Performance profiling in sports coaching: a review

Butterworth, Andrew; O’Donoghue, Peter; Cropley, Brendan

doi:10.1080/24748668.2013.11868672

Cited by 43 publications

(49 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In recent years, researchers made great efforts to develop meaningful and useful performance profiles in sport (Butterworth et al, 2013;Eugster, 2012;Hughes et al, 2001;James et al, 2005;O'Donoghue, 2005;. The main profiling techniques were discussed and compared by O'Donoghue (2013).…”

Section: Introductionmentioning

confidence: 99%

Performance profiles of football teams in the UEFA Champions League considering situational efficiency

Liu

Giménez

et al. 2015

International Journal of Performance Analysis in Sport

View full text Add to dashboard Cite

Performance of football teams varies constantly due to the dynamic nature of this sport, whilst the typical performance and its spread can be represented by profiles combining different performance-related variables based on data from multiple matches. The current study aims to use a profiling technique to evaluate and compare match performance of football teams in the UEFA Champions League incorporating three situational variables (i.e. strength of team and opponent, match outcome and match location). Match statistics of 72 teams, 496 games across four seasons (2008-09 to 2012-13) of this competition were analysed. Sixteen performance-related events were included: shots, shots on target, shots from open play, shots from set piece, shots from counter attack, passes, pass accuracy (%), crosses, through balls, corners, dribbles, possession, aerial success (%), fouls, tackles, and yellow cards. Teams were classified into three levels of strength by a k-cluster analysis. Profiles of overall performance and profiles incorporating three situational variables for teams of all three levels of strength were set up by presenting the mean, standard deviation, median, lower and upper quartiles of the counts of each event to represent their typical performances and spreads. Means were compared by using one-way ANOVA and independent sample t test (for match location, home and away differences), and were plotted into the same radar charts after unifying all the event counts by standardised score. Established profiles can present straightforwardly typical performances of football teams of different levels playing in different situations, which could provide detailed references for coaches and analysts to evaluate performances of upcoming opposition and of their own.

show abstract

Section: Introductionmentioning

confidence: 99%

Performance profiles of football teams in the UEFA Champions League considering situational efficiency

Liu

Giménez

et al. 2015

International Journal of Performance Analysis in Sport

View full text Add to dashboard Cite

show abstract

“…Performance variables that were more associated with RQ were identified from all variables. This is the first study to identify the correlation between match performance variables and RQ and to assess the performance of female players in four Grand Slams via the regression based method (Butterworth et al, 2013;O'Donoghue & Cullinane, 2011).…”

Section: Discussionmentioning

confidence: 99%

“…This statement could be argued via emphasizing that tennis is a highly individual but interacting sport that varied constantly either from match to match or from player to player (O'Donoghue, 2009). Hence, this characteristic determines that the building, interpreting and eventually predicting players' performance should be done acknowledging the variability of the game and different types of players (Butterworth, O'Donoghue, & Cropley, 2013). In the present, some studies attempted to explore the issue of variability by, for example, analyzing the players' physical and technical performance variation during consecutive days of simulated matches (Gescheit et al, 2015;Gescheit et al, 2016).…”

Section: Limitation and Issues Of Current Investigationmentioning

confidence: 99%

“…For example, the percentage of second serve points won in Body zone of the deuce court was the variable that had stronger relationship in Roland Garros as it is a preferred zone for female players to place their second serves (Hizan et al, 2015). Also, it could be implied that in further research, the selection of performance variables should could be altered by the necessity of coach and players, according to different match and opponents (Butterworth et al, 2013).…”

Section: Relationship Between Match Variables and Relative Quality Anmentioning

confidence: 99%

“…Former studies tended to analyze the performance variables in isolation: assessing solely serve and return performance variables likes aces, first serve points in, first serve points won, second serve points won (Hizan et al, 2011); or break point performance variables (O'Donoghue, 2012); or physical performance variables (Pereira et al, 2017). In order to address this issue, it is necessary to bring together all performance variables of interest and to evaluate them comprehensively if we want to represent the performance of elite female players in different slams (Butterworth et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploring match performance of elite tennis players : the multifactorial game-related effects in Grand Slams

Cui¹

View full text Add to dashboard Cite

Tennis is a complex and dynamic sport where coaches are required to prescribe accurate training programming and match planning, and players are expected to execute the strategy by constantly adjusting their tactics, in order to increase the winning probability. Match performance analysis provides them with accurate, valid and less subjective information that is beneficial for the improvement of player behaviors. Currently, the research of performance analysis in tennis is a well-established realm that addresses the technical-tactical, physical and physiological performance of players. However, there still exist some limitations that need to be further solved, such as the lack of consideration of contextual constraints, the exclusive focus on general performance instead of accounting for within-player variation in personal attributes and performance, and the failure to assess the non-linear relationships among different influencing components from macro-, meso-, micro-and nano-level and match performance. Therefore, in order to address these issues, five independent and correlated studies were undertaken in the thesis.The first study analyzed the set-to-set variation in performance using match statistics of 146 completed main-draw matches in Australian Open and US Open 2016-2017 men's singles.Comparisons of technical-tactical and physical performance variables were done among different sets; and the coefficients of variation (CV) of these variables were contrasted between match winning and losing players. All comparisons were realized via standardized (Cohen's d) mean differences and uncertainty in the true differences were assessed using nonclinical magnitude-based inferences. Results showed that there was possibly to very likely decreases in serve, net and running related variables and an increase in return and winner related variables in the last sets when compared to the initial sets, indicating the influence of match fatigue and the player's adaption of match tactics and pacing strategy. Besides, winning players were revealed to have lower CV values in most of performance variables except for second serve, winner, and physical performance variables, which indicated that they would sacrifice the consistency to gain more aggressiveness and to dominate the match.The second study was aimed to (i) analyze the match performance of professional female tennis players in different Grand Slams; (ii) model the relationships between match performance variables and ranking-based relative quality; and (iii) build typical performance profiles for female players in Grand Slams using regression-based approach. Data of a total of X 1369 matches were collected within 2014-2017 four Grand Slams. Correlations between 37 performance variables and relative quality were determined and automatically classified into two groups of magnitudes via two-step cluster analysis. Higher-correlated variables were used to build players' typical performance profiles via regression-based technique to give percentage evaluation scores (%ES). Player...

show abstract

Player Profiling and Monitoring in Basketball: A Delphi Study of the Most Important Non-Game Performance Indicators from the Perspective of Elite Athlete Coaches

et al. 2021

View full text Add to dashboard Cite

Performance profiling in sports coaching: a review

Cited by 43 publications

References 1 publication

Performance profiles of football teams in the UEFA Champions League considering situational efficiency

Performance profiles of football teams in the UEFA Champions League considering situational efficiency

Exploring match performance of elite tennis players : the multifactorial game-related effects in Grand Slams

Player Profiling and Monitoring in Basketball: A Delphi Study of the Most Important Non-Game Performance Indicators from the Perspective of Elite Athlete Coaches

Contact Info

Product

Resources

About