Profiling elite male squash performance using a situation awareness approach enabled by automated tracking technology

The pioneering research into squash performance by Sanderson and Way (1977) tested the hypothesis that “an individual exhibits a pattern of play which is relatively stable over time and independent of the opponent”. This belief influenced research in this area for many years. Many studies attempted to discover these stable patterns of play e.g. Hughes, 1985; McGarry and Franks, 1996; Hughes, Evans and Wells, 2001; and presented, what were suggested as indicative, playing profiles e.g. Murray and Hughes, 2001; Hughes, Watts, White and Hughes, 2006. This PhD aimed to analyse squash match play at a more detailed level by using movement data to supplement shot information related to shot types, court area etc. Elite male squash matches were filmed using a fixed overhead camera and images processed in software to semi-automatically calculate player movement information as well as allow an operator to manually input shot information. The two data streams were synchronised in Matlab before reliability and accuracy testing. Good levels of reliability were found for all court locations and shot information (agreement > 90%) although when an operator coded a long match without a break some percentage agreements had less than 90% agreement, presumably due to fatigue effects. Error testing, using a series of queries, specific to each data type, following data collection and prior to data analysis, discovered multiple errors in the data which were corrected. The physical demands and rally characteristics of elite-standard men's squash had not been well documented since recent rule changes (scoring and tin height). Rallies were split into four ball-in-play duration categories using the 25th (short), 75th (medium), 95th percentiles (long) and maximum values. The frequencies of shots played from different areas of the court had not changed after the adoption of new rules but there was less time available to return shots. Chapter 5 considered how expert squash players use Situation Awareness (SA) to decide on what shot to play. Shot type, ball location, players’ positions on court and movement parameters were captured 25 times per second for shots that achieved their objective. Six SA clusters were named to relate to the outcome of a shot ranging from a defensive shot played under pressure to create time to an attempted winner played under no pressure with the opponent out of position. The pressure exerted on a squash player is a coupling of the two players’ SA abilities. The same variables used for Chapter 5 were used except all shots (excluding serves and rally ending shots) were used, producing five main SA clusters, where a greater proportion of shots were categorised in the greater pressure clusters and less in the lower pressure ones. Individual matches were presented using cluster profile infographics which demonstrated how individual players played differently in different matches.

[1]  Nic James,et al.  A new method for assessing squash tactics using 15 court areas for ball locations. , 2014, Human movement science.

[2]  Nic James,et al.  Measurement error associated with the SAGIT/Squash computer tracking software , 2010 .

[3]  F. Sanderson,et al.  The development of objective methods of game analysis in squash rackets [proceedings] , 1977, British journal of sports medicine.

[4]  Edward M Winter,et al.  Metrics of meaningfulness as opposed to sleights of significance , 2014, Journal of sports sciences.

[5]  I. Franks,et al.  On the presence and absence of behavioural traits in sport: an example from championship squash match-play. , 1999, Journal of sports sciences.

[6]  Nic James,et al.  The effect of court location and available time on the tactical shot selection of elite squash players. , 2013, Journal of sports science & medicine.

[7]  Craig Pulling,et al.  Offloads in Rugby Union: Northern and Southern Hemisphere International Teams , 2015 .

[8]  Matej Kristan,et al.  Analysis of Player Motion in Sport Matches , 2008, Computer Science in Sport - Mission and Methods.

[9]  Paul M Salmon,et al.  Never blame the umpire – a review of Situation Awareness models and methods for examining the performance of officials in sport , 2016, Ergonomics.

[10]  Hsinchun Chen,et al.  Sports Data Mining , 2010 .

[11]  Nic James,et al.  Effects of rule changes on physical demands and shot characteristics of elite-standard men’s squash and implications for training , 2016, Journal of sports sciences.

[12]  Xianggui Qu,et al.  Multivariate Data Analysis , 2007, Technometrics.

[13]  John Patrick,et al.  The role of situation awareness in sport , 2004 .

[14]  Annett Baier,et al.  Modern Educational Dance , 2016 .

[15]  K. A. Ericsson,et al.  Deliberate practice and the acquisition and maintenance of expert performance in medicine and related domains. , 2004, Academic medicine : journal of the Association of American Medical Colleges.

[16]  Lloyd L. Messersmith,et al.  The Distance Traversed by a Basketball Player , 2013 .

[17]  B. Abernethy,et al.  Anticipation in squash: differences in advance cue utilization between expert and novice players. , 1990, Journal of sports sciences.

[18]  M. Norusis IBM SPSS Statistics 19 Statistical Procedures Companion , 2011 .

[19]  J. Sampaio,et al.  Rugby Game-Related Statistics that Discriminate Between Winning and Losing Teams in Irb and Super Twelve Close Games. , 2010, Journal of sports science & medicine.

[20]  Nic James,et al.  Using a situation awareness approach to determine decision-making behaviour in squash , 2018, Journal of sports sciences.

[21]  Nic James,et al.  Performance analysis of golf: Reflections on the past and a vision of the future , 2009 .

[22]  Niklas Gloeckner Visual Perception And Action In Sport , 2016 .

[23]  G. Klein,et al.  A recognition-primed decision (RPD) model of rapid decision making. , 1993 .

[24]  Ian M Franks,et al.  Sport competition as a dynamical self-organizing system , 2002, Journal of sports sciences.

[25]  Andrew Borrie,et al.  Temporal pattern analysis and its applicability in sport: an explanation and exemplar data , 2002, Journal of sports sciences.

[26]  Nic James,et al.  THE DISTANCE COVERED BY WINNING AND LOSING PLAYERS IN ELITE SQUASH MATCHES RAZLIKE V OBSEGU GIBANJA MED ZMAGOVALCI IN PORAŽENCI AKTIVNIH , 2011 .

[27]  Christopher F. Chabris,et al.  Visualization, pattern recognition, and forward search: effects of playing speed and sight of the position on grandmaster chess errors , 2003, Cogn. Sci..

[28]  I M Franks,et al.  Dynamic patterns of movement of squash players of different standards in winning and losing rallies. , 1994, Ergonomics.

[29]  B. Abernethy,et al.  Expertise and the Perception of Kinematic and Situational Probability Information , 2001, Perception.

[30]  Robert J. Mackenzie,et al.  Performance analysis in football: A critical review and implications for future research , 2013, Journal of sports sciences.

[31]  T. Reilly A motion analysis of work-rate in different positional roles in professional football match-play , 1976 .

[32]  Joseph G. Johnson,et al.  Expertise-based differences in search and option-generation strategies. , 2007, Journal of experimental psychology. Applied.

[33]  N. James,et al.  An exercise protocol that simulates the activity patterns of elite junior squash , 2006, Journal of sports sciences.

[34]  Mike Hughes,et al.  The Evolution of Computerised Notational Analysis Through the Example of Racket Sports , 2008 .

[35]  K. Davids,et al.  The ecological dynamics of decision making in sport , 2006 .

[36]  Bahadorreza Ofoghi,et al.  Data Mining in Elite Sports: A Review and a Framework , 2013 .

[37]  Stephen-Mark Cooper,et al.  Analysis procedures for non-parametric data from performance analysis , 2002 .

[38]  P. O'Donoghue Sources of variability in time-motion data; measurement error and within player variability in work-rate , 2004 .

[39]  Mike Hughes,et al.  Establishing normative profiles in performance analysis. , 2001 .

[40]  Paul J. Feltovich,et al.  Categorization and Representation of Physics Problems by Experts and Novices , 1981, Cogn. Sci..

[41]  Richard Pollard,et al.  Charles Reep (1904-2002): pioneer of notational and performance analysis in football , 2002 .

[42]  Nic James,et al.  Reliability procedures for categorical data in Performance Analysis , 2007 .

[43]  Nic James,et al.  Tactical use of the T area in squash by players of differing standard , 2009, Journal of sports sciences.

[44]  G. Ermentrout Dynamic patterns: The self-organization of brain and behavior , 1997 .

[45]  J. Gréhaigne,et al.  Tactical Knowledge in Team Sports From a Constructivist and Cognitivist Perspective , 1995 .

[46]  Mica R. Endsley,et al.  Toward a Theory of Situation Awareness in Dynamic Systems , 1995, Hum. Factors.

[47]  M S Magnusson,et al.  Discovering hidden time patterns in behavior: T-patterns and their detection , 2000, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[48]  Tessa E. Morris,et al.  The specificity of training prescription and physiological assessment: A review , 2009, Journal of sports sciences.

[49]  K. A. Ericsson,et al.  The Influence of Experience and Deliberate Practice on the Development of Superior Expert Performance , 2006 .

[50]  Cyril Bossard,et al.  Defensive Soccer Players’ Decision Making , 2014 .

[51]  A. Williams,et al.  Quantifying the nature of anticipation in professional tennis , 2013, Journal of sports sciences.

[52]  Anne-Claire Macquet,et al.  Recognition Within the Decision-Making Process: A Case Study of Expert Volleyball Players , 2009 .

[53]  G Atkinson,et al.  Statistical Methods For Assessing Measurement Error (Reliability) in Variables Relevant to Sports Medicine , 1998, Sports medicine.