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# Analysis of penalties taken in shoot-outs

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## Abstract and Figures

Penalties are now a subject of myth, romance, excitement, dread, fear and pressure - depending upon whether you are watching or taking them. Many soccer managers and coaches have said that they are a lottery and many are of the opinion that practising them are a waste of time, because it is not possible to replicate the pressure. Little research has been completed on penalty shoot-outs, and most of this has been on the performance of goalkeepers. The aim of this work is to use notation to analyse the performances of the penalty takers and goalkeepers in penalty shoot-outs taken from the FIFA World Cup finals and also the finals of the European Champions League, and present these data so that a successful profile of optimal performance can be defined. A notation system was designed to input data directly into Access, 129 penalties were notated with an intention to analyse the time in preparing the shot, the number of paces taken to approach the ball, their relative pace, the pace of the shot, its placement and the outcome. It was found that:- • One in five saved (20%; 3/15), one in fifteen missed (7%; 1/15) and three in four scored (73%; 11/15). • 25% of shots a fast run are saved because the player then tried either 50% or 75% power. • Best success ratios are from an even run up of 4, 5 and 6 paces. • There is no laterality in the success ratios – left footed and right footed strikers have the same success when the frequencies are represented as percentages. • No shots above waist height were saved, although 18% of those shots missed. • In every case, the goalkeeper moved off the line before the ball was struck. • There is only a small data set, but the goalkeepers who took a pace forward and stood up while the striker approached the ball, had the best save and miss ratios. • The profile of Germany’s penalty takers show a consistent pattern that is very different from the average, indicating analysis and training. Analysis of penalties taken in shoot-outs All authors Mike Hughes & Julia Wells https://doi.org/10.1080/24748668.2002.11868261 Published online 03 April 2017 Table 1 Respective frequency of the different paces of striking the ball with the outcomes expressed as percentages CSVDisplay Table It was concluded that these data analyses demonstrate that there are optimal strategies in taking and saving penalties. These point to ways of enhancing the individual performance of the players in these closed skills. Coaches in this team sport will be helped by methods used in individual sports such as golf and racket sports, where the emphasis is on the attainment of expert technique.
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Analysis of penalties taken in shoot-outs.
Mike Hughes and Julia Wells,
CPA, UWIC, Cyncoed, Cardiff CF23 6XD.
Abstract
Penalties are now a subject of myth, romance, excitement, dread, fear and pressure –
depending upon whether you are watching or taking them. Many soccer managers and
coaches have said that they are a lottery and many are of the opinion that practising them
are a waste of time, because it is not possible to replicate the pressure. Little research has
been completed on penalty sho ot-outs, and most of this has been on the performance of
goalkeepers. The aim of this work is to use notation to analyse the performances of the
penalty takers and goalkeepers in penalty shoot-outs taken from the FIFA World Cup
finals and also the finals of the European Champions League, and present these data so
that a successful profile of optimal performance can be defined.
A notation system was designed to input data directly into Access, 129 penalties were
notated with an intention to analyse the time in preparing the shot, the number of paces
taken to approach the ball, their relative pace, the pace of the shot, its placement and the
outcome.
It was found that:-
· One in five saved (20%; 3/15), one in fifteen missed (7%; 1/15) and three in four
scored (73%; 11/15).
Table 1. Respective frequency of the different paces of striking the ball with the
outcomes expressed as percentages.
Power of shot Frequency Goal Missed Saved
50% 12% 47% 0% 53%
75% 70% 81% 1% 18%
100% 18% 63 % 31% 7%
· 25% of shots a fast run are saved because the player then tried either 50% or 75%
power.
· Best success ratios are from an even run up of 4, 5 and 6 paces.
56
· There is no laterality in the success ratios – left footed and right footed strikers have
the same success when the frequencies are represented as percentages.
· No shots above waist height were saved, although 18% of those shots missed.
· In every case, the goalkeeper moved off the line before the ball was struck.
· There is only a small data set, but the goalkeepers who took a pace forward and stood
up while the striker approached the ball, had the best save and miss ratios.
· The profile of Germany’s penalty takers show a consistent pattern that is very
different from the average, indicating analysis and train ing.
It was concluded that these data analyses demonstrate that there are optimal strategies in
taking and saving penalties. These point to ways of enhancing the individual performance
of the players in these closed skills. Coaches in this team sport will be helped by methods
used in individual sports such as golf and racket sports, where the emphasis is on the
attainment of expert technique.
Introduction
Penalties are now a subject of myth, romance, excitement, dread, fear and pressure –
depending upon whether you are watching or taking them. They have helped careers of
footballers and destroyed them. Many soccer managers and coaches have said that they
are a lottery and many are of the opinion that practising them are a waste of time, because
it is not possible to replicate the pressure. Yet little research has been completed on
penalty shoot-outs, and most of this has been on the performance of goalkeepers.
Franks and Hanvey (1997) researched the cues that goalkeepers can use to anticipate
the direction of the shot by the penalty taker, by initially analysing penalty shoot-outs
from four FIFA World Cup competitions. Several cues were considered independently,
but only three were found to reliably predict the position of the shot above an acceptable
level of 80%. Placement of the non-kicking foot was chosen as the most appropriate
response cue because it allows the goalkeeper time to make his move to the position of
the ball. An experiment was then designed to test whether training with this cue could
improve performance of the goalkeeper – using a lab-based simulation. Subjects
significantly improved their response accuracy and reaction time. Franks and Hanvey
went on to devise a training programme that enabled goalkeepers to successfully predict
shot position and react within the allowable time constraints of the penalty kick situation.
Savelsbergh et al. (2002) solely used a lab-based approach in their attempt to research
visual search, anticipation and expertise in soccer goalkeepers. Expert and novice
goalkeepers used a joystick to respond to images shown them on film. Visual search
behaviour was examined by means of an eye movement registration system. Expert
goalkeepers used a more efficient search strategy and were more accurate in predicting
the direction of the penalty kick, using the kicking leg, the non-kicking leg and the ball
57
areas as cues. They presented an informed overview of the area and discussed in depth
the implications for improving anticipation skill at penalty kicks.
The aim of this work is to use notation to analyse the performances of the penalty
takers and goalkeepers in penalty shoot-outs taken from the FIFA World Cup finals and
also the finals of the European Champions League, and present these data so that a
successful profile of optimal performance can be defined.
Method
A notation system was designed to input data directly into Access, 129 penalties were
notated with an intention to analyse the time in preparing the shot, the number of paces
taken to approach the ball, their relative pace, the pace of the shot, its placement and the
outcome (see Table 1 for example data).
Table 1. Data entry system in Access for the actions of the penalty taker
Taker ID Pen
No
Approach
Time
Start
Placement
Finish &
Striking
Start
Paces
Back
Paces
In
Strikin g
Time
Finish
Ru n I n Approach
Direction
Placement
no n-
striking
foot
Strike
Fo ot
Part of
Foot
Pace of
Strike
Shot
Directio n
De Kock 1 04:26:00 04:4 8:02 14 8 04:58:20 Slow Left Curve Left Right Instep 100 Left 2
Zidane 1 04 :58:20 0 5:29:06 7 5 05 :38:14 Medium L eft Curve Right Right Side 75 Left 4
R. De
Boar
2 05:38:14 05:5 7:00 7 4 06:04 :15 Medium Straig ht Straight Right Side 75 Le ft 4
In addition the actions of the goalkeeper were notated – position, body shape, movements
as the player approached, his first movements and the subsequent direction, the outcome
(see Table 2 for example data).
Table 2. Data entry system in Access for the actions of the goalkeeper
GK GK Position Body Shape Movement Made Intitial Movement Dive Outcome
If Saved, How?
Centre Crouched Still Forward No Dive Goal
Van der Sar Centre Arms & Legs Wide Shuffle Forward Low Left Goal
Centre Crouched Still Forward Low Right Goal
An intra-observer reliability test on 50 of the penalties resulted in percentage agreements
ranging from 96% to 100%, depending upon the data analysed. Not all video recordings
enabled all of these data to be notated, so in the subsequent analyses some of the totals
are 128 and 127. Case studies were completed on 2 teams, England and Germany as an
example of the power of this type of analysis.
Results and Discussion
The data in Table 3 show that generally penalties will result in one in five saved (3/15),
one in fifteen missed (1/15) and three in four scored (11/15).
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Decisions in taking a penalty
The player taking a penalty is faced with a series of simple decisions – how hard to hit
the shot, whether to attempt to place the ball and, if so, where to place it. Then the player
must decide on the pace of the run up, and the number of paces, to achieve the aims.
Table 3. Outcomes of penalties
Outcome Frequency %
Goal 94 73
Missed 9 7
Saved 26 20
Pace of strike
The largest percentage hit the ball at 75% of maximum power, placing the ball (70%,
87/128), this is the most efficient way of striking a penalty (see Table 2). Slow shots have
only a 47% success rate, blasting the ball at 100% effort has only 63% success (31% miss
the goal), whilst placing the ball has 81% success
Table 4. Respective frequency of the different paces of striking the ball with the
outcomes expressed as percentages
Pace of Strike
(% of max)
Outcome Frequency %
50 Goal 7 47
50 Missed 0 0
50 Saved 8 53
75 Goal 71 81
75 Missed 1 1
75 Saved 15 18
100 Goal 16 63
100 Missed 8 31
100 Saved 2 7
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Fig. 1. Respective frequency of the different paces of striking the ball
Fig. 2. Frequency of the different outcome at different pace of shot.
The frequencies of goals, misses and saved shots give an indication of the relative
success of the different types of shots, but this is clouded by the different amounts of
each types of shots (Fig. 2). By non-dimensionalising them, expressing each as a
percentage of the total of each type of shot the picture becomes more clear. As the pace
of the shot increases there are less saves, but more misses. The message is clear – if
players can improve their accuracy at pace then they will score more goals..
15
87
26
50% max 75% max 100% max
0
10
20
30
40
50
60
70
80
50 75 100
Goal
Missed
Saved
60
Fig. 3. Frequency of the different outcome at different pace of shot expressed as
percentages of the total of each set..
Approach to Ball
Pace of Approach
Often players will try to deceive the goalkeeper with the run up, varying the pace and the
direction. The ‘check’ was the term given to a slow run with a feint in it, the other speed
terms are self-explanatory.
Fig. 4. Relative percentages of the different types of approach runs to the ball.
Most players place the ball at 75% of maximum pace so the distribution of the different
types of run up reflect this, with the medium being the most common. Quite large
numbers however, have a slow run up and still hit the ball at 75%, and also a 75% pace
shot from a fast pace run-up these types of data are further analysed later.
Pace of approach to the ball
3%
32%
50%
15%
Check Fast Medium Slow
0
10
20
30
40
50
60
70
80
% success
50 75 100
% of maximum pace shot
Goal
Missed
Saved
61
Table 5. Relative percentages of the different types of approach runs to the ball, with the
different outcomes of the shots.
Run In Outcome Frequency %
Other Saved 1 20
Check Step Goal 3 60
Check Step Saved 1 20
Fast Goal 30 68
Fast Missed 3 7
Fast Saved 11 25
Medium Goal 47 83
Medium Missed 5 8.5
Medium Saved 5 8.5
Slow Goal 14 61
Slow Missed 1 4
Slow Saved 8 35
Figs. 5 and 6. The frequencies of the outcomes of each of the approaches, and these
frequencies expressed as percentages of the totals of each approach.
Figures 5 and 6 show a similar message to the pace of the shot – a balanced even paced
(medium) run will give low misses, low saves and high success. The slower the run,
usually trying to disguise the shot (check and slow) results in high percentages of saves.
High speed also has a high relatively high percentage of saves and misses. The interesting
part of these data, and those data of the pace of shot, is that players have different success
rates when they combine fast run-ups with 75% shots, and so on. Consequently the data
were further analysed.
0
30
60
90
% Outcome
Check
Fast
Medium
Slow
Pace
Goal
Missed
Saved
0
10
20
30
40
50
Freq uency
Check
Fast
Medium
Slow
Pace
Goal
Missed
Saved
62
Table 6. Speed of run up and the pace of shot.
Run In Pace of Strike Outcome Frequency
Check Step 50 Saved 1
Check Step 75 Goal 3
Fast 50 Goal 1
Fast 50 Saved 2
Fast 75 Goal 19
Fast 75 Saved 8
Fast 100 Goal 10
Fast 100 Missed 3
Fast 100 Saved 1
Medium 50 Goal 4
Medium 75 Goal 39
Medium 75 Missed 1
Medium 75 Saved 4
Medium 100 Goal 4
Medium 100 Missed 4
Medium 100 Saved 1
Slow 50 Goal 2
Slow 50 Saved 5
Slow 75 Goal 10
Slow 75 Saved 3
Slow 100 Goal 2
Slow 100 Missed 1
Fig. 7. Fast run up with different pace strikes and the respective outcomes.
0
10
20
30
40
50
60
70
50% MAX 75% MAX 100%
Goal
Saved
Missed
63
Fig. 8. Medium speed run up with different pace strikes and the respective outcomes.
Fig. 9. Slow speed run up with different pace strikes and the respective outcomes.
Table 7. Frequency of penalty shots taken with the number paces of the approach run.
Paces In Frequency
1 2
2 1
3 10
4 27
5 37
6 29
7 11
8 8
9 1
10 2
The figures 8 and 9 show how the players find it difficult to mix the speed of the run- up
with the pace of the strike and sustain accuracy – compare these data with the overall
performance of 73% goals, 7% missed and 20% saved. Only the medium paced run up
gives better results, and then not with the 100% effort in the pace of the shot.
0
20
40
60
80
100
50% MAX 75% MAX 100%
Goal
Saved
Missed
0
10
20
30
40
50
60
70
80
50% MAX 75% MAX 100%
Goal
Saved
Missed
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The number of paces in the run up
The data in Table 7 follows almost a normal distribution (Kurtosis =-0.73477; Skewness
= 0.902751) about a maximum frequency of 5 paces, very much as expected, which of
these show the best outcomes?
Table 8. Frequency and % of pena lty shots taken with the number paces of the approach.
Paces In Outcome Frequency %
1 Goal 2 100
2 Goal 1 100
3 Goal 8 80
3 Saved 2 20
4 Goal 17 63
4 Missed 2 7
4 Saved 8 30
5 Goal 29 79
5 Missed 2 5
5 Saved 6 16
6 Goal 22 75
6 Missed 3 10
6 Saved 4 15
7 Goal 5 46
7 Missed 2 18
7 Saved 4 36
8 Goal 7 87
8 Saved 1 13
9 Goal 1 100
10 Goal 2 100
Fig. 10. The frequency of outcomes associated with each of the different paced run ups.
0
5
10
15
20
25
30
1 2 3 4 5 6 7 8 9 10
Goal
Missed
Saved
65
Fig. 11. The frequency of outcomes associated with each of the different paced run ups
expressed as percentages of the total number of shots at that number of paces. The very
low and very high number of paces have been removed because of the small data
samples.
The normalised data in Fig. 11 show that the balanced approach over 5 paces gives the
player the chance to achieve a smooth approach, being marginally better than 4 or 6 in
terms of percentage achievement. Although 8 and 3 paces appear to be better, these data
samples are very small.
Strike Foot
The data show no significant differences in the performance of left and right footed
strikers, once the data has been normalised to balance the respective overall frequencies.
Table 9. Laterality of striking foot and outcome
Strike Foot Outcome Frequency %
Left Goal 18 72
Left Missed 2 8
Left Saved 5 20
Right Goal 76 75
Right Missed 7 6
Right Saved 20 19
Shot Direction
To analyse the position of the shot with respect to the goal, it was divided into 8 areas
within the goal, and 4 areas outside the goals to define misses.
0
10
20
30
40
50
60
70
80
90
3 4 5 6 7 8
Goal
Missed
Saved
66
Fig. 12. The cell division of the goal and surround for direction of strike from the view of
the striker.
The above divisions were linked with the outcomes of goal, saved and missed.
Fig. 13. The cell division of the goal and surround for direction of strikes (the misses are
extrapolated into the top cells).
L3
L2
L4
L1
R3
R2 R1
R4
High Left High Right
High Centre
Left Right
Goal
1 & 2
3 & 4
L L R R
16
32
9
7
19
27
9
9
0
50
Frequency
Height
Side
67
Fig. 14. The cell division of the goal and surround for direction of strike that are saved.
If the data from these figures are combined, the % efficacy of each of these cell divisions
of the goal can be calculated. The conversion rates show a stark message in shooting on
the floor, particularly close to the goalkeeper. Lifting the ball means that the shot will not
be saved (none of the shots at the upper cells were saved – but there is the miss factor). It
can be seen that goalkeepers have more success to their right – the strikers’ less success
to their left.
Fig. 15. The respective % conversion rates in different parts of the goal
Goal-keeper data
In spite of the clear rules about movement, the GK moves off the line, before the ball is
struck, for 80% of the analysed penalties. The forward movement achieves the highest
Shot direction for goals saved
6
9
5
6
Left 3
Left 4
Right 3
Right 4
Shot direction
Number of goals
1 & 2
3 & 4
L L R R
88
72
77
25
88
78
77
44
0
50
100
% Strike
Rate
Height
Side
68
number of saves, but if the data is examined as a percentage of its own total (Fig. 22), the
standing still ploy then has the highest success rates
Fig. 16. Movement made by the GK and the outcome before the ball is struck
Fig. 17. Percentage movement made by the GK and the outcome before the ball is struck
Frequency Percentages
Figs. 18 and 19. Movement made by the GK and the outcome before the ball is struck,
and expressed as a percentage (N.B. this right is the strikers’ right – the GK’s left).
0
5
10
15
20
25
30
35
Arms Jumping Still
Goal
Saved
Missed
0
10
20
30
40
50
60
70
80
90
100
Arms Jumping Still
Goal
Saved
Missed
0
10
20
30
40
50
60
70
80
90
Forward Right
Goal
Saved
Mi s sed
0
10
20
30
40
50
60
70
80
Forward Right
Goal
Saved
Mi s sed
69
It is not clear what the skewing of the percentage data means – other than the anticipation
to the right side gives a high % return. This data set is so small it cannot be taken as
meaningful, but it could be an area for further research with larger sets of data.
Performance Profiling
Although there was not a great amount of data available, profiles were analysed for
England and Germany to investigate whether there were indications that the data would
show distinct individual patterns. Summaries of these data are presented below. There are
clear messages in these two data sets that fly in the face of common perceptions – that it
is a lottery and that England are poor at taking penalties.
England’s Performances
The data analysed for England comes from 4 tournaments:-
Germany Lost 5-4
Germany Lost 5-6
Argentina Lost 5-4
Spain Won 4-2
England took 20 penalties, scoring 15, missing 1 and had 4 saved.
· This means that their overall % performance was above average, despite the fact that
they lost 3 of the 4 shoot-outs.
Goals Missed Saved
Average 73% 7% 20%
England 75% 5% 20%
· The pace of the shots shows no slow shots taken, 3 saved at 75% pace and only 1 out
of 3 scored at 100% power, 1 missed, 1 saved.
Goals Missed Saved
Average (75% power) 81% 1% 18%
England (75% power) 82% 0% 18%
Average (100% power) 63% 31% 7%
England (100% power) 33% 33% 33%
· Four out of the 5 saves and misses were penalties delivered off a fast run up – even
though 2 of these were then only at 75% power.
· Pacing – those at the extreme ends all scored, so nothing unusual.
· Accuracy, this is critical – 4 inaccurate shots. One accurate shot was saved, but the
other 3 saves were all at L3, and of course a miss.
Germany’s Performances
The data analysed for Germany comes from 5 tournaments:-
70
England Won 5-4
England Won 6-5
Czech Lost 5-4
France Won 5-4
Mexico Won 4-1
Germany took 24 penalties, scoring 22, missing 1 and had 1 saved.
· This means that their overall % performance was well above average.
Goals Missed Saved
Average 73% 7% 20%
Germany 92% 4% 4%
· The pace of the shots shows 3 slow shots taken (1 saved), 13 at 75% pace (all goals)
and 7 out of 8 scored at 100% power, 1 missed.
Goals Missed Saved
Average (75% power) 81% 1% 18%
Germany (75% power) 100% 0% 0%
Average (100% power) 63% 31% 7%
Germany (100% power) 88% 12% 0%
· Accuracy, this is critical – only 3 inaccurate shots, one of these scored. Surprisingly
they were very accurate at 100% power – this was the real difference between
Germany and England. The data suggest that this is probably the outcome of
considerable analysis, training and practice – see the average at 100% power.
Summary and Conclusions
It was a pleasant surprise to find that very clear messages emerged from this data set. The
‘competition within a competition’ enables a far simpler analysis than the game of soccer
itself.
The conclusions are summarised below.
One in five saved (20%; 3/15), one in fifteen missed (7%; 1/15) and three in four
scored (73%; 11/15).
Table 10. Respective frequency of the different paces of striking the ball with the
outcomes expressed as percentages.
Power of shot Frequency Goal Missed Saved
50% 12% 47% 0% 53%
75% 70% 81% 1% 18%
100% 18% 63 % 31% 7%
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Table 11. Relative percentages of the different types of approach runs to the ball, with the
different outcomes of the shots.
Pace Frequency Goal Missed Saved
Check step 4 3% 60 0 40
Slow 23 15% 61 4 35
Medium 57 50% 83 8.5 8.5
Fast 44 32% 68 7 25
25% saved off a fast run because the player then tried either 50% or 75% power.
Best success ratios are from an even run up of 4, 5 and 6 paces.
There is no laterality in the success ratios – left footed and right footed strikers have
the same success when the frequencies are represented as percentages.
Table 12. Schematic representation of the success ratios of shooting at the different areas
of the goal.
% Success Rates Goal
Upper 88 77 77 88
Lower 72 25 44 78
Left Right
The figures in the upper half include the shots that went over the bar – No shots
above waist height were saved.
In every case, the goalkeeper moved off the line before the ball was struck.
Although there is only a small data set, the goalkeepers who took a pace forward and
stood up while the striker approached the ball, had the best save and miss ratios.
Although there is only a small data set, the profile of Germany’s penalty takers show
a consistent pattern that is very different from the average, indicating analysis and
training.
These data analyses demonstrate that there are optimal strategies in taking and saving
penalties. These along with the goalkeeper research of Franks and Hanvey (1997) and
Savelsbergh et al. (2002), point to ways of enhancing the individual performance of the
players in these closed skills. Perhaps the coaches in this team sport will be helped by
methods used in individual sports such as golf and racket sports, where the emphasis is
on the attainment of expert technique.
References
Franks, I.M. and Hanvey, T. (1997). Cues for goalkeepers: high-techmethods used to
measure penalty shot response. Soccer Journal. 42, 30-33.
72
Savelsbergh, G.J.P., William, A. M., Van der Kamp, J. and Ward, P. (2002). Visual
search, anticipation and expertise in soccer goalkeepers. Journal of Sports
Sciences, 20 279-287.
... The first version of the new penalty kick analysis system was created, based on the collection of several variables from previous studies (Hughes and Wells, 2002;Timmis et al., 2014Timmis et al., , 2018Noël et al., 2015;Almeida et al., 2016;Comas et al., 2018). Characteristics were selected that are likely to distinguish the profile of successful or unsuccessful penalty kicks and strategies. ...
... The first version of the new penalty kick analysis system was created, based on the collection of several variables from previous studies (Hughes and Wells, 2002;Timmis et al., 2014Timmis et al., , 2018Noël et al., 2015;Almeida et al., 2016;Furley et al., 2017;Comas et al., 2018). Characteristics were selected that are likely to distinguish the profile of successful or unsuccessful penalty kicks and strategies. ...
Thesis
Full-text available
The present dissertation is a combination of publications that aim to expand the scientific and practical knowledge in performance analysis in elite football. Chapter one focused on the analysis of penalty kicks. The process of designing and validating a new instrument for analyzing penalty kicks in football (OSPAF) is described. Utilizing the expert-validated tool (i.e., OSPAF), an empirical study was carried out to distinguish the strategies adopted by the penalty taker and goalkeeper. Subsequently, a novel approach was adopted to automatically detect body angle orientation from video data (i.e., OpenPose), combined with the notational analysis proposed earlier. The developed system (OSPAF) evidenced content validity, inter-and intra-reliability for analyzing penalty kicks in football, using a gold standard methodology for instrument validation. Body orientation analysis using Openpose has shown sufficient reliability and provides practical applications for analyzing the strategies adopted by goalkeepers in penalty kicks in elite football. Chapter two addresses relevant aspects of performance analysis related to investigating the physical and physiological demands of different training tasks. The aim was to examine the differences in external and internal load during pre-season training sessions with different small-sided games (SSGs) and a friendly match in top-class professional football players. The present findings indicated that external and internal loads differ across different SSGs and a friendly match (FM) during the pre-season. Performance analysis is a discipline of sports science that presents a broad analytical approach. The results in chapters one and two provided practical applications for coaches and other football professionals and offered support for future research.
... According to the statistics, the odds of scoring are highest for shots with a power of 75%, while they are lowest for shots with a power of 50% and 100%. [9] ...
Research Proposal
Full-text available
Penalties are now a subject of mystery, romance, excitement, fear, and stress-better explained with the Spanish phrase "casarse de penalti." In order to tackle a real-world problem, like the banana shot, we constructed mathematical models to examine the trajectory of the ball during a penalty kick and obtained values for a number of factors. Whether you're shooting for the upper corner or somewhere else in the "Best Region," a speed between 110-140 km/h and a spin of 86.69 rad/s are determined. Our developed model directly supports the penalty statistics of better to score with 75% of shot power, aiming anywhere in the best region. 1
... Second, we examined the relationships between stress and laterality in understanding soccer penalty situations. Previous research has shown mixed results here (Baumann, Friehe, & Wedow, 2011;Hughes & Wells, 2002). Based on the negative perceptual frequency advantage, our hypothesis was that under normal conditions (like preliminary rounds), goalkeepers would be less successful against left-footed kickers than against right-footed players and that this effect would increase in more stressful situations (like play-off rounds), because goalkeepers' anticipation skills should decrease in situations that are less familiar (Hagemann, 2009;Loffing et al., 2012;McMorris & Colenso, 1996;Schorer et al., 2012). ...
... Also, Carey et al. (2001) argued a priori that relatively "rare" left-footed soccer players may have a strategic advantage in that a defender may be less prepared to duel against left-footed players and a structural advantage in that left-footedness may predispose the player toward a favored use of contralateral visuospatial networks of the right cerebral hemisphere during game-play events. However, in opposition to these theorized performance relationships with upper limb nondominance (Grouios, 2004), these assumptions may not always hold for lower limb dominance preferences (Carey et al., 2001;Hughes and Wells, 2002). Although additional physiological or anatomic factors may differentiate athletes who prefer to use right and left limbs (Grouios, 2004), they do not seem to meaningfully impact skill-related futsal behavior that was similar in our study for all players, irrespective of their lower limb dominance. ...
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... The first version of the new penalty kick analysis system was created, based on the collection of several variables from previous studies (Hughes and Wells, 2002;Timmis et al., 2014Timmis et al., , 2018Noël et al., 2015;Almeida et al., 2016;Furley et al., 2017;Comas et al., 2018). Characteristics were selected that are likely to distinguish the profile of successful or unsuccessful penalty kicks and strategies. ...
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... However, the interplay between the two players has received much less attention. An important impetus for the research seems that the statistics (approximate success rate 75%; Kropp and Trapp, 1999;Hughes and Wells, 2002;Morya et al., 2005) indicate that penalty takers convert fewer kicks than expected and, conversely, that goalkeepers still save a considerable number of kicks, while they ought to be without a chance. Yet interpretation of success rates is not straightforward. ...
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