARLOS F. MESA-ORJUELA, LINA M.
VALENCIA-BERMUDEZ, LUIS A. CARDOZO--------------------------------------------------------------------------------------------------------------------------------------The profiles generated through hierarchical analysis revealed significant differences primarily inanthropometric variables, while the electromyographic variables showed little difference among the groups.
Despite the limited variation in electromyographic variables, the analysis successfully recognized differences
between the identified profiles, allowing for the grouping of athletes with sufficient internal validity, as shown in
Table 4. As observed, Profile 3 (Cluster 3), which is composed of athletes with larger dimensions inanthropometric variables and higher grip strength (as measured by upper limb dynamometry), consists
exclusively of male athletes (n=10). In descending order of anthropometric values, Profile 2 (Cluster 2) iscomposed of 12 men and four women. Finally, Profile 1 (Cluster 1) consists of athletes with the smallest
anthropometric values and grip strength, comprising 20 women and two men.

Figure 2. A Dendrogram for Profiling Basketball Players Based on Clustering.
Note: The blue shaded area represents Cluster 1, red represents Cluster 2, and green represents Cluster 3.
Regarding fatigue indices, athletes of Profile 3 present more significant isometric fatigue in the dorsal
and triceps muscles (taller athletes) than the other profiles. In contrast, more significant dynamic fatigue wasobserved in the dorsal and trapezius muscles in the athletes of Profile 2. Regarding the Maximum Voluntary
Contraction (MVC), evidence shows that there was greater neuromuscular activation in athletes from Profile 1,characterized by shorter stature and smaller measurements in anthropometric variables.
Table 4. Comparison of Average Scores by Clusters.
Variable
Anthropometry
Size (m)Mass C (kg)LADMD (cm)LADMI (cm)PH (cm)
APTCDD (cm)
APTCDI (cm)APDMPD (cm)APDMPI (cm)Dynamometry
DMSD (kg)DMSI (kg)
EMG
FIsoD
FIsoT
FDinD
FDinTraX_T (MVC)
X_B (MVC)

Cluster 1(n = 22)

Cluster 2(n = 16)

Cluster 3(n = 10)F(2, 45)

ω²

Post-hoc

1.63±0.05
61.43±8.81
73.41±2.9273.14±2.6233.16±1.71
17.59±1.09
17.63±0.94
19.50±1.40
19.66±1.51

1.75±0.06
75.38±12.29
79.66±3.15
79.19±3.02
36.63±1.83
19.22±0.93
19.09±0.82
21.19±1.08
21.03±1.19

1.85±0.06
85.96±14.42
85.60±2.8785.60±2.76
38.25±1.30
20.85±0.75
20.90±0.97
23.35±1.4223.30±1.57

69.26**
17.78**
60.80**
71.96**
38.19**
40.45**
46.19**
30.93**
22.55**

0.740
0.411
0.714
0.747
0.608
0.622
0.653
0.555
0.473

3>2 & 1, 2>1†
3>2 & 1, 2>1†
3>2 & 1, 2>1†
3>2 & 1, 2>1†
3>2 & 1, 2>1†
3>2 & 1, 2>1†
3>2 & 1, 2>1†
3>2 & 1, 2>1†
3>2 & 1, 2>1†

25.60±4.16
23.99±5.38

36.11±7.82
34.03±7.60

44.98±5.1242.22±5.2341.49**
32.96**

0.628 3>2 & 1, 2>1†
0.571 3>2 & 1, 2>1†

9.94±2.21
10.37±2.52
17.99±6.07
7.76±1.67
6.95±2.60
6.55±3.27

9.77±2.30
7.63±3.01
20.34±9.99
8.99±2.22
4.52±1.91
3.28±1.07

12.20±2.43
11.42±4.60
11.77±4.03
6.88±1.06
4.35±2.13
2.63±1.62

4.12*
5.28*
4.30*
4.67*
7.04*
H(2) 18.30**

0.115
0.151
0.121
0.133
0.201
0.329

3>2 & 1†3>2 & 1>2†
2>3†
2>3†
1>2 & 3†
1>2 & 3‡----------------------------------------------------------------------------------------------------------------------------1989
JPES ® www.efsupit.roNATALI RENDÓN-GALVEZ, KAROL B. GARCIA-SOLANO, JULIALBA CASTELLANOS-RUIZ, JOSÉ A.
VIDARTE-CLAROS, , CARLOS A. CASTILLO-DAZA, CARLOS F. MESA-ORJUELA, LINA M.
VALENCIA-BERMUDEZ, LUIS A. CARDOZO--------------------------------------------------------------------------------------------------------------------------------------Note: Mass C: Body mass; LADMD: Acromion to right middle finger length; LADMI: Acromion to left middle
finger length; PH: Shoulder girth; APTCDD: Carpal tunnel perimeter grip to right middle finger; APTCDI:
Carpal tunnel perimeter grip to left middle finger; APDMPD: Tip of little finger to right thumb perimeter grip;
APDMPI: Tip of little finger to left thumb perimeter grip; DMSD: Right upper limb dynamometry; DMSI: Leftupper limb dynamometry; FIsoD: Dorsal isometric fatigue; FIsoT: Triceps isometric fatigue; FDinD: Dorsal
dynamic fatigue; FDinTra: Trapezius dynamic fatigue; X_T: Triceps average; X_B: Biceps average. **: p< .001;*: p< .05; † Significant differences between groups (Pos Hoc scheffe p < .05); ‡: Dunn nonparametric; F: Anova
statistic when Levene's test indicates equality of variances p > .05; H: Kruskal-Wallis nonparametric statistic.
PCA Analysis
Through the use of Principal Component Analysis (PCA), variables were grouped into categories
including anthropometry, average normalized muscle activation by MVC (mean_mvc), maximum normalizedmuscle activation by MVC (max_mvc), isometric fatigue (fiso), and dynamic fatigue (fdin). Within eachcategory, the contribution level of each variable was determined. This contribution is shown in Figure 3.
Subsequently, the grouping analysis was performed considering the categories of playing positions.

Figure 3. Variable Contribution Graph by Category.

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VIDARTE-CLAROS, , CARLOS A. CASTILLO-DAZA, CARLOS F. MESA-ORJUELA, LINA M.
VALENCIA-BERMUDEZ, LUIS A. CARDOZO---------------------------------------------------------------------------------------------------------------------------------------

Figure 4. The behavior of the Variables by Cluster According to the Playing Position Graph.
Discussion
The objectives of this research were clearly defined as follows: to determine Anthropometric andElectromyographic Characteristics in the free throw shooting gesture along with possible associations between
the study variables and to explore athlete profiles using unsupervised machine learning algorithms in collegelevel basketball players.
Body Composition and Anthropometric Characteristics
Body mass index (BMI) is widely used as a simple indicator of weight-for-height ratio in adults to
identify overweight individuals and obesity. According to the World Health Organization (WHO, 2022), being
overweight is defined as a BMI equal to or greater than 25, and obesity as a BMI equal to or greater than 30.
Even though the population evaluated in this study consists of athletes with regular sports practice, more than
30% show tendencies to be overweight or obese. This finding could be aligned with the research of Bustamante
et al. (2017), who studied 36 adolescent basketball players and found that the valuable time of each training
session impacts the athlete's BMI. Their results indicated that groups of athletes with higher average training
volume showed improved health and reduced BMI compared to the beginning of the season, in contrast to
athletes with lower training volume. This suggests that some college athletes in this study may not meet theminimum physical activity recommendations, and their eating habits may be unhealthy, contributing to increased
BMI.
----------------------------------------------------------------------------------------------------------------------------1991
JPES ® www.efsupit.roNATALI RENDÓN-GALVEZ, KAROL B. GARCIA-SOLANO, JULIALBA CASTELLANOS-RUIZ, JOSÉ A.
VIDARTE-CLAROS, , CARLOS A. CASTILLO-DAZA, CARLOS F. MESA-ORJUELA, LINA M.
VALENCIA-BERMUDEZ, LUIS A. CARDOZO--------------------------------------------------------------------------------------------------------------------------------------Barut et al. (2008) suggest that anthropometric measurements of hand and grip strength may be sportand gender dependent. They found significant differences in these variables when comparing basketball,volleyball, and handball athletes between nine and 18 years of age. Their study reports average measurements of
170.43 mm and 171.40 mm for right- and left-hand length, respectively, in 145 basketball players. These
measures differ from the results obtained in the present study. Similarly, Pizzigalli et al. (2017) suggest that
prehensile strength in basketball athletes is directly related to age and sports level. They report a right prehensile
strength of 38.8±7.7 kg and a left prehensile strength of 38.5±8.2 kg for the 19- to 20-year-old Italian female
athletes, similar to those found in the present study according to the mean age of the population evaluated.
Electromyographic Behavior in LaunchingThis study highlights that the medial deltoid, longissimus dorsi, triceps, and flexor radialis longus
muscles are the primary muscles involved in throwing in basketball. However, a high coefficient of variation isobserved in the activation normalized by maximal voluntary contraction (MVC) in latissimus dorsi andpectoralis major. This suggests that these muscles are prone to increased fatigue and exhibit low endurance
despite their high demand in sports practice.
The latissimus dorsi muscle is crucial in extending the glenohumeral and scapulothoracic joints. In
addition, it collaborates in internal rotation and acts as an adductor muscle. It works with the pectoralis major to
generate resistance in abduction and limit scapulothoracic flexion (Kapandji, 2006). Comel et al. (2018)highlight the pectoralis as a stabilizing shoulder joint muscle connecting the upper limb to the thorax. Weakness
in these muscles can lead to recruitment and overload of other muscles, such as the trapezius, during functional
activities.
This underscores the need to strengthen and focus training on these muscles, especially given
their high coefficient of variation. Strength training is pivotal in helping players adapt their neuromuscularsystem to increase the force they can produce, thus improving the nervous response and optimizing the muscle
contraction process (Bompa & Haff, 2009). Speed and synchronization of nerve signals improve with maximal
strength training, contributing to progress in muscular endurance (Kraemer & Looney, 2012).
This study also highlights the increase in the fatigue index in dynamic tests compared to isometric tests,
which is related to the high weekly training volume. Tavares et al. (2017) suggest that the density of intensive
training programs can limit recovery, leading to muscle fatigue and affecting performance. Basketball athletes
exposed to high-load/speed contraction training can experience fatigue and decreased performance (Kraemer &Looney, 2012). Therefore, the study emphasizes the importance of considering various factors to avoid athletes'
overload and fatigue, as Argus et al. (2010) pointed out in rugby athletes.
In team sports such as basketball, continuous eccentric actions, such as changing direction or propellinga ball, generate a stretch-shortening cycle and can cause muscle fatigue due to the disruption of cross-bridges
and the disruption of contractile and structural proteins in the muscle cell (Farthing & Chilibeck, 2003; Fridén &Lieber, 2001).
Association between Study Variables and the Exploration of Profiles
The association analyses observed significant inverse correlations between anthropometric variables
and electromyographic (EMG) activity of the trapezius, triceps, and biceps muscles. Specifically, athletes withgreater dimensions, lengths, and perimeters in their extremities showed less muscular activity when performing
the throwing gesture. This suggests that the relationship between body size, body mass, and limb length
influences the strength required to execute the throw with a relatively lightweight sports implement. This
relationship also aligns with human muscle growth and development processes in humans (Herzog et al., 2011).
Alternatively, athletes with shorter stature and limb lengths exhibited higher muscle activity than their peers,
supporting evidence of the relationship between muscle length and force generation (Winters et al., 1993; Nuzzo,2023). This finding suggests that shorter athletes may need more muscular effort to achieve similar performancelevels as their larger counterparts, possibly due to less mechanical advantage and smaller muscle mass.
Three distinct clusters were identified in the exploration of profiles using hierarchical analysis. Cluster 3
primarily comprised men who were more prominent in size, with greater body mass, anthropometric lengths,
circumferences, and strength as measured by dynamometry. Following Cluster 3, Cluster 2 was primarily
composed of men, and Cluster 1 predominantly consisted of women. These results suggest that the fatigue
indices and EMG recordings show variability in all athletes and do not allow for the formation of distinctive
groups (clusters) based on fatigue indices or EMG activity. This finding is consistent with previous research thatfound no significant sex differences in neuromuscular activation in certain types of contraction (Hunter &Enoka, 2001; Kavanagh et al., 2020).
These findings highlight the significant influence of sex on anthropometric variables conditioned bydiverse hormonal environments, as previously confirmed in studies (Handelsman et al., 2018). Althoughanthropometric variables are relevant for profiling basketball players, future research needs to investigate therelationship between these variables and performance and sports success.
PCA Analysis
According to Figure 3, the variables that contribute the most to this study from the category ofanthropometry are the grip perimeter from the carpal tunnel to the left and right middle fingers and, in terms of
1992---------------------------------------------------------------------------------------------------------------------------JPES ® www.efsupit.roNATALI RENDÓN-GALVEZ, KAROL B. GARCIA-SOLANO, JULIALBA CASTELLANOS-RUIZ, JOSÉ A.
VIDARTE-CLAROS, , CARLOS A. CASTILLO-DAZA, CARLOS F. MESA-ORJUELA, LINA M.
VALENCIA-BERMUDEZ, LUIS A. CARDOZO--------------------------------------------------------------------------------------------------------------------------------------height, the length from the acromion to the left and right middle fingers. This is directly related to the lever arms
and the movement dynamics during the free throw and for gripping and directing the ball. This finding agrees
with that investigated by Koley (2010), who found strong correlations between grip strength and several
anthropometric traits, such as length, size, and muscle activation, in improving basketball shooting or throwing.
Additionally, he reported that the longer the fingers, the better the shooting or throwing accuracy in the game.
Therefore, some researchers have examined a series of factors and anthropometric variables that explain thisissue, as done by Fallahi (2011), who demonstrated that in basketball, various movements depend on thecontinuous use of muscles such as the wrist and finger flexors since they are of great importance in catching,holding, shooting, and throwing the ball.
Previous studies indicate that body fat percentage is significantly associated with speed and agility
concerning ball grip. In contrast, hand grip strength and weight status are associated with upper extremity
explosive strength. Changes in body composition, years of experience, and weight status influenced players'
physical performance and motor skills, especially speed, agility, and explosive strength of the upper limbs(Rinaldo, 2020).
At the level of average muscle activation, the triceps, pectoralis major, biceps, and upper trapezius
make the most significant contributions. This is particularly important in athletic throwing because, during thatphase, elastic energy storage will be released later in the acceleration phase (Roach, 2012; Roach et al., 2013).
During the arming phase, EMG activity of the trapezius and pectoralis major helps stabilize the scapula and thus
generate adequate torque for subsequent humeral internal rotation (Paine, 1993; Digiovine, 1992; Hirashima,2002). In addition, the EMG pattern of biceps activation and deactivation is vital in preventing and allowing
centrifugal forces to extend the elbow, thus facilitating adequate elastic energy storage during the arming phaseand the generation of rapid elbow extension during the acceleration phase (Roach et al., 2013; Werner, 1993).
This is relevant because, as Gowan et al. (1987) reported, expert athletes have significantly higher activation of
the pectoralis major muscle, while amateurs show significant biceps brachii activation during the entire throwing
motion. In addition, a selective deactivation of the biceps brachii by the experts during acceleration is observed,
with no significant differences between the groups for the trapezius muscle, showing, for this study, a possible
selection for the group of athletes.
A study by Pakosz et al. (2021) evaluated muscle activation time and free throw effectiveness inbasketball. It concluded that significant differences were found in muscle activation in novice, intermediate, andexpert subjects, with the triceps brachii showing a significant variation in the coefficient of variation andefficiency of 81%. The activation time of the arm muscles during a free throw and its variations change
according to the training experience of basketball players.
While at the level of maximum muscle activation, the most outstanding contributions are evidenced by
the biceps, wrist extensor, pectoralis major, and medial deltoid. During overhead throwing, high rotator cuff
muscle activity is generated to help resist the high distraction forces of the shoulder. During arm preparation, themaximal rotator cuff activity is 49% to 99% of a maximal voluntary isometric contraction; during arm
deceleration, it is 37% to 84% (Escamilla et al., 2009).
Maximal scapular muscle activity is also high during the arm preparation and deceleration phases ofthrowing, with maximal serratus anterior activity of 69% to 106%, maximal upper, middle, and lower trapezius
activity of 51% to 78%, maximal rhomboid activity of 41% to 45% and maximal levator scapulae activity of
33% to 72%. In addition, the maximum activity of the serratus anterior was approximately 60% during throws(Escamilla, 2009).
From the fatigue analysis, at the isometric level, the muscles with the most significant contribution were
the biceps, hand extensor, and pectoralis major. These EMG frequency responses are due to increased fatigue,
resulting in increased muscle activation to meet force demand and reduced action potential conduction velocity.
During voluntary contractions, the timing and coherence of motor units are often quantified simultaneously toprovide complementary information about the adjustments made by the nervous system (NS) adjustments during
various tasks (Dartnall, 2008). These EMG frequency responses are due to increased fatigue, resulting inincreased muscle activation to meet force demand and reduced action potential conduction velocity. However,regarding the muscles of the upper extremities, changes have been observed in the synchronization of motor
units during isometric contractions compared to when forces are exerted in different directions. For example,
Semmler (2002) suggests that isometric contractions alter the synaptic input sent to the motor units involved.
The above is a highly discussed and accepted aspect by different authors since it is a commonly accepted opinion
that certain types of exercise, such as fatigue or strength training, can improve the correlated activity of motor
units (Gandevia 2001; Semmler 2002; Kidgell et al., 2006).
Compared to that found by Clough (1968), most measurements of motor unit synchronization strength
are made in the muscles of the hand or wrist, as these have corticospinal solid projections to motor neurons(Phillips, 1964). The biceps brachii muscle's corticospinal inputs to motor neurons are generally weaker than
hand muscles (Palmer, 1992), reflected in reduced synchrony in proximal muscles (Kim et al., 2001). However,alterations in recurrent inhibition, mainly directed toward low-threshold motor units associated with fatigue----------------------------------------------------------------------------------------------------------------------------1993
JPES ® www.efsupit.roNATALI RENDÓN-GALVEZ, KAROL B. GARCIA-SOLANO, JULIALBA CASTELLANOS-RUIZ, JOSÉ A.
VIDARTE-CLAROS, , CARLOS A. CASTILLO-DAZA, CARLOS F. MESA-ORJUELA, LINA M.
VALENCIA-BERMUDEZ, LUIS A. CARDOZO--------------------------------------------------------------------------------------------------------------------------------------(Katz & Pierrot-Deseilligny, 1999), could induce changes in inhibitory inputs standard to motor neurons at the
spinal level after exercise. The effects of muscle damage result in a shift in the length-tension relationshiptoward longer optimal muscle lengths for maximum force generation, an increase in passive tension throughout
the muscle, and a reduction in maximum force output (Proske & Allen, 2005), leading to a series ofphysiological and mechanical events that cause muscle damage (such as fatigue) from exercise and influence theneural inputs responsible for the correlated discharge of the motor unit.
The medial deltoid, upper trapezius, hand flexor, and triceps were the muscles that contributed the mostto the dynamic component. From the analysis of the clustering graph by categories according to playing position,
athletes in the power forward position are the only ones who exhibit behavior different from the other positions.
This is because, in this position, they must secure rebounds on both the defensive and offensive sides, which
contributes to controlling ball possession. Additionally, they block shots from the opposing team, challenge
opponents trying to penetrate, and protect the painted area, among other actions that require high physicaldemands.
The present investigation has limitations, including a wide age range for a small sample of men andwomen. Therefore, future studies with a larger population sample are suggested to enable comparisons between
different age groups. At the same time, athletes from different competitive levels should be included.
Conclusions
The present study showed that of the 24 participants in the study, the majority are overweight, Cluster
3 was characterized by being made up only of men, having anthropometric characteristics superior to the otherclusters specifically in size, lengths and body perimeters such as grip strength. , greater isometric fatigue in thedorsal and triceps muscles for taller athletes), compared to the other profiles. On the contrary, greater dynamic
fatigue was observed in the dorsal and trapezius muscles in the athletes of profile 2. Regarding the MCV, greater
neuromuscular activation was evident in the athletes of profile 1 (athletes of shorter height and record in theanthropometric variables evaluated) Likewise, a higher fatigue index was established in the dynamic tests than in
the isometric tests. Through these results and their analysis, coaches and athletes will have a bibliographic basis
to discuss the inclusion of strengthening exercises for under-trained muscles that play a significant role in highly
demanding gestures. Their efforts in changing training programs can generate benefits around performance
during competitions and prevent upper extremity injuries.
Acknowledgments
Thanks to the Organization of Ibero-American States for Education, Science and Culture (OEI) in
alliance with the Ministry of Science, Technology and Innovation (MINCIENCIAS) of the Government ofColombia in the framework of the +Women +Science +Equity program, the Autonoma University of Manizales,
as well as the team of the Movement Analysis Laboratory, the tutors who were part of the development of the
program as mentioned above and the athletes who participated in the study.
Conflict of Interest
The authors report no conflict of interest in developing this research.
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