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Difference in shot's attributes between winning and losing teams in the 2018 FIFA World Cup 2018 ‫القدم‬ ‫لكرة‬ ‫العالم‬ ‫كأس‬ ‫في‬ ‫والخاسرة‬ ‫الفائزة‬ ‫املنتخبات‬ ‫بين‬ ‫التسديد‬ ‫سمات‬ ‫في‬ ‫الفرق‬


Abstract and Figures

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. ‫امللخص:‬ ‫(ا‬ ‫اة‬ ‫املبار‬ ‫ونتائج‬ ‫التسديد‬ ‫سمات‬ ‫بين‬ ‫العالقة‬ ‫في‬ ‫التحقيق‬ ‫هو‬ ‫اسة‬ ‫الدر‬ ‫هذه‬ ‫من‬ ‫الهدف‬ ‫كأس‬ ‫في‬ ‫الخسارة)‬ ‫مقابل‬ ‫لفوز‬ ‫العالم‬ 2018. ‫نمط‬ ‫هي:‬ ‫التسديدة‬ ‫سمات‬ ‫اللعب،‬ ‫األصل،‬ ‫النتيجة،‬ ‫الالعب‬ ‫وموقع‬ ‫التسديدات‬ ‫اسة‬ ‫در‬ ‫تمت‬. 1313 ‫تسديدة‬ ‫من‬ 105 ‫اة‬ ‫مبار‬ ‫متوازنة‬ ‫غير‬ ‫لكل‬ ‫شركة‬ ‫قبل‬ ‫من‬ ‫البيانات‬ ‫توفير‬ ‫تم‬ ‫فريق.‬ StatsBomb. ‫النتائج‬ ‫أظهرت‬ ‫انه‬ ‫بالفرق‬ ‫مقارنة‬ ، ‫الخاسرة‬ ‫ف‬ ‫ال‬ ‫أن‬ ‫الثابتة‬ ‫ات‬ ‫الكر‬ ‫من‬ ‫التسديدات‬ ‫من‬ ‫املزيد‬ ‫تستخدم‬ ‫الفائزة‬ ‫فرق‬ (P <0.05 ‫؛‬ ES = 0.25) ‫من‬ ‫أكثر‬ ‫وتسديد‬ ، ‫مضاد‬ ‫هجوم‬ (P <0.05 ‫؛‬ ES = 0.22) ‫العادي‬ ‫اللعب‬ ‫ومن‬ ، (P <0.05 ‫؛‬ ES = 0.28) ‫ا‬ ً ‫نجاح‬ ‫أكثر‬ ‫تسديدات‬ ‫لديها‬ ، (P <0.001 ‫؛‬ ES = 0.42) ‫أكبر‬ ‫نجاح‬ ‫نسبة‬ ، (P <0.01 ‫؛‬ ES = 0.38) ‫من‬ ‫املزيد‬ ‫اك‬ ‫إشر‬ ‫إلى‬ ‫تميل‬ ، ‫العبي‬ ‫املهاجم‬ ‫الوسط‬ ‫خط‬ (P = 0.087 ‫؛‬ ES = 0.18) ‫واألمام‬ ، (P = 0.86 ‫؛‬ ES = 0.2) ‫الهدف‬ ‫من‬ ‫بالقرب‬ ‫الغالب‬ ‫في‬ ‫الناجحة‬ ‫لقطاتهم‬ ‫وتتركز‬ ، (P = 0.06 ‫؛‬ ES = 0.3) ، ‫وت‬ ‫م‬ ‫ا‬ ً ‫نسبي‬ ‫ركز‬ (P <0.05 ‫؛‬ ES = 0.32). . ‫النتائج‬ ‫هذه‬ ‫استخدام‬ ‫للمدربين‬ ‫يمكن‬ ‫العبيهم‬ ‫أداء‬ ‫لتحسين‬ .-‫الكلمات‬ ‫املفتاحية:‬ ‫العالم‬ ‫كاس‬ 2018 ‫اة‬ ‫املبار‬ ‫نتيجة‬ ‫التسديدات،‬ , A. Hadji
Content may be subject to copyright.
The journal « sports creativity »
Volume: (12) / N°: (01)-(2021), p167-..181
Corresponding author: A. Hadji e-mail:
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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
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
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
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
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).
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
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
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
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
A shot that was stopped from continuing by a defender
A shot that was deemed to cross the goal-line by officials
Off Target
A shot that’s initial trajectory ended outside the posts
A shot that hit one of the three posts
A shot that was saved by the opposing team’s keeper
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
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)
( A significance level was set at p ≤ 0.05.
Table 2. Comparison of shots attributes between losing and winning teams
Shots attributes
Losing team
Winning team
Pattern of play
Free Kick
(N=51) 0.45±0.58
(N=51) 0.88±0.91
0.016 *
Open Play
(N=51) 11.43±5.61
(N=51) 12.59±4.97
Free Kick%
(N=51) 3.98±5.31
(N=51) 7.5±7.93
0.027 *
Open Play%
(N=51) 96.03±5.31
(N=51) 92.5±7.93
0.027 *
From Corner
(N=51) 2.1±2.06
(N=51) 2.18±1.85
From Counter
(N=51) 0.33±0.68
(N=51) 0.65±0.82
0.023 *
From Free Kick
(N=51) 2.35±1.74
(N=51) 2.51±1.55
From Goal Kick
(N=51) 0.33±0.59
(N=51) 0.37±0.69
From Keeper
(N=51) 0.12±0.33
(N=51) 0.14±0.4
From Kick Off
(N=51) 0.1±0.36
(N=51) 0.06±0.24
From Throw-In
(N=51) 2.14±1.81
(N=51) 1.73±1.22
Regular Play
(N=51) 4.39±3.13
(N=51) 5.82±3.33
0.015 *
(N=51) 3.57±2.73
(N=51) 3.71±2.11
(N=51) 0.47±0.7
(N=51) 1.94±1.14
Off T
(N=51) 4.33±2.37
(N=51) 4.39±2.1
(N=51) 0.06±0.24
(N=51) 0.26±0.44
(N=51) 2.55±1.99
(N=51) 2.55±1.80
(N=51) 1.04±0.98
(N=51) 0.88±1.13
(N=51) 0.65±0.96
(N=51) 1.26±1.67
(N=51) 1.33±1.4
(N=51) 1.33±1.49
(N=51) 0.69±1.21
(N=51) 0.67±1.09
(N=51) 2.92±2.24
(N=51) 3.71±2.44
(N=51) 2.71±2.52
(N=51) 3.22±3.2
(N=51) 2.71±2.33
(N=51) 2.28±2.75
(N=51) 1±1.02
(N=51) 1.28±1.17
Total Shots
(N=51) 12.02±5.64
(N=51) 13.73±4.85
0.030 *
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
Table 3. Comparison of shots success between winning and losing team
Shots success
Losing teams
Winning teams
(N=51) 8.94 ± 4.40
(N=51) 8.98 ± 3.55
(N=51) 3.08 ± 2.11
(N=51) 4.75 ± 2.37
% Success
(N=51) 25.4% ± 14.8%
(N=51) 35.3% ± 14.2%
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
Outcome coordinate
(N=48) 17.79±5.34
(N=50) 17.74±4.26
(N=19) 12.8±5.61
(N=50) 9.93±4.91
Off T_X
(N=48) 17.99±5.46
(N=50) 18.54±5.94
(N=3) 13±12.17
(N=13) 17.31±7.79
(N=44) 15.77±4.36
(N=45) 15.45±6.16
(N=35) 11.88±6.5
(N=28) 12.23±6.77
(N=48) 3.85±8.27
(N=50) 4.47±5.72
(N=19) 8.45±5.61
(N=50) 5.22±6.01
0.039 *
Off T_Y
(N=48) 5.15±6.6
(N=50) 5.59±5.36
(N=3) 5±1.73
(N=13) 5.77±11.92
(N=44) 4.99±7.36
(N=45) 5.64±6.58
(N=35) 4.69±7.28
(N=28) 2.14±7.41
Average Location
(N=51) 16.74±2.64
(N=51) 16.19±2.65
(N=51) 29.76±3.49
(N=51) 29.73±2.93
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.
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
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
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.
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
Future investigations are necessary to validate the kinds of conclusions
that can be drawn from this study. Future studies could investigate shot
A. Hadji
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Soccer is the most popular sport in the world. Despite this global popularity, European teams in contrast to African ones, have dominated the Fédération Internationale de Football Association (FIFA) World Cup tournaments for many decades. Therefore, the aim of this study was to examine the performance indicators that differentiated between African and European teams in the 2018 FIFA World Cup. Thirty matches played by five European (n = 15) and five African teams (n = 15) from the group stages of the World Cup were analysed using the InStat video system. The results showed that European teams had higher averages than African teams on the following performance variables: total shots, shots on target, goals scored from open play and set pieces, ball possession, short passes, medium passes, total passes, accurate passes and corner kicks. Therefore, soccer coaches should take note of these findings as they could serve as a benchmark for African teams to set trends and improve their performance at FIFA World Cup tournaments.
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l’objectif de ce travail est d’identifier les indicateurs de performance technico- tactique observés pouvant distinguer un match gagné d’un match nul et/ou perdu. L’étude est portée sur onze (11) matchs de l’équipe nationale d’Algérie de football (senior). Nous avons utilisé le logiciel Dartfish pour l’analyse. Les éléments observés sont partagés en deux catégories ; 1) éléments d’attaque (nombre et barycentre des passes réussies et non réussies, tirs et % tirs cadrés) et 2) éléments de défense. Les résultats de la comparaison indiquent que le pourcentage de possession de ballon lors des matchs gagnés (64 % ± 5 %) est supérieur à celui des matchs nuls (51 % ± 6 %) (p<0,01) et celui des matchs perdus (41 % ± 0,5) (p<0,001). Pour le nombre de passes réussies, la moyenne des matchs gagnés est aussi supérieure (p<0,01) à celle des matchs perdus ou nuls. La position du barycentre des récupérations de ballon est plus haute lors des matchs gagnés (p<0,01). Le pourcentage de possession est le seul élément qui prédit le résultat final des matchs (gagné > nuls > perdu) (coefficient de structure CS 0,35 > 0,30).
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The aim of the present study was to identify the game-related statistics which discriminate between winning, drawing and losing teams in Chinese Soccer Association Super League. The sample included 1056 balance games from the 2012–2017 Chinese Soccer Association Super League. Physical and technical game-related statistics were gathered. A one-way analysis of variance and discriminant analysis of data was done. The results showed that winning teams were significantly higher for the following game statistics: shots, shots on target, 50–50 challenge won, offsides, sprinting distance, sprinting effort, sprinting distance in ball possession and high-speed-running distance in ball possession. Losing teams had significantly higher averages in the variable crosses, passes, forward passes, sprinting distance out of ball possession and high-speed-running distance out of ball possession. Discriminant analysis concluded the following: the variables that discriminate between winning, drawing and losing teams were the shots on target, sprinting distance in ball possession, quality of opposition, passes and forward passes. Coaches and players should be aware of these different profiles in order to design and evaluate practices and competitions for their teams.
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Identifying match statistics that strongly contribute to winning in football matches is a very important step towards a more predictive and prescriptive performance analysis. The current study aimed to determine relationships between 24 match statistics and the match outcome (win, loss and draw) in all games and close games of the group stage of FIFA World Cup (2014, Brazil) by employing the generalised linear model. The cumulative logistic regression was run in the model taking the value of each match statistic as independent variable to predict the logarithm of the odds of winning. Relationships were assessed as effects of a two-standard-deviation increase in the value of each variable on the change in the probability of a team winning a match. Non-clinical magnitude-based inferences were employed and were evaluated by using the smallest worthwhile change. Results showed that for all the games, nine match statistics had clearly positive effects on the probability of winning (Shot, Shot on Target, Shot from Counter Attack, Shot from Inside Area, Ball Possession, Short Pass, Average Pass Streak, Aerial Advantage and Tackle), four had clearly negative effects (Shot Blocked, Cross, Dribble and Red Card), other 12 statistics had either trivial or unclear effects. While for the close games, the effects of Aerial Advantage and Yellow Card turned to trivial and clearly negative, respectively. Information from the tactical modelling can provide a more thorough and objective match understanding to coaches and performance analysts for evaluating post-match performances and for scouting upcoming oppositions.
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The purpose of the present study was to identify performance indicators that may discriminate between games a soccer team won, drew and lost. A second aim was to identify those variables that best predict success for the team. The sample comprised of 46 matches played by a League One soccer team during the 2012-2013 domestic season. Offensive and defensive game-related statistics were gathered Match location was also considered. A Kruskal Wallis test and binary logistic regression were used to identify those indicators associated with success (wins). The Kruskal Wallis test identified significant differences in the number of passes, percentage of successful passes and passes made in the opposition half Significantly more passes and passes in the opposition half were made when the team lost compared to when they won and drew games (P Keywords: LOGISTIC REGRESSION ANALYSIS; MATCH ANALYSIS; PERFORMANCE INDICATORS; SOCCER Document Type: Research Article Publication date: December 1, 2014 More about this publication? Editorial Board Information for Authors Subscribe to this Title Terms & Conditions ingentaconnect is not responsible for the content or availability of external websites $(document).ready(function() { var shortdescription = $(".originaldescription").text().replace(/\\&/g, '&').replace(/\\, '<').replace(/\\>/g, '>').replace(/\\t/g, ' ').replace(/\\n/g, ''); if (shortdescription.length > 350){ shortdescription = "" + shortdescription.substring(0,250) + "... more"; } $(".descriptionitem").prepend(shortdescription); $(".shortdescription a").click(function() { $(".shortdescription").hide(); $(".originaldescription").slideDown(); return false; }); }); Related content In this: publication By this: publisher In this Subject: Internal Medicine By this author: Harrop, Kerys ; Nevill, Alan GA_googleFillSlot("Horizontal_banner_bottom");
To compare the tactical, physical and technical indicators between winners and losers of the group and final stages of the 2018 FIFA World Cup (WC). Eighteen variables were analysed, divided into tactical (ball possession), physical (distance covered, sprints and intensity zones 1 [low] to 5 [high]) and technical indicators (passing, shots and corners). The losing teams had lower ball possession in the group stage when compared to the winners (p = .04). There was a tendency (p = .06) for winners to spend more time in high intensity (group stage). Winners also had a tendency towards greater passing success (p = .06), shots (p = .05) and shots on target (p < .01). In summary, the winning teams have greater ball possession and pass success during the group stage of the 2018 FIFA WC, in addition to spending more time at high intensities and achieving more shots and shots on target independent of whether the match was performed in the group or final knockout stage. Our findings do not suggest any additional physical, tactical or technical variable in the determination of success in high-level soccer competition.
In association football, crosses from the wide areas of the pitch in the attacking third is a standard tactic for creating goal-scoring opportunities. But recent studies show that crosses adversely impact goals. Regression run in this paper on data from the premier soccer leagues of England, Spain, Germany, France and Italy for 2016-2017 season also found this inverse relation. However, there is no research that explains the reason for this inverse relation between crosses and goals. A game-theoretical model developed in this paper explains why crosses adversely affect goal-scoring. The model identifies a mixed strategy Nash equilibrium (MSNE), wherein the attacking team's probability of playing a cross decreases with increase in their crossing accuracy, heading accuracy and probability of winning aerial balls. If the attacking team is good in terms of these parameters, the defending team's probability of using an offside trap increases and that forces the attacking team to use crosses less frequently. In the MSNE, teams with a greater chance of scoring from crosses use the crosses less frequently than teams having a smaller chance of scoring from crosses. The theory was subsequently validated using the data of the 2016-2017 football season.
Recent research suggests that match-to-match variation adds important information to performance descriptors in team sports, as it helps measure how players fine-tune their tactical behaviours and technical actions to the extreme dynamical environments. The current study aims to identify the differences in technical performance of players from strong and weak teams and to explore match-to-match variation of players’ technical match performance. Performance data of all the 380 matches of season 2012–2013 in the Spanish First Division Professional Football League were analysed. Twenty-one performance-related match actions and events were chosen as variables in the analyses. Players’ technical performance profiles were established by unifying count values of each action or event of each player per match into the same scale. Means of these count values of players from Top3 and Bottom3 teams were compared and plotted into radar charts. Coefficient of variation of each match action or event within a player was calculated to represent his match-to-match variation of technical performance. Differences in the variation of technical performances of players across different match contexts (team and opposition strength, match outcome and match location) were compared. All the comparisons were achieved by the magnitude-based inferences. Results showed that technical performances differed between players of strong and weak teams from different perspectives across different field positions. Furthermore, the variation of the players’ technical performance is affected by the match context, with effects from team and opposition strength greater than effects from match location and match outcome.
Statistical analysis for the soccer matches of the First Bundesliga; i.e., the first national soccer league of Germany, during the period Aug. 5 to Nov. 27, 2011 is made in order to see which parameters are more important for the result of a match. It is found that the goal efficiency, defined by the number of goals divided by the number of shots, is by far the most important parameter. The parameters of the second to the fourth importance are the number of shots, the number of passes and the number of ball contacts respectively. The present analysis shows that the quality of shots, represented by the goal efficiency, is more important than the quantity of shots for winning a soccer game. Regarding the long time dispute over the issue whether "direct play" or "possession play" is more effective, the present result favours "direct play" because it has higher goal efficiency compared to "possession play" according to the literature. Nevertheless, the length of passing sequence may not be the major factor related to the goal efficiency. Effective use of team possessions to create favourable shooting conditions may be more important for raising the goal efficiency. Much further analysis remains to be carried out in this direction.