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Investigating the impact of the mid-season winter break on technical performance levels across European Football – Does a break in play affect team momentum?

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Using game-level data, this study examines what impact the mid-season winter break in football fixtures has on technical performance across European football leagues. 38 technical measures pertaining to the actions of passing and shooting are assessed for 3,494 team match observations from the German Bundesliga, Spanish La Liga, French Ligue 1 and English Premier League across 5 seasons from 2013/14 to 2017/18. Kruskal-Wallis One Way ANOVA’s were conducted to investigate the differences between three groups: PREPRE (4-6 fixtures prior to the break); PRE (1-3 fixtures prior to the break); and POST (1-3 fixtures after the break). Shooting performance declined significantly post winter break in the German Bundesliga (13/21 metrics) which had an average break of 32 days. Passing performance deteriorated significantly in the French Ligue 1 (4/17 metrics) which had an average break of 19 days. The Spanish La Liga had a 13 day break on average and remained unaffected as did the English Premier League which had no mid-season winter break. Evidence suggests that a mid-season winter break of less than 13 days will not affect technical performance levels but breaks that last longer can act as a catalyst that halt momentum and cause performances to deteriorate.
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This is an Accepted Manuscript of an article published in International Journal of Performance
Analysis in Sport on 04 May 2020, available online:
https://doi.org/10.1080/24748668.2020.1753980
Version: Accepted for publication
Publisher: © Taylor & Francis
Rights: This work is made available according to the conditions of the Creative Commons
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Please cite the published version.
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International Journal of Performance Analysis in Sport
Investigating the Impact of the Mid-Season Winter Break on
Technical Performance Levels across European Football Does a
Break in Play Affect Team Momentum?
M. Jamil, S.A. McErlain-Naylor, and M. Beato.
School of Health and Sports Sciences, University of Suffolk, Ipswich, IP4 1QJ, UK
ABSTRACT
Using game-level data, this study examines what impact the mid-season winter break in
football fixtures has on technical performance across European football leagues. 38
technical measures pertaining to the actions of passing and shooting are assessed for
3,494 team match observations from the German Bundesliga, Spanish La Liga, French
Ligue 1 and English Premier League across 5 seasons from 2013/14 to 2017/18. Kruskal-
Wallis One Way ANOVA’s were conducted to investigate the differences between three
groups: PREPRE (4-6 fixtures prior to the break); PRE (1-3 fixtures prior to the break); and
POST (1-3 fixtures after the break). Shooting performance declined significantly post winter
break in the German Bundesliga (13/21 metrics) which had an average break of 32 days.
Passing performance deteriorated significantly in the French Ligue 1 (4/17 metrics) which
had an average break of 19 days. The Spanish La Liga had a 13 day break on average
and remained unaffected as did the English Premier League which had no mid-season
winter break. Evidence suggests that a mid-season winter break of less than 13 days will
not affect technical performance levels but breaks that last longer can act as a catalyst that
halt momentum and cause performances to deteriorate.
Keywords: Soccer; Passing; Shooting; German Bundesliga; French Ligue 1; Spanish La
Liga; English Premier League
INTRODUCTION
In recent years the field of performance analysis in football has been the focus of
much research and interest in this field continues to grow (Lago, 2009; Mackenzie and
Cushion, 2013). Much of this research has found that performance metrics pertaining
to possession of the ball, successful passing and shooting are key determinants of
success in football. Various aspects of possession and the passing attribute have been
extensively reviewed such as, passing accuracy, passing range, longevity of passing
sequences, and recovery of possession (Carmichael, Thomas and Ward, 2000; Jones,
James and Mellalieu, 2004; Hughes and Churchill 2005; Lago-Penas, Lago-
Ballesteros, Dellal and Gomez, 2010; Vogelbein, Nopp and Hökelmann 2014, Almeida,
Ferreira and Volossovitch, 2014; Barreira, Garganta, Guimarães, MacHado and
Anguera, 2014; Hughes and Lovell, 2019; Jamil, 2019). The importance of effective,
accurate and frequent shooting has also been emphasised in previous research
(Carmichael et al., 2000; Hughes and Churchill, 2005; Lago 2007; Carmichael and
Thomas, 2008; Lago- Penas et al., 2010).
Something that has been generally overlooked is the potential impact of the mid-
season winter break on these technical aspects of performance in football. Although
there is some evidence to suggest a mid-season break is beneficial with regards to
physical recuperation and injury prevention (Faude, Kellman, Ammann, Schnittker and
Meyer, 2011; Funten, Faude, Lensch and Meyer, 2014; Ekstrand, Spreco and Davison,
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2018), there is little evidence of the impact on technical aspects of team performances
and in particular, team momentum built up throughout the early parts of the season.
As proposed by Vallerand, Colavecchio and Pelletier (1988) momentum begins
with a catalyst, which is followed by a sequence of events that result in a change in
performance. This definition is echoed by Taylor and Demick (1994) who explain
momentum as a multidimensional construct in which a precipitating event will set off a
chain of events, that ultimately lead to an eventual change in performance. Momentum
in sport is a concept that has been studied extensively and exists in two forms:
behavioural and psychological (Mortimer and Burt, 2014). Behavioural momentum
refers to observable actions that lead to measurable progress towards or away from a
successful outcome (Wanzek, Houlihan and Homan, 2012). Psychological momentum,
on the other hand, revolves around positive and negative perceptions of individual
athletes or teams moving towards or away from a successful outcome (Cotterill, 2013).
Previous research has been conducted on both concepts of momentum with
somewhat mixed results in team sports. In a study on hockey, Leard and Doyle (2011)
discovered the existence of a momentum effect and concluded that a two-game or
three-game winning streak would have a positive impact on the probability of winning.
On the contrary, Kniffin and Mihalek (2014), discovered no evidence of any momentum
effects in hockey and concluded that neither victory nor the margin of victory in a match
had any bearing on the outcome of the next match. Arkes and Martinez (2011)
investigated the existence of momentum in-between games in NBA basketball and
concluded that success in the previous 3-5 matches led to an increased probability of
winning the next match. Similarly, poor performances in previous matches led to a
decreased chance of winning the next match. Morgulev, Azar and Bar-Eli (2018) also
investigated momentum in NBA basketball and, contradictory to Arkes and Martinez
(2011), they discovered no momentum effects.
Previous research has also focussed upon short term breaks in play acting as
precipitating events that could potentially shift momentum. In the sport of volleyball,
Wanzek et al. (2012) investigated whether a called timeout disrupted momentum but
discovered that points scored post timeout were not affected. On the contrary, Gomez,
Jimenez, Navarro, Lago-Penas and Sampaio (2011) discovered that both offensive
and defensive performance levels enhanced post timeout in their study on basketball.
The purpose of this study is to examine momentum effects in football by
examining the potential change in passing and shooting performances between three
time periods (two before and one after the mid-season winter break). As stated by
Mackenzie and Cushion (2013) and Mitrotasios, Gonzalez-Rodenas, Armatas and
Aranda (2019), inter-league and inter-nation differences between technical
performance levels have been relatively overlooked in previous research as well as
several other aspects of match analysis. This particular concern will be addressed by
this study as technical performance levels will be examined individually across several
European football leagues, over a 5 season sample period, before and after their mid-
season winter break thereby identifying the presence of any momentum effects.
The mid-season winter break will be treated as a long-term timeout and therefore,
the model proposed by Taylor and Demick (1994) will be adapted by removing the
psychological aspects leaving a three stage model akin to that utilised by Mortimer and
Burt (2014). Our model therefore consists of three stages defined as a “trigger”
followed by a “change in behaviour”, followed by “the outcome”. For the purposes of
this study the “trigger” is defined as the start of the winter break. A mid-season winter
break in football typically consists of several days away (usually unsupervised)
followed by a return to training and build up to the first post winter break fixture. The
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lack of training whilst on break, the lack of supervision and the lack of competitive
fixtures is therefore defined as the “change in behaviour” with "the outcome” being the
subsequent change in performance upon the players return post break, which is to be
determined by the study.
METHODS
Experimental Design
Throughout the five season sample period (2013/14 to 2017/18), the German
Bundesliga, French Ligue 1 and Spanish La Liga all had mid-season winter breaks,
unlike the English Premier League (EPL) which had no mid-season winter break
allowing the latter to be used for comparison. A dataset consisting of 38 technical
performance metrics (tables 1.1 4.2) relating to the actions of passing and shooting
from teams performing in these European Leagues was compiled in order to allow an
investigation into the levels of performance 4-6 fixtures prior to the break (PREPRE);
1-3 fixtures prior to the break (PRE); and 1-3 fixtures after the break (POST).
Sample
Data sets were prepared which consisted of team match observations from the
German Bundesliga (GB; n = 810), Spanish La Liga (SLL; n = 892) and French Ligue
1 (FL; n = 892). The samples each ranged across 5 seasons between 2013-14 and
2017/18. Three groups were formed: PREPRE; PRE; and POST. PREPRE consisted
of 270 (GB), 300 (SLL) and 298 (FL) team, match observations, occurring between 4-
6 gameweeks (match days) prior to the start of the winter break. PRE consisted of 268
(GB), 292 (SLL) and 296 (FL) team, match observations occurring 1-3 gameweeks
prior to the start of the winter break. POST consisted of 272 (GB), 300 (SLL) and 298
(FL) team, match observations occurring 1-3 gameweeks after the end of the winter
break.
The three groups were formed with the intention of including three fixtures for
each team in each league, ensuring equally sized groups, however this was not
possible due to some fixtures being postponed and fixture scheduling (some
gameweeks consisting of less than the full quota of fixtures). The formation of equally
sized groups was further complicated by the participation of German and Spanish
teams in the FIFA Club World Cup championship (annually occurring in the second
and third week of December) which subsequently caused some of the fixture
rescheduling and postponements referred to above.
To further investigate what impact a mid-season winter break has on technical
performance, each of the European leagues assessed above were compared to the
English Premier League (EPL) (n = 900 team match observations), which during the
same sample period had no mid-season winter break. In order to ensure as much
consistency as possible, 9 consecutive rounds of fixtures (gameweeks) were selected
(in order: 3 representing PREPRE, 3 representing PRE and 3 representing POST).
Each of the three groups, PREPRE, PRE and POST consisted of 300 fixtures. To
ensure further consistency, these 9 selected gameweeks closely matched the same
time period as the samples for the European leagues they were compared against
(matches played from mid-November through to early January). Technical
performance data utilised in this study was provided by OPTA sports, renowned for
having a high degree of accuracy (Liu, Hopkins, Gómez and Molinuevo, 2013; Beato,
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Jamil and Devereux, 2018; Jamil, 2019). Tables 1.1 and 1.2 below present official
OPTA definitions for each of the metrics utilised in this study.
*** Tables 1.1 and 1.2 here ***
Statistical Analysis
Parametric assumption tests were conducted for each of the 38 technical
measures analysed throughout this study and assumption violations were discovered
meaning a non-parametric method was required. Consequently, Kruskal-Wallis One
Way ANOVA tests were conducted to test for differences in means between PREPRE,
PRE and POST for each of the 38 technical measures of performance analysed in this
study. Post-hoc tests consisting of pairwise comparisons were also conducted in order
to compare all different combinations of groups and identify differences in means
between them. A 95% (p < 0.05) significance value was set initially, with significance
values adjusted by the Bonferroni correction (Field 2014). Effect sizes, assessed as
Pearson’s r, were also calculated as they provide an objective measure of the
magnitude of an effect (Field 2014). The widely used thresholds for small (0.1 0.3),
medium (0.3 0.5) and large effects (> 0.5) set by Cohen (1992) were utilised in this
study.
RESULTS
Overall ANOVA results revealed significant effects for a total of thirteen technical
measures (out of twenty one) pertaining to the action of shooting in German Bundesliga
football (Table 1.3). Pairwise Comparisons (Table 1.3) revealed that eleven of these
thirteen significant differences in means were associated with at least one of the two
groups representing performance prior to the mid-season winter break (PREPRE and
PRE) and the group representing performance after the break (POST). A closer
analysis of the difference in means reveals that shooting accuracy and frequency
deteriorates after the mid-season winter break. Metrics pertaining to the technical
action of passing in the German Bundesliga were unaffected by the mid-season winter
break (Table 1.4).
*** Tables 1.3 and 1.4 here ***
Shooting was unaffected by the mid-season winter break in the French Ligue 1
(Table 2.1), however metrics pertaining to the technical action of passing revealed
significant differences in means between PRE and POST break performance (Table
2.2). Four out of seventeen measures pertaining to the technical action of passing were
revealed to be significant (initially 6 measures prior to Bonferroni correction). Similarly
to the Bundesliga shooting results, pairwise comparisons of mean differences between
groups in the French Ligue 1, revealed that passing performance deteriorated after the
mid-season winter break. All significant differences discovered in the German
Bundesliga and French Ligue 1 were revealed to have small effect sizes however this
is to be expected due to the multifaceted nature of football where a combination of
variables contribute to overall performance (Mackenzie and Cushion, 2013).
*** Tables 2.1 and 2.2 here ***
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No significant differences in means for either shooting or passing were
discovered in the Spanish La Liga (Tables 3.1 and 3.2). Similarly, no significant
differences in means for either shooting or passing were discovered in the English
Premier League (Tables 4.1 and 4.2).
*** Tables 3.1, 3.2, 4.1 and 4.2 here ***
DISCUSSION
The results reveal that the technical performance levels of professional football
players deteriorates post mid-season winter break but only in the German Bundesliga
and the French Ligue 1. The Spanish La Liga remains unaffected by the mid-season
winter break. Throughout the sample period, the average length in number of days for
the winter break in the German Bundesliga was 32 days (between the last fixture in the
PRE phase and the first fixture in the POST phase). In the French League 1, the length
of the winter break averaged 18.6 days, whereas the break only lasted 12.2 days on
average in the Spanish La Liga.
The results obtained suggest that in cases where the mid-season winter break
lasts longer than 13 days, the start of the break acts as a trigger that disrupts momentum
by leading to a change in behaviour, causing an ultimate change in performance.
Specifically, the change in performance precipitated by the mid-season winter break is
a deterioration of shooting frequency and accuracy in the German Bundesliga and
passing frequency and accuracy in the French Ligue 1. Furthermore, this deterioration
of shooting frequency and accuracy in the German Bundesliga and passing frequency
and accuracy in the French Ligue 1 both occur in attacking areas of the playing field
implying that attacking fluency is most affected by the winter breaks in these countries.
The lack of significant changes in means discovered from a parallel analysis
conducted on the English Premier League that has no mid-season winter break further
reinforces the notion that the winter break, particularly if greater in length than 13 days,
disrupts performances and leads to an eventual decline in technical performance levels
upon players return post break.
A closer look at the pairwise comparisons results reveals that almost all significant
differences in means were discovered between the PREPRE - POST phase and/or the
PRE - POST phase or both. Pairwise comparisons revealed that there were only a
minority of significant differences in means between the PREPRE and PRE phases
(which both occur prior to the mid-season winter break), lending further support to the
argument that the mid-season winter break acts as a catalyst/precipitating event and is
partly responsible for the decline in technical performance levels discovered in this study
in the top divisions of both, German and French football.
The fact that performance deteriorates during the POST phase after the winter
break could be explained by a number of reasons, such as a lack of training, an
unfavourable diet or a lack of physical conditioning post winter break. In a study on
Rugby players, Jensen, Gleason and VanNess (2018) discovered that a winter break of
greater than 4 weeks resulted in a change in some of the players physical shape.
Specifically, Jensen et al. (2018) discovered that the body mass of players increased
largely due to an increase in body fat percentage. Jensen et al. (2018) concluded that
although rugby players do not necessarily need to be prescribed exercise routines
during the mid-season break they would benefit from some structured nutritional advice.
Furthermore, In a study on women’s hockey Jones and McGregor (2010) discovered
that general fitness levels dipped after their midseason Christmas break, which the
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authors attributed to poor weather prohibiting athletes from exercising/training and
potentially the athletes’ poor adherence to their personal training regimes. In a study on
English Premiership Academy teams, Moore, Cloke, Avery, Beasley and Deehan (2011)
discovered that the peak injury period for academy players was post mid-season winter
break which the authors attributed to a lack of adequate physical conditioning when
players returned from the break.
From a practical perspective the results obtained in this study offer some insight
to the governing bodies of German and French football (DFB and FFF) with regards to
the quality of football played post winter break and they could explore shortening their
mid-season winter breaks in order to enhance this quality (weather and broadcasting
contracts permitting). These results also offer some guidance to the governing bodies
of other nations around the world that are yet to formally introduce a mid-season winter
break, to ensure their break is less than 13 days in length. From a coaching perspective,
the results obtained in this study highlight the lack of player sharpness when returning
post break in Germany and France, suggesting the need for greater supervision during
the break and perhaps more intensive training and conditioning prior to the first few
fixtures played post break.
Previous studies have revealed physical benefits of a mid-season winter break
thus future studies should focus on whether the deterioration in technical performance
and the subsequent loss of momentum caused by these mid-season winter breaks is
offset by their previously proven physical benefits. Future studies could also investigate
the optimal balance between ensuring high levels of technical performance whilst also
receiving physical benefits of mid-season winter breaks. Furthermore, this study
revealed shooting performance deteriorates in the German Bundesliga and passing
performance deteriorates in the French Ligue 1. It is beyond the scope of this present
study to ascertain why one technical action was affected and not the other in each
nation, however this could be investigated in follow-up studies.
CONCLUSION
The results obtained from this study revealed that technical performance levels of
professional football players performing in the German Bundesliga and French Ligue 1
are negatively affected by the mid-season winter break. Specifically, the frequency and
accuracy of crucial actions such as shooting and passing are significantly affected by
the mid-season winter break. The results of this study also suggest that the longer this
mid-season winter break the greater the level of deterioration in technical performance
levels. More specifically, if a mid-season winter break is longer in duration than 13 days,
then it appears to act as a catalyst/precipitating event which ultimately contribute
towards negative momentum effects in European football.
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Table 1.1 Variable Definitions List for Shooting Metrics
(1) Shots on target including goals
Any goal or goal attempt that:
- Goes into the net regardless of intent.
- Is a clear attempt to score that would have gone into the net but for being saved by the goalkeeper or is stopped
by a player who is the last-man with the goalkeeper having no chance of preventing the goal (last line block).
(2) Shots off target including
woodwork
Any clear attempt to score that:
- Goes over or wide of the goal without making contact with another player.
- Would have gone over or wide of the goal but for being stopped by a goalkeeper's save or by an outfield player.
- Directly hits the frame of the goal and a goal is not scored.
(3) Direct free Kick on target
Direct free kick shots created directly from the free kick itself (unassisted) and (1)
(4) Direct free Kick off target
Direct free kick shots created directly from the free kick itself (unassisted) and (2)
(5) Shots on From Inside Box
(1) from inside the 18-yard box
(6) Shots off From Inside Box
(2) from outside the 18-yard box
(7) Goals from inside box
Number of goals scored from inside the 18-yard box
(8) Goals from outside box
Number of goals scored from outside the 18-yard box
(9) Shots on Target Outside Box
(1) from outside the 18-yard box
(10) Right foot shots on target
(1) attempted with the right foot
(11) Left foot shots on target
(1) attempted with the left foot
(12) Goals conceded inside box
Number of goals conceded from inside the 18-yard box
(13) Goals conceded outside box
Number of goals conceded from outside the 18-yard box
(14) Shots on conceded
Number of shots on target (1) conceded
(15) Shots on conceded inside box
Number of shots on target (1) conceded from inside the 18-yard box
(16) Shots on conceded outside
box
Number of shots on target (1) conceded from outside the 18-yard box
(17) Total Shots Conceded
Total number of shots (1) and (2) conceded
(18) Right Foot Shots
Total number of shots attempted with the right foot
(19) Left Foot Shots
Total number of shots attempted with the left foot
(20) Shooting accuracy right foot
A calculation of shots on target divided by all shots (excluding blocked attempts and own goals) (right foot only)
(21) Shooting accuracy left foot
A calculation of shots on target divided by all shots (excluding blocked attempts and own goals) (left foot only)
Table 1.2 Variable Definitions List for Passing Metrics
(1) Total successful passes excluding
crosses and corners
Any intentional played ball from one player to another (successfully received by the intended recipient
without a touch from an opposing player). Passes include open play passes, goal kicks and free kicks played as
a pass.
(2) Total unsuccessful passes excluding
crosses and corners
Any intentional played ball from one player to another (unsuccessfully received by the intended recipient).
Passes include open play passes, goal kicks and free kicks played as a pass.
(3) Successful passes own half
(1) played in a subject team’s own half
(4) Unsuccessful passes own half
(2) played in a subject team’s own half
(5) Successful passes opposition half
(1) played in an opposing team’s half
(6) Unsuccessful passes opposition half
(2) played in an opposing team’s half
(7) Successful passes defensive third
(1) played in a subject team’s defensive third
(8) Unsuccessful passes defensive third
(2) played in a subject team’s defensive third
(9) Successful passes middle third
(1) played in a subject team’s middle third
(10) Unsuccessful passes middle third
(2) played in a subject team’s middle third
(11) Successful passes final third
(1) played in a subject team’s final third
(12) Unsuccessful passes final third
(2) played in a subject team’s final third
(13) Successful short passes
(1) under 32 metres in distance
(14) Unsuccessful short passes
(2) under 32 metres in distance
(15) Successful long passes
(1) over 32 metres in distance
(16) Unsuccessful long passes
(2) over 32 metres in distance
(17) Through Ball
Ball played through for player making an attacking run to create a chance on goal
Table 1.3 Technical measures pertaining to shooting assessed in the German Bundesliga
Technical Measure
H
P - value
Pairwise difference
Mean Analysis
Direction (Mean Rank Values)
P value
(post-hoc)
Effect Size (r)
Shots on target including goals
12.368
0.002**
PREPRE POST
Decrease post break (437 368)
0.002**
0.149
Shots off target including woodwork
1.699
0.428+
-
-
-
-
Direct free Kick on target
5.517
0.063+
-
-
-
-
Direct free Kick off target
9.375
0.009**
PREPRE POST
Decrease post break (427 383)
0.007**
0.132
Shots on From Inside Box
9.650
0.008**
PREPRE - POST
Decrease post break (423 370)
0.023*
0.115
PRE - POST
Decrease post break (424 370)
0.021*
0.116
Shots off From Inside Box
0.761
0.684+
-
-
-
-
Goals from inside box
3.396
0.183+
-
-
-
-
Goals from outside box
6.482
0.039*
PREPRE - PRE
Decrease pre break (423 390)
0.034*
0.109
Shots on Target Outside Box
8.011
0.018*
PREPRE - PRE
Decrease pre break (437 389)
0.038*
0.108
PREPRE - POST
Decrease post break (437 391)
0.048*
0.103
Right foot shots on target
7.997
0.018*
PREPRE - POST
Decrease post break (431 375)
0.016*
0.12
Left foot shots on target
5.982
0.05+
-
-
-
-
Goals conceded inside box
3.396
0.183+
-
-
-
-
Goals conceded outside box
6.482
0.039*
PREPRE PRE
Decrease post break (423 390)
0.034*
0.109
Shots on conceded
12.724
0.002**
PREPRE POST
Decrease post break (438 367)
0.001**
0.152
Shots on conceded inside box
9.671
0.008**
PREPRE POST
Decrease post break (424 370)
0.019*
0.118
PRE - POST
Decrease post break (423 370)
0.025*
0.114
Shots on conceded outside box
8.011
0.018*
PREPRE - PRE
Decrease post break (437 389)
0.038*
0.108
PREPRE - POST
Decrease post break (437 391)
0.048*
0.103
Total Shots Conceded
9.482
0.009**
PREPRE POST
Decrease post break (436 375)
0.006**
0.132
Right Foot Shots
3.089
0.213+
-
-
-
-
Left Foot Shots
12.622
0.002**
PREPRE POST
Decrease post break (442 371)
0.001**
0.152
Shooting accuracy right foot
7.895
0.019*
PREPRE POST
Decrease post break (428 374)
0.021*
0.116
Shooting accuracy left foot
0.485
0.785+
-
-
-
-
PREPRE = 4-6 fixtures prior to the winter break, PRE = 1-3 fixtures prior to the winter break, POST = 1-3 fixtures after the winter break
** = Significant at 99% CI, * = Significant at 95% CI, + = Insignificant
Mean rank figures are displayed to nearest whole number
Table 1.4 Technical measures pertaining to passing assessed in the German Bundesliga
Technical Measure
H
P - value
Pairwise difference
Mean Analysis
Direction (Mean Rank Values)
P value
(post-hoc)
Effect Size (r)
Total successful passes excl crosses and corners
0.368
0.832+
-
-
-
-
Total unsuccessful passes excl crosses and corners
3.441
0.179+
-
-
-
-
Successful passes own half
0.936
0.626+
-
-
-
-
Unsuccessful passes own half
0.818
0.664+
-
-
-
-
Successful passes opposition half
0.504
0.777+
-
-
-
-
Unsuccessful passes opposition half
5.864
0.053+
-
-
-
-
Successful passes defensive third
2.390
0.303+
-
-
-
-
Unsuccessful passes defensive third
2.826
0.243+
-
-
-
-
Successful passes middle third
0.643
0.725+
-
-
-
-
Unsuccessful passes middle third
1.831
0.400+
-
-
-
-
Successful passes final third
0.404
0.817+
-
-
-
-
Unsuccessful passes final third
3.018
0.221+
-
-
-
-
Successful short passes
0.440
0.817+
-
-
-
-
Unsuccessful short passes
2.667
0.264+
-
-
-
-
Successful long passes
3.167
0.205+
-
-
-
-
Unsuccessful long passes
2.407
0.300+
-
-
-
-
Through Ball
4.272
0.118+
-
-
-
-
PREPRE = 4-6 fixtures prior to the winter break, PRE = 1-3 fixtures prior to the winter break, POST = 1-3 fixtures after the winter break
** = Significant at 99% CI, * = Significant at 95% CI, + = Insignificant
Table 2.1 Technical measures pertaining to shooting assessed in the French Ligue 1
Technical Measure
H
P - value
Pairwise difference
Mean Analysis
Direction (Mean Rank Values)
P value
(post-hoc)
Effect Size (r)
Shots on target including goals
0.858
0.651+
-
-
-
-
Shots off target including woodwork
0.418
0.812+
-
-
-
-
Direct free Kick on target
0.471
0.790+
-
-
-
-
Direct free Kick off target
1.455
0.483+
-
-
-
-
Shots on From Inside Box
0.221
0.895+
-
-
-
-
Shots off From Inside Box
1.253
0.534+
-
-
-
-
Goals from inside box
1.844
0.398+
-
-
-
-
Goals from outside box
1.744
0.418+
-
-
-
-
Shots on Target Outside Box
2.608
0.272+
-
-
-
-
Right foot shots on target
0.521
0.771+
-
-
-
-
Left foot shots on target
1.211
0.546+
-
-
-
-
Goals conceded inside box
1.844
0.398+
-
-
-
-
Goals conceded outside box
1.744
0.418+
-
-
-
-
Shots on conceded
0.588
0.745+
-
-
-
-
Shots on conceded inside box
0.070
0.965+
-
-
-
-
Shots on conceded outside box
2.608
0.272+
-
-
-
-
Total Shots Conceded
0.992
0.609+
-
-
-
-
Right Foot Shots
0.230
0.892+
-
-
-
-
Left Foot Shots
4.735
0.094+
-
-
-
-
Shooting accuracy right foot
1.679
0.432+
-
-
-
-
Shooting accuracy left foot
1.729
0.421+
-
-
-
-
PREPRE = 4-6 fixtures prior to the winter break, PRE = 1-3 fixtures prior to the winter break, POST = 1-3 fixtures after the winter break
** = Significant at 99% CI, * = Significant at 95% CI, + = Insignificant
Table 2.2 Technical measures pertaining to passing assessed in the French Ligue 1
Technical Measure
H
P - value
Pairwise difference
Mean Analysis
Direction (Mean Rank Values)
P value
(post-hoc)
Effect Size (r)
Total successful passes excl crosses and corners
0.484
0.785
-
-
-
-
Total unsuccessful passes excl crosses and
corners
7.711
0.021*
PRE POST
Increase post break (415 473)
0.018*
0.113
Successful passes own half
0.963
0.618+
-
-
-
-
Unsuccessful passes own half
6.386
0.041*
PRE - POST
Increase post break (429 477)
0.069+
-
Successful passes opposition half
0.391
0.822+
-
-
-
-
Unsuccessful passes opposition half
6.901
0.032*
PRE POST
Increase post break (415 469)
0.034*
0.104
Successful passes defensive third
1.826
0.401+
-
-
-
-
Unsuccessful passes defensive third
5.179
0.075+
-
-
-
-
Successful passes middle third
0.609
0.737+
-
-
-
-
Unsuccessful passes middle third
6.985
0.030*
PRE POST
Increase post break (419 475)
0.025*
0.108
Successful passes final third
0.944
0.624+
-
-
-
-
Unsuccessful passes final third
1.502
0.472+
-
-
-
-
Successful short passes
0.506
0.777+
Unsuccessful short passes
10.902
0.004**
PRE POST
Increase post break (410 480)
0.003**
0.136
Successful long passes
1.097
0.578+
-
-
-
-
Unsuccessful long passes
0.987
0.611+
-
-
-
-
Through Ball
6.177
0.046*
PREPRE POST
Decrease post break (462 421)
0.067+
-
PREPRE = 4-6 fixtures prior to the winter break, PRE = 1-3 fixtures prior to the winter break, POST = 1-3 fixtures after the winter break
** = Significant at 99% CI, * = Significant at 95% CI, + = Insignificant
Mean rank figures are displayed to nearest whole number
Table 3.1 Technical measures pertaining to shooting assessed in the Spanish La Liga
Technical Measure
H
P - value
Pairwise difference
Mean Analysis
Direction (Mean Rank Values)
P value
(post-hoc)
Effect Size (r)
Shots on target including goals
5.211
0.074+
-
-
-
-
Shots off target including woodwork
0.402
0.818+
-
-
-
-
Direct free Kick on target
2.441
0.295+
-
-
-
-
Direct free Kick off target
4.453
0.108+
-
-
-
-
Shots on From Inside Box
3.537
0.171+
-
-
-
-
Shots off From Inside Box
0.804
0.669+
-
-
-
-
Goals from inside box
0.588
0.745+
-
-
-
-
Goals from outside box
0.874
0.646+
-
-
-
-
Shots on Target Outside Box
2.146
0.342+
-
-
-
-
Right foot shots on target
6.792
0.034*
PREPRE - PRE
Decrease pre break (463 415)
0.065+
-
Left foot shots on target
3.344
0.188+
-
-
-
-
Goals conceded inside box
0.722
0.697+
-
-
-
-
Goals conceded outside box
0.464
0.793+
-
-
-
-
Shots on conceded
5.400
0.067+
-
-
-
-
Shots on conceded inside box
3.711
0.156+
-
-
-
-
Shots on conceded outside box
2.146
0.342+
-
-
-
-
Total Shots Conceded
0.735
0.693+
-
-
-
-
Right Foot Shots
0.456
0.796+
-
-
-
-
Left Foot Shots
0.847
0.655+
-
-
-
-
Shooting accuracy right foot
3.973
0.137+
-
-
-
-
Shooting accuracy left foot
1.856
0.395+
-
-
-
-
PREPRE = 4-6 fixtures prior to the winter break, PRE = 1-3 fixtures prior to the winter break, POST = 1-3 fixtures after the winter break
** = Significant at 99% CI, * = Significant at 95% CI, + = Insignificant
Mean rank figures are displayed to nearest whole number
Table 3.2 Technical measures pertaining to passing assessed in the Spanish La Liga
Technical Measure
H
P - value
Pairwise difference
Mean Analysis
Direction (Mean Rank Values)
P value
(post-hoc)
Effect Size (r)
Total successful passes excl crosses and corners
0.664
0.717+
-
-
-
-
Total unsuccessful passes excl crosses and
corners
0.061
0.970+
-
-
-
-
Successful passes own half
1.552
0.460+
-
-
-
-
Unsuccessful passes own half
4.191
0.123+
-
-
-
-
Successful passes opposition half
0.394
0.821+
-
-
-
-
Unsuccessful passes opposition half
0.491
0.782+
-
-
-
-
Successful passes defensive third
1.576
0.455+
-
-
-
-
Unsuccessful passes defensive third
5.721
0.057+
-
-
-
-
Successful passes middle third
0.571
0.751+
-
-
-
-
Unsuccessful passes middle third
0.005
0.998+
-
-
-
-
Successful passes final third
1.555
0.460+
-
-
-
-
Unsuccessful passes final third
0.070
0.966+
-
-
-
-
Successful short passes
0.810
0.667+
-
-
-
-
Unsuccessful short passes
0.816
0.665+
-
-
-
-
Successful long passes
0.943
0.624+
-
-
-
-
Unsuccessful long passes
0.163
0.922+
-
-
-
-
Through Ball
0.729
0.695+
-
-
-
-
PREPRE = 4-6 fixtures prior to the winter break, PRE = 1-3 fixtures prior to the winter break, POST = 1-3 fixtures after the winter break
** = Significant at 99% CI, * = Significant at 95% CI, + = Insignificant
Table 4.1 Technical measures pertaining to shooting assessed in the English Premier League
Technical Measure
H
P - value
Pairwise difference
Mean Analysis
Direction (Mean Rank Values)
P value
(post-hoc)
Effect Size (r)
Shots on target including goals
0.249
0.883+
-
-
-
-
Shots off target including woodwork
2.137
0.343+
-
-
-
-
Direct free Kick on target
0.904
0.636+
-
-
-
-
Direct free Kick off target
1.427
0.477+
-
-
-
-
Shots on From Inside Box
1.233
0.540+
-
-
-
-
Shots off From Inside Box
2.111
0.348+
-
-
-
-
Goals from inside box
0.447
0.800+
-
-
-
-
Goals from outside box
0.755
0.686+
-
-
-
-
Shots on Target Outside Box
1.279
0.528+
-
-
-
-
Right foot shots on target
0.253
0.881+
-
-
-
-
Left foot shots on target
0.395
0.821+
-
-
-
-
Goals conceded inside box
0.447
0.800+
-
-
-
-
Goals conceded outside box
0.755
0.686+
-
-
-
-
Shots on conceded
0.110
0.946+
-
-
-
-
Shots on conceded inside box
0.863
0.650+
-
-
-
-
Shots on conceded outside box
1.279
0.528+
-
-
-
-
Total Shots Conceded
0.460
0.795+
-
-
-
-
Right Foot Shots
0.519
0.771+
-
-
-
-
Left Foot Shots
1.912
0.384+
-
-
-
-
Shooting accuracy right foot
0.516
0.773+
-
-
-
-
Shooting accuracy left foot
0.517
0.772+
-
-
-
-
PREPRE = 4-6 fixtures prior to the winter break, PRE = 1-3 fixtures prior to the winter break, POST = 1-3 fixtures after the winter break
** = Significant at 99% CI, * = Significant at 95% CI, + = Insignificant
Table 4.2 Technical measures pertaining to passing assessed in the English Premier League
Technical Measure
H
P - value
Pairwise difference
Mean Analysis
Direction (Mean Rank Values)
P value
(post-hoc)
Effect Size (r)
Total successful passes excl crosses and corners
1.011
0.772+
-
-
-
-
Total unsuccessful passes excl crosses and
corners
0.604
0.739+
-
-
-
-
Successful passes own half
2.385
0.303+
-
-
-
-
Unsuccessful passes own half
0.955
0.620+
-
-
-
-
Successful passes opposition half
0.513
0.774+
-
-
-
-
Unsuccessful passes opposition half
0.083
0.959+
-
-
-
-
Successful passes defensive third
4.313
0.116+
-
-
-
-
Unsuccessful passes defensive third
0.326
0.850+
-
-
-
-
Successful passes middle third
1.023
0.600+
-
-
-
-
Unsuccessful passes middle third
0.867
0.648+
-
-
-
-
Successful passes final third
1.011
0.603+
-
-
-
-
Unsuccessful passes final third
0.677
0.713+
-
-
-
-
Successful short passes
0.970
0.616+
-
-
-
-
Unsuccessful short passes
0.322
0.851+
-
-
-
-
Successful long passes
0.085
0.958+
-
-
-
-
Unsuccessful long passes
1.089
0.580+
-
-
-
-
Through Ball
2.746
0.253+
-
-
-
-
PREPRE = 4-6 fixtures prior to the winter break, PRE = 1-3 fixtures prior to the winter break, POST = 1-3 fixtures after the winter break
** = Significant at 99% CI, * = Significant at 95% CI, + = Insignificant
... Something that has been relatively overlooked in the previous research, however, is the technical development of players and the physical and physiological demands of football in the specific nation and league it is played in. Previous research on peak performance ages in football has tended to collate information from several European football teams, but football is practised differently in every country due to various reasons such as differences in the technical skill levels of players, tactics, the quality of coaching, individual player development, as well as historical, social and cultural aspects of each country, the influence of which vary nation to nation (Gai et al., 2019;Jamil, McErlain-Naylor et al., 2020;Mitrotasios et al., 2019;Sarmento et al., 2013). In a recent study, inter-league and inter-nation variations were discovered between strategies adopted to score penalty kicks, emphasising the varying tactical approaches exhibited in different footballing leagues and nations (Jamil, Littman et al., 2020). ...
... In this retrospective case study, technical performance data was utilised as it has been previously suggested that technical variables can be more informative than physical parameters when conducting research in football (Liu et al., 2016). Performance data utilised in this study were provided by Opta sports, renowned for having a high degree of accuracy (Jamil, 2019;Jamil, McErlain-Naylor et al., 2020;Liu et al., 2013). ...
... Variables for each position were identified by previous literature (Hughes et al., 2012;Jamil, in press;Jamil, McErlain-Naylor et al., 2020;Liu et al., 2016Liu et al., , 2013Oberstone, 2010;Zhou et al., 2018) and therefore consisted of 13 variables for goalkeepers, 14 variables for full-backs, 11 variables for central defenders, 16 variables for central midfielders, 18 variables for wingers, and 8 variables for forward players. Table 1 presents a list of variable definitions; all definitions were obtained from either the official Opta F24 appendices or the Opta website*. ...
Article
Seasonal statistics for 637 professional football players performing in the English Premier League (EPL) across 3 intermittent seasons were analysed via a series of Kruskal-Wallis tests in order to determine the most productive (peak) years of players' careers. Contrary to previous research, results revealed that age had no bearing on the technical performance levels of goalkeepers, fullbacks , central defenders or central midfielders performing in the EPL. Wingers aged between 16-20 and 21-25 have significantly more shots on target (p = 0.022, p = 0.040) and more attempts from open play (p = 0.012, p = 0.028) than wingers over the age of 26. Results also revealed that strikers aged between 21 and 25 are more adept at executing specific attacking actions such as scoring goals from outside the box (p = 0.024) and shooting on target from outside the box (p = 0.021) than older strikers aged between 26 and 30. Evidence is discovered proving that ageing trends are present but not uniform across the sport of football. The authors conclude that further league specific case studies are required in order to identify the unique characteristics and peculiarities of foreign leagues enabling a more objective approach to recruitment decisions and individualised coaching plans.
... Recent years have seen much research focussed on factors that impact player performance in football with some research focussing upon the physical aspects of player performance (Andrzejewski et al., 2013;Di Salvo et al., 2009), technical aspects of player performance (Fernandez-Navarro et al., 2016;Jamil, McErlain-Naylor, et al., 2020;Liu et al., 2013), or both physical and technical parameters (Bush et al., 2015;Zhou et al., 2018). It has been widely acknowledged that performance in football is influenced by a combination of physiological, psychological, tactical and technical variables (Hughes et al., 2012) demonstrating the multifaceted nature of performances in football where many parameters contribute to success (Mackenzie & Cushion, 2013). ...
... With regards to technical variables previous literature has emphasised the importance of defensive actions (Hughes et al., 2012;Lago-Peñas et al., 2010), as well as accurate passing and shooting (Collet, 2013;Hughes et al., 2012). Metrics pertaining to the actions of passing and shooting in particular have received much attention in recent years (Hughes et al., 2012;Jamil, McErlain-Naylor, et al., 2020;Lago-Peñas et al., 2010;Liu et al., 2015Liu et al., , 2016Zhou et al., 2018) and there is now a substantial body of research indicating technical skills such as passing, ball control, dribbling and shooting are the most prominent aspects of performance that are evaluated by coaches and scouts in talent identification systems when distinguishing between the skilled and less-skilled youth football players (Larkin & Reeves, 2018;Sarmento et al., 2018). ...
... Something that has been relatively overlooked in previous research however is what influence geographical and their associated cultural factors can have on players technical abilities. Football is practiced differently in every country due to various reasons such as, differences in the technical skill levels of players, playing tactics, physical abilities, the quality of coaching and the socio-cultural aspects of each country football is played in, the influence of which vary nation to nation (Jamil, McErlain-Naylor, et al., 2020;Mitrotasios et al., 2019;Sarmento et al., 2013). Playing styles of football players can be influenced by their country of origin (Bush et al., 2015;Gai et al., 2019) meaning these geographical factors can influence and characterise the technical profiles of professional players and guide recruitment policies. ...
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Opta seasonal sum total statistics for 1,533 professional football players representing 88 nations around the world were analysed in order to determine where the most proficient technical football players originate from. A series of Kruskal-Wallis tests were conducted and results revealed that South American players were significantly better at scoring the first goal (p = .044), scoring penalties (p = .034) and attempting shots (p = .018) than their European counterparts. Both European and South American players were revealed to be more adept at passing actions than their African, Asian or North American counterparts. Both South American and African players committed significantly more errors than their European, Asian or North American counterparts, with South American players more frequently apprehended by the referee than Asian players (p = .031), European players (p = .020) and North American players (p = .034). African players were revealed to be caught offside (p = .039) as well as have more unsuccessful ball touches (p = .001) and be dispossessed (p = .036) significantly more often than European players. It is concluded that a player’s geographical origin can impact their technical proficiency in football and this theme needs further investigation. Keywords: Opta; Player performance; Technical performance; Soccer; Player nationality; Performance analysis, Match analysis.
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The theory of momentum is contentious within performance analysis in sport and has been subject to much academic investigation; with many researchers exploring the scale of its existence. Within handball, previous research has focused predominantly on psychological momentum with no statistical analysis of behavioural momentum, as found in research into basketball and other sports. In this study, a method derived from Taylor and Demick (1994) was used to investigate the existence and effect of behavioural momentum in 45 elite handball matches. Results highlighted momentum as being present within elite handball, and having a positive effect on match outcome in 86% of the matches analysed. Further assessment of the results indicated turnovers in possession, at stage two of the three-stage momentum model, to be a key performance indicator. It is hoped this study can be used to develop future research that incorporates a more complex analysis of the existence and effect of momentum in elite sport.
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Context: The winter break in the top 2 German professional soccer leagues was shortened from 6.5 to 3.5 weeks in the 2009-2010 season. Objective: To investigate whether this change affected injury characteristics by comparing the second half of the 2008-2009 (long winter break) with the equivalent period in the 2009-2010 season (short winter break). Design: Prospective cohort study. Setting: German male professional soccer leagues. Patients or other participants: Seven professional German male soccer teams (184 players in the 2008-2009 season, 188 players in the 2009-2010 season). Main outcome measure(s): Injury incidences and injury characteristics (cause of injury, location, severity, type, diagnosis), including their monthly distribution, were recorded. Results: A total of 300 time-loss injuries (2008-2009 n = 151, 2009-2010 n = 149) occurred. The overall injury incidence per 1000 soccer hours was 5.90 (95% confidence interval = 5.03, 6.82) in 2008-2009 and 6.55 (5.58, 7.69) in 2009-2010. Match injuries per 1000 hours were 31.5 (25.0, 38.0) in the first season and 26.5 (20.2, 32.7) in the second season; the corresponding training values were 2.67 (2.08-3.44) and 3.98 (3.19-4.95), respectively. The training injury incidence (incidence rate ratio = 1.49 [95% confidence interval = 1.07, 2.08], P = .02) and the risk of sustaining a knee injury (incidence rate ratio = 1.66 [1.00, 2.76], P = .049) were higher in 2009-2010 after the short winter break; the incidence of moderate and severe injuries (time loss >7 days) trended higher (incidence rate ratio = 1.34 [0.96, 1.86], P = .09). Conclusions: Shortening the winter break from 6.5 to 3.5 weeks did not change the overall injury incidence; however, a higher number of training, knee, and possibly more severe injuries (time loss >7 days) occurred.