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

Redwood-Brown, Athalie; O’Donoghue, Peter; Nevill, Alan M.; Saward, Chris; Sunderland, Caroline

doi:10.1371/journal.pone.0211707

Cited by 24 publications

(26 citation statements)

References 40 publications

(138 reference statements)

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“…Perhaps as a consequence, losers experienced significantly less player possessions in defense but also in midfield. Moreover, psychological factors that may impact a player's performance on a losing team need to be considered as researchers have recently suggested that if the outcome of a match is known in the second half due to superior opposition or a perceived unsurmountable lead, then player motivation may decline and potentially result in a decrease in work rate and reduced effort attempting to regain possession [26,27].…”

Section: Discussionmentioning

confidence: 99%

Temporal Changes in Team Performance Indicators Which Differentiate Between Winning and Losing in Elite Gaelic Football

Gamble¹,

Carren²,

Bradley³

et al. 2019

AJSS

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This study evaluated changes in performance indicators within winning and losing elite Gaelic football teams. Seven game statistics and 83 technical and tactical variables were measured during 24 games. Game statistics showed a significant decrease in ball in play time and increase in stoppage time between the first and second halves and between the first and fourth quarters. Significant differences between the first and second halves were evident in 10 variables (3 positive: 7 negative) in winning teams compared to 16 variables (4 positive: 12 negative) in losing teams. When the fourth quarter was compared to the first, significant differences were also found in 8 variables (negative) in winners and 23 variables (11 positive: 12 negative) in losers. Although certain technical and tactical variables improved in winners during halves and in losers during halves and quarters, when overall performance is evaluated across both match periods, decrements were more pronounced in both winners (15 vs. 3) and losers (24 vs. 15). Both winners and losers experienced significant reductions in the frequency of team possession in the fourth quarter. Losers also demonstrated significant declines in total time in both team and individual player possession from the first to the second half and in passing (hand and kick) profiles across both halves and quarters. There was a significant increase in the percentage of successful free kick passes by winners in the second half and an increase in successful free kick passes and kick outs in losers in the fourth quarter. Both winners and losers reported significant reductions in turnover variables across halves and quarters. Defensive efficiency declined significantly in winners across match periods, which coincided with the significant increase in attacking efficiency of losers in the second half and fourth quarter. The attacking frequency declined significantly in winners in the second half and the fourth quarter and attacks originating in defense were also significantly lower in both winners and losers in the second half. Although both positive and negative differences were observed in the temporal changes exhibited by winners and losers across the match periods examined, the findings demonstrate that winners did not experience the same extant of technical and tactical performance decrement as losers, which partly explains the match outcomes associated with these games. In conclusion, knowledge of these temporal changes can be used to inform current preparation practices to enhance technical and tactical components and optimize match performance strategies.

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Section: Discussionmentioning

confidence: 99%

Temporal Changes in Team Performance Indicators Which Differentiate Between Winning and Losing in Elite Gaelic Football

Gamble¹,

Carren²,

Bradley³

et al. 2019

AJSS

View full text Add to dashboard Cite

show abstract

“…In addition, researchers indicate a number of other determinants of players’ activity during a match, including quality of opposition ( Bilek and Ulas, 2019 ), team and opposition ability ( Redwood-Brown et al, 2019 ), team formation ( Aquino et al, 2020 ), match status ( Gonzalez Rodenas et al, 2019 ), players’ age and experience ( Lorenzo-Martínez et al, 2019 ), and climate conditions ( Coker et al, 2020 ). However, during a soccer match, players experience all these factors simultaneously, which is why modern studies focus on multifactorial analyses, using advanced statistical methods ( Moura et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

The Impact of Match Location and Players’ Physical and Technical Activities on Winning in the German Bundesliga

et al. 2020

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This study aimed to examine whether the physical and technical activities of soccer players and match locations can be associated with higher or lower odds of winning matches whose outcome can be described as “close.” The study comprised 7972 individual observations of German Bundesliga players during the 2014/2015 ( n = 2794), 2015/2016 ( n = 2494), and 2016/2017 ( n = 2684) seasons. A selection of “close matches” was made, which were defined as those in which the difference in numbers of scored goals was ≤1. Players’ five pitch positions were considered: central defenders, fullbacks, central midfielders, wide midfielders, and forwards. Data on 12 physical and 10 technical activities performed by players during matches as well as on match location were retrieved from the Impire AG (Germany) match analysis system. The study results show that the odds of winning at home are different for each playing position: from 41.99% for wide midfielders to 91.34% for central midfielders. Another conclusion was that one of the key components of the predictive model for forwards is the percentage of overall distance covered at speeds >24 km/h (7.99%), which is a variable with an increasing trend. The proposed model predicts that each 1% increase in this variable will theoretically be associated with a 4.08% raise of the odds of winning in further seasons. The presented statistical model may be used by trainers to identify players’ physical and technical activities and contextual variables that may significantly affect the match outcome. In addition, it can help to determine the individual training load related to the player’s position on the pitch.

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“…The increased accessibility of detailed match data is largely due to the progression made in semi-and fully-automated tracking [1][2][3][4]. Noticeably, the technical and tactical match performance of professional soccer players, has been shown to be affected by several different contextual, situational and positional attributes [5][6][7][8][9][10]. Thus, the continuous movement dynamic and the corresponding Sensors 2020, 20, 3213 2 of 13 movement within the context of the match produces thousands of different values that reflect in total what the player, players, team or teams do together as a whole during the game.…”

Section: Introductionmentioning

confidence: 99%

Predicting Wins, Losses and Attributes’ Sensitivities in the Soccer World Cup 2018 Using Neural Network Analysis

Hassan

Akl

Hassan

et al. 2020

Sensors

Self Cite

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Predicting the results of soccer competitions and the contributions of match attributes, in particular, has gained popularity in recent years. Big data processing obtained from different sensors, cameras and analysis systems needs modern tools that can provide a deep understanding of the relationship between this huge amount of data produced by sensors and cameras, both linear and non-linear data. Using data mining tools does not appear sufficient to provide a deep understanding of the relationship between the match attributes and results and how to predict or optimize the results based upon performance variables. This study aimed to suggest a different approach to predict wins, losses and attributes’ sensitivities which enables the prediction of match results based on the most sensitive attributes that affect it as a second step. A radial basis function neural network model has successfully weighted the effectiveness of all match attributes and classified the team results into the target groups as a win or loss. The neural network model’s output demonstrated a correct percentage of win and loss of 83.3% and 72.7% respectively, with a low Root Mean Square training error of 2.9% and testing error of 0.37%. Out of 75 match attributes, 19 were identified as powerful predictors of success. The most powerful respectively were: the Total Team Medium Pass Attempted (MBA) 100%; the Distance Covered Team Average in zone 3 (15–20 km/h; Zone3_TA) 99%; the Team Average ball delivery into the attacking third of the field (TA_DAT) 80.9%; the Total Team Covered Distance without Ball Possession (Not in_Poss_TT) 76.8%; and the Average Distance Covered by Team (Game TA) 75.1%. Therefore, the novel radial based function neural network model can be employed by sports scientists to adapt training, tactics and opposition analysis to improve performance.

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

Cited by 24 publications

References 40 publications

Temporal Changes in Team Performance Indicators Which Differentiate Between Winning and Losing in Elite Gaelic Football

Temporal Changes in Team Performance Indicators Which Differentiate Between Winning and Losing in Elite Gaelic Football

The Impact of Match Location and Players’ Physical and Technical Activities on Winning in the German Bundesliga

Predicting Wins, Losses and Attributes’ Sensitivities in the Soccer World Cup 2018 Using Neural Network Analysis

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