Research Article: Effects of playing position, pitch location, opposition ability and team ability on the technical performance of elite soccer players in different score line states

Date Published: February 5, 2019

Publisher: Public Library of Science

Author(s): Athalie J. Redwood-Brown, Peter G. O’Donoghue, Alan M. Nevill, Chris Saward, Caroline Sunderland, Anthony C. Constantinou.


The purpose of this study was to investigate the effects of playing position, pitch location, team ability and opposition ability on technical performance variables (pass, cross, corner, free kick accuracy) of English Premier League Soccer players in difference score line states. A validated automatic tracking system (Venatrack) was used to code player actions in real time for passing accuracy, cross accuracy, corner accuracy and free kick accuracy. In total 376 of the 380 games played during the 2011–12 English premier League season were recorded, resulting in activity profiles of 570 players and over 35’000 rows of data. These data were analysed using multi-level modelling. 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 temporal variables were found to predict cross accuracy. A number of score line effects were present across the temporal factors which should be considered by coaches and managers when preparing and selecting teams in order to maximise performance. The current study highlighted the need for more sensitive score line definitions in which to consider score line effects.

Partial Text

There has been much speculation about the influence of score line (i.e. scoring and conceding goals and/or whether a team is winning, drawing or losing) on player performance [1,2,3,4,5]. Such speculation has motivated academic researchers [1,2,3,4,5] to ascertain the influence of score line on different aspects of sports performance. Score line is generally defined as winning, losing or drawing state, however more recently smaller data sets (e.g. World Cup Tournaments) have included specific score lines or goal differences (GD) (e.g., 1:0, 1:1, 2:0 etc.) in an attempt to understand how the size of the lead or deficit affects player performance [6,7].

A total of 570 players across 376 games were analysed, with the maximum number of appearances from one player being 38 and the minimum being 1 game. Table 1 presents the technical performance for each of the teams included in the analysis across the three match statuses (winning, drawing, losing). The average passing accuracy per player per games was 73.6 ± 5.5% per game. With regards to corners, crosses and free kicks players performed on average 19.7 ± 2.6%, 45.4 ± 8.3% and 63.9 ± 12.1% accuracy respectively.

The aim of the present study was to investigate the effect of playing position, pitch location, team ability and opposition ability on the technical performance of English Premier League players across various goal differences (GD). In support of previous research [2,5,7,9,23], the results suggested that passing accuracy (when playing both at home and away) and corner accuracy (when playing away from home) changed systematically in relation to the goal difference (e.g., winning: +3, +2, +1, drawing: 0, or losing: -1, -2, -3 etc.) in a non-linear manner. Specifically, significant difference between matches, specifically teams showed higher passing accuracies in extremes of GD (e.g., -5 and +5) and the lowest when winning by only a smaller number of goals (e.g., +1 to +3). On the other hand teams were found to have the lowest corner accuracy (away from home) when losing by a small margin (e.g., 1 to 2 goals). Although free kick accuracy was varied across pitch location, playing position and team ability no association was found with goal difference. Crossing accuracy was not found to vary across goal difference or any of the temporal factors considered in the model, suggesting its limited impact on overall team performance and its absence in much of the previous research investigating score line and performance.

Although the definition used for score line in the current study was more sensitive than the traditional win, loss, draw it did not give an indication to the actual evolving score line; e.g. 2–0 could be perceived by players differently to 4–2 but would have the same GD. This should therefore be investigated in future research. Another consideration/limitation of the current study was the number of pitch zones used. Although pitch location was included in the multi-level modelling, unlike more recent studies only 3 zones were used. Splitting the pitch further (e.g., nine or twelve zones) would further highlight any variation between pitch zone. Adding additional playing positions (e.g., into wide and central midfielder) would also help to highlight differences between playing positions. It would also be interesting to investigate the extent that individual differences contribute to the overall team, or in this case, the overall mean of their playing position given research [54,55] has suggested variability between players with regards performance accomplishments and success and failure.

Although previous studies have investigated the effect of score line on player performance, few have considered score line outside of match status (e.g. winning, drawing, and losing). The current study was the first to consider a more sensitive score line with a large data set (35’000 rows of data) including an entire season of data from every team in the English Premier League. The current study also considered a much greater number of matches across one season in an attempt to eradicate the high match-to-match variation. By using only one validated system and an entire season of games more generalisations can also be made by reducing the error seen when trying to compare multiple measurement systems [23].




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