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Difference In Shot's Attributes Between Winning And Losing Teams In The 2018 FIFA World Cup

Authors:
The journal « sports creativity »
Volume: (12) / N°: (01)-(2021), p167-..181
Corresponding author: A. Hadji e-mail: Abderrahmen.hadji@univ-bejaia.dz
- Publication rights are reserved for M'sila University - website: https://www.asjp.cerist.dz/en/PresentationRevue/316
167
Difference in shot’s attributes between winning and losing teams in the 2018 FIFA
World Cup

Abderrahmen Hadji
Department of Sciences and Technique of Physical and Sports Activities, University of Bejaia, Algeria
Abderrahmen.hadji@univ-bejaia.dz
Received: 08/01/2021 Accepted: 20/04/2021 Published:01/06/2021
Abstract: The aim of this study is to investigate the relationship between shot attributes and match
outcome (Winning vs losing) in the 2018 FIFA World cup. Shot’s attributes are: Pattern of play, Origin,
Outcome, Player (shooter) and coordinate of shots. 1313 shots from 105 unbalance game by team were
studied. Data was provided by StatsBomb company. The results showed that, comparing to losing teams,
winning teams use more shots from set pieces (P<0.05; ES=0.25), shoot more often from a counter attack
(P<0.05; ES=0.22), and from regular play (P<0.05; ES=0.28), have more successful shots (P<0.001;
ES=0.42), more ratio of success(P<0.01; ES=0.38), tend to involve more their attacking-midfield(P=0.087;
ES=0.18), and forward (P=0.86; ES=0.2) at shooting, and their successful shots are mostly concentrated
closer to target (P=0.06; ES=0.3), and relatively centered (P<0.05; ES=0.32). Coaches can use these
findings to enhance their own players' performance.
Keywords: FIFA World Cup 2018, Shots, Match outcome

 
 StatsBomb. 
 (P <0.05 ES = 0.25) 
 (P <0.05 ES = 0.22)  ( P <0.05 ES = 0.28)
 (P <0.001
ES = 0.42)  (P <0.01 ES = 0.38)  (P = 0.087
ES = 0.18)  (P = 0.86 ES = 0.2)  (P = 0.06 ES = 0.3) 
 (P <0.05 ES = 0.32) .

A. Hadji
168
Introduction
Performance analysis (PA) consists of quantifying, analyzing and
objectively studying the factors that define performance during it events in
competition and training. It “is a tool aimed specifically at improving future
performances through the analysis and dissemination of information relating to
previous training and match performances to an athlete or player” (Mackenzie &
Cushion, 2016, p. 540) and a team. With technological development,
performance analysis has been closely linked to technological tools in order to
become dependent. Baca (2015, p. X) defines it as “the objective way to record
and interpret sports performance using the latest technologies so that key
elements can be quantified in a valid and consistent manner. This knowledge is
then used to enhance athlete performance and effective decision-making”
through practical exercises or theoretical-mental practice (Guettaoui &
Boumasjed, 2020).
In soccer, science has tried to find keys to identify performance among
technical and tactical attributes. It has been shown that, in soccer, success is
linked to passes (Lago-Peñas & Dellal, 2010; Oberstone, 2009), successful passes
(Rampinini et al., 2009), possession (Castellano et al., 2012), duel (Liu, Gomez, et
al., 2015), off-side (Zhou et al., 2018) and corner (Hadji et al., 2020). On the
other hand, the lack of success was linked to crosses (Sarkar, 2018; Vecer, 2014),
foul committed (Hadji et al., 2020; Oberstone, 2009), passes (Harrop & Nevill,
2014). In almost all previous studies that focus on performance indicators, shot
and shot on target were always major part of the analysis.
For shots. It has been shown that successful teams in FIFA World cup
made more shots than other teams: 1990 World Cups (Hughes & Franks, 2005)
2002 (Szwarc, 2004), 2006 and 2010 (Castellano et al., 2012) 2014 (Liu, Gomez,
et al., 2015) and 2018 (Alves et al., 2019). Similar results were observed in the
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domestic competitions: La Liga (Spanish) 2008/09 (Lago-Ballesteros & Lago-
Peñas, 2010) and Serie A (Italian) 2004/05 (Rampinini et al., 2009). The top four
Spanish teams (2008/09) make more shots than those from the middle or
bottom of the table (16, 13 and 13 respectively) (Lago-Ballesteros & Lago-Peñas,
2010). The analysis of the shots is always related to its success, each shot is
identified as "on target " or "off target". The researchers placed great importance
on this difference in relation to success in football. Liu, Gomez, et al. (2015)
showed that at the 2014 World Cup, shots and shots on target increased the
chances of victory by 13% and 48%, respectively. In addition, a previous study
found similar results, suggesting that total shots and shots on target are crucial to
winning during the 2002, 2006 and 2010 World Cups (Castellano et al., 2012).
Although, Szwarc (2004), reported that the winning teams made only four more
shots than the defeated teams, but the efficiency of their shots was three times
higher. Similar results were obtained by Yamanaka et al. (1993) for the national
teams participating in the 1990 World Cup in Italy. It would seem that what best
discriminates a team's performance is the number of shots on target, not the total
number of shots. It appears that high-performing teams only use to shoot when
the situation is fortunate to score a goal, unlike other teams that use it in the
absence of clear and safe solutions. The FIFA report (FIFA, 2018) reports a
decline in the number of “shots per goal between” 2010 and 2018 and (12.8, 10
and 9.8 respectively). It is important to point out that this index reflects the use of
shots with regard to scored goals, the lower the value, the better the efficiency. At
the 2018 World Cup, France and Croatia recorded very small values (6 and 8.5
respectively) compared to the tournament average (9.8). This confirms that the
number of shots is not an absolute performance index in itself against its
effectiveness (successful shots). From another angle, Lago (2007) and Moura et
al. (2014) reveal that the number of shots and shots on target is related to the
A. Hadji
170
performance in the group stage of the 2006 World Cup. On the other hand, Lago
(2007) finds no difference in the knockout matches between the winners and
the losers.
Problematic of the study : The very nature of the competition itself can affect
the technical-tactical activity and performance indicators. As far as we know, no
previous research has investigated only shots attribute and its relation with
success in soccer. With regard to all differences and opposite conclusions we
investigate the following question:
Are shot attributes linked to the match
outcome in the 2018 FIFA World Cup?
The Aim of the study :The aim of this study is to identify which shot attributes
are linked to successes in the 2018 World Cup tournament. Once these
attributes are found, they can be used as augmented feedback to enhance
individual and collective performance (Chettouh et al., 2020).
Methods
We use descriptive approach to conduct this research.
Data: The match-related data was obtained from the company StatsBomb in
JSON format files (StatsBomb, 2019). StatsBomb is one of the most reliable
companies in the market that provides data and analysis of all European league
and worldwide competition (Bundesliga, La Liga, Ligue 1, Serie A, MLS). Data
specification was provided with the dataset (V 1.1) (StatsBomb data
specifications, 2020). A Python package was used to parser the JSON’s data files
into separate CSV files (Package from Khan (2020) in GitHub). CSV’s files were
then managed by the Microsoft Excel to extract different specifications related to
shots. Data specifications for shot attributes are used as operational definitions
in this study (see Table 1). For the coordinate, originally the axis starts at the
right-down corner. We change the axis in a way to express distances far from the
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target (opponent goal) in Y coordinate, and center of the target for X coordinate
(see Fig. 1).
Sample: From sixty-four (64) games in the World Cup, we used fifty-one (51)
games that end with a winner and a loser outcome (unbalanced games). A total
of 1313 shots were analyzed in 102 (51 x 2) team game.
Table 1. Operational definition used by the StatsBomb (Stats Bomb data specifications, 2020, p. 34)
Shot’s attributes
Definition
Shot
An attempt to score a goal, made with any (legal) part of the body
Pattern of play
Free Kick
shot is from a set-piece
Open Play
shot is from an open play
Origin
From Corner
Shot direct from a corner kick
From Counter
Shot is from a counter-attack
From Free Kick
Shot is from a direct free kick
From Goal Kick
Shot is from a set-piece “Goal kick.”
From Keeper
Shot is from the keeper’s long pass
From Kick Off
Shot directly from kick off
From Throw-In
Shot directly after a throw in
Outcome
Blocked
A shot that was stopped from continuing by a defender
Goal
A shot that was deemed to cross the goal-line by officials
Off Target
A shot that’s initial trajectory ended outside the posts
Post
A shot that hit one of the three posts
Saved
A shot that was saved by the opposing team’s keeper
Wayward
An unthreatening shot that was way off target or did not have enough power to reach the goal line (or a miskick
where the player didn’t make contact with the ball)
Fig. 1. X and Y coordinate used for shot location.
Statistics: Data were presented as means (M) and standard deviations (SD). A
non-parametric test was used (Mann-Whitney) to compare shots attributes
A. Hadji
172
between teams that won and lost. Rank-biserial correlation is used as Effect size
(ES). All statistical analyses were computed using JASP (Version 0.13.1)
(www.jasp-stats.org). A significance level was set at p ≤ 0.05.
Results
Table 2. Comparison of shots attributes between losing and winning teams
Shots attributes
Losing team
Winning team
U
P-Value
ES
Pattern of play
Free Kick
(N=51) 0.45±0.58
(N=51) 0.88±0.91
972
0.016 *
0.253
Open Play
(N=51) 11.43±5.61
(N=51) 12.59±4.97
1069
0.122
0.178
Free Kick%
(N=51) 3.98±5.31
(N=51) 7.5±7.93
991.5
0.027 *
0.238
Open Play%
(N=51) 96.03±5.31
(N=51) 92.5±7.93
1609.5
0.027 *
0.238
Origin
From Corner
(N=51) 2.1±2.06
(N=51) 2.18±1.85
1224
0.604
0.059
From Counter
(N=51) 0.33±0.68
(N=51) 0.65±0.82
1014.5
0.023 *
0.220
From Free Kick
(N=51) 2.35±1.74
(N=51) 2.51±1.55
1189.5
0.450
0.085
From Goal Kick
(N=51) 0.33±0.59
(N=51) 0.37±0.69
1292
0.945
0.007
From Keeper
(N=51) 0.12±0.33
(N=51) 0.14±0.4
1297.5
0.976
0.002
From Kick Off
(N=51) 0.1±0.36
(N=51) 0.06±0.24
1327.5
0.686
0.021
From Throw-In
(N=51) 2.14±1.81
(N=51) 1.73±1.22
1438.
0.348
0.106
Regular Play
(N=51) 4.39±3.13
(N=51) 5.82±3.33
938
0.015 *
0.279
Outcome
Blocked
(N=51) 3.57±2.73
(N=51) 3.71±2.11
1177
0.407
0.095
Goal
(N=51) 0.47±0.7
(N=51) 1.94±1.14
302
<.001***
0.768
Off T
(N=51) 4.33±2.37
(N=51) 4.39±2.1
1263.5
0.805
0.028
Post
(N=51) 0.06±0.24
(N=51) 0.26±0.44
1045.5
0.007**
0.196
Saved
(N=51) 2.55±1.99
(N=51) 2.55±1.80
1270.5
0.841
0.023
Wayward
(N=51) 1.04±0.98
(N=51) 0.88±1.13
1466
0.240
0.127
Player
AM
(N=51) 0.65±0.96
(N=51) 1.26±1.67
1067.5
0.087
0.179
CB
(N=51) 1.33±1.4
(N=51) 1.33±1.49
1313.5
0.931
0.010
DM
(N=51) 0.69±1.21
(N=51) 0.67±1.09
1223
0.545
0.060
F
(N=51) 2.92±2.24
(N=51) 3.71±2.44
1047
0.086
0.195
M
(N=51) 2.71±2.52
(N=51) 3.22±3.2
1251.5
0.742
0.038
W
(N=51) 2.71±2.33
(N=51) 2.28±2.75
1483.5
0.210
0.141
WB
(N=51) 1±1.02
(N=51) 1.28±1.17
1133.
0.242
0.129
Total Shots
(N=51) 12.02±5.64
(N=51) 13.73±4.85
977.5
0.030 *
0.248
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Significance levels: *:0.05; **:0.01; ***:0.001. AM: attacking-midfield; CB: central-back; DM:
defensive-midfield; F: forward; M: midfield; W: wing; WB: wing-back.
For the pattern of play, most shots recorded were from open play for
both losing and winning teams (93%-96% respectively) with less than 8% from a
free kick. Although in average by game, winning team shoots more often from a
free kick than the losing team (0.88±0.91; 0.45±0.58; P=0.016; ES=0.25). For the
origin of the shot, winning team recorded more shots from counter-attacks
(0.65±0.82; 0.33±0.68; P=0.023; ES=0.22) and from regular play (5.82±3.33;
4.39±3.13; P=0.015; ES=0.28). No difference was found for other origin’s
attributes (Corner, Free Kick, Goal Kick, Keeper, Kick Off and Throw In). With
regard to the outcome, winning teams recorded more shots on target for both
goals (1.94±1.14;0.47±0.7; P<0.001; ES=0.77) and on the post shots (0.26±0.44;
0.06±0.24; P=0.007; ES=0.19) than the losing teams. Which means that winning
teams score one goal from a shot every 46 Mn, compared to one goal every 190
Mn for the losing team. For players involved in shots, attacking midfield (AM)
and forward (F) from the winning team tend to shoot more often than their
counterparts from the losing teams (1.26±1.67; 0.65±0.96; P=0.087; ES=0.18
and 3.71±2.44; 2.92±2.24; P=0.086; ES=0.2 respectively).
Figure 1. Shots distribution on the field for winning and losing teams
Losing team
Winning Team
A. Hadji
174
Table 3. Comparison of shots success between winning and losing team
Shots success
Losing teams
Winning teams
U
P-Value
ES
Unsuccessful
(N=51) 8.94 ± 4.40
(N=51) 8.98 ± 3.55
1211
0.55
0.069
Successful
(N=51) 3.08 ± 2.11
(N=51) 4.75 ± 2.37
754.5
<.001***
0.42
% Success
(N=51) 25.4% ± 14.8%
(N=51) 35.3% ± 14.2%
811.5
0.001**
0.376
Successful shots = (Goal + Post + Saved); Unsuccessful shots = (off targe t + Wayward + blocked).
Significant level; **=0.01; ***=0.001
For the unsuccessful shots, no significant differences were found
between the losing and the winning team. However, the winning team record
more successful shots than the losing team (4.75 ± 2.37;3.08 ± 2.11; P<0.001;
ES=0.42), that’s mean 50% more successful shot for every game. For success
ratio, winning teams’ shots are more accurate than the losing teams (35.3% ±
14.2%; 25.4% ± 14.8%; P=0.001; ES=0.38).
Table 4. Comparison of shot location between losing and winning teams.
Shots attributes
Losing team
Winning team
U
P-Value
ES
Outcome coordinate
Blocked_X
(N=48) 17.79±5.34
(N=50) 17.74±4.26
1193.5
0.966
0.005
Goal_X
(N=19) 12.8±5.61
(N=50) 9.93±4.91
615
0.061
0.295
Off T_X
(N=48) 17.99±5.46
(N=50) 18.54±5.94
1129
0.616
0.059
Post_X
(N=3) 13±12.17
(N=13) 17.31±7.79
12.5
0.381
0.359
Saved_X
(N=44) 15.77±4.36
(N=45) 15.45±6.16
1119
0.291
0.130
Wayward_X
(N=35) 11.88±6.5
(N=28) 12.23±6.77
485.5
0.956
0.009
Blocked_Y
(N=48) 3.85±8.27
(N=50) 4.47±5.72
1154
0.746
0.038
Goal_Y
(N=19) 8.45±5.61
(N=50) 5.22±6.01
629
0.039 *
0.324
Off T_Y
(N=48) 5.15±6.6
(N=50) 5.59±5.36
1195.5
0.977
0.004
Post_Y
(N=3) 5±1.73
(N=13) 5.77±11.92
18
0.893
0.077
Saved_Y
(N=44) 4.99±7.36
(N=45) 5.64±6.58
972
0.886
0.018
Wayward_Y
(N=35) 4.69±7.28
(N=28) 2.14±7.41
580
0.215
0.184
Average Location
X
(N=51) 16.74±2.64
(N=51) 16.19±2.65
1418.5
0.432
0.091
Y
(N=51) 29.76±3.49
(N=51) 29.73±2.93
1289.5
0.944
0.008
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The shot outcome doesn’t show a relation with the location of execution
when comparing losing and winning teams (P>0.05), except for the Y coordinate
of goals that appear to be closer to the goal-line when winning than when losing
(5.22±6.01; 8.45±5.61; P<0.01; ES:0.32). Also, the X coordinate for goals when
winning tends to be different from losing. The winning team have a tendency to
score more goals when they shoot near to the center of the field (9.93±4.91;
12.8±5.61; P=0.061; ES=0.3).
Shots average location for both teams show no difference for neither
distance from the goal-line and distance from the axial line. For all shot outcome
and for both teams, the mean location from the axial-line tends to be left-sided.
Figure 1 shows how winning team shots are less dispersed, closer to the goal-line
and the axial-line compared to losing team.
Discussion
The aim of this study was to search for the relationship between shots
attributes and success (match outcome) in the 2018 Russia FIFA World Cup
games. This study indicated that successful teams attempt more shots from set
pieces than unsuccessful teams. Carmichael et al. (2000) also found that
successful teams were more efficient than unsuccessful teams in scoring from set
plays. Kubayi and Toriola (2020) found that African teams (unsuccessful)
conceded more goals from set pieces. Those finding light up the importance of
set pieces in modern soccer in both way defensive and offensive approach.
The study also shows that successful teams shoot more often from a
counter-attack and from regular play. Hughes and Franks (2005) found that
there were significantly more shots per possession at longer passing sequences
than there were at shorter passing sequences for successful teams. The
conversion ratio of shots to goals is better for direct play than for possession
play. Which indicates that successful teams are more able to create chances of
A. Hadji
176
shooting from both direct and indirect play. It seems that they aim to make their
good shooters in the best condition to increase the accuracy of shots. In regular
play, by moving collectively fast, the team creates space in front of the penalty
area that can give space and time for midfielder to shoot without a big pressure.
The shot’s outcome shows only differences in “Goal” and “On post”
attributes. Successful teams are more accurate at shooting (Table 3). Those
results are in good agreement with other studies which have shown that shot on
target are one of the most powerful performance indicators has ever been
identified (Broich et al., 2014; Castellano et al., 2012; Delgado-Bordonau et al.,
2013; Hadji et al., 2019, 2020; Hughes & Bartlett, 2002; Lago-Ballesteros & Lago-
Peñas, 2010; Lago-Peñas et al., 2011).
The result show that attacking-midfield and forwarded from the
winning team are more involved in shooting. Which highlights the importance of
those two playing-positions in the attacking success. A team that have,
especially, midfield players with offensive traits are more likely to be successful.
A similar conclusion was reached by Liu, Gómez, et al. (2015) in the Spanish First
Division Professional Football League. They found that forward and midfielder
belonging to the top 3 teams have recorded more shots and shots on target than
those from bottom 3 teams.
With regard to shot location on the field, the results show no difference
in overall shots or outcome shots, except for shots ending with goals. Successful
teams tend to score goals from closer regions to the target and from centred-
area. Kapidžić et al. (2010) showed that shots from inside the penalty area are
the most powerful predictor of success in the 2008 European championship.
Oberstone (2009) found that the ratio of goals from outside the penalty area is
one of the predictors of the final league ranking in the English Premier League.
Successful teams’ shots are also less dispersing, tends to be much closer to the
177
penalty area and more centered. A good team organization at the offensive
phases will led to a better collective possession which give the ability to take the
ball as closer as possible to the target. This shot distribution don’t mean that
these teams have lineup the best shooters to achieve it. It means that these teams
are able to produce plays’ configuration that will create enough time-space
windows allowing higher number of shots with higher probability of success.
Conclusion
The main purpose of the study is to inspect the relation between shots
attributes and success in the 2018 FIFA World Cup. We showed that shot
attributes are indeed linked to success. Our results demonstrated that winning
teams when compared to losing teams:
̶ Uses more shots from set pieces;
̶ Shoot more often from a counter-attack and from regular play;
̶ Have more successful shots and higher accuracy when shooting;
̶ Involve more their attacking-midfield and forward at shooting;
̶ Shots are mostly concentrated closer to target and relatively centred.
Recommendation for practical use
Those finding can be used by coaches to improve tactical and technical
training. Training should be oriented to improve shots accuracy from different
areas on the field. Working on developing team ability of making a lot of
configurations that allow midfielders to be frequently in a shot situation. Teach
players how to make the right decision when it comes to choosing between
shooting and passing, shots are great goals’ source but also a big source of ball
loose.
Future investigations are necessary to validate the kinds of conclusions
that can be drawn from this study. Future studies could investigate shot
A. Hadji
178
attributes with more contextual variables, as line score, home advantage and
part of execution.
References
Alves, D. L., Osiecki, R., Palumbo, D. P., Moiano-Junior, J. V. M., Oneda, G., & Cruz, R. (2019).
What variables can differentiate winning and losing teams in the group and final
stages of the 2018 FIFA World Cup?
International Journal of Performance Analysis in
Sport
,
19
(2), 248-257. https://doi.org/10.1080/24748668.2019.1593096
Broich, H., Mester, J., Seifriz, F., & Yue, Z. (2014). Statistical analysis for the First Bundesliga in
the current soccer season.
Progress in Applied Mathematics
,
7
(2), 1-8.
Carmichael, F., Thomas, D., & Ward, R. (2000). Team performance: the case of English
premiership football.
Managerial and decision Economics
,
21
(1), 31-45.
Castellano, J., Casamichana, D., & Lago, C. (2012). The use of match statistics that discriminate
between successful and unsuccessful soccer teams.
Journal of Human Kinetics
,
31
,
139-147. https://doi.org/10.2478/v10078-012-0015-7
Chettouh, F., Hadji, A., & Bougandoura, F. (2020). Progress in motor learning through
augmented feedback (video feedback from a model and video feedback from its
own realization), the case of the long jump technics.
The journal « sports creativity »
,
11
(3), 253-277.
Delgado-Bordonau, J. L., Domenech-Monforte, C., Guzm??n, J. F., & Mendez-Villanueva, A.
(2013). Offensive and defensive team performance: Relation to successful and
unsuccessful participation in the 2010 Soccer World Cup.
Journal of Human Sport
and Exercise
,
8
, 894-904. https://doi.org/10.4100/jhse.2013.84.02
FIFA. (2018).
Rapport technique de la coupe du monde 2018
.
https://resources.fifa.com/image/upload/2018-fifa-world-cup-russia-technical-
study-group-report.pdf?cloudid=vnyavkozqglkj1omdaoi
Guettaoui, M., & Boumasjed, A. E. (2020). The Effect Of A Suggested Training Program On The
Levels Of Bodily-kinesthetic Intelligence Of Football Beginners Less Than 15 Years.
The journal « sports creativity »
,
11
(2), 421-433.
https://www.asjp.cerist.dz/en/article/137975
179
Hadji, A., Benmesbah, K., & Benbousta, R. (2019). Les indicateurs technico-tactiques de
performance chez l’équipe nationale algérienne de football.
Revue des Sciences et
Technologie Des Activités Physique et Sportive
,
16
(1), 1-13.
https://www.asjp.cerist.dz/en/article/91954
Hadji, A., Benmesbah, K., & Benbousta, R. (2020). Les Indicateurs Technico-tactiques de
derformance dans la Ligue de Football Professionnel « 1 » d’algérie.
Journal of Sport
Science Technology and Physical Activities
,
17
(3), 71-86.
https://www.asjp.cerist.dz/en/article/136595
Harrop, K., & Nevill, A. (2014). Performance indicators that predict success in English
professional League One soccer team.
International Journal of Performance Analysis
in Sport
,
14
, 907-920. https://doi.org/10.1080/24748668.2014.11868767
Hughes, M. D., & Bartlett, R. M. (2002). The use of performance indicators in performance
analysis.
Journal of Sports Sciences
,
20
, 739-754.
https://doi.org/10.1080/026404102320675602
Hughes, M. D., & Franks, I. M. (2005). Analysis of passing sequences, shots and goals in soccer.
Journal of Sports Sciences
,
23
, 509-514.
https://doi.org/10.1080/02640410410001716779
Kapidžić, A., Mejremić, E., Bilalić, J., & Bečirović, E. (2010). Differences in some parameters of
situation efficiency between winning and defeated teams at two levels of
competition.
Sport Scientific & Practical Aspects
,
7
(2).
Khan, I. (2020).
statsbomb-parser
. Retrieved 02/06/2019 from
https://github.com/imrankhan17/statsbomb-parser
Kubayi, A., & Toriola, A. (2020). Differentiating African Teams from European Teams:
Identifying the Key Performance Indicators in the FIFA World Cup 2018.
Journal of
Human Kinetics
,
73
(1), 203-208.
Lago-Ballesteros, J., & Lago-Peñas, C. (2010). Performance in team sports: Identifying the keys
to success in soccer.
Journal of Human Kinetics
,
25
, 85-91.
https://doi.org/10.2478/v10078-010-0035-0
Lago-Peñas, C., & Dellal, A. (2010). Ball possession strategies in elite soccer according to the
evolution of the match-score: The influence of situational variables.
Journal of
Human Kinetics
,
25
, 93-100. https://doi.org/10.2478/v10078-010-0036-z
A. Hadji
180
Lago-Peñas, C., Lago-Ballesteros, J., & Rey, E. (2011). Differences in performance indicators
between winning and losing teams in the UEFA Champions League.
Journal of
Human Kinetics
,
27
, 135-146. https://doi.org/10.2478/v10078-011-0011-3
Lago, C. (2007). Are winners different from losers? Performance and chance in the FIFA World
Cup Germany 2006.
International Journal of Performance Analysis in Sport
,
7
, 36-
47.
Liu, H., Gomez, M.-Á., Lago-Peñas, C., & Sampaio, J. (2015). Match statistics related to winning
in the group stage of 2014 Brazil FIFA World Cup.
Journal of Sports Sciences
,
33
(12),
1205-1213. https://doi.org/10.1080/02640414.2015.1022578
Liu, H., Gómez, M. A., Gonçalves, B., & Sampaio, J. (2015). Technical performance and match-
to-match variation in elite football teams.
Journal of Sports Sciences
,
34
, 509-518.
https://doi.org/10.1080/02640414.2015.1117121
Mackenzie, R., & Cushion, C. (2016). Match Performance Analysis: Player and Team
Assessments. In A. Strudwick (Ed.),
soccer science
. Human Kinetics.
Moura, F. A., Martins, L. E. B., & Cunha, S. A. (2014). Analysis of football game-related statistics
using multivariate techniques.
Journal of Sports Sciences
,
32
(20), 1881-1887.
https://doi.org/10.1080/02640414.2013.853130
Oberstone, J. (2009). Differentiating the top English premier league football clubs from the rest
of the pack: Identifying the keys to success.
Journal of Quantitative Analysis in
Sports
,
5
(3).
Rampinini, E., Impellizzeri, F. M., Castagna, C., Coutts, A. J., & Wisløff, U. (2009). Technical
performance during soccer matches of the Italian Serie A league: Effect of fatigue and
competitive level.
Journal of Science and Medicine in Sport
,
12
, 227-233.
https://doi.org/10.1016/j.jsams.2007.10.002
Sarkar, S. (2018). Paradox of crosses in association football (soccer) a game-theoretic
explanation.
Journal of Quantitative Analysis in Sports
,
14
(1), 25.
https://doi.org/10.1515/jqas-2017-0073
Szwarc, A. (2004). Effectiveness of Brazilian and German teams and the teams defeated by
them during the 17th Fifa World Cup.
Kinesiology
,
36
(1), 83-89.
https://hrcak.srce.hr/4224
181
Vecer, J. (2014). Crossing in soccer has a strong negative impact on scoring: Evidence from the
English premier league the German Bundesliga and the World Cup 2014.
Available
at SSRN 2225728
. https://doi.org/http://dx.doi.org/10.2139/ssrn.2225728
Yamanaka, K., Hughes, M. D., Lott, M., Reilly, T., Clarys, J., & Stibbe, A. (1993).
Science and
football II
(Vol. II).
Zhou, C., Zhang, S., Lorenzo Calvo, A., & Cui, Y. (2018). Chinese soccer association super
league, 20122017: key performance indicators in balance games.
International
Journal of Performance Analysis in Sport
,
18
(4), 645-656.
https://doi.org/10.1080/24748668.2018.1509254
DATA
StatsBomb. (2019).
Open data
[JSON]. https://github.com/statsbomb/open-data
StatsBomb data specifications. (2020).
Open data Statsbomb specifications V1.1
.
https://github.com/statsbomb/open-
data/blob/master/doc/StatsBomb%20Open%20Data%20Specification%20v1.1.pdf
ResearchGate has not been able to resolve any citations for this publication.
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