Effects of score line on match performance in professional soccer players

Redwood-Brown, A ORCID: https://orcid.org/0000-0002-9198-0088, 2019. Effects of score line on match performance in professional soccer players. PhD, Nottingham Trent University.

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Abstract

Investigating the impact of score line on performance in professional soccer players is of major interest to managers and coaches within professional soccer. However, the majority of studies investigating score line have omitted important components of performance such as team and opposition ability, goal difference and fatigue. Little is also known, regarding the impact of score line on the performance of different playing positions in different areas of the pitch. Thus, the aim of this thesis was to examine the effects of score line on the technical, physical and psychological components of performance across a number of variables (team ability, opposition ability, match location, pitch position, playing position) using new methods of automated tracking and data collection. The majority of participants were recruited from the English Premier League consisting of 1027 players and 501 games analysed across multiple seasons (2004-2005, 2007-2008, 2011-2012). The exception to this was Chapter 5 where the 75 participants were recruited from both EPL and Championship teams. Across all chapters playing position was characterised as striker (attacker), defender and midfielder and ability (both team and opposition) was defined as final league finish position. Chapter 3 revealed in the 5 minutes that preceded a goal, the scoring team played a significantly greater percentage of passes accurately (72.4 ± 12.7) compared to the average for the half (70.2 ± 7.5) (P<0.017) while the conceding team played significantly fewer passes before (19.3 ± 8.4) compared to the average for the half (22.9 ± 4.3) (P<0.017). After the goal was scored, the scoring team played significantly fewer passes (21.5 ± 11.1) and a lower percentage of passes were played accurately (67.3 ± 14.7) than the average for the half of the match where the goal was scored (23.2 ± 5.2) (70.2 ± 7.5) (P<0.017). Chapter 4 established a typical fatigue pattern using data from 79 player performances during five 0-0 drawn English FA Premier League matches. This typical fatigue pattern was used to adjust the work-rate of 90 player performances in five English FA Premier League matches. There was a significant interaction between player position and score-line (p = .010) with forwards spending a greater percentage of time moving at 4 m.s-1 or faster when their team was leading than when level while defenders spent a greater percentage of time moving at 4 m.s-1 or faster when their team was trailing than when level. Chapter 5 provided insight into the key experiences of psychological momentum (PM) and the strategies associated with positive and negative momentum using both questionnaires and interviews. Scoring or conceding a goal was an important factor that affected players’ perceptions of positive and negative momentum, respectively. In addition, “feeling confident”, “having a positive attitude” and “being cohesive as a team” were important aspects of positive PM. A “perceived lack of ability” and “feeling anxious” were the most frequently reported experiences of negative PM. The majority of key responses reported in the interviews were supported by the questionnaire data. The similarity of results from both methods support the measure as a useful tool for coaches to collect data pertaining to players’ experiences and perceptions of PM. In order to investigate player movement in different score lines in Chapter 6, the validity of the Venatrack automated tracking system was tested. The system was compared to calibrated speed gates within a stadium environment. For all the runs combined the mean speed recorded by the automated system was 15.4 ± 5.5 km·h-1 compared with the recorded mean speed of 15.2 ± 5.4 km·h-1 and the mean difference and 95% limits of agreement were -0.25 ± 0.64 km·h-1. Pearson correlations (r) among timing gate speed and automated tracking speed were ≥ 0.99 (P<0.001), except the 20 m sprint, with 90° turn (r > 0.7). The results demonstrate good validity over a range of soccer specific movements and speeds, up to and including sprinting. In Chapter 7a multi-level regression revealed an inverted “u” shaped association between total distance covered and goal difference (GD), with greater distances covered when GD was zero and reduced distances when GD was either positive or negative. A similar “u” shaped association was found with high speed distance covered at home. In addition, distance covered (both at home and away) were predicted by playing position. All activity profiles (with the exception of sprint distance at home) were predicted by pitch location and time scored. Lastly, distance away from home and high speed running at home were predicted by opposition ability. In Chapter 7b multi-level regression revealed a “u” shaped association between passing accuracy and goal difference (GD) with greater accuracy occurring at extremes of GD e.g., when the score was either positive or negative. The same pattern was seen for corner accuracy away from home e.g., corner accuracy was lowest when the score was close with the lowest accuracy at extremes of GD. Although free-kicks were not associated with GD, team ability, playing position and pitch location were found to predict accuracy. No situational variables were found to predict cross accuracy. The results of this thesis suggest that a number of variables are associated with both the physical and technical performance of players in difference score lines and that such effects may be related to the perception of events (as shown in Chapter 5) rather than fatigue, ability or opposition as previously thought. The current study also highlighted the need for more sensitive score line definitions in which to consider score line effects using technological advancements such as automated tracking systems.

Item Type:	Thesis
Creators:	Redwood-Brown, A.
Date:	February 2019
Rights:	This work is the intellectual property of the author. You may copy up to 15% of this work for private study, or personal, non-commercial research. Any re-use of the information contained within this document should be fully referenced, quoting the author, title, university, degree level and pagination. Queries or request for any other use, or if more substantial copy is required, should be directed to the author.
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	17 Apr 2020 10:41
Last Modified:	17 Apr 2020 10:41
URI:	https://irep.ntu.ac.uk/id/eprint/39673

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