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Patterns of Injury in the Spanish Football League Players

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Background: The study of football injuries is a subject that concerns the scientific community. The problem of most of the available research is that it is mainly descriptive. The objective of this study is to discover and analyse the patterns of injury in the Spanish Football League (2016-2017 season). Methods: The sample data consisted of 136 given injuries identified by the official physicians of the football clubs. The analysis was performed by using traditional statistic tests, T-pattern detection and polar coordinate analysis. Results: The analysis revealed several patterns of injury: (a) The defender suffered a rupture of the hamstring muscles after a sprint, (b) knee sprains happened due to a received tackle, (c) fibrillar adductor rupture appeared mostly among defenders and (d) fibrillar ruptures took place mostly throughout the first part. Conclusions: There is a marked shift in the tendency regarding the player who gets more injured, from the midfielder to the defender. The most common injury was fibrillar rupture. The most common scenario in which this injury occurred was that in which the player injured himself after a sprint (24%). A week without competing seems to be insufficient as a prevention mechanism for injuries.
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Citation: Prieto-Lage, I.;
Argibay-González, J.C.;
Paramés-González, A.;
Pichel-Represas, A.;
Bermúdez-Fernández, D.;
Gutiérrez-Santiago, A. Patterns of
Injury in the Spanish Football League
Players. Int. J. Environ. Res. Public
Health 2022,19, 252. https://doi.org/
10.3390/ijerph19010252
Academic Editors:
Krzysztof Ma´ckała and
Hubert Makaruk
Received: 6 November 2021
Accepted: 24 December 2021
Published: 27 December 2021
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International Journal of
Environmental Research
and Public Health
Article
Patterns of Injury in the Spanish Football League Players
Iván Prieto-Lage , Juan Carlos Argibay-González *, Adrián Paramés-González , Alexandra Pichel-Represas,
Diego Bermúdez-Fernández and Alfonso Gutiérrez-Santiago
Observational Research Group, Faculty of Education and Sport, University of Vigo, 36005 Pontevedra, Spain;
ivanprieto@uvigo.es (I.P.-L.); aparames@uvigo.es (A.P.-G.); alexandrapichel19@gmail.com (A.P.-R.);
diegobf87@gmail.com (D.B.-F.); ags@uvigo.es (A.G.-S.)
*Correspondence: juan.carlos.argibay@uvigo.es
Abstract:
Background: The study of football injuries is a subject that concerns the scientific community.
The problem of most of the available research is that it is mainly descriptive. The objective of this study
is to discover and analyse the patterns of injury in the Spanish Football League (2016–2017 season).
Methods: The sample data consisted of 136 given injuries identified by the official physicians of the
football clubs. The analysis was performed by using traditional statistic tests, T-pattern detection
and polar coordinate analysis. Results: The analysis revealed several patterns of injury:
(a) The
defender suffered a rupture of the hamstring muscles after a sprint, (b) knee sprains happened due to
a received tackle, (c) fibrillar adductor rupture appeared mostly among defenders and
(d) fibrillar
ruptures took place mostly throughout the first part. Conclusions: There is a marked shift in the
tendency regarding the player who gets more injured, from the midfielder to the defender. The most
common injury was fibrillar rupture. The most common scenario in which this injury occurred was
that in which the player injured himself after a sprint (24%). A week without competing seems to be
insufficient as a prevention mechanism for injuries.
Keywords: injury; football; pattern; video analysis
1. Introduction
The Spanish Football League (LaLiga) is one of the main sports competitions in
the world, with a high economic and social impact, representing 1.37% of the Spanish
GDP, generating around 200,000 jobs per year and generating an amount of more than
4000 million
euros per year in tax contributions [
1
]. Studies on similar competitions to the
Spanish one have calculated that an injury can cost up to 500,000 euros if the injury period
is 1 month [
2
]. Likewise, an investigation of the English Premier League teams determined
that in the 2013–2014 season, for injuries of more than 30 days, clubs would pay more than
£100,000,000 in wages [3].
Throughout the past decade, several studies have been carried out on injures in
football [
4
]. The number of injuries is higher in a competition than in training due to the
number of minutes of exposure [
5
,
6
]. Authors have shown that 25–28 injuries are suffered
for every 1000 h of competition and 7–10 injuries occur for every 1000 h of training [
4
9
].
Additionally, the largest percentage of injuries happen in the lower extremities (70–95%
compared to other parts of the body) [
5
,
6
,
8
10
]. If we take into account the leg that suffers
the injury, some authors [
11
] indicate that the dominant leg suffers the injury in 55.8% of
the cases, while the non-dominant leg suffers 34.3% of the injuries. Similar results have
been found in previous studies [12,13].
The most frequent injuries are in the thigh (especially the back section), knee, ankle,
and groin [
7
,
13
]. As found lately [
5
], 1% of the injuries are caused by hard tackles and,
therefore, punished by the rulebook. Among that percentage, the most common ones are
ankle sprain, at 15%, while knee sprains were only 9% [
7
,
13
]. The studies differ among the
way the injury is suffered; some indicate that up to 80% of the injuries take place through
Int. J. Environ. Res. Public Health 2022,19, 252. https://doi.org/10.3390/ijerph19010252 https://www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2022,19, 252 2 of 12
contact [
14
], while others state the opposite (just 20%) [
12
]. Other research provides more
even data, at 46–54% [15] and 42–58% [13].
In contrast, some studies [
4
,
9
] point out that the nature of the injury is not the same in a
competition and in training, because traumatic injuries happen more often in a competition,
while the injuries suffered in training are often more related to overuse.
The midfielder is the position that suffers more injuries (37.6%), followed by the
defenders (29.6%), strikers (20.5%) and, finally, the goalkeepers (8.3%) [8].
There are no significant differences in the distribution of injuries comparing the first
half with the second half, although there are significant differences in the distribution in
the way that injuries take place, because injuries without contact are common during the
second half [
9
]. Moreover, the occurrence of injuries increases significantly as the time in
each half advances, as demonstrated in previous studies [5,13,16].
Research has revealed that there are no significant differences between different age
groups (under 25 years old, between 25 and 30 years old, and older than 30 years) while
studying the seriousness and rate of the injuries [8].
Taking into account how advanced the season is, data indicate that higher injury
occurrence takes place during the preseason [
9
]. If we take into account the training, most
of the injuries occur during the first part of the season; but if we only concentrate on the
competitions, the opposite occurs—the higher injury occurrence happens along the second
half of the season.
It goes without discussion that the study of football injuries is a subject that concerns
the scientific community. The problem of most of the studies is that they are especially
descriptive, without establishing what causes these injuries. Therefore, the objective of
this study is to discover and analyse the patterns of injury in the Spanish Football League
during the 2016–2017 season. The results of this research will generate knowledge of the
patterns of injury of football players, which will be useful to coaches, trainers and players
to improve the training and competition systems.
2. Materials and Methods
2.1. Design
We used the observational method because it allowed us to study the player’s actions
that lead to an injury in a natural way and with the necessary rigor and flexibility. The kind
of observation carried out was systematic, open and non-participant [17].
The observational design [
18
] used was nomothetic (all injuries were studied indepen-
dently), follow-up (one season) and unidimensional (one level of response). A series of
decisions about the participants, instruments and the analytical process was derived from
this design.
2.2. Participants
The participants of this study were the players of the first division of the Spanish
Football League for men in the 2016–2017 season who suffered an injury because of which
they were withdrawn from the field. A sample of 136 injuries was obtained from the
38 league
days. The information about the injuries was taken from the official medical
records of the clubs and from TransferMarkt, a valid sports database [
19
] that provides
information about injuries. The injuries were analysed in accordance with the ethical
principles of the Helsinki Declaration using audiovisual material in the public domain [
20
].
According to the American Psychological Association [
21
], an observational study in a
natural environment, with public videos obtained from the mass media that does not imply
experimentation, does not require informed consent from the participants. The study was
approved by the ethics committee of the Faculty of Education and Sport Science (University
of Vigo, application 02/1019).
Int. J. Environ. Res. Public Health 2022,19, 252 3 of 12
2.3. Instruments
The observation instrument designed ad hoc for this study is OI-INJURIES-FOOTBALL,
a category system that contemplates a collectiveness of criteria that allows us to determine
the football injuries’ characteristics [
18
]. Each dimension gives rise to a system of cate-
gories that accomplish the conditions of exhaustiveness and mutual exclusivity (E/ME). A
detailed description of the observation instrument appears in Table 1, where the criteria,
categories and subcategories of the instrument are shown. In Figure 1, the established field
areas are detailed.
Table 1. Descriptive obtained values and χ2test intra-criteria.
Criteria Category Subcategory n % χ2(p-Value) Criteria Category Subcategory n % χ2(p-Value)
Injury SPRAIN 33 24.3 5.327 (0.070) aTime 1st Half: 00–45074 54.4 1.441 (0.230)
Ankle sprain 15 11.0 00–15025 18.4
Knee sprain 8 5.9 160–30027 19.9
Acromioclavicular sprain 1 0.7 310–450+ added time 22 16.2
Anterior cruciate ligament rupture 9 6.6 2nd Half: 460–90062 45.6
STRAIN 45 33.1 460–60031 22.8
Hamstring fibrillar rupture 25 18.4 610–75017 12.5
Quadricep fibrillar rupture 5 3.7 760–900+ added time 14 10.3
Soleus-gastrocnemius fibrillar
rupture 3 2.2
How the
injury took
place
ALONE 71 52.2 0.265 (0.607) c
Adductor fibrillar rupture 11 8.1 Sprint 33 24.3
Psoas fibrillar rupture 1 0.7 Turn 8 5.9
CONTUSION 26 19.1 Shooting 9 6.6
Head, face or neck contusion 3 2.2 Ball control 2 1.5
Lower extremity contusion 20 14.7 RIVAL 65 47.8
Trunk contusion 3 2.2 Jump 19 14.0
FRACTURE 8 5.9 Collision 13 9.6
Head, face or neck fracture 3 2.2 Received tackle 37 27.2
Trunk fracture 1 0.7 Performed tackle 12 8.8
Upper extremity fracture 2 1.5 Hit by ball 2 1.5
Lower extremity fracture 2 1.5 Goalkeeper0s save 1 0.7
DISLOCATION 4 2.9 Player
laterality
Right-footed 104 76.5 39.474 (0.000) b
Upper extremity dislocation 3 2.2 Left-footed 31 22.8
Lower extremity dislocation 1 0.7 Ambidextrous 1 0.7
OVERUSE 14 10.3 Age <18 years 0 0.0 77.882 (0.000) b
Hamstring overuse 6 4.4 18–25 years 44 32.4
Gluteus overuse 1 0.7 26–34 years 88 64.7
Adductor overuse 3 2.2 >34 years 4 2.9
Quadriceps overuse 3 2.2 Position Goalkeeper 7 5.1 21.767 (0.000) b
Gastrocnemius overuse 1 0.7 Defender 67 49.3
OTHERS (wound, concussion, etc.) 6 4.4 Midfielder 37 27.2
Months August 8 5.9 33.559 (0.000) Forward 25 18.4
September 22 16.2 Total accu-
mulated
minutes
00–500046 33.8 9.588 (0.022)
October 18 13.2 5010–1000039 28.7
November 9 6.6 10010–1500023 16.9
December 7 5.1 >1500028 20.6
January 15 11.0 Accumulated
minutes
after resting
(>7 days)
00–200068 50.0 60.294 (0.000)
February 15 11.0 2010–400041 30.1
March 10 7.4 4010–600015 11.0
April 27 19.9 >600012 8.8
May 5 3.7 Zone DEFENSIVE ZONE 93 68.4 18.382 (0.000)
Stadium Local 67 49.3 0.029 (0.864) Zone 1 21 15.4
Visitor 69 50.7 Zone 2 24 17.6
Injury
location
Head, face or neck 8 5.9 276.882 (0.000) Zone 3 24 17.6
Lower extremity: from waist to feet 118 86.8 Zone 4 24 17.6
Upper extremity: from shoulder to
hands 5 3.7 OFFENSIVE ZONE 43 31.6
Trunk-back: from neck to waist 5 3.7 Zone 5 12 8.8
Leg injury Right leg injuries 64 47.1 0.847 (0.357) bZone 6 16 11.8
Left leg injuries 54 39.7 Zone 7 7 5.1
No leg injury 18 13.2 Zone 8 8 5.9
Moment in
the season
Days 1–19 76 55.9 1.882 (0.170)
Days 20–38 60 44.1
Note.
a
The chi-square test was performed among the three categories with the highest frequency;
b
the chi-square
test was calculated by eliminating the lowest-frequency category;
c
the chi-square test was calculated with the
categories (alone-rival).
Int. J. Environ. Res. Public Health 2022,19, 252 4 of 12
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 4 of 12
Left leg injuries 54 39.7 Zone 77 5.1
No leg injury 18 13.2 Zone 88 5.9
Moment in the
season
Days 1–19 76 55.9 1.882 (0.170)
Days 20–38 60 44.1
Note.
a
The chi-square test was performed among the three categories with the highest frequency;
b
the chi-square test was calculated by eliminating the lowest-frequency category;
c
the chi-square
test was calculated with the categories (alone-rival).
Figure 1. Field areas.
The categorisation of the type of injury followed the UEFA model [22]. To designate
the part of the body where the injury occurred, a classification endorsed by other authors
was used [23–25]. Four demarcations were used for the analysis [14,19,25,26] and divided
the match into 15 min intervals [5,9].
All the injuries were codified using the software LINCE v.1.4. [27]. With this instru-
ment, all the data were registered. This software is a free multimedia interactive pro-
gramme that allows simultaneous viewing and registering of the filmed material in a com-
puter that is used to support the observational analysis in a systematic way. This software
has been used in numerous investigations of football [28,29].
2.4. Procedure
The sample was obtained through the Wyscout platform [30], a paid online scouting
platform for football.
Behind the design of the observational instrument OI-INJURIES-FOOTBALL, the va-
lidity of its construct was determined through its coherence with the theoretical frame-
work and through a consultation with two experts in the observational methodology, in-
juries and football, that needed to show the degree of agreement, achieving a level of
agreement of 92%.
After adequate training in the use of the register instrument and the observational
instrument OI-INJURIES-FOOTBALL, the data were registered by two observational ex-
perts. To guarantee rigor in the codification process [31], the quality of the registered data
was controlled through a calculation of the intra- and inter-observers’ compliance using
Cohen’s kappa coefficient calculated using the software LINCE. The intra-observer com-
pliance was previously calculated among a third of the injuries (n = 45; not belonging to
the final sample), obtaining a kappa value of 0.97 for observer 1 and 0.86 for observer 2.
Subsequently, the inter-observer agreement achieved a kappa value of 0.83. Afterwards,
the data were recorded by observer 2.
After registering all the injuries, a Microsoft Excel file was obtained with the sequence
of all the codes of the registered behaviours, with the temporality and duration expressed
in frames. The versatility of this file allowed us to conduct successive transformation for
the different analyses: T-patterns and polar coordinates.
Figure 1. Field areas.
The categorisation of the type of injury followed the UEFA model [
22
]. To designate
the part of the body where the injury occurred, a classification endorsed by other authors
was used [
23
25
]. Four demarcations were used for the analysis [
14
,
19
,
25
,
26
] and divided
the match into 15 min intervals [5,9].
All the injuries were codified using the software LINCE v.1.4. [
27
]. With this instru-
ment, all the data were registered. This software is a free multimedia interactive programme
that allows simultaneous viewing and registering of the filmed material in a computer that
is used to support the observational analysis in a systematic way. This software has been
used in numerous investigations of football [28,29].
2.4. Procedure
The sample was obtained through the Wyscout platform [
30
], a paid online scouting
platform for football.
Behind the design of the observational instrument OI-INJURIES-FOOTBALL, the va-
lidity of its construct was determined through its coherence with the theoretical framework
and through a consultation with two experts in the observational methodology, injuries
and football, that needed to show the degree of agreement, achieving a level of agreement
of 92%.
After adequate training in the use of the register instrument and the observational
instrument OI-INJURIES-FOOTBALL, the data were registered by two observational ex-
perts. To guarantee rigor in the codification process [
31
], the quality of the registered
data was controlled through a calculation of the intra- and inter-observers’ compliance
using Cohen’s kappa coefficient calculated using the software LINCE. The intra-observer
compliance was previously calculated among a third of the injuries (n = 45; not belonging
to the final sample), obtaining a kappa value of 0.97 for observer 1 and 0.86 for observer 2.
Subsequently, the inter-observer agreement achieved a kappa value of 0.83. Afterwards,
the data were recorded by observer 2.
After registering all the injuries, a Microsoft Excel file was obtained with the sequence
of all the codes of the registered behaviours, with the temporality and duration expressed
in frames. The versatility of this file allowed us to conduct successive transformation for
the different analyses: T-patterns and polar coordinates.
2.5. Data Analysis
All the statistical analysis was carried out using IBM Statistical Package for the Social
Sciences, version 25.0 (IBM-SPSS Inc., Chicago, IL, USA). It calculated the relationship be-
tween the different categories that were studied by using the chi-square (
χ2
) test. Statistical
significance was assumed as p< 0.05.
To analyse the patterns of injury, the detection of T-patterns was carried out using
Theme v.5.0 [
32
], with a significance level of 0.005, which means that the percentage of
accepting a critical range due to chance is 0.5%. The minimum number of occurrences in
the search of T-patterns was three (the minimum possible without a mistake in processing
Int. J. Environ. Res. Public Health 2022,19, 252 5 of 12
the information because of an excessive number of series). Furthermore, the reduction
in redundancies was activated to 90% to avoid the occurrence of similar T-patterns. This
software reveals hidden structures and non-observable aspects in sports science [
32
,
33
]. The
graphic representation in dendrograms guides the discovery of existing linkages among
the different aspects of the injuries. The left quadrant represents the connection among
the different categories, which must be read from top to bottom. Meanwhile, the right
quadrant allows us to see how many times such connections occur through lines that go
from top to bottom.
The sequential analysis of delays was executed through GSEQ5 [
34
] using only the
subsequent calculation of polar coordinates. According to other studies [
35
], the major
delays considered were 1.96, with a significance of p< 0.05, that implied a relation of
activation between the conduct criteria and the conditioned. The results that were less than
or equal to 1.96 were considered equally significant (p< 0.05), which was implied in the
relation of inhibition between the conduct criteria and the conditioned.
The analysis of polar coordinates was carried out using the HOISAN programme [
36
]
following the analytic technique of Sackett [
37
] in the variant of genuine retrospective [
38
],
as used in similar studies. We considered as significant (p< 0.05) the relations between
the focal categories and the conditioned categories when the length of the vector was
higher than 1.96. The behavioural connection is determined by the quadrant in which the
behaviour is represented and the angle. This way, quadrant I indicates both behaviours are
mutually activated in both directions. Quadrant II indicates that the conditioned behaviour
activates the focal behaviour, which then inhibits the conditioned behaviour. Quadrant
III indicates that the focal behaviour and the conditioned behaviour are both inhibited
mutually in both directions. Quadrant IV indicates that the focal behaviour activates the
conditioned behaviour, which then inhibits the focal behaviour. The three most common
types of injury were used as focal behaviours for the analysis (strain, sprain and contusion).
3. Results
3.1. Statistical Analysis
In Table 2, a descriptive analysis and the χ2test intra-criteria are presented.
Table 2. Analysis of the selective search for T-patterns.
Description of T-Patterns with Three Occurrences N
Total T-patterns detected 8571
Non-useful T-patterns (do not meet selection criterion) 7773 (91%)
T-patterns not excluded 798 (9%)
T-patterns with strain, sprain or contusion 2461
T-patterns with defender, midfielder or forward 3170
T-patterns with defender and strain 450
T-patterns with defender and sprain 55
T-patterns with defender and contusion 63
T-patterns with midfielder and strain 11
T-patterns with midfielder and sprain 121
T-patterns with midfielder and contusion 46
T-patterns with forward and strain 21
T-patterns with forward and sprain 26
T-patterns with forward and contusion 5
Significant differences were verified among the different categories of the age criterion.
Subjects between the age of 26 and 34 years were the ones who suffered injuries more
frequently (65%), even though this is the range that refers to the most common age of the
studied players. The injured player ’s position also showed significant statistical differences.
In this case, the defenders (49%) followed by the midfielders (27%) were the most frequently
Int. J. Environ. Res. Public Health 2022,19, 252 6 of 12
injured players. With regard to the location where the injuries occur more often, it was
proven that injuries occur more often in the defensive zone rather than the attacking zone
(68% vs. 32%). There were also significant statistical differences (
χ2
= 75,132; p= 0.000) in
the most common injury, strains being the most common ones (33%), followed by sprains
(24%) and contusions (19%). There were no significant differences if we performed the
analysis only with these three types of injuries.
Most of the injuries occurred in the lower extremity (87%) and in the right leg (47%).
The months when most of the injuries occurred were September (16%) and April (20%). We
did not assess big differences among the injuries suffered in the first and second parts of the
season (56–44%) or among the first and second periods of the match (54–46%); nevertheless,
we did assess a higher occurrence within the first 15 min of the second half (23%).
There were statistically significant differences between the different ways of getting
injured. The players were more often injured alone (52%) after a sprint (24%). Another
common way was to be injured by an opponent (48%) after a hard tackle (27%).
Without differentiating the type of injury, these were recorded when players accu-
mulated between 0–500 min in the season (34%). In addition, up to 50% of the injuries
occurred before 200 min accumulated after a rest of more than 7 days. There were significant
statistical differences in both criteria.
3.2. Identification of Temporal Patterns (T-Patterns)
3.2.1. General Description of T-Patterns
The Table 3below shows an analysis of the number of patterns found through a
selective search with three occurrences. As a selection criterion, the presence of the position
category was used (defence, midfield and forward) combined with one of three most
frequent injuries (strain, sprain or contusion).
Table 3.
T-patterns according to position (defender, midfielder and forward) and most frequent injury
(strain, sprain and contusion).
T-Pattern O L
((defender (lower extremity strain)) (hamstring fibrillar rupture (alone sprint))) 14 6
((defender (lower extremity strain)) AMAR 0–200) 18 4
(defensive zone ((defender (lower extremity sprain)) (ankle sprain (alone jump)))) 4 7
(defender ((lower extremity contusion) (lower extremity contusion rival))) 8 5
(day 20–38 (defensive zone (defender ((lower extremity contusion) (lower extremity contusion rival)))) 5 7
((midfielder (lower extremity strain)) (hamstring fibrillar rupture alone)) 3 5
((day 1–19 first half) ((age 26–34 midfielder) (lower extremity strain))) 3 5
(v1 ((mid (lower extremity sprain))(rival tackle received))) 8 6
((midfielder (lower extremity sprain)) TAM 0–500) 8 4
((day 1–19 second half) (defensive zone (midfielder contusion))) 5 5
(midfielder (contusion rival)) 8 3
(((day 1–19 first half) (offensive zone ((forward lower extremity) (strain TAM 0–500)))) AMAR 200–400) 3 8
offensive zone ((forward (lower extremity strain)) (alone sprint))) 6 6
((first half age 26–34) (forward ((lower extremity sprain) (TAM 0–500 AMAR 0–200)))) 4 7
(forward sprain) (rival tackle received)) 4 4
(forward (contusion rival)) 3 3
(second half (forward contusion)) 3 3
Note. TAM: total accumulated minutes; AMAR: accumulated minutes after resting. O: occurrence; L: length.
The table included below shows the most relevant T-patterns organised by posi-
tion, type of injury and number of minutes accumulated during the season (total and/or
after rest).
Int. J. Environ. Res. Public Health 2022,19, 252 7 of 12
3.2.2. T-Patterns in Defenders
Of the 31 strain injuries that were recorded, up to 14 times (45%), the player was injured
by a hamstring rupture after a sprint without the presence of an opponent (Figure 2A). The
evidence suggests that these injuries occur after less than 200 min accumulated after rest
and predominantly in the opponent’s field. Of the 11 adductor rupture injuries observed in
total in this investigation, 9 took place in defenders (82%). Strain mostly occurred when
the player played between 500 and 1000 min during the season (36%). High values were
detected when the player accumulated between 1000 and 1500 min (26%).
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 8 of 12
Most of the sprains (4/9, 44%), the same as the previously mentioned midfields, were
caused by a hard tackle during the first part of the season. Most of them were registered
in the first half (67%) after accumulating less than 500 min in the season (56%) (Figure 2C).
Almost all the contusions were caused by an opponent (3/4; 75%) in the second half
of the match (75%). The accumulated time played was not relevant.
Figure 2. T-patterns of the investigation: (A) defender, (B) midfielder and (C) forward.
3.3. Analysis of the Polar Coordinates
The results of the analysis of polar coordinates revealed significant statistical connec-
tions among the focal behaviours (strain, sprain and contusion) and the conditioned be-
haviours (the rest of the categories).
The polar coordinate of the strain (Figure 3A) showed that it is more common in de-
fenders and that it occurs in the absence of an opponent (usually after a sprint).
Figure 3B indicates that the sprain was more frequent during the first part of the sea-
son and during the first half of matches. Defenders and midfielders were more likely to
suffer this type of injury, which occurred after a tackle or a jump.
Contusion (Figure 3C) usually occurred in the second half of the matches and was
more common in defenders and midfielders. They were usually caused by a collision or a
hard tackle.
Figure 2. T-patterns of the investigation: (A) defender, (B) midfielder and (C) forward.
Up to 40% of the sprains (4/10) were on the ankle after a jump without the presence of
an opposing player and in their own field. The T-patterns also reflected that they occurred
indistinctly in the first and second parts of the season, as well as in the first or second half.
The predominant age for this injury was between 26 and 34 years.
Most of the contusions affected a lower extremity (8/11, 73%) and were caused by a
rival. They happened more frequently in the first part of a match (65%) and in the second
part of the season of a competition (55%).
3.2.3. T-Patterns in Midfields
The study did not evidence many strains in midfields (only five). Most of them (60%)
were hamstring fibrillar ruptures, in which there was no intervention of an opponent.
They were more frequent in the first part of the season (80%) and in the first half of the
matches (80%).
Int. J. Environ. Res. Public Health 2022,19, 252 8 of 12
Of the 12 sprains registered, 8 (67%) occurred in the first part of the season (before
having playing 500 min in the season) due to an opponent’s tackle (Figure 2B).
Contusions were also mostly caused by an opponent (8/9; 89%). They were more
frequent in the defensive zone (78%), in the second half (67%) and in the first part of the
season (78%).
3.2.4. T-Patterns in Forwards
Most of the strains (6/8) happened in the offensive zone, after a sprint and without
the presence of an opponent (75%). They were more frequent in the first part of the season,
(63%), in the first half (63%) and with less than 500 accumulated minutes (38%).
Most of the sprains (4/9, 44%), the same as the previously mentioned midfields, were
caused by a hard tackle during the first part of the season. Most of them were registered in
the first half (67%) after accumulating less than 500 min in the season (56%) (Figure 2C).
Almost all the contusions were caused by an opponent (3/4; 75%) in the second half
of the match (75%). The accumulated time played was not relevant.
3.3. Analysis of the Polar Coordinates
The results of the analysis of polar coordinates revealed significant statistical con-
nections among the focal behaviours (strain, sprain and contusion) and the conditioned
behaviours (the rest of the categories).
The polar coordinate of the strain (Figure 3A) showed that it is more common in
defenders and that it occurs in the absence of an opponent (usually after a sprint).
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 9 of 12
Figure 3. Polar coordinates of the study: (A) strain, (B) sprain and (C) contusion.
4. Discussion
It has been confirmed that the most frequent injury is strain, in particular fibrillar
rupture (33%), a finding that agrees with the results given in the Bundesliga (30.3%) [19]
or with the 31% obtained by other authors [39]. With regard to the injuries of ligaments
(sprains), the results found in our study were similar (24%) to those produced in other
studies [19]. Analysing the contusions, the values of 19% obtained differ from the ones
reported in other studies: 8.5% [19] and 16% [7].
With regard to the way the injury takes place, studies [40,41] agree that most of the
injuries occur without previous contact (approximately 70%), a fact that was also proved
in our study but at a lower frequency (52%).
As it might seem logical, the lower extremity showed a higher injury percentage
(87%), similar to the 89.6% previously reported [9].
With regard to the affected leg, the reported data match those found in this study.
Recent studies have shown values of 54.4% (right leg) and 36.5% (left leg) [40], meanwhile
others have reported values of 52% and 38.7% [16].
According to the specialised literature, at the end of every half of the match, there is
a higher risk of suffering an injury [5,16], a circumstance that is completely opposite to the
results of this study, given that the higher occurrence of injury was identified from 15 to
30 min and 46 to 60 min at 20% and 23%, respectively. Considering these last data, it could
be sensed that at the beginning of the second period, the players do not manage to get
predisposed to the effort at a physiological level.
There are no big differences regarding the number of injuries between the first and
the second half (54% and 46%, respectively), something also cross-checked by other stud-
ies [4,40], which have revealed 58.1–41.9% and 51–49% injuries between the first and the
second half.
As regards the position of the player in the play field, the results are clear: defenders
(49%) are the ones who get more injured, followed by midfielders (28%), forwards (18%)
and goalkeepers (5%). These results vary from those found in other studies, where the
midfielders’ position had the highest percentage: 37.6% [8], 39% [6] and 37.7% [19].
There were differences between the age ranges where an injury occurs in this study;
this may be due to the fact that the 26–34-year age range has the most players. This result
does not coincide with those found in the Major Soccer League [8], where there were no
differences between the three age ranges studied.
The months with the highest number of injuries in our study were September, Octo-
ber and April, similar to what was found in a longitudinal study in the Spanish Football
Figure 3. Polar coordinates of the study: (A) strain, (B) sprain and (C) contusion.
Figure 3B indicates that the sprain was more frequent during the first part of the
season and during the first half of matches. Defenders and midfielders were more likely to
suffer this type of injury, which occurred after a tackle or a jump.
Contusion (Figure 3C) usually occurred in the second half of the matches and was
more common in defenders and midfielders. They were usually caused by a collision or a
hard tackle.
4. Discussion
It has been confirmed that the most frequent injury is strain, in particular fibrillar
rupture (33%), a finding that agrees with the results given in the Bundesliga (30.3%) [
19
]
or with the 31% obtained by other authors [
39
]. With regard to the injuries of ligaments
(sprains), the results found in our study were similar (24%) to those produced in other
studies [
19
]. Analysing the contusions, the values of 19% obtained differ from the ones
reported in other studies: 8.5% [19] and 16% [7].
Int. J. Environ. Res. Public Health 2022,19, 252 9 of 12
With regard to the way the injury takes place, studies [
40
,
41
] agree that most of the
injuries occur without previous contact (approximately 70%), a fact that was also proved in
our study but at a lower frequency (52%).
As it might seem logical, the lower extremity showed a higher injury percentage (87%),
similar to the 89.6% previously reported [9].
With regard to the affected leg, the reported data match those found in this study.
Recent studies have shown values of 54.4% (right leg) and 36.5% (left leg) [
40
], meanwhile
others have reported values of 52% and 38.7% [16].
According to the specialised literature, at the end of every half of the match, there is
a higher risk of suffering an injury [
5
,
16
], a circumstance that is completely opposite to
the results of this study, given that the higher occurrence of injury was identified from 15
to 30 min and 46 to 60 min at 20% and 23%, respectively. Considering these last data, it
could be sensed that at the beginning of the second period, the players do not manage to
get predisposed to the effort at a physiological level.
There are no big differences regarding the number of injuries between the first and
the second half (54% and 46%, respectively), something also cross-checked by other
studies
[4,40]
, which have revealed 58.1–41.9% and 51–49% injuries between the first and
the second half.
As regards the position of the player in the play field, the results are clear: defenders
(49%) are the ones who get more injured, followed by midfielders (28%), forwards (18%)
and goalkeepers (5%). These results vary from those found in other studies, where the
midfielders’ position had the highest percentage: 37.6% [8], 39% [6] and 37.7% [19].
There were differences between the age ranges where an injury occurs in this study;
this may be due to the fact that the 26–34-year age range has the most players. This result
does not coincide with those found in the Major Soccer League [
8
], where there were no
differences between the three age ranges studied.
The months with the highest number of injuries in our study were September, October
and April, similar to what was found in a longitudinal study in the Spanish Football League
between 2012 and 2016 [
11
]. Another investigation in this league in the 2008–2009 season
showed that March and May are the months with the highest number of injuries [9].
In contrast to what was reported in the preceding investigation [
9
], in our case, most
of the injuries originated in the first part of the season.
Finally, focusing on the frequency of injuries suffered as a function of total accumulated
minutes in the season, and accumulated minutes after rest, it has been observed that the
highest number of injuries is found among the categories with the least accumulated
playing time (33% in TAM 0–500 and 50% in AMAR 0–200).
The first case can be due to the high amount of training in the preseason, which
generates excessive fatigue that translates in an injury at the beginning of the season,
something that has been evidenced in other studies [
9
,
13
]. For the second case, research
that confirms these findings has not been found, although it can be noted that this might
occur, in spite of the rest, due to competitive stress or the accumulation of fatigue from the
season (weekly rest will not be enough as an injury prevention mechanism).
From a practical point of view, it would be important for coaches to take into account
the injury data presented, especially those related to strains, in order to avoid injuries of
this type as far as possible. It seems that the players are not physiologically prepared for
the effort at the beginning of the second half, so the rest time should not be used exclusively
for tactical aspects but also for physical aspects, preparing them for the restart. Weekly rest
(>7 days) is not sufficient as an injury prevention mechanism, so coaches should consider
rotation systems that provide sufficient rest to prevent injury.
The other types of injuries are usually caused by the opposing player, so it is more
difficult to carry out preventive work.
Int. J. Environ. Res. Public Health 2022,19, 252 10 of 12
5. Conclusions
The most common injury is strain, followed by sprains and contusions. Fibrillar
rupture after a sprint and without contact with the opponent is the most frequent injury.
There is a change in the tendency of the player who gets injured the most: before the
midfielders and now the defenders.
It has been noted that the rotation of weeks (resting without competing) is not enough
as an injury prevention mechanism. The largest number of injuries take place during
the first 200 min after the rest period. In addition, players are mostly injured before the
first
500 cumulative
minutes of the season have elapsed. Therefore, injury prevention
programmes during preseason and the start of the season are important.
Defenders are the most injured players, with hamstring strains after a sprint being
the most common. In general, fibrillar ruptures occur mainly in the first half. Adductor
fibrillar ruptures occur mainly in defenders. The accumulation of minutes in the season in
this position (and not in the others) increases the number of strains.
Injuries due to a tackle received or made are also frequent. In this respect, defenders
are frequently injured by a tackle made, while midfielders are equally injured by a tackle
made or received. Sprains are more common in midfielders and forwards.
Author Contributions:
Conceptualization, J.C.A.-G., A.P.-G. and A.P.-R.; methodology, I.P.-L. and
A.G.-S.; software, J.C.A.-G. and D.B.-F.; validation, A.P.-R. and A.G.-S.; formal analysis, I.P.-L., J.C.A.-
G., A.P.-G., A.P.-R.; investigation, I.P.-L., J.C.A.-G., A.P.-G., A.P.-R., D.B.-F. and A.G.-S.; resources,
J.C.A.-G., A.P.-G. and D.B.-F.; data curation, J.C.A.-G. and A.P.-R.; writing—original draft preparation,
I.P.-L., A.P.-G., A.P.-R. and A.G.-S.; writing—review and editing, I.P.-L. and A.G.-S.; visualization,
J.C.A.-G., A.P.-R. and D.B.-F.; supervision, I.P.-L. and A.G.-S.; project administration, I.P.-L. and
A.G.-S.; funding acquisition, not applicable. All authors have read and agreed to the published
version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement:
The study was approved by the ethics committee of the
Faculty of Education and Sport Science (University of Vigo, application 02/1019).
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
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A thorough look at the different research applications of temporal pattern detection and analysis using specially developed software, THEME (TM). The T-system is a model for discovering hidden recurring patterns in observable behavior and can be useful to researchers in neuroscience, psychology, biology, robotics, finance, medicine, and many other fields. This system forms the basis for the search algorithms in THEME (TM) , now in its 6th edition and available in both educational and fully commercial versions. Each chapter describes the methodology used and discusses the findings in detail, providing a highly useful primer for advanced students and researchers.
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The aim was to compare the epidemiology of injuries between elite male and female football players from the same club. Injuries and individual exposure time in a male team and a female team, both playing in the Spanish first division, were prospectively recorded by the club's medical staff for five seasons (2010-2015) following the FIFA consensus statement. Total, training and match exposure hours per player-season were 20% higher for men compared to women (P < 0.01). Total, training and match injury incidence were 30-40% higher in men (P ≤ 0.04) mainly due to a 4.82 [95% confidence interval (CI) 2.30-10.08] times higher incidence of contusions, as there were no differences in the incidence of muscle and joint/ligament injuries (P ≥ 0.44). The total number of absence days was 21% larger in women owing to a 5.36 (95% CI 1.11-25.79) times higher incidence of severe knee and ankle ligament injuries. Hamstring strains and pubalgia cases were 1.93 (95% CI 1.16-3.20) and 11.10 (95% CI 1.48-83.44) times more frequent in men, respectively; whereas quadriceps strains, anterior cruciate ligament ruptures and ankle syndesmosis injuries were 2.25 (95% CI 1.22-4.17), 4.59 (95% CI 0.93-22.76) and 5.36 (95% CI 1.11-25.79) times more common in women, respectively. In conclusion, prevention strategies should be tailored to the needs of male and female football players, with men more predisposed to hamstring strains and hip/groin injuries, and women to quadriceps strains and severe knee and ankle ligament injuries. This article is protected by copyright. All rights reserved.
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The study objective was to describe the types, localizations and severity of injuries among first division Bundesliga football players, and to study the effect of playing position on match and training injury incidence and severity, based on information from the public media. Exposure and injuries data from 1 448 players over 6 consecutive seasons were collected from a media-based register. In total, 3 358 injuries were documented. The incidence rate for match and training injuries was 11.5 per 1 000 match-hours (95% confidence interval [CI]: 10.9-12.2), and 61.4 per 100 player-seasons (95% CI: 58.8-64.1), respectively. Strains (30.3%) and sprains (16.7%) were the major injury types, with the latter causing significantly longer lay-off times than the former. Significant differences between the playing positions were found regarding injury incidence and injury burden (lay-off time per incidence-rate), with wing-defenders sustaining significantly lower incidence-rates of groin injuries compared to forwards (rate ratio: 0.43, 95% CI: 0.17-0.96). Wing-midfielders had the highest incidence-rate and injury burden from match injuries, whereas central-defenders sustained the highest incidence-rate and injury burden from training injuries. There were also significant differences in match availability due to an injury across the playing positions, with midfielders sustaining the highest unavailability rates from a match and training injury. Injury-risk and patterns seem to vary substantially between different playing positions. Identifying positional differences in injury-risk may be of major importance to medical practitioners when considering preventive measures. © Georg Thieme Verlag KG Stuttgart · New York.
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This study introduces a new concept of retrospectivity in polar coordinates analysis. We present an application to tennis professional sport in competition games through instrument made by Gorospe (1999). We have done both retrospectivity analysis, and show the differences between them.