'7Ò7 6 t ¡. 7Zç 10242680 €vt+ Mc00 'lUC o

illilt I r lil]llil il]il ilil I ilt ililt

E STABLI SHING NORMATIVE TEMPLATE S

IN PERFORMANCE ANALYSIS OF BADMINTON

0t

>. CYNCOED

¡.ii,l. '

TEPHEN EV

ÈC)R u5Ê il\ ']BRARY ONLV

A project submitted in partial fulfilment of the requirements for the Degree of Master of Science (Coaching Science)

School of Graduate and Continuing Education Faculty of Education and Sport University of Wales Institute, Cardiff September 1999 PRIF"TSGOL CYMRU UNTVERSITY OF }VALES

THE DEGREE OF MASTER OF SCIENCE

Declaration of student in respect to their work

I CERTIF"Y that this work has not been previously accepted in substance for any degree, and is not being concurrently submitted in candidature for any other degree.

I further certi$r that the whole of this work is the result of my individual effort, except where otherwise stated. All quotations from books and journals have been acknowledged. A list ofreferences is appended.

I hereby give consent for my dissertation, if accepted, to be available for photocopying, and for interJibrary loan, and for the title and summary to be made available to outside organisations.

Signed: (candidate) Date: sql1 l33

Certificate of supervising tutor in respect of the student's individual work

I am SATISFIED that this work is the result of the above-named student's own effort.

Signed:

Date: 'I am playing all the right shots,

but not necessarily in the right order.'

Adapted from an Eric Morecambe and Ernie Wise sketch

With thanks to

Dr. Mike Hughes for his experience

and my mother for her support.

ll A notation system, designed to record rally-end variables in Badminton, \¡ras shown to be both valid and reliable. Inter and intra reliability ranged from 98.6% (Rally length) to gl.3% (Position). Percentage differences between data from side, and end observations of the same match were not greater than for the intra-reliability data thus different court viewing angles had little effect on notation. Previous literature declared profiles of performance without adequately tackling the problem of quantifuing of the data required in creating a normative template. The badminton notation system was used to examine the cumulative means of selected variables over a series of 1l matches of a player. A template, at match \n¡, was established when these means became stable within set limits of error (LE). T{ests on the variable means in games won, and games lost

established the existence of winning and losing templates for winners and effors. Match

descriptors (rallies, shots and shots per rally) were independent of match outcome.

General values of NtBl established for data types, (10% LE), were 3 matches (descriptive

variables), 4 (winners/errors (w/e)), 6 (smash + øe), 7 (position + de). Respective

values at 5Yo LE were '7, 5,8 and lO.There was little difference in the values of NtBl when

variable means were analysed by game than by match. For the working performance

analyst the results provide an estimate of the minimum number of matches to profile an

opponent's rally-end play. Whilst the results may be limited to badminton, men's singles

and the individual, the methodology of using graphical plots of cumulative means in

attempting to establish templates of performance has been served.

lll TABLE OF CONTENTS

PAGE

DECLARATION

ACKNOWLEDGEMENTS l1

ABSTRACT

LIST OF TABLES vtn

LIST OF FIGURES xl

CHAPTER ONE INTRODUCTION I

1.1 Notation and badminton I

1.2 The bacþround for the study 1

1.3 The statement of the problems 5

1.4 The hypotheses 6

lv 1.5 The limitations and de-limitations 7

CHAPTER TWO REVIEV/ OF LITERATURE 9

2.1 Reviewing the literature 9

2.2.1 Notational analysis in badminton 9

2.2.2 Defining profiles, templates and models 1l

2.2.3 Creating arally ending template l3

2.2.4 The use of performance indicators 15

2.3.1 Using research to define methodology t7

2.3.2 Definition of sample size 18

2.3.3 Dividing the playing surface t9

2.3.4 Defining actions and outcomes 2t

2.3.7 System validation and reliability 22

2.3.6 Reliability and camera position 24

2.3.7 Statistical analysis 25

2.4 The rationale of the study 27

v CHAPTER THREE METHODOLOGY 29

3.1 Notation system design 29

3.2 The notation database 32

J.J Subject and matches 4l

3.4 Equipment 4l

3.5 System validation 46

3.6 Testing system reliability 46

3.7 Reliability and camera position 48

3.8 Collecting the data 48

3.9 The analysis procedures employed 49

CHAPTER FOUR RESULTS 52

CHAPTERFIVE DISCUSSION 96

5.1.1 Inter-observer reliability 96

5.1.2 Intra-observer reliability 97

5.1.3 Reliability and camera angle 99

5.2 Research design 101

11 5.3 The descriptive summary data 103

5.4 Establishing templates of winning 104

and losing

5.5 Establishing templates of matches 105

and games

5.6 The definition of templates 106

CHAPTER SIX CONCLUSIONS 110

CHAPTER SEVEN REFERENCES 111

CHAPTER EIGI{T APPENDIX lt8

vll LIST OF TABLES

TABLE PAGE

31 Abbreviation codes for actions and outcomes 30

3.2 List of matches involving Player A" in order of analysis 42

J.J Match list of wins by Player A 42

3.4 Match list of losses by Player A 42

3.5 The definitions of actions and outcomes 43

3.6 An example of winner/error interpretation and the effect

on other variables 47

4.1 Results of intra-observer reliability study 52

4.2 Results of inter-observer reliability study 52

4.3 Results of camera angle reliability study 52

4.4 Intra-observer differences betvieen each set ofvariables 53

4.5 Inter-observer differences between each set ofvariables 54

4.6 Camera view observation differences between each set

of variables 55

4.7 Description information of the matches analysed 56

vlll 4.8 Rally frequency information for different match groupings 57

4.9 Shot frequency information for different match groupings 58

4.lO Shot per rally information for different match groupings 58

4.ll The means and percentage error range of rallies by match 59

4.12 The means and percentage error range of shots by match 59

4.13 The means and percentage effor range of shots per

rally by match 62

4.14 Shot and rally information for fìrst games 64

4.15 Shot and rally information for first games 65

4.16 The means and limits of error of rallies by game 67

4.17 The means and limits of error of shots by game 69

4. l8 The means and limits of error of shots/rally by game 7t

4.19 Summary of the number of matches to establish

variable templates 73

4.20 Summary of the number of matches to establish

variable templates 73

4.21 Frequency of winners by match outcome for Player A 74

4.22 Frequency of errors by match outcome for Player A 74

4.23 Frequency of winners by match outcome for Opponent 75

4.24 Frequency of errors by match outcome for Opponent 75

lx 4.25 Frequency of winners by game outcome for Player A 76

4.26 Frequency of errors by game outcome for Player A 76

4.27 Frequency of winners by game outcome for Opponent 76

4.28 Frequency of errors by game outcome for Opponent 76

4.29 Summary of the number of games to establish awinner

and an error profile when Player Awins 86

4.30 Summary of the number of games to establish a winner

and an error profile when Player A loses 86

4.31 Overall summary ofN(E) for all variable measured

at each limit of error 95

x LIST OF FIGURES

FIGURE PAGE

3.1 The division of the badminton court into position cells 3l

3.2 The list of tables in the Rally-end database 34

3.3 A list of queries in the Access Rally-end database 35

3.4 Furtherqueries in the Access Rally-end database 36

3.5 The design view of the rally table 37

3.6 An example of the data input table 38

3.7 The design view of a database query 39

3.8 An example of a database query output 40

4.1 Mean number of rallies by match 60

4.2 Mean number of shots by match 6l

4.3 Mean number of shots per rally by match 63

4.4 Mean number of rallies by game 68

4.5 Mean number of shots by game 70

4.6 Mean number of shots per rally by game 72

4.7 Player A's winners when Player Awins the game 78

4.8 Player A's errors when Player Awins the game 79

xl 4.9 Opponent's winners when Player A wins the game 80

4.10 Opponent's effors when Player Awins the game 81

4.ll Player A's winners when Player A loses the game 82

4.12 Player A's errors when Player A loses the game 83

4.13 Opponent's winners when Player A loses the game 84

4.14 Opponent's errors when Player A loses the game 85

4.15 Player A's smash winners by match 88

4.16 Player A's smash errors by match 89

4.17 Opponent's smash winners by match 90

4.18 Opponent's smash errors by match 9t

4.19 Player A's winners from position 3 per match 9l

4.20 Player A's errors from position 3 per match 92

4.21 Opponent's winners from position 3 per match 93

4.22 Opponent's errors from position 3 per match 94

xll CHAPTERI

INTRODUCTION 1.1 Notation and badminton

Notation has been utilised throughout history to describe and define movement although its use to facilitate improvements in sport perfonnance is a recent development. Studies of sport performance have highlighted the difficulties coaches experience in recalling key behavioural events (Franks and Miller, 1986; l99l; Franks, 1993).In men's badminton

singles, at top world class level, the shuttle is in play for approximately 40olo of the time, with match duration's ranging anywhere between 20 and 120 minutes. It has been shown that on average one shot is played per second (Liddle and O'Donoghue, 1998), with

shuttles struck at up to 162 miles per hour. The duration of and the speed at whictU the game is played present difficulties in providing an accurate version of match events.

There is a vast amount of valuable information that can be obtained from a badminton match. An important role for the badminton coach is to decide what information will be of most benefit to the coaching process and to use the skills of the performance analyst to provide the objective measures required.

1.2 The background for the study

The whole process of analysis and feedback of performance has many practical diffïculties. In badminton the National Governing Bodies control player entry into

I international tournaments. At these international events the English national coaches are often in charge of large numbers of players (up to twenty), playing in any of five

disciplines spread through out the day. A twelve-hour day of match play is not

uncommon, especially during the first three days of an event. Players expect to play twice

or three times in one day, particularly if they have entered more than one discipline.

Under these conditions it is impossible for the practising notational analyst to provide

detailed performance analysis support for every player between matches or even over night. The emphasis has to be to help the coaches have a more effective impact on

developing optimal performance in certain key players. Due to the nature of the sport and its tournament structure, it is preferable that the majority of the analysis support is

prepared before the event. Even so the national coaches are well advised to prioritise and use the services of the performance analyst in the most effective way to achieve the goals

set for that tournament and in the long term.

The performance analyst working in this applied environment will experience strict

deadlines and acute time pressures defined by the date of the next tournament, the

schedule and the draw. It is usual for the Badminton Association of England to receive a

copy of the draw just one week before the tournament start. The need then is to provide coaches with accurate information on as many of the likely opposition players in the

amount of time available. This may be achieved by the instigation of a library of player

2 analysis files, which can be extended over time and receive frequent updating. There should be a regular assessment of the effectiveness and effrciency of the notation and analysis procedures. One method of improving the effrciency of the notation and analysis procedures is to notate the minimum number of matches necessary to provide an

accurate, reliable player analysis for the player data library. At present this required number of matches is not known and may even be different for individual players, player groups, gender groups, playing standard and the different sport disciplines. Player files must be regularly updated by adding analysis from recent matches to the database held on each player. Potter (1999) reinforced the theory of the greater the database the more accurate the model against which to compare future performances. However it can be argued that as a database increases in size then it will become more insensitive to change of playing pattems. Mosteller (1979) advised the use of match weighting in performance modelling, with recent games weighted much more heavily than earlier games. If the amount of data is known to establish statistically a player template, then as additional matches are added to the database the oldest dated matches may be dropped from the model. This will enable the model to respond to any long-term changes in a player's game.

Hughes et al. (1999) established performance profiles for women squash players at recreational, county and elite levels of play. Fundamental questions were raised regarding

J the research process in terms of the consistency of the means of the measured variables

from match to match. In previous notation research there has been little statistical basis to quantifu the number of matches analysed in providing a performance profile. The

essential question is whether a consistent state of performance per match has been reached to classify the data as a profile. Large variation in the frequencies of the individual variables between matches gives no credibility to the presentation of this data as a performance profile. McGarry and Franks (199a) suggested that a player exhibit

greater consistency in play when matched against the same opponent than against a different opponent. The data was reanalysed (McGarry and Franks, 1996) and some of the reasons why they were unable to establish an individual template of athletic performance against different opposition was examined. The authors concluded that invariant athletic behaviour is dependent to a certain degree on the level of analysis used.

Other problems present themselves to the working analyst. In determining the validity of a post event badminton notation system, Blomqvist et al. (1998) positioned his video camera behind the court. Difüculties were reported in recording accurately the positional variables and shot height although movement was easier to observe across the width of the court. These were partly explained by the view from the camera and it was stated that the study would be improved if a more ideal shooting angle and height for the camera

4 were found. In conclusion the analysis system used was reliable and valid for player time, player position and the type and quantity of the shots for the camera placement chosen.

From experience, the best place to film a badminton match is from an elevated position behind the court. The whole court is in view and the camera is near to the action. When recording from the side of the court the camera has to be fuither away to cover the court, even with the advantage of a wide-angle lens attachment. A camera placement above court level means that play is not hidden behind the line judges, players and the net.

However there are many problems associated with setting up a tripod and camera at a major badminton event to obtain a camera position raised behind the court. The design of the hall and the layout of the courts dictate where a camera can be positioned. A raised balcony or stand is preferable; even so the courts may be laid with their side to it.

In essence the performance analyst would expect a variety of camera angles and heights at badminton events. It is important to discover whether this alters the reliability of any subsequent notational analysis.

1.3 The statement of the problems

This study will develop a notation system to record the rally outcome variables of badminton, and test system validity and reliability. This system will be used to tackle two

5 of these basic problems that affect the analyst working in this area of performance analysis. Firstly, the research provides conflicting opinion on how much data is required to create normative profiles. Studies have also shown these 'profiles' are dependent on match outcome. However in most profile analyses, the number of matches selected has been arbitrary. The affect of new match data on the accumulative mean of each variable of the rally ending badminton profile will be examined in an attempt to quantify the matches necessary to establish templates. Secondly the notation of the same match from two different camera angles will be tested for reliability.

1.4 The hypotheses

The notation system devised to collect rally-ending data for badminton is valid and reliable for the men's singles discipline.

There will be a number of matches, (Np¡), after which the means of the data will fall into the limits of error of the overall mean. The value of (N1r¡) will depend on the type of data.

A match template will be not be independent of match outcome, thus a player will have a winning profile and a losing profile.

6 The position of the camera relative to the badminton court will have no effect on the reliability of notation of rally ending situations.

1.5 The limitations and delimitations

It was decided to select matches played only in Europe to reduce the effect of environmental factors such as hall temperature and humidity on the patterns of play.

Conditions are very different between the Far East and Europe. The heat and humidity in countries such as Malaysia, Thailand, Korea and China alter the tactics adopted by all players, particularly those from Europe. From observational experience, playing style in these environments is based more on shorter attacking rallies than on attrition. Therefore it is possible that a player may have two profiles of play, a European venue profile and an

Asian venue profile.

The player studied sustained a serious injury for many months of the badminton season.

Following his recovery the player was not selected by the national governing body to represent England at the international team competitions. At the two individual events attended by the subject player abroad, the performance analyst was not present. This limited the overall number of matches collected and those recorded against foreign opposition.

,1 It is reasonable to expect that the five badminton disciplines will all have a different style of play. Thus the application of the study results to other related areas of the sport is de- limited in focusing the research on an individual male singles player. However the theoretical issues of normative templates and the methodology of establishing them will be explored for all the disciplines of badminton, and for other sports where possible.

8 CHAPTERII

REVIEW OF LITERATURE 2.1 Reviewing the literature.

There is a historical background to research in analysis of racket sports. Downey (1973) developed the first extensive notation system for a racket sport in Britain. The practical usage of this lawn tennis hand notation system was limited but it served as a sound development base for future racket sport systems. Sanderson and Way (1977) and

Sanderson (1983) analysed tactics in squash based on Downey's system and Hughes

(1985) later adapted Sanderson and Way's (1977) system for computers. However it is not the intention here to repeat in detail the history of notation but review the literature in two main areas. First, to research the principal areas of concern with the study, badminton, rally ending analyses, performance indicators and establishing templates of play. Second, to review the research methodology to address the difficulties in developing a valid and reliable notation system.

2.2.1 Notational analysis in badminton.

Previous studies of badminton have primarily focused on the physiological aspects of the sport (Hughes, 1995, Dias and Ghosh, 1995, Liddle et al., 1996). These time and motion analyses of badminton have assisted physiologists in defining the sport's specific physiological demands. For example, a study by Liddle and O'Donoghue (1998)

9 recorded the duration of rallies in badminton from which work to rest ratios and mean shots per second were calculated for each discipline. The main conclusion was that each discipline was characterised by different distributions of rally lengths, rest times and shot rates.

Blomqvist et al. (1998) devised a computerised badminton notation system using "Sage

Game Manager for Badminton" software. The purpose of the study was to determine the validity of the system. The notation was post event and every shot of the match was coded for twelve input variables. There were a total of 413 shots in the recorded match that was notated twice by three observers, each with a different background in a net/wall sport. A range of intra- and inter-observer calculations was made and the overall results indicated that the system was reliable and valid, particularly for the quantitative variables measured. The reliability of shot type and court position variables was established with

413 inputs (413 shots in the match). However the number of rallies was not disclosed so the reliability of the outcome variable, winner, forced error and unforced error, was established over an undisclosed number of observations. At estimate seven shots per rally fifty-nine rallies in total was notated. This is a small number of observations for a reliability test particularly as the rally outcome is subjective.

10 2.2.2 Defining profiles, templates and models

The words profile, template and model are often synonymous when used to describe a performance or performances in sport. The term 'profiling' is commonly associated with the physiological and psychological branches of sport science. A profile may also be considered as an outline of play, a representation of the tactical and technical characteristics of the sport. A profile is not, in itself a method of assessment, (Hitchcock,

1990) but a basis for communication between the coach and the player and when used over a period of time they provide a continuous record of player development. Hughes

(1999) recommended for any sport that individual match data should be compared to previous performance data and equivalent aggregated performance data of a peer group.

Aggregated data over a number of match performances are often referred to in the literature as a template. A template is a pattern that helps to shape performance accurately, (Hanks, 1979). This accuracy implies a negligible or permissible deviation from a standard, which suggests a greater degree of accuracy for a template than a profile outline. Franks et al. (1983) recognised that computerised notation can provide a database of match play from which a model of tactical play may be formed. A model of play is a representative style that McGarry and Franks (1996) suggested is an underlying signature of sport performance. Thus a 'model' may be considered as a prediction of future performance based on real performances, the performance template.

ll In previous research (Hughes, 1986; Hughes and Franks, 1994; Hughes and Knight

1995; Hong et al., 1996; Hughes and Robertson, 1998) a variety of sample sizes and data sets had been used in an effort to provide a normal playing pattern for squash (see section

2.3.2). Whether referred to as profile, template or model, the existence of any such patterns was not statistically established in any of these studies. Wells (1998) tested whether a normal playing profile could be established after eight, nine or ten ladies squash matches. The mean values of three variables of eight matches against nine matches, and nine matches against ten matches were tested for significant differences.

However the limited number of tested variables meant that a normative playing profile could only be established for, the total number of shots per match, the total number of rallies per match and the total number of shots per rally. The results showed that for the elite playing standard a normal playing pattern for these variables was established after ten matches. It is important that variables used in a template should be fïrst proven to be normative.

It is expected that matches at the elite level will differ to some extent from each other as players do not always perform the same way. McGarry and Franks (1996) suggested that player-player interaction has influence on stable athletic behaviour, thus a player's profile may not be apparent because of the way different opponents play against them. Matches may differ by chance, through adverse environmental conditions, a diffrcult tournament

t2 draw, or injury worries. Hughes (1998) noted that tactical models change with time and cited the decrease in average number of shots per rally in squash, (from 20 to 14 in fifteen years) as an example. A pattern of play may not in fact exist, in which case there is little benefit to the coaching process in measuring rally outcome. It may not be practical to establish playing patterns, thus recording rally outcome is of no value to the working coach.

2.2.3 Creating a rally ending template

When analysing badminton one may focus attention on different technical and tactical points within a rally. A 'Rally Start' analysis will concentrate on the service and return of service although it may include the third shot, particularly if notating doubles play. A full rally analysis will provide information on the frequency, position and distribution of all shots played in a match. A rally ending analysis will just emphasise frequency, position and distribution of the winners and effors (the nth shot) hit, although it is possible to extend this to include the last but one shot (n - l) of the rally and even the last but two shot (n - 2). Sanderson (1983) developed the analysis system of Sanderson and Way

(1977) further, to undertake a detailed examination of the rally ending shot patterns of squash by recording the rally ending shot (n) along with the opponent's shot before it, (n

- l) and the shot before that, (n - 2). This research showed that the squash players

13 analysed exhibited consistent patterns of play irrespective of whether they were winning or losing.

Hughes (1985, 1986) compared the differences in playing patterns between national, county and recreational squash players. It was found that recreational players were not able to maintain a taúical plan, and whilst these players hit more winners they also hit more effors. County players hit significantly more winners with the straight drive and showed simple game tactics, pressurising the opponent's backhand corner. The average length of rallies between nationally ranked players was longer and it was stated that these players employed the more complex tactics, using an 'all court' game. Rally-ending situations were analysed by Hughes and Knight (1995) to compare the playing patterns exhibited by elite squash players under two different scoring systems. The selected rallies were notated and analysed using a ne\¡/ computerised system developed for squash, validated by Brown and Hughes (1995). The analysis of the distribution of shots revealed a difference in the patterns of play between the English and American scoring systems.

The study also highlighted that significantly more winners were played under point-per- rally scoring. Brown and Hughes (1995) evaluated patterns of unforced errors to assess the effectiveness of the quantitative and qualitative feedback they had provided to junior squash players. Hughes and Robertson (1998) used the ratio of winners to errors to profile all the different categories of shots between winning and losing players.

l4 Significant differences (p<0.01) were shown between winning and losing players for all the shot categories. In conclusion it was suggested that similar modelling of performance could be replicated in other sports. The implications of these dataarethat a player may have more than one profile, a winning profile and a losing profile. McGarry and Franks

(1996) stated that it was evident that winners and effors distinguish successful performance in squash and tennis, so why not badminton?

2.2.4 The use of Per{ormance Indicators

Hughes (1999) defined a performance indicator as an action variable, or combination of action variables, that aim to define performance in part or as a whole. Winners and errors may be categorised as scoring indicators of performance. Sanderson (1933) used a ratio of number of winners played to errors made as a performance indicator. He discovered that the squash player who achieved a winner to error ratio (w/e) greater than one usually won the match. Hughes (1999) highlighted how misinterpretation of performance can occur when indicators are used in isolation. It is preferable that performance indicators are used in a comparative way with previous performances and I or an aggregated peer performance. Performance is also relative to the opponent's play and it is advised that any indicators be referenced to the opponent's profile. It was suggested that w/e ratios be written as Nr:Nz (:Nr/I.[z) for player and opponent. This method of expressing this data

l5 will provide an insight into the length of the match, its competitiveness and the aggressive nature of the players in addition to whom won or lost.

Hughes (1999) also questioned the accuracy of positional distributions of rally-end shots.

It was shown that the normalisation of the cell winner and error frequencies with the total number of shots played from that cell is a more accurate performance indicator. Similar normalisation procedures should also be applied to shot type and shot direction frequencies. However these total frequency figures are not available from just notating rally-ending situations. A full rally analysis is necessary to obtain these frequency totals, which takes more work and time to complete. This is not ideal for the practical performance analyst in search of eflicient working practices. The number of rallies will vary from match to match therefore direct comparison between matches may also be unreliable. In an aim to maintain homogeneity Hughes and Franks (1994) collected data from only the first three games of squash matches regardless of whether they went to four or five games. Furthermore Hughes and Knight (1995) analysed a set number of rallies (2 x 500) rather than matches, or games, so that the information obtained under the two different scoring systems would be directly quantifiable. Hughes (1999) urges careful consideration in the use of perforrnance indicators to avoid misleading interpretations and furthermore reminds the research community that success is relative.

t6 2.3.1 Using research to define methodology

Hughes and Franks (1997) recommended the first step in designing a data collection system was to decide what was required from the system. This helps to simplify defining the data input and reduce collection of irrelevant information. For example unforced elTors are a common concern among coaches of all sports. In an attempt to reduce these effors a badminton coach may require information on how rallies end in this way. The collection of data concerning frequency of unforced errors, type of error, type of shot played, and court position would be required. The variables recorded in any notational analysis system fall into four categories (Hughes and Franks, 7997): l. Player

2. Position

3. Action - and the subsequent outcome(s).

4. Time

Whether one or all of these variables are necessary is dependent on what is required of the system. A¡eas of research specifïc to this badminton study are establishing system validity and reliability, the effect of camera position on reliability and statistically establishing a player profile.

I7 2.3.2 Definition of sample size

Studies by Hughes (1985,1986) investigated the patterns of play at different levels of squash. Six elite, fifteen county and ten recreational players were used in establishing these patterns of play. A later study by Hughes and Franks (1994) used a sample size of six players for each group, (elite, and provincial, clubs A/B and club C/D). Alternatively

Hughes and Knight (1995) examined a set number of squash rallies (500) under each of the English and American scoring systems with male top 30 players. Recent research by

Hong et al. (199ó) profiled the world's top male squash players by notating ten matches whilst Hughes and Robertson (1998) used five matches of elite players (world top 20) to provide their tactical model of squash at this level.

Players have been classified into broad bands of ability in previous research. Levels exist in all sports and 'Elite' is a subjective term, which can encompass a wide variety of standards. It is preferable to relate the standard of player to a recognised benchmark such as a world ranking. In choosing a cut off ranking then numbers which reflect a tournament draw's seeding levels is appropriate, such as 8, 16, 24,32. For example if a player can attain a ranking of twenty-four, they will then enter the main round draw automatically avoiding the qualification rounds for the final eight places. Similarly a player reaching the top sixteen will be drawn against a player ranked 17 to 32 in the first

18 round as opposed to a player seeded I to 16. However when attempting to differentiate between standards in elite sport it may be more appropriate to identify any large natural breaks in the world ranking points, rather than select an arbitrary position such as ten or sixteen.

The notation of player variables is easier in an individual sport, such as badminton, than for a team sport. A notator of rugby for instance may have thirty players to identi$r not including the substitutes, yet in badminton an observer only has to differentiate between two players, or four for a doubles event. However, it is helpful to record the referee's introduction made at the start of each badminton match to identify at later date any unfamiliar players.

2.3.3 Dividing the playing surface.

There is always a compromise between notating accurate positional data and obtaining significant data across all the cells when defrning a playing area. Downey (1973),

Sanderson and Way (1977) and Sanderson (1983) employed a series of scatter diagrams to record player, position and action outcome variables. However Hughes (1983) digitised the input variables of the earlier squash systems, but was limited to nine positional variables due to the memory capacity of computers at that time. Advancements

19 in computer technology allowed Hughes (1986) to increase the number of position cells on the court to sixteen. Hughes et al (1989) collected positional data using a 'Power Pad' in a study that analysed squash movement. A video image of the playing area was mixed and aligned with that of the source tape and a tracking stylus accurately traced the player's movements onto the simulated playing area in real time.

Badminton has some basic differences to squash and tennis when considering court coding. In squash it is preferable not to hit the ball into the centre of the court. However in Badminton it is a common tactic to hit the shuttle into the body of the opponent or place it in mid-court to narro\¡/ angles of attack. Thus consideration must be given to the definition of the central areas of the court. Tennis does not have the same movement patterns as badminton as large forward and backward motion is rare in tennis so more longitudinal definition would be beneficial in badminton. Court lines are a consideration when deciding how to dissect a court for notation purposes. The court lines on a badminton court suit a two by two dissection however this will not provide detailed information on shots hit from and towards centre and mid court areas.

Previous racket sport literature identified the cell position variable as the position of contact between the racket with the object ball. In badminton, a player will regularly

20 jump to hit the shuttle and may land in a different place from take off This makes the recording of a single body position impractical thus recording contact position is sensible.

2.3.4 Defining actions and outcomes.

Some systems use invented symbols that represent actions and outcomes (Hughes and

Franks, 1997). Sanderson and Way (1977) categorised seventeen different strokes in squash using illustrative symbols. These symbols were felt easier to learn and remember than codes. Hughes (1985) digitised these variables. Significant letters, such as 'v' to record a volley and 'w' to record a winner, used to represent actions and outcomes in a system reduce the number of characters to be recorded to a minimum.

The main problem for actions and their respective outcomes is one of definition due to the complexity of the rally end situation. Blomqvist et al. (1998b) divided rally outcome into three categories, successful shots unforced errors and forced errors. Previous research has classed unforced effors by a player as a winner to the opponent. The advantage of reducing the number of classifications is to minimise the subjective decision-making and also to ensure sufücient data in each defined category. However a side effect of classifying a forced elror as a winner to the opponent is that the rally length is reduced by one shot. Hong et al. (1996a) also classified squash shot types as 'effective'

2I or 'ineffective' and 'winning' or 'losing'. An order of shot priority was also established ranging from drive length (-61%) to boasts (5%) and lobs (5%). Another finding was the average number of shots per game tobe252. Hong et al. (1996a) concluded that winning

performances were produced at this elite level with a "pressure and aftack game" .

Research has shown that visual feedback has a powerful positive effect on performance when used in an appropriate manner. Videos of performance are generally more effective for advanced athletes as opposed to beginners (Rothstein and Arnold, 1976). It is necessary to record the time of an action or outcome when it is important to relate an analysis back to the video to provide visual feedback. Time is an essential variable to record when duration data is required from the notation system. These are generally for time/motion analyses such as work and rest duration (Liddle and O'Donoghue, 1998,

Liddle et a1.,1996) or movement velocities and accelerations (Hughes and Franks, 1991).

2.3.5 System validation and reliability

Hughes (1994) noted a growing sophistication in the validation of notation systems particularly since the first systems were published in the nineteen seventies. The use of video recordings is invaluable in establishing the reliability and validity of a notation system. However using computers can lead to increases in errors through operator error

22 or hardware and software errors, (Hughes and Franks, 1997). Brown and Hughes (1995) validated a computerised system devised for squash by comparing the results of the computerised system with those of the hand notation. There were no discrepancies found between the two outputs. Wilson and Barnes (1998) determined the concurrent validity of their computerised table tennis system by measuring the overall percentage agreement between six games notated by computer and by hand. The authors reported a percentage agreement of 95.2Yo. However a similar validity study was not reported by Blomqvist et al. (1998) in the validation of a computerised badminton notational analysis system.

Blomqvist et al. (199S) tested the reliability, inter and intra-observer, of the data collection system with three trained observers who each analysed the match twice. The results indicated significant intra- and inter-observer reliability correlation in all variables. However reliability problems v/ere apparent in the two qualitative variables of shot execution and shot decision. It was reported that each observer had coaching experience in a different net/wall game and intra-observer reliability was best for the badminton specialist. Wilson and Barnes (1998) reliability data suggested that only observers who have suffrcient familiarisation with the system and considerable table tennis experience are able use the table tennis system in a valid and reliable way. The authors stressed quality control in data entry as this affects the reliability and validity of output and analysis. It was suggested that, in future notation studies, validity and

23 reliability should be quantifïed before reporting results obtained from system analysis. In a study of European level badmintonLiddle and O'Donoghue (1998) reported an inter- observer reliability of 94.9Yo for live analyses of rally length. It was remarked that all the rally lengths, which varied between the two obsenrers, differed by only one shot. It would be expected that an increased reliability would be shown when post match analysis is used. Wilson and Barnes (1998) measured concurrent validity by using overall percentage agreement and the authors cited Rushall (1977) when defining an acceptable limit of 80% forthis method.

2.3.6 Reliability and camera position

An important decision, which affects the number of variables able to be entered in a system, is whether to notate a match in real or lapse time. Accurate analysis is fundamental to the entire coaching process. The high speed at which most sport is played hinders detailed analysis of it. The use of video offers coaches and players an opportunity to reflect on a perforrnance and enhance their memory of it, enabling them to check performance accurately. As highlighted in the introduction at major badminton events designated areas available for performance analysts to set up a video camera vary from venue to venue and between organising NGB's. Wilson and Barnes (1998) noted the position of the video camera relative to the table tennis table in their procedure. How this

24 practical issue affects the reliability of a notation system has received no research attention to date.

2.3.7 Statisticalanalysis

In notation an observation is classified into one discrete variable category and these observations are then counted. Data grouped in this way is specifically referred to as frequency data and is an example of a nominal scale. Nominal scales do not meet the assumption of normality and are thus non-parametric (Vincent, 1995). The mode, the most frequent score, is the only measure of central tendency that can be used with nominal data. Vincent (1995) stated that a ratio scale has equal distance between scale points, zero is used to represent the absence of value and proportional comparisons can be made. Frequency data is able to meet these requirements. The increment between scale points is unity, a zero value shows no frequency, and four occurrences are twice the frequency of two occulrences, a proportional comparison. Thus it may be argued that observation frequencies also adhere to the conditions of a ratio scale. For ratio data the mean is the most appropriate central tendency measure and provides for further statistical calculations that will yield more information on the data.

25 Variability of a set of data is a measure of its dispersion from the mean. Standard deviation is the most common measure of the variability of a data set (Vincent, 1995).

Like the mean it is possible to provide further information about data from the standard deviation with additional statistical calculations. Small standard deviations from the group mean shows that the group scores are compact. When a large standard deviation occurs there is more spread among the group scores. However the sample size (N) is important in the interpretation of standard deviations. Vincent (1995) stated that there are usually five or six standard deviations within the range of a data set when N is large (- >

50) and the sample is close to a normal distribution. However if N is small, as in this study, this rule does not apply.

Sets of data can be compared when their central tendency and their variability are known.

Wells (1998) used the t-test to establish the existence of a normal playing pattern for the elite standard of play. The mean of each of three variables for eight matches was compared with those of nine matches and similarly the nine match means with ten.

Assumptions of data normality and homogeneity of variance were made in this study.

The t test is quite robust when its assumptions are not entirely met although Vincent

(1995) advised of the selection of a conservative level of confidence, (p<0.01), in such situations to avoid effors of inference. An independent t test may be used to compare the variable means of winning matches with the like means from losing matches. However

26 this statistical method cannot be applied to establish the existence of a perforrnance template as the variable means for 'n' matches and those for 'n + 1' matches are not independent of each other.

Chi-square is a non-parametric statistical technique that tests the significance of the discrepancy between the observed and the expected results. Thomas and Nelson (1996, pp.l98) stated that "observations must be independent and the categories mutually exclusive." Further limitations of Chi-square aÍe that the test does not apply well to small samples and that total frequencies should be at least twenty with cell values not less than five (Vincent, 1995). Whilst Chi-square is a common test for differences among sets of nominal frequency data (Hughes and Knight, 1995; Blomqvist et al., 1998b; Hughes and

Moore, 1998) it is not a useful statistical tool for determining a normative template.

When comparing 'n' matches with 'n + l' matches the data sets cannot be independent of each other.

2.4 The rationale of the study

Whilst Hughes and McGarry (1989) and Hughes et al. (1999) have attempted to validate their perforrnance profiles, no-one has tackled fully the problem of quantifying the data required to create a normative template. The aim of this study is, by using a rally-end

27 analysis of badminton, to examine the cumulative means of selected variables over a series of matches and determine when these means become stable. These procedures of defining normative templates will be applied for those variables, which suggest dependence on match outcome to profile winning and losing. The reliability of these data from different angles will also be quantifïed.

28 CHAPTER III

METHODOLOGY 3.1. Notation system design.

After review of previous notation literature six general areas were considered important in the development of a full rally-end analysis system for badminton. These are listed below: l. Rally description

2. Player

3. Court position

4. Action

5. Outcome

6. Time

A pilot study was undertaken to ensure the notation system devised collected relevant data in an efficient manner to meet the system requirements. The pilot match notated was the men's final of the Bath Grand Slam 1998 between Peter Knowles (England no.2) and

Darren Hall (England no.l). It was essential to distinguish between rallies so rallies were labelled 'l' to 'n' by match. The rally description variable was rally length" i.e. the total number of shots in the rally. Letter codes were allocated for the two players, 'a' and 'b', and all action and outcome variables to reduce the amount of writing necessary for notation. There were initially thirteen action variables defined. However from difficulties in defining a small number of shots two more variables were listed. Judgement was to

29 describe when no shot was played and unclassified covered unusual or lucky shots. It was also decided to differentiate between backhand and forehand shots although around-the-

head shots were considered as being forehand. The codes for action and outcome variables are listed in Table 3.1.

Table 3.1 Abbreviation codes for action and outcome variables.

Block B Low serve LS Clear C High serve HS Drop D Flick Serve FS Drive V Judgement j Flick F Hit net n Kill K Out wide o Lift L Out long q Net Return N Miss hit m Push P Winner w Smash S Error e Unclassifred U

The playing area was dissected into nine cells, (numbered 1 to 9), each side of the net, as

shown in Figure 3. l. Each side of the court was coded on an opposite diagonal so a shot

position recorded is not altered when a player changes ends. (i.e, section 1 is always the

forehand back corner for a right handed player, and section 7 is always the backhand net

for a left handed player). It was important to notate the start time of each rally as it

provided a quick future reference to that rally's data to the video recording.

30 Figure 3.1 The division of the badminton court into position cells

1 2 3

4 5 6

7 8 9

9 8 7

6 5 4

3 2 1

31 3.2 The notation database.

The experience gained from the pilot study finalised the rally-end variables and their definitions and an Access database was created in which to record them. The database tables and queries are shown in Figures3.2,3.3 and 3.4, with examples of the design

view and input/output tables shown in Figures 3.5,3.6,3.7 and 3.8. The creation and

linking of the database tables and queries was very time consuming particularly as the researcher had no previous knowledge of setting up or using a database. It was understood that typing the input data into a database table increased the probability of

recording error however this was compensated by the increased accuracy of the data

processing. The subsequent improvements in the speed of processing the match data

proved very worthwhile.

Information \¡/as recorded in the database for each rally under the column headings as

shown in Figure 3.6. An explanation of each column heading is as follows:

Rally ID - The 'nth' rally of the database, (automatically increases by one).

Rally No - The 'nth' rally (rally number) of the match.

Time S - The start time of the rally with the commencement of the service

action. The clock is zeroed with the first serve of the match.

32 Shot No The total number of shots played in the rally.

Player The player who is deemed to have hit the rally outcome shot. wÆ The nature of the rally outcome shot, a winner or an effor.

Shot Abbr The type of outcome shot hit.

To The cell position of contact between the racket and shuttle for the

outcome shot

From The cell position where the shuttle hit the floor or where it was

intended to land.

Side The racket side of the outcome shots, either forehand or backhand.

Error When an effor is made the nature of that error.

Game The game code for this game of the match.

Time F The finish time of each game.

Due to the design of the database it was necessary to make a change in the player codes,

from letters to numbers. Also an additional variable was introduced to the system from the pilot study to define the type of error made.

33 Figure3.2 The list of tables in the Access Rally-end database.

ffim :*{ Mtcrnsnil Aenr:s-s lt.l.tll9 r:n11 l)at.th;rrcl Hls

t:t lirrl t--t

Côt60þty clð3s Error Event

t:t t':--:t t=::::l t=:i::t la::l EfîI Liri-::I t-g, Gdne Match Plèyerj R¡lly 5hott

l:=ï¡ r-tl=:=l ITilI Typ€ W/E Dat nlt on

34 Figure 3.3 A list of queries in the Access Rally-end database

Flrcrnsolt Âeeess lHallu end l).rt.rh.rsel wffiffi

Al - Fr-ü match inforrotbn C3 - w/e shots by player - naly r*o by Matrh C'l - w/e shot and position by player (broad) @ -na @ F @ nt -nlmotchinformation iñil C4 - w/e ihot ånd porition by player (detaiD æ ff nz - nuåy tnf-matin by qðme d mðtch F C5 - Enor type by playa # @ nz - nuly Inf*mðtim by mdch # C6 - w/e and rdþ lmgh by phyer F ffi ns - nuly inf.matim by rrdth (in sequre) F C7 - w¡le on first { shoÈs by pleyer F S eo - ra*cn eatirq pr¡nt ü-t F CB - w/e by ra[y length Ol-tË by playa @ S eo - w¡e by qame md match # (8 - w/e by rally length 06"12 by flðya ri& @ et -wiebymotch F CB - w/e by ralþ hrìgth l3-r8 by flaya # S ez-wiepøtionbyffitch æ (B - w1e by rally lenqth l8 -24 by phyer F ff æ-wfe=hotrbymrtch æ C9 - w/e shots by position ard flayer æ ff a+ - wie shot and positim by match (lroad) H[ Eross tðb 0l-0+ Ð ffi e+ - w/e shot and position by match (dettril) E[ cross tab 0$0Ê æ fip 85 - Eror type by match E cross tðb 09-12 ffi S eS-enutypebyshotmdmatch Hi crosr tab 13-99 æ S ee - *¡" *d rdly length by match æ D.2- F ffi es - wie rhots by posäm md match F D3- EI T S co-*¡.byq*andphyer æ D90 EI T # ct -w¡e¡yploy.r @ 090 B f ff cz - wie posit¡rm bv pl¿ya @ ost - rnu and raþ lmgh by mtch and playa EI T

35 Figure 3.4 Further queries in the Access Rally-end database.

NKÑ l'{rcrosnlt Â.necss lHallv en.i l)ntðhðsel NISN

DBI - Winner ard raly lendh by retch and dayer T - RðXy Info gðrÉ I @ orz - nutty i*ømation by mdch and player (ir æqueme) ffi r-naaytnroqðre2 ffi er - uot.h irformatim þ cdegory (doubles) @ r - nUy I*ormation by match @ ez - uut.h inform¿tion by c¡teæry (¡indes) rru ff es- e*u* una frequency of WiE by rategory ff r-naÍyttogamet S e+ - w¡f tt'*s and freqøry by c*egory ffi t-naþruoqamez S es - w¡e on fir*4 shotsby rategory S ee - rt"q*n y d W[ by cateqory S Mc - aror stnts S nc-enotypebymatch F Mc - */e by game and match @ tøc - w/e by match S Mc-winningshots S oRe-enortypebymatch ff occ-*¡"Uyqareandmatdr @ om - w/e by retch El r - ot-o+ El r - os-æ ffil r-æ-rz ffi r-r:-æ

36 Figure 3.5 The design view of the rally table.

llrerr'snlt Âcccss ll-l.rllu ì ¡hlcl ñ

Lmo Intmer lncrffir*

l¿s lltJo [Ì¡lk*e¡)

37 Figure 3.6 An example of the data input table.

Itr Mrcr¡rsoll Àe ecss lHallv lahlcl

tï1:07:43i 3 50i e tÐ:07:56i 25 52,w n U0:08:39¡ 4 50: e Þ l0:09:36i 1 52, e HS It1:t9:44i 6 52tw 94i 2le n 94i 9758 94i 975! n 941 97ffi 38 0 94i wÊ1 3gt m:1f :tE 2i 5" n 94i

9762 1 978! 41 00:1 976{ 42i ûj2ú1 4 9764 43i tïl:12:14 31 52,e s7æ 44i ffij225 3: äl,e 9767 45i UO:'12:S 2i 50rw 97æ 46i 00:12:45 q{

9769i 47i U0:13:07 I I b .94 977U: 48i ff1:13:45 32 94 eitii 4e: mi4,o, 1 I 94 9772 50i ffi: 4 0 m 95 oà 977i 51i ffi: 14 34i 1 4 9771 52i U0:14:¡f6: 1 4 o ,95 9771 53i 00:14:57i 50e 1 .9Í q77À 7 h qq

38 Figure 3.7 The design view of a database query

Mrcrnsolt Aeeess lllll w/c hy eame an.l m.rtch llt¡eryl "clect

Rally l'Jo Time 5 silot No

tr) Abbr NO ID

F Abbr

39 Figure 3.8 An example of a data query output.

Mrcrnsolt Á.eeess llìll w/e hl, oame ðnd malch I -rosstah ll¡¡n¡vl ffiËTffi

...... li 19 t6 Winner 11 3: Enor 4

40 3.3 Subject and matches.

An English male player was selected for this study by the researcher after consultation with the Performance Director of the Badminton Association of England. The player was ranked in the top four in England in March 1998 and according to the English national

coaches possessed the potentialto atlun a top sixteen world ranking. His national and

international, (in Europe only), competitive matches from March 1998 through to June

1999 were video recorded for analysis. A list of the matches (Table 3.2), were fuither

subdivided by outcome (Tables 3.3 and3.4).

3.4 Equipment used.

The following list of equipment was used in the process of this research:

Digital video camera @anasonic EZI), digital tapes (Sony DV63) and tripod.

Digital video recorder (Sony DSR 30 P) with jog shuttle control.

Television (Hitachi l4').

Laptop computer (Acer Extensa 368T).

Windows '97 Access database.

4I Table 3 .2 List of matches involving Player A" in order of analysis.

I Player A England 2 Opponent Denmark 0 England tU03/98 1 Opponent Malaysia 2 Player A England 0 England t2103/98 'Wales J Player A England 2 Opponent England 0 27103/98 4 Player A England 2 Opponent England 0 Wales 28103198 5 Player A England 2 Opponent England 0 Wales 29103198 6 Opponent England 2 Player A England 0 Wales 30103/98 7 Opponent Denmark 2 Player A England 0 Bulgaria 18104/98 8 Player A England 2 Opponent Sweden 0 Bulgaria 20104198 9 Opponent Denmark 2 Player A England I Bulgaria 2U04/98 10 Player A England 2 Opponent England 0 England 0s102/99 l1 Opponent England 2 Player A England I England 06102199

Table 3.3 Match list ofwins by Player A

I Player A England 2 Opponent Denmark 0 England LU03lgE 3 Player A England 2 Opponent England 0 Wales 27103198 4 Player A England 2 Opponent England 0 Wales 28103/98 5 Player A England 2 Opponent England 0 Wales 29103/98 8 Player A England 2 Opponent Sweden 0 Bulgaria 20104198 10 Player A England 2 Opponent England 0 England 0s102/e9

Table 3.4 Match list of losses by Player A.

2 Opponent Malaysia 2 Player A England 0 England 12103198 6 Opponent England 2 Player A England 0 Wales 30/03198 7 Opponent Denmark 2 Player A England 0 Bulgaria t8104/98 9 Opponent Denmark 2 Player A England I Bulgaria 2U04198 11 Opponent England 2 Player A England I England 06/02/99

42 Table 3.5 The definitions of actions and outcomes. (Andrew, 1999)

Block a powerful shot by the opponent is blocked from mid-court, and the shuttle

passes low over the net.

Clear the shuttle is hit overhead, high into the air from one end of the court to

the other.

Drive the shuttle is hit hard from chest/shoulder height to travel parallel to the

floor towards the mid- or rear coutt.

Drop the shuttle is hit softly from overhead to travel in a downward direction

from the mid- or rear court.

Flick the shuttle is hit from net height to pass just over the opponent to the rear

court.

Judgement the shuttle is left to hit the floor out of court only for it to land in bounds.

43 Kill the shuttle is hit hard in a downward direction from above the top the net

from the front of the court.

Lift the shuttle is hit high to the back of the court from below the height of the

net.

Net Return the shuttle is struck softly to loop just over the net. Usually played from

the forecourt although sometimes played from mid-court.

Push the shuttle is struck with medium pace to travel flat and just pass to the

side of an opponent. Usually played from the mid- or front court to the

mid-court.

Smash the shuttle is hit powerfully from overhead to travel in a downward

direction from the mid- or rear court.

High Serve the shuttle is hit high in the air so that it will drop almost vertically as near

to the base line as possible.

Low Serve the shuttle flies very close to the top of the net so that it will land near to

or on the short service line.

44 Flick Serve the shuttle is hit just high enough to pass over the reach of the receiver to

the rear court

Unclassified this is an unusual or lucky shot not accounted for in the above shot

definitions.

Winner a shot that is struck so that an opponent cannot reach the shuttle before it

lands within the court boundaries or be reasonably expected to return the

shuttle back over the net if it can be reached.

Error an action that causes the loss of a point or the right to serve which is

independent ofthe action ofthe opponent

Hit net the shuttle is struck into the net

Out wide the shuttle lands outside the side line boundaries on the opponent's side of

the court

Out long the shuttle lands beyond the rear of the opponent's court.

45 The subject's badminton matches were taped using a digital camera because of its high quality of recording. All rallies were analysed using a digital video recorder to display the matches on a fourteen-inch monitor screen. Notation was by typed input into a specially designed Access database on a laptop computer.

3.5 System validation.

The action and outcome definitions were reviewed an English national badminton coach.

These variables and their definitions are given in Table 3.5. The validity of the database queries was established by analysing the reliability match by hand and comparing the analysis to the computer output (see appendix). The differences that did appear between the two analyses were discovered to be, on re-checking the hand notation data Table, to be manual counting effors. The data processing by hand was very time consuming and this one match analysis took three hours to complete with accvracy checks.

3.6 Testing system reliability.

A study was undertaken to assess the intra observer reliability of the notation system devised. The rally end variables were notated for all 162 rallies of the men's singles world championship (1999) semi-final between Peter Gade-Christensen and Fung

46 Permadi. The notation of the match into the database took three hours to complete. There was a twelve-day duration before the re-notation. A comparison was made between the

data sets ofthe first and second notation and the percentage difference of each variable was calculated (see section 3.9). However, a difference in interpretation of whether a

winner or an erïor occurred affected the records of the other variables so these rallies were not included in the percentage calculation of the other variables. In the example

shown in Table 3.6, the difference in the notation of Rally End of an error by Knowles

and a winner by Hall changed the records of Surname, Shot No, Shot, From, and To.

Table 3.6 An example of winner/error interpretation and the effect on other variables

2 00:00:17 Knowles iError 6 Net Return 97 1 2 00:00:17 Hall Winner 5 iDrop 1 7 1

The notation for the entire study was carried out by one person, however an inter-

observer test was also completed in order to assess the wider application of the system.

One of the English national coaches was employed as the second observer. The coach

had previously assisted in validating the variables and their definitions. Blank paper

templates of the recording sheet were provided for the notation together with the

variables, their abbreviations and their definitions. The notation method was carefully

47 explained and the coach was not required to record the time and game number. The data was transferred into the computer database by the researcher and the subsequent print out triple checked for any input errors. A comparison was made between the data sets of the observer and coach and the percentage difference of each variable was calculated. Once again rallies with winnerlenor differences were excluded from the percentage difference calculations of the other variables. A maximum percentage difference of ten percent discrepancy of the total observations was set for each variable in all three reliability tests.

3.7 Reliability and camera position

The Bath Grand Slam 1998 final (Peter Knowles vs. Darren Hall) was recorded from two positions, from behind the court and from the side of the court. Each view of the match was notated with a time gap of eight days between each notation. The data tables were compared for differences and the percentage differences for each variable was compared with those from the intra-reliability test differences.

3.8 Collecting the data.

Prior to using the computer database to record the subject's matches the observer had gathered experience in recording data from over twenty other matches. The data for the

48 study was notated post event using video. The jog shuttle control on the video cassette recorder was invaluable for replaying missed observations and marginal decisions at a

controlled slow speed. The rate of data collection was approximately ten minutes of video footage per half-hour real time. Regular breaks, taken approximately every hour, were taken to minimise observational and data input errors. A guide to using the rally-end database is provided in the appendix.

3.9 The analysis procedures employed.

The transfer of data from the Access database queries to the Excel program was through

exporting files between the software programs. This process eliminated errors that may have occurred by manual data transfer. Percentage difference calculations were used to

evaluate the reliability of system, for intra-, inter and camera view observations.

Percentage difference: (No. of differing observations / Total no. of observations) x 100

The ftequencies of each variable per match \¡/ere summated and the mean and standard deviation of the data set calculated. Evaluating the relative size of the standard deviation to the mean (S.D./lvlean Yo) enabled for comparison of the variability of the data between different variables.

49 S.D./TVIean o¿ : (Standard deviation / Mean) x 100

Variable differences between matches/games won by Player A and lost by Player A were

assessed in order to identify the existence of a winning and losing template. T-tests were used to evaluate the signifrcance of differences between these independent means. A

significance level of p: 0.01 was set as some assumption of the t-test were not met.

In certain cases frequency data was norrnalised to allow for true comparison of data from

matches and games of differing lengths.

Normalised Match Frequency : (Match frequency / No. of rallies in match) x 100

Normalised Game Frequency : (Game frequency / No. of rallies in game) x 50

The cumulative means of each variable were examined over a series of matches/games.

At the first point, the number of matches, '\E¡', where the cumulative mean consistently

lay within set 'limits of error' was recorded as the establishment of a normative template

of play. These limits of error are a percentage deviation (+l- l%; +/- 5Yo; +/- l0%) of the

overall data mean about the overall mean.

50 Let n the variable 'number of matches'

ð(' the variable ' number of games'

Ncet value of n to reach limits of error

Ntrr total number of matches

Cumulative mean: Sum of the frequencies of 'n' / n

Limits of error (lO%): Mean Ncrt + (Mean Ño x 0.1)

Limits of error (5%): MeanNcrl + (MeanNcrl x 0.05)

Limits of error (lyo): MeanNcrl + (MeanNo x 0.01)

5l CHAPTERIV

RESULTS Table 4.1 Results of intra-observer reliability study

Rally end r62 4 2.5 Shot no. 158 2 1.3 Shot 158 6 3.8 From 158 10 6.3 To 158 l2 7.6

Table 4.2 Results of inter-observer reliability study

Rally end 162 6 3.7 Shot no. 156 2 1.3 Shot 156 1l 7.1 From 156 l0 6.4 To 156 13 8.3

Table 4.3 Results of camera angle reliability study

Rally end 147 J 2.0 Shot no. r44 I 0.7 Shot 144 2 t.4 From r44 10 6.9 To 144 72 83

52 Table 4.4 Intra-observer observation differences between each set of variables

Judgement E Clear W 3 I 3 6 a Block E Smash W 5 8 2 J Net return E Net return W 2 3 5 3 LiftE NetreturnW 8 7 4 6 J I 4 I

5 1 J 4 5 6 5 J 5 8 5 6 2 5 6 J 5 2 6 J 2 I 6 J

Flick Lift 2t 19 Unclass. Drop 7 8 Drive Kilt Flick Drive Push Kill Drop Smash

53 Table 4.5 Inter-observer observation differences between each set of variables

Judgement E Clear W J I J 6 Net return W Lift E 8 9 2 J Net return E Net return W 8 7 5 J Lift E Net return W J 1 4 6 Net returnW Lift E 5 2 4 I Block E Smash W 5 I 4 1 5 6 J 4 2 5 5 6 5 ) 4 I 2 J 5 6 6 J 7 4 J 6

Lift Flick 18 t9 Unclass. Drop 9 8 Lift Flick Smash Drop Drive Kill Smash Drive Flick Lift Flick Drive Smash Drive Push Kill Drop Smash

54 Table 4.6 Camera view observation differences between each set of variables

Net return E Drop W 2 3 7 4 Net return W Net return E 1 2 5 6 Net return W Net return E Ja 2 9 6 6 5 J 6 J 6 9 6 9 8 6 2 6 9 5 2 2 5 4 7 J 2 9 6 a 5 9 J 6 I 4 2 5

Drop Smash 5 7 Push Flick

55 Table 4.7 Description information of the matches analysed

I 104 968 9.3 8.0 39 2 68 607 8.9 5.8 JJ a J 88 702 8.0 5.0 24 4 120 9s2 7.9 5.7 26 5 118 lt96 10.1 7.1 35 6 64 545 8.5 6.5 3l 7 97 789 8.1 5.6 28 8 78 576 7.4 4.9 27 9 t24 896 7.2 5.4 JJ 10 to4 713 6.9 4.r t9 t1 153 7252 8.2 6.3 49 Range 89 707 3.2 nla 30 Median 104 789 8.1 nla 31 Mean 101.6 836.0 8.2 5.9 31.3 St.Dev. 21.4 229.8 0.9 1.1 5.1 SD/IVfean % 2t.o 27.5 to.7 18.3 16.2

56 Table 4.8 Rally frequency information for different match groupings

ll Wins/Losses 101.6 26.6 26 6 Wins 102.0 t6.s l6 5 Losses t0t.2 37.8 37

Ho : There is no significant difFerence between the mean number of rallies

in matches won and lost by Player A

H1 : There is a significant difference between the mean number of rallies

in matches won and lost by Player A t(obs) :0.047 < t(cr): 1.833 [p:0.1, df :9]l Therefore accept Ho

Table 4.9 Shot frequency information for different match groupings

tl Wins/Losses 836.0 240.2 29 6 Wins 851.2 227.9 27 5 Losses 817.8 280.3 34

Ho : There is no significant difference between the mean number of shots

in matches won and lost by Player A

Hl : There is a significant difference between the mean number of shots

in matches won and lost by Player A t(obs) :0.218 < t(cr) : 1.833 [p:0.1, df : 9] Therefore accept Ho

s'.'l Table 4.10 Shot per rally information for different match groupings

11 Wins/Losses 8.2 1.0 t2 6 Wins 8.3 1.2 t4 5 Losses 8.2 0.6 7

Ho : There is no significant difference between the mean number of shots

per rally in matches won and lost by Player A

Hl : There is a significant difference between the mean number of shots

per rally in matches won and lost by Player A t(obs):0.169 < t(cr): 1.833 [p:0.1, df : 9]l Therefore accept Ho

58 Table 4.11 The means and percentage effor range of rallies by match (Fig a.l)

104.0 101.6 111.8 91.5 106.7 96.6 102.7 100.6 86.0 101.6 111.8 91.5 106.7 96.6 102.7 100.6 86.7 101.6 lll.8 9r.5 106.7 96.6 t02.7 100.6 ,95,:0, 101.6 It 1.8 :9'L,:5: 106.7 96.6 102.7 100.6 99.6 101.6 111.8 91.5 106.7 96.6 102.7 100.ó 93.7 101.6 I 11.8 91.5 106.7 96.6 102.7 100.6 94.1 101.6 111.8 9t.5 106.7 96.6 102.7 100.6 92.r 101.6 111.8 91.5 106.7 96.6 102.7 100.6 95.7 101.6 1il.8 91.5 706.7 96.6 102.7 100.6 96.5 101.6 111.8 91.5 106.7 96.6 102.7 100.6 101.6 101.6 tll.8 91.5 106.7 96.6 102.7 100.6

Table 4.I2 The means and percentage effor range of shots by match (Fig.a.z)

968.0 836.0 919.6 752.4 877.8 794.2 844.4 827.6 /$],,$,, 836.0 794.2 844.4 827.6 759.0 836.0 9t9.6 752.4 877.8 794.2 844.4 827.6 807.3 836.0 919.6 752.4 877.8 794.2 844.4 827.6 885.0 836.0 919.6 752.4 877.8 794.2 844.4 827.6 828.3 836.0 919.6 752.4 877.8 794.2 844.4 827.6 822.7 836.0 919.6 752.4 877.8 794.2 844.4 827.6 791.9 836.0 9t9.6 752.4 877.8 794.2 844.4 827.6 8CI3:4.'... 836.0 919.6 752.4,8XX,:8,,,,,,794.2 844.4 827.6 794.4 836.0 919.6 752.4 877.8 794.2 844.4 827.6 836.0 836.0 9t9.6 752.4 877.8 794.2 844.4 827.6

59 I

ll

*Cumulativemean *Plus 10% *Less 10% -'€FPlus 5olo #less 5%

60 Mean number of shots by

I

l! o 3h É

*Cumulative mean #Plus l0o/o 1l-ess l0% 4FPlus 5% #less 5%

6t Table 4.13 The means and percentage error range of shots per raþ by match @igure 4.3)

1 9.3 8.2 9.1 7.4 8.6 7.8 8.3 8.2 2 9.1 8.2 9.1 7.4 8.6 7.8 8.3 8.2 r tii:iti:.::a:i;t: r::it:t i it: i t.:t i tì.t:i¡i: ::::::::::::4rr: : : : : :i:::r:r:r::rr::::::i :r:ä r#rr:::::r:r:::: ::: !!i!!!iiri!-',,,,,',,',,,.,i.,.,,,:,,,,,,.,lti:'1,i,,.i,,.,',,,,,,, 'i ljÌ 8.2 ì i üig; i ì ili.'ii f f if i ; 8.6 7.8 8.3 8.2 4 8.5 8.2 9.1 7.4 8.6 7.8 8.3 8.2 5 8.9 8.2 9.1 7.4 8.6 7.8 8.3 8.2 6 8.8 8.2 9.1 7.4 8.6 7.8 8.3 8.2 7 8.7 8.2 9.1 7.4 8.6 7.8 8.3 8.2 ir.i'::':..-i8::::;::i.:i:ì:i::iiiiÌi:i$$:i:::iiÌiiii:ì: 8.2 9.7 7.4 ï ¡" tr6i¡,,,¡11¡¡;i f.. ¡;;;, 8.3 8.2 9 8.4 8.2 9.1 7.4 8.6 7.8 8.3 8.2 l0 8.2 8.2 9.1 7.4 8.6 7.8 8.3 8.2 ll 8.2 8.2 9.1 7.4 8.6 7.8 8.3 8.2

62 *Cumulative mean *Plus l0% ...Gl.ess l0% +FPlus 5% #less 5%

63 Table 4.74 Shot and rally information for first games

I 46 428 9.3 8.0 39 2 44 373 8.5 6.1 JJ J 40 350 8.8 5.4 24 4 66 507 7.7 5.0 2l 5 48 476 9.9 7.2 33 6 3t 299 9.6 7.2 31 7 37 292 7.9 6.3 28 8 34 2ro 6.2 4.3 22 9 34 268 7.9 5.4 25 l0 57 401 7.0 4.4 t9 1l 53 456 8.6 5.8 24 Range 297 3.7 nla 20 Median 373 8.5 nla 25 Mean ¡f,9,f...... i.Iii...... ,,8.3',,,,,,, 5.9 27.2 ,,, St.Dev. 1 00,3 l .) , ,,, , 1.3 6.3 SD/IVÍean % 27.2 14j 21.2 23.1

64 Table 4.15 Shot and rally information for second games

1 58 540 9.3 8.1 39 2 24 234 9.8 5.2 l9 3 48 352 7.3 4.5 18 4 54 445 8.2 6.5 26 5 70 720 10.3 7.t 35 6 JJ 246 7.5 5.6 23 7 60 497 8.3 5.2 2t 8 44 366 8.3 5.3 27 9 58 4t6 7.2 5.7 55 10 47 3t2 6.6 3.6 t5 1l 50 414 8.3 5.2 26 Range 46 486 3.6 nla 24 Median 50 414 8.3 n/a 26 Mean',,,,,49.:6,,,,," :::412,.9' :: .,,'.,,',8;3 5.6 25.6

St.Dev.,,,15,.0,,,,,,,,, 1.61,;,8.....,,,.'.i...,.l.:.l,,,, ' 1.2 7.5 iD/lVlean ot 30.2 39.2 12.8 21.5 29.4

65 Ho : There is no significant difference in the mean number of rallies

between the first and second games of matches

Hl : There is a significant difference in the mean number of rallies

between the first and second games of matches t(obs) : 0.910 < t(cr) : 1.725 [p:0.1], dÈ20 Therefore accept Ho

Ho : There is no significant difference in the mean number of shots

between the first and second games of matches

Hl : There is a significant difference in the mean number of shots

between the first and second games of matches t(obs) :0.763 < t(cr) : 1.725 [p:0.1], df¿I Therefore accept Ho

Ho : There is no significant difference in the mean number of shots per rally

between the first and second games of matches

Hl : There is a significant difference in the mean number of shots per rally

between the first and second games of matches t(obs) : 0.204 < t(cr) : 1.725 [p:0.1], df :20 Therefore accept Ho

66 Table 4.16 The means and limits of error of rallies by game (Fig.a.a)

46 46.0 47.0 51.7 42.3 49.4 44.7 47.5 46.5 58 52.0 47.0 5t.7 42.3 49.4 44.7 47.5 46.5 44 49.3 47.0 51.7 423 49.4 44.7 47.s 46.5 24 43.0 47.0 51.7 423 49.4 44.7 47.5 46.5 40 42.4 47.0 51.7 42.3 49.4 44.7 47.5 46.5 48 43,.3 47.0 5I:7 42,3:49.4 44.7 47.5 46.5 66 46.6 47.0 51.7 42.3 49.4 44.7 47.5 46.5 54 47.5 47.0 51.7 42.3 49.4 44.7 47.5 46.5 58 48.7 47.0 51.7 42.3 49.4 44.7 47.s 46.5 70 50.8 47.0 5r.7 42.3 49.4 44.7 47.s 46.5 3l 47.0 51.7 42.3 ,49,4,, 44..7' 47.5 46.5 33 47.7 47.0 51.7 42.3 49.4 44.7 47.5 46.5 37 46.8 47.0 5t.7 42.3 49.4 44.7 47.5 46.5 60 47.8 47.0 51.7 42.3 49.4 44.7 47.5 46.5 34 46.9 47.0 51.7 42.3 49.4 44.7 47.5 46.5 44 46.7 47.0 51.7 42.3 49.4 44.7 47.5 46.5 34 45.9 47.0 51.7 42.3 49.4 44.7 47.5 46.5 58 46.6 47.0 5r.7 42.3 49.4 44.7 47.5 46.5 32 45.8 47.0 51.7 42.3 49.4 44.7 47.s 46.5 57 46.4 47.0 51.7 42.3 49.4 44.7 47.5 46.5 47 464 47.0 57.7 42.3 49.4 44.7 47.5 46.s 53 47.0 51.7 42.3 49.4 44.7 ,,41,.' 46,5 50 46.9 47.0 57.7 42.3 49.4 44.7 47.5 46.5 50 47.0 47.0 51.7 42.3 49.4 44.7 47.5 46.5 Sum tt28 Mean 47.0 St.Dev. t2.o SD/Ave % 25.6

67 ,Number of games,

*Cumulative Mean +'+lïVo 4'-l0o/o 1l-'*5o/o +'-5yo *'*lo/o #'-lo/o

68 Table 4.17 The means and limits of error of shots by game (Fig. a.5)

I 428 428.0 383.2 421.s 344.9 402.3 364.0 387.0 379.3 2 540 484.0 383.2 421.5 344.9 402.3 364.0 387.0 379.3 J 373 447.O 383.2 421.5 344.9 402.3 364.0 387.0 379.3 4 234 393.8 383.2 42t.5 344.9 402.3 364.0 387.0 379.3 5 350 385.0 383.2 421.5 344.9 402.3 364.0 387.0 379.3 6 352 379.5 383.2 421.5 344.9 402.3 364.0 387.0 379.3 7 507 397.7 383.2 427.5 344.9 402.3 364.0 387.0 379.3 8 445 403.6 383.2 421.5 344.9 402.3 364.0 387.0 379.3 9 476 411.7 383.2 421.5 344.9 402.3 364.0 387.0 379.3 720 442.s 383.2 421,.s 344.9 402.3 364.0 387.0 379.3 299 429.5 383.2 42t.5 344.9 402.3 364.0 387.0 379.3 246 ,,,,,41:4.2 383.2 421,,5',344.9, 402.3 364.0 387.0 379.3 292 404.8 383.2 421.5 344.9 402.3 364.0 387.0 379.3 497 411.4 383.2 421.5 344.9 402.3 364.0 387.0 379.3 1'...... :402;3: l,...... L.5... 2to , ,,,,3,97;,9 ,,,, 383.2 421.5 344.9 364.0 387.0 379.3 t6 366 395.9 383.2 421.s 344.9 402.3 364.0 387.0 379.3 t7 268 388.4 383.2 421.5 344.9 402.3 364.0 387 0 379.3 l8 4r6 389.9 383.2 421.s 344.9 402.3 364.0 387.0 379.3 l9 2t2 380.6 383.2 42t.5 344.9 402.3 364.0 387.0 379.3 20 40r 381.6 383.2 42r.5 344.9 402.3 364.0 387.0 379.3 2l 3t2 378.3 383.2 42r.5 344.9 402.3 364.0 387.0 379.3 1',' 456 38r'8 : 383.2 42r.5 344.9 402.3 364.0 '387:0: 3:79.3 23 4t4 383.2 383.2 421.5 344.9 402.3 364.0 387.0 379.3 24 382 383.2 383.2 42t.5 344.9 402.3 364.0 387.0 379.3 Sum 9196 Mean 383.2 St.Dev. 118.9 SD/Ave % 31.0

69 *Crunulativemean +'+10olo +'-lOVo +l-'+syo 4ts'-s%o #t*lo/o #'-lo/o

70 Table 4.18 The means and limits of error of shots/rally by game (Fig. a.6)

9.3 9.3 8.2 9.0 7.4 8.6 7.8 8.3 8.1 9.3 9.3 8.2 9.0 7.4 8.6 7.8 8.3 8.1 8.5 9.0 8.2 9.0 7.4 8.6 7.8 8.3 8.1 9.8 9.2 8.2 9.0 7.4 8.6 7.8 8.3 8.1 8.8 9.t 8.2 9.0 7.4 8.6 7.8 8.3 8.1 7.3 .8rS 8.2 8.6 7.8 8.3 8.1 7.7 8.2 9.0 7.4 8.6 7.8 8.3 8.1 8.2 8.2 9.0 7.4 8.6 7.8 8.3 8.1 9.9 8.2 9.0 7.4 8.6 7.8 8.3 8.1 10.3 8.2 9.0 7.4 8.6 7.8 8.3 8.1 9.6 8.2 9.0 7.4 8.6 7.8 8.3 8.1 7.5 8.2 9.0 7.4 8.6 7.8 8.3 8.1 7.9 8.2 9.0 7.4 8.6 7.8 8.3 Ll 8.3 8.2 9.0 7.4 8.6 7.8 8.3 8.1 i8,6 6.2 8.2 9.0 7.4 .,7:.8 8.3 8.1 8.3 8.2 9.0 7.4 8.6 7.8 8.3 8.1 7.9 8.2 9.0 7.4 8.6 7.8 8.3 8.1 7.2 8.2 9.0 7.4 8.6 7.8 8.3 8.1 6.6 8.2 9.0 7.4 8.ó 7.8 8.3 8.1 7.0 8.2 9.0 7.4 8.6 7.8 8.3 8.1 6.6 8.2 9.0 7.4 8.6 7.8 8,3, ,8:.I. 8.6 8.2 8.2 9.0 7.4 8.ó 7.8 8.3 8.1 8.3 8.2 8.2 9.0 7.4 8.6 7.8 8.3 8.1 7.6 8.2 8.2 9.0 7.4 8.6 7.8 8.3 8.1 Sum 196.7 Mean 8.2 St.Dev. l.l SD/Ave % 13.6

7I :Number of games,,,,,,,,,,,,,,,,,,

*Cumulative Mean +'+lj%o 4'-l0o/o 1I-'+5%o #'-5Yo +'+lyo #'-lo/o

'12 Table 4.19 Summary of the number of matches to establish variable templates

Rallies 4 10 >ll matches Shots 2 9 >ll matches Shots/rally J 8 >ll matches All variables 4 10 >11 matches

Table 4.20 Summary of the number of games to establish variable templates

Rallies 6 1l 22 games aa Shots t2 15 LL games Shots/rally 6 15 2t games All variables l2 15 22 games All variables 6 7 1l matches

73 Table 4.21 Frequency of winners by match outcome for Player A

1t Wins/Losses 23.6 8.7 37 6 Wins 29.7 4.4 t5 5 Losses 16.2 63 39

Ho: There is no significant difference between the mean number of winners

hit by Player A in matches won and lost by Player A

Hl : There is a significant difference between the mean number of winners

hit by Player A in matches won and lost by Player A t (obs) : 4.192 > t (cr) : 3.250 [p : 0.01, df : 9] Reject Ho, accept Hl

Table 4.22 Frequency of errors by match outcome for Player A

l1 Wins/Losses 28.2 5.9 2t 6 \{ins 25.4 5.3 2T 5 Losses 31.5 5.1 l6

Ho : There is no significant difference between the mean number of errors

hit by Player A in matches won and lost by Player A

Hl : There is a significant difference between the mean number of errors

hit by Player A in matches won and lost by Player A t (obs) :1.932 > t (cr): 1.833 [p:0 l, df :9] - Accept Ho, required p:0.01

74 Table 4,23 Frequency of winners by match outcome for Opponent

11 Wins/Losses 22.1 5.6 25 6 Wins 18.2 3.2 l8 5 Losses 26.8 3.8 t4

Ho : There is no significant difference between the mean number of winners

hit by opponent in matches won and lost by Player A

Hl : There is a significant difference between the mean number ofwinners

hit by opponent in matches won and lost by Player A t (obs) :4.080 > t (cr) :3.250 [p : 0.01, df :9] Reject Ho, accept Hl

Table 4.24 Frequency of errors by match outcome for Opponent

1l Winsilosses 26.0 4.9 19 6 Wins 26.5 6.7 25 5 Losses 25.4 1.9 8

Ho : There is no significant difference between the mean number of errors

hit by opponent in matches won and lost by Player A

Hl : There is a significant difference between the mean number of errors

hit by opponent in matches won and lost by Player A t (obs) :0.352 < t (cr) : 1.833 [p : 0.1, df : 9] Accept Ho

75 Table 4.25 Frequency of winners by game outcome for Player A

t4 Wins 14.t 2.8 20 l0 Losses 8.4 4.2 50

Ho : There is no significant difference between the mean number ofwinners

hit by Player A in games won and lost by Player A

Hl : There is a significant difference between the mean number of winners

hit by Player A in games won and lost by Player A t (obs) :3.121>t (cr) : 2.819 [p : 0.01, df: 22] Reject Ho, accept Hl

Table 4.26 Frequency of errors by game outcome for Player A

t4 Wins t2.l 3.6 30 10 Losses 17.0 4.5 26

Ho : There is no significant difFerence between the mean number of errors

hit by Player A in games won and lost by Player A

Hl : There is a significant difference between the mean number of errors

hit by Player A in games won and lost by Player A t (obs) : 2.968 > t (cr) : 2.819 [p : 0.01, df : 22] Reject Ho, accept Hl

76 Table 4.27 Frequency of winners by game outcome for Opponent

74 Wins 9.2 2.5 27 10 Losses 14.0 4.7 JJ

Ho : There is no significant difference between the mean number of winners

hit by opponent in games won and lost by Player A

Hl : There is a significant difference between the mean number ofwinners

hit by opponent in games won and lost by Player A t (obs) : 3.254 > t (cr) : 2.819 [p : 0.01, df : 22] Reject Ho, accept Hl

Table 4.28 Frequency of errors by game outcome for Opponent

74 Wins 14.5 4.8 33 10 Losses 10.7 3.9 36

Ho : There is no significant difference between the mean number of errors

hit by opponent in games won and lost by Player A

Hl : There is a significant difference between the mean number of errors

hit by opponent in games won and lost by Player A t (obs): 1.900 > t (cr) :1.717 [p:0.1, df: 22] Ãccept Ho, p:0.01 required

77 X'igure 4.7 Player

JFCumulative Mean ++10% +-ljVo +I-+syo +-5yo

78 (tsr Õ ::,. || t2.0 ....'' :ì]::: :.1:

00

-|ts Cumulative Mean -++10% + -l0%o +l- +5Vo + -5yo

79 JtsCumulative Mean + +lÙVo + -lïyo +l- +5yo --€- -5%

80 JtsMean 4+I0o/o -æ-l0yo 1l-*5o/o +--5%

81 t2

JtsCumulative Mean -++lj%o + -10% 1F-+5o/o +- -5Vo

82 -'t

*Mean '++lÙVo 4-l0o/o +l-+syo +-syo

83 JtsMean ++lïyo +-70Vo +l-+syo +-5Yo

84 *Mean 4+l0o/o +-l0%o 1l-+5o/o +-sYo

85 Table 4.29 Summary of the number of games to establish a winner

and an error profile when Player A wins

Player As winners 5 5 10 games Player As errors 10 ll t2 games Opponent's winners 4 10 ll games Opponent's errors 9 10 t2 games All variables l0 11 12 games

Table 4.30 Summary of the number of games to establish a winner

and an error profile when Player A loses

Player lt's winners 8 >10 >10 games Player As errors 4 9 >10 games Opponent's winners 4 >10 >10 games Opponent's effors 8 >10 >10 games All variables 8 >10 >10 games

86 -)tsCumulativemean {FPlus l0% +Less l0olo -*FPlus 5% #less 5olo

87 *Cumulativemean -#Plus l0% +Less 10olo #Plus 5% #less 5%

88 Less 5% + less 5o/o

89 -)FCumulativemean -#Plus lÙYo +Less l0% -&Plus 5% #less 5%

90 3

: : : : 6: : : : : :: : : : : : : : :.7

Number of 'matches'

-*Cumulativemean êPlus lïVo 4Less l0oá 4l-Plus 5% -#less 5%

91 o

Number of

*Cumulative Mean 4 +l0o/o + -lïyo +I-+syo 4I- -5o/o

92 ...&lessîo/o -l(-Cumulativemean *Plus ljYo +Less l0% 4l-Plus 5o/o

93 {tsCumulative Mean *+10% +--l0o/o 1l-+5Vo +-5Vo

94 Table 4.31 Overall surnmary of ñplfor all variables measured at each limit of error

Rallies Both Match Any 4 X Shots Both Match Any 2 X Both Match J x Rallies Both Game Any

Smash Winners A Match (N) Any I (6) o Smash Errors A Match (N) Any 8 o Smash Winners opp Match (N) Any 7 o Smash Errors opp Match cN) Anv x o Position 3 Winners A Match (N) Anv 6 l0 o Position 3 Errors A Match (N) Anv 6 l0 o Position 3 Winners Opp Match (N) Anv 7 ro (8) o Position 3 Errors Opp Match (N) Anv 6 x o

x - ñBl not established with the matches / games available o - ñBl not assessed

N - Normalised data

*NB - shaded area shows game values not match values ofÑsl

from the cumulative means plot. Bracketed values are subjective amendments made

95 CHAPTERV

DISCUSSION 5.1.1 Intra-observer reliability.

A match consisting of a large number of rallies was chosen for the intra-observer reliability study. For each rally end variable 162 observations were made. This was approximately three times the number of rallies in the validity study conducted by

Blomqvist et al. (1998). The percentage disagreement for all five variables was less than

the ITYo limit set. The variables of 'Rally end', 'Shot No.' and 'Shot' were over 95o% reliable (Table a.l). The term'percentage difference'was used in preference to

'percentage error' because it was not certain which record of the two observations was the true representation, if either. The lowest variable disagreement was that of 'Shot No.' which exhibited 98.7% reliability. Fewer disagreements than expected were made

between notation and re-notation of winners or errors (2.5%) and shot type (3.8%). The

observer's responsibility for the design of the whole system meant that the variable

definitions were very well understood which might explain these fewer disagreements.

Positional variables were the least reliable as in all of the three reliability investigations,

(discussed further in sections 5.1.2 and 5.1.3).

96 5.1.2 fnter-observer reliability.

The researcher (a badminton performance analyst) had considerable badminton analysis experience and was obviously familiar with the system. The national coach involved in the inter-observer test was considered the 'badminton expert' and it was ensured he had a good working knowledge of the system and the definitions before commencement of notation. In accordance with the validity and reliability findings of Wilson and Barnes

(1998) for table tennis, these attributes enabled both researcher and expert to use the badminton system in a valid and reliable way. Disagreements between researcher and

expert were more marked for rally outcome (3.7%) and type of shot played (7 1%) when

compared to the intra-observer study, (2.5% and 3.8Yo respectively). This occurred

despite the expert's prior agreement with the variable's definitions. This shows that

disagreements will occur between observers even for well-defined variables. Shot

differences in shot observations were principally due to the interpretation of shuttle

height, (Table 4.5). The lift/flick, flick/drive and drivelkill have a'grey area' between

them in terms of height, which is very difficult to clarify through a definition. Similar

difficulties arose between the smash and the drop shot in the grey area of pace It is within

the subsections of these two badminton shots, the half smash and the fast drop, where the

observation confusion occurred. These shots were not originally defined because of

concern about suffrcient frequency counts being made. In the consideration of a winner or

97 an error for the rally end, the quality of the last but one shot of the rally (n-l) \Ã/as a difücult area of judgement. All of the inter-observer rally-end observations were a disagreement in (n-1) shot quality, (Table 4.5).

As for the intra-observer study, rally length only ever differed by one or two shots. This reliability (98.7%) was greater than Liddle and O'Donoghue (1998) who stated an inter- observer reliability of 94.9Yo for live analysis of rally length. A higher reliability than reported by Liddle and O'Donoghue (1998) was expected, as post match analyses provides the opportunity to replay a rally if shot count is lost rather than take an educated guess. The positional variables, 'To' and 'From' showed 6.3Yo and 8.3% disagreement respectively between the two sets of data. In fact the 'To' variable showed the highest disagreement in all reliability tests. The definition of the 'To' variable contained the phrase, "where the shuttle intended to land" for those occasions where the shuttle did not reach the opponent's court, These imaginary shuttle outcomes may have led to observational differences where none truly existed. For example, a straight smash into the net from the forehand corner may be marked 'from 1, to 6' in the first data set and 'from l, to 3' in the second. The observer will record a straight smash in both instances however a disagreement will be marked.

98 5.1.3 Reliability and camera angle.

As mentioned in the introduction, camera position can be restricted to areas designated by the organising National Governing Body and thus the choice of camera angle and height is beyond the control of the cameÍa operator. The reliability test between two sets of match data notated from different positions relative to the court was to address this limitation and stimulate further research in this area. There is little difference in the reliability of the five variables between the intra-observer test and the camera angle test,

(Tables 4.1 and 4.2). This finding would suggest those differences in observation from the two camera angles is a consequence of intra-observer error. In fact percentage disagreements for 'Rally end' and 'Shot' observations from different camera positions

are less than inter- and intra-observer reliabilities. The side camera presents a good view along the line of the net so it is easier to judge the quality of shot tight to the net. This influences an observer's decision on whether the rally-end shot is a forced error (winner) or an unforced error (error). In all the reliability studies, eight from thirteen winner or

error disagreements arose from interpretation of the quality of the net shot. For example an apparent lift error should be considered forced if the previous net shot was of

exceptional quality, reversing the rally outcome to a net return winner.

99 A subjective observation of the whole notation process was that many of the video replays were made to confirm the positional variables. The position cell of racket to shuttle contact was not easy to ascertain from either camera angle. An overhead camera view would be useful for observation of position. However by simplifying the positional variables the time taken to record the rally-ending situation would be less. One improvement to the system would be to change the design of 'To' variable from a cell position to a direction. Shots hit from the sides could be labelled as straight (s), crosscourt (x), or middle (m). Shots from the centre of the court may be hit to the middle

(m), to the left (l) or right (r). A reduction in the number of positional cells from nine to six, or perhaps to four, was considered. As badminton players make particular use the mid- and centre court areas in their play, the detail of the positional variable 'From' would be lost.

Only two camera angles were used for the reliability study, both from a raised balcony.

For a fuller investigation into this aÍea, Íecording from a wider variety of camera angles and heights is required. It is suggested that future studies reference their camera position relative to the playing area when post match analysis is used. Also when notating live, observer position should also be indicated. It is also important to register the type of video footage used. Television coverage of badminton can show replays from many angles, which allows for more accurate notation, (or greater indecision). Whilst none of

100 the match recordings were taken from the extreme side view, different cameÍa positions were used to record some of the matches. The reliability fïndings (Tables 4.7 and 4.3)

address this limitation of the study. In conclusion this study has shown that for the two camera positions used, the angle of view of the court had no effect on the reliability of the

badminton rally-end notation system.

5.2 Research design.

The main limitation of the study was that the subject chosen sustained an injury for much

of the 1998/1999 badminton season. This culminated in only eleven matches recorded for

analysis. Consequently the researcher did not have the flexibility to reject a match from

the database if it were a one-sided contest. One could argue exists that one-sided matches

are part of a player's profile. However a performance analyst wishes to profile close

competitors with whom the home player should expect a close match, thus it will be

beneficial to profile the opponent under this level of competitiveness. The quality of

player A's opponents ranged from, a top five world ranked player to an English county

player. In accordance with the recommendations of Hughes (1999) it was desirable to

reference Player A's templates with those of the opponents. However the range of

playing standard limited the application of any templates to other groups of players.

10r One may debate about the use of mean calculations on frequencies (nominal data) and the use of further statistical tests (t-tests) on these means. However, means were used in this study on the foundation that frequency data would fit most of the assumptions of ratio data, on which means are a valid descriptor, and the dearth of any credible options. There are statistical limitations in establishing a normalised template of perficrmance variables.

The data of the 'nth' match and the (n+l) match are dependent by nature. This rules out the use of chi-square analysis and the independent t-test to evaluate any differences between the data. However a variable template would be established when its mean value is proven to be stable over a succession of matches. A normative template of play was considered established at the first point, (number of matches, N(s)), where the cumulative mean consistently lay within set 'limits of error'. These limits of error are a percentage deviation of the overall mean, (Mearyr¡), about the Mean

A graphical representation of a cumulative mean enabled the researcher to see the trend of the plot and not just the point of long term entry into the limits of error as the cumulative mean / limits of error table (e.g. Table 4.16). Furthermore a cumulative means plot will show up any extreme frequencies and visually impress their effect on the stability of the plot, (e.g. Figures 4.9 tr8/9, 4.15 17 and 4.24 n:8). How these stable

to2 cumulative means relate to statistical inference is a future dilemma for further mathematical investigation.

It is not conducive in attempting to establish a normative template to have an 'extreme' match as the last match in an analysis sequence, because of the effect on sample mean

and the limits of error (see Figure 4.13). However the eleventh match in the series was by

far the longest match, in terms of rallies and shots. The number of match rally-ending

situations in a match will affect the frequencies of winners and elrors of both player and

opponent. Thus most data was normalised to rounded figures of one hundred rallies

representing a match, and fifty rallies representing a game. The average number of rallies

per match was 102 [01.6], (Table 4.15), and for a game was 47 (Table a.l6). The

benefits of normalising data (Hughes, 1999) was discussed in the literature review this

has a further benefit of enabling future inter study comparisons.

5.3 The descriptive summary data.

The summary data and descriptive statistics for the number of shots, rallies and shots per

rally for each match, is displayed in Table 4.7. The total number of shots ranged from

545 for a one sided match to 1252 for a hard fought match played to three sets. The same

two matches, both lost by Player A incidentally, also gave the extremes of the total

103 number of rallies, 64 and 153 respectively. The average match of Player A has 102 rallies and 836 shots with a mean of eight (8.2) shots per rally. From calculations of rally and shot timings in Liddle and O'Donoghue (1998), there were approximately nine (9.1) shots per rally on average in European circuit badminton (men's singles).

The standard deviation/mean percentage (S.D./lvlean Yo) provided a between variable comparison of the size of the standard deviation. The S.D./MeanYo values ranged from

8% (Player A's errors / 100 rallies when Player A loses) to l27Yo (Player A's winners from position 6). It was expected that for variables with a larger S.D./IVIean Yo, a greater value of Not would be required to establish a template than in those with a smaller

S.D./lVfean o/o. However the relationship between S.D.iIVfean Yo and\r¡, if any existed, is an area for future research.

5.4 Establishing templates of winning and losing

Recording matches played only in Europe addressed the potential limitation of extreme environmental factors. Players play a different way in the heat and humidity of the Asian countries, and thus might have two templates of play. Similarly some studies (Hughes and Robertson, 1998; Hughes and Moore, 1998), suggested that a player might have a winning and a losing profile. Therefore the data was examined to discover whether this

104 occuffed for Player A and Opponent. A t-test is valid test on group means in this case because the two groups of matches, those won and those lost, are independent. A t-test on the means of 'rallies per match' for matches (100 rallies) won and matches lost by Player

A revealed no significant difference between them (Table 4.8). Furthermore no significant differences were exposed for t-tests on the means of 'shots per match' (Table

4.9) andthe means of 'shots per rally' (Table 4.10). The implication of these t-test results was that a match template consisting of these variables would be independent of match outcome. However t-test results on norrnalised winners and errors, hit by Player A

(Tables 4.21 and 4.22) and the opponents (Tables 4.23 and 4.24), showed that whilst the frequency of winners were related to the match outcome, the frequency of effors were not. Further t-tests on winners and effors per game (50 rallies) showed significant differences in both for Player A between match outcome. For the opponent group the null hypothesis was rejected when p:0.1 because a value of p:0.01 was necessary to compensate for infringements on t-test assumptions. These t-test results suggest both player and opponent will possess a winning and losing template of rally-ending play.

5.5 Establishing templates of matches and gâmes

There are two benefits of analysing cumulative means of games rather than matches.

Firstly a larger Not will reduce the effect of extreme frequencies on the mearyr¡, and

105 better represent the true mean leading to more accurate limits of error. This is particularly relevant when eleven matches are split in six winning matches and five losing matches.

Secondly, play can change over the course of a match that can lead to match means being a performance compromise. Play can change throughout a game, although it is usually after an inter-game break, when a player has time to reflect on his performance and receives advice from a third party, when a change in the pattern of play is noticeable.

The eleven matches consisted of twenty-four games, fourteen won and ten lost by Player

A. A t-test on the game one and game two means, independent groups, established no

significant differences in the numbers of rallies, shots and shots per rally, as a

consequence of game order. Game three means were not tested as only two matches went to three sets so the assumption was made that these third games did not difïer

signifïcantly from fïrst and second games. A further assumption was made that no

significant differences existed between games one, two and three in the other variables.

5.ó The definition of templates

The number of games andlor matches, \r; for each performance variable required to

attain a stable cumulative mean within the set limits of error is summarised in Table 4.31.

These values of Nel, (shaded areas) were calculated from the cumulative mean tables

106 (e.g. Table 4.17). The fìgures, based on these tables, provided visual evidence of the stability of the cumulative means. Also the visual impact of figure's plot lead to recommendations for adjustment on \n¡ values due to extreme frequency counts. For example, Nu) : 12 for'shots per game' at the 80% confidence level. When compared with the other values of ñBt games (Table 4.20) it was not in keeping with the data type.

The number of shots played in the tenth notated game \ryas unusually high (Freq. trot:

720, Mean (24) : 383) which took the cumulative mean temporarily out of the limits of error, as shown clearly in Figure 4.4. Recommendations for amendment of the ñpt value

are shown as bracketed values in Table 4.31.

The examination of the cumulative means by games and by matches for rallies, shots and

shots per rally provided for verification of Nrrl. However the game Nt¡l values (Table

4.20) were all less than the equivalent N1rl matches previously established (Table 4.19), if

the 'shots per game' amendment above is accepted. Direct comparison between games

and matches will always be estimation as there are possibly or two three games in a

match. As discussed in section 5.5 this may be because there is less within game variation

of play than within match variation. If a game template is established with less data than

for a match then it is more efficient for the performance analyst to examine data in this

way, frequencies permitting. Investigation of other data is necessary to confirm or deny

these conclusions.

107 From the derived values of NrB), (Table 4.31)it can be clearly seen that the number of matches to constitute a template varied between the types of data and the individual variables. A performance template is essentially a collection of individual variable templates and the matches necessary to establish such a template should equate to the largest variable value of ñrt An estimation of NrBl for the four types of data identified in this study, (descriptive match data, winner and error data, shot winner and error data and position winner and error data) is 3, 4, 6 and 7 with l0% limits of error and 7,5, 8,and 10 at the 5Yo level. However the study was unable to establish templates of most combination variables, as match frequencies, let alone game frequencies, were too small.

The number of matches to be analysed will also depend on the level of confidence required. The greater level of confidence required that a template will accurately represent performance the greater the number of matches to analyse. One may question whether a template established within limits of error of ßYo is a sound assessment of an opponent, and whether it would support or enhance the subjective opinions of the coach or player. This study was inspired a drive for effrciency of notation procedures, to reduce the number of matches analysed to a minimum whilst maintaining confidence in the findings.

108 Bland and Altman (1986) compared the agreements between two sets of data with a

simple plot of one test measurement against the other. A further plot of the differences

between the test measurements against their means, the Bland and Altman plot, may be

more informative (Nevill, 1996) in the assessment of agreement. The plot of cumulative

means over a number of matches, with reference to the methods of agreement comparison

described by Bland and Altman (1986) stimulates further theoretical research into the

application of statistical methods to establish performance templates

Another area of attention for future research is that of the relationship between the size of

the S.D./VleanYo and ñet. If a relationship and an agreement can be establish then the

use of the S.D./Mean Yo of a data set to estimate it's ñs) would be an effrcient way of

establishing a template. In future research, when declaring a set of data as a performance

template a consideration as to the stability of the mean of that data should be made. This

cumulative mean plot is a method which shows that means are stable or are reaching

stability and it is not only applicable to badminton or racket sports, but to sport in

general.

109 CHAPTERVI

CONCLUSIONS The notation system devised to collect rally-ending datafor badminton is valid and reliable for the men's singles discipline. Similar reliability results would be expected for other disciplines of badminton. However further reliability analyses, particularly of doubles play would support this supposition. The viewing angle of the badminton court had no effect on the reliability of the notation of rally ending situations. Further examination of this area, particularly for different camera heights, is required.

The number of matches N(E), required to establish a template depends on the type of

data. For the badminton men's singles player analysed descriptive match data templates

were achieved in three to seven matches depending on the limits of error used and were

independent of match outcome. For outcome data, winners and errors, players possessed

a winning profile and a losing profile. Generally it took four to five matches to establish

templates of both of these. However establishing templates of combination variables was

diffrcult as frequency counts were low and standard deviations were high. Templates

were established for the highest frequency combination variables in six to ten matches.

Future studies that proclaim data as a template of performance should provide supportive

evidence that the variable means are stable. A cumulative means plot over a series of

matches is one such technique.

ll0 CHAPTERVII

REFERENCES Andrew, M. (1999), Badminton Level II Coaching Manual. Milton Keynes:

Badminton Association of England.

Blabd, J.M. and Altman, D.G. (1986), Statistical methods for assessing agreement

between two methods of clinical measurement. Lancet, 1,307 - 310.

Blomqvist, M., Luhtanen" P. and Laakso, L. (1998), Validation of a notation system in

badminton. Journal of Human Movement Studies, 35,137 - 150.

Blomqvist, M., Luhtanen, P. and Laakso, L. (1998b), Game performance and game

understanding in badminton of Finnish primary school children. In A. Lees et al.

(eds) Science and Racket Sports II. London 8. &, F.N. Spon, pp.27l -221.

Brown, D. and Hughes, M. (1995), The effectiveness of quantitative and qualitative

feedback on performance in squash. In T. Reilly et al. (eds) Science and Racket

Sports. London: E. & F.N. Spon, pp. 232 - 237.

Dias, R. and Ghosh, A.K. (1995), Physiological evaluation of specifrc training in

badminton. In T. Reilly et al. (eds) Science and Racket Sports. London: E. &

F.N. Spon, pp. 38 - 43.

1ll Downey, J.C. (1973), The Singtes Game. London: E.P.Publications

Franks, I.M. (1993), The effects of experience on the detection and location of

performance differences in a gymnastic technique. Research Quarterly for

Exercise and Sport,64 (2),227 -231.

Franks, I.M. and Millar, G. (1986), Eyewitness testimony in sport. Journal of Sports

Behaviour,9,39 - 45.

Franks, I.M. and Millar, G. (1991), Training coaches to observe and remember. Journal

of Sports Sciences, 9,285 -297

Franks, I.M., Goodman, D. and Millar, G. (1983), Analysis of performance: qualitative or

quantitative. Scientific Periodical in Research and Technology of Sport,

March.

Hanks, P. (ed.) (1979), Collins English Dictionary. Glasgow: William Collins Sons and

Co. Ltd.

t12 Hitchcock, G. (1990), Profiles and Profiling: A practical introduction. Harlow:

Longman Group Ltd.,

Hong, Y., Robinson, P.D., Chan, W.K., Clark,C.R. and Choi, T. (1996), Notational

analysis on game strategy used by the world's top male squash players in

international competition. The Australian Journal of Sciences and MedÍcine in

Sport, 28 (1), 17-22.

Hughes, M. (1985), A comparison of the patterns of play of squash. In I.D.Brown et al.

(eds) International Ergonomics '85. London: Taylor and Francis, pp. 139 - l4l.

Hughes, M. (1986), A review of patterns of play in squash at different competitive levels.

In J.Watkins et al. (eds) Sport Science. London: E. & F.N. Spon, pp. 363 - 368.

Hughes, M. (1991), A time-motion analysis of squash players using a mixed-image video

tracking system. Ergonomics, 37 (l), 23 -29.

Hughes, M. (1994), Computerised notation of racket sports, In T. Reilly et al. (eds)

Science and Racket Sports. London: E. & F.N. Spon, pp.249 -256.

113 Hughes, M. (1998), The application of notational analysis to racket sports. In Science

and Racket Sports II, (eds. A. Lees, I. Maynard, M. Hughes and T. Reilly),

London: E. & F.N. Spon, PP.2ll -227.

Hughes, M. and McGarry, T. (1989), Computerised notational analysis of squash. In

Proceedings of Science in Squash Conference, Liverpool: Liverpool

Polytechnic Press.

Hughes, M. and Knight, P. (1995), A comparison of playing patterns of elite squash

players, using English and point-per-rally scoring. In T. Reilly et al. (eds) Science

and Racket Sports. London: E. & F.N. Spon, pp.257 -259.

Hughes, M. and Moore, P. (1998), Movement analysis of elite male 'serve and volley'

tennis players. In A. Lees et al. (eds) Science and Racket Sports II. London: E.

& F.N. Spon, pp.254 -259.

Hughes, M. and Robertson, C. (1998), Using computerised notational analysis to create a

template for elite squash and its subsequent use in designing hand notation

systems for player development. In A. Lees et al. (eds) Science and Racket

Sports II. London: E. & F.N. Spon, pp.254 -259.

I14 McGarry, T. and Franks, I.M. (1996), In search of invariant athletic behaviour in sport:

An example from championship squash match play. Journal of Sports Sciences,

14,445 - 456.

Mosteller, F. (1979), A resistant analysis of l97l and 1972 professional football. In

J.H.Goldstein (ed) Sports, Games and Play. New Jersey: Lawrence Erlbaum

Associates, pp. 371 - 401.

Nevill, A. (1996), Validity and measurement agreement in sports performance. Jourmal

of Sports Sciences, 14, editorial.

Potter, G. and Hughes, M. (1999), Modelling in competitive sports. In M. Hughes (ed)

Notational Analysis of Sport ltr. Cardiff: U.W.I.C., pp.67 - 83

Rushall, B.S. (1977), Two observation schedules for sporting and physical education

environments. Canadian Journal of Applied Sports Science, 2,15 -21.

Sanderson, F.H. (1983), A notation system for analysing squash. Physical Education

Review, 6,19 -23.

116 Sanderson, F.H. and Way, K.I.M. (7977), The development of an objective method of

game analysis in squash rackets. British Journal of Sports Medicine, 11, 188.

Thomas, J.R. and Nelson, J.K. (1996), Research Methods in Physical Activity,

Champaign, Il. : Human Kinetics.

Vincent, W.J. (1995), Statistics in Kinesiolgy, Champaign, Il. : Human Kinetics

Wells, J. (1998), Establishing "normal playing patterns' for women squash players at

elite, county and recreational standards. Unpublished dissertation, Bd University

of Wales.

tt7 XICNflddY Table x. lntra-observer reliability notation 1

1 Gecle-Christensen Enor 13 Smash 2 Gacle-Ghristensen Winner 16 Smash 3 Permadi Enor 3 Judgement 4 Gade-Christensen Enor 3 Smash 5 Permadi Winner 11 Kiil Þ Permadi Winner 5 Smash 7 Permadi Enor 3 Block I Gade-Christensen Enor 21 Flick 9 Gade-Christensen Enor 8 Net Retum 10 Gade-Christensen Winner I Drop 11 Gade-Christensen Winner 17 Smash 12 Gad+Christensen Winner 5 Kiil t3 Gacte-Chrislensen Enor 3 Lift 14 Permadi Eror 1 High serve 15 Gade-Christensen Enor 3 Unclassified 16 Permadi Eror 3 Lift 17 Gade-Ghristensen Enor 5 Flick 18 Gade-Christensen Winner 2 Smash 19 Permadi Enor I Net Retum 20 Permadi Winner I Smash 21 Gade-Christensen Enor 2 Clear 22 GadeGhrislensen Winner 2 Smash 23 Permacli Enor 2 Net Retum 24 Gade-Christensen Enor 13 Net Retum 25 Permadi Enor 13 Drive 26 Permadi Enor 7 Judgement 27 Gade-Christensen \Mnner 9 Drop 28 Gade-Christensen Enor 5 Smash 29 Gade-Christensen Winner 4 Net Retum 30 Permadi Winner 1E Net Retum 31 Gade-Christensen Enor 2 Smash 32 Gade'Christensen Winner 2 Smash 33 Gade-Ghrilensen Winner 3 Smash 34 Gade-Christensen Enor 5 Net Return 35 Gade-Christensen Winner 2 Smash 36 Gade-Christensen Enor 3 L¡ft 37 Gade-Christensen Enor I Smash 38 Gade-Christensen Error 2 Flick 39 Permadi Winner 5 Smash 40 Gade-Christensen Winner 5 Drive 41 Permadi Winner 26 Smash 42 Gade-Christensen Enor 2 Flick 43 Gade-Christensen Winner 16 Kiil 44 Gade-Christensen Enor 5 Smash 45 Permadi Winner 7 Smash 46 Permadi Error 11 Smash 47 Gade-Christensen Winner 11 Drop 48 Permadi Winner 6 Net Retum 49 Gade-Christensen Winner 4 Net Retum 50 Gade-Ghristensen Error 5 Clear 51 Gade-Christensen Enor 4 Net Return '52 Gade-Chrislensen Enor 14 Drop 53 GedrGhristensen Winner 2 Smash 54 Permadí Winner I Kiil Table x. lntra-observer reliability notation 1

55 Gade-Christensen Enor 2 Smash 2 4 1 56 Gade-Christensen Winner Smash 57 Gade'Ghrislensen Winner Smash 58 Gacle-Christenæn Enor Net Retum 59 Gacle-Christensen Enor Drop 60 Gade-Christensen Winner Net Retum 61 Gade-Ghristensen Winner Smash 62 Gacle-Christensen Enor Drive 63 Gade-Christensen Enor Flick 64 Gacle-Christensen Enor Flick 65 Gade-Christensen Winner Smash oo Permadi Enor Drop 67 Gade-Christensen Winner Net Retum 68 Gade-Christensen Enor Clear 69 Gade-Christensen Winner Smash 70 Permadi Winner Net Retum 71 Gacle-Christensen Winner Smash 72 Gade-Christensen Winner Smash 73 Permadi Enor Net Retum 74 Gade-Christensen Winner Smash 75 Gade-Christensen Winner Smash 76 Gade-Christensen Winner Net Retum 77 Gade-Christensen Winner Smash 78 Gade-Ghristensen Winner Net Retum 79 Gade-Christensen Enor Block 80 Gade-Christensen Winner Smash 81 Gade-Christensen Winner Smash 82 Permadi Enor Smash 83 Gade-Christensen Enor Drop E4 Gade-Christensen Winner Net Retum 85 Gade-Christensen Winner Flick E6 Gade-Christensen Winner Smash 87 Gade-Christensen Winner Smash 88 Gade-Christensen Winner Smash 89 Permadi Enor Net Retum 90 Permadi Enor Smash 91 Gad+Christensen Enor Smash 92 Gade-Christensen Winner Smash 93 Gade-Christensen Enor Smash 94 Gade-Christensen Winner Kilr 95 Gade-Ghristensen Enor Drop 96 Gade-Christensen Enor Clear 97 Gade-Christensen Winner Drive 98 Gade-Christensen Enor Net Retum 99 Permadi Enor Net Retum 100 Permadi Enor Flick 101 Gade-Christensen Winner Smash 102 Permadi Winner Smash 103 Gade-Christensen Enor Flick 104 Gade-Christensen Winner Kiil 105 Gade-Christensen Enor Net Retum 106 Gade-Christensen Winner Smash 107 Permadi Enor Block 108 Gade-Christensen Enor Smash Table x. lntra-observer reliability notation 1

109 Gacle-Christensen Enor Smash 110 Gade-Christensen Winner Net Retum 111 Gade-Christensen Enor Lifl 112 Gade-Christensen Winner Smash 113 Gacle-Christensen Winner Smash 114 Permadi Winner Net Retum 115 Gade-Christensen Enor Smash 116 Gade-Christensen Enor K¡II 117 Gade-Christensen Winner Flíck 118 Permadi Winner Kiil 119 Gade-Christensen Enor Net Retum 120 Gade-Christensen Winner Smash 121 Permadi Enor Flick 122 Gade-Christensen Winner Smash 123 Gade-Christensen Enor Lifr 124 Gade-Christensen Enor Clear 125 Gede-Christensen Winner Smash 126 Permadi Winner Smash 127 Permadi Enor Lin 128 Gade-Christensen Enor Net Retum 129 Gade-Christensen Winner Net Retum 130 Gade-Christensen Enor Push 131 Permadi Winner Smash 132 Gade-Ghristensen Enor Drop 133 Permadi Enor L¡ft 134 Permadi Enor Drop 135 Gade-Christensen Winner Smash r36 Permadi Winner Net Retum 137 Gade-Christensen Winner Smash 138 Permadi Enor Drop 139 Permadi Winner Smash 140 Gade-Christensen Winner Push 141 Gade-Christensen Enor Net Return 142 Permadi Enor Flick 143 Gade-Christensen Winner Smash 144 Gade-Christensen Enor Smash 145 Gade-Christensen Winner Smash 146 Gade-Christensen Enor Lifl 147 Gade'Christensen Winner Smash 148 Gacle-Christensen Enor Smash 149 Gade-Christensen Winner Smash 150 Permadi Winner Smash 151 Gade-Christensen Enor Flick 152 Gade-Christensen Enor Flick 153 Gade-Christensen Error Smash 1V Permadi Winner Flick 155 Gade-Christensen Winner Kiil 156 Gade-Christensen Winner Smash 157 Gade-.Christensen Enor Smash 158 Gade-Christensen Enor Block 159 Gade-Christensen Winner Smash 160 Gad+Christensen Winner Smash 161 Gade-Christensen Enor Net Retum 162 Gade-Chrilensen Enor Net Retum Table x. lntra-observer reliability notation 2

1 Gade-Christensen Enor 13 Smash 5 3 1

2 Gacle-Christensen Winner 16 Smash 3 3 1

3 Permadi Enor 3 Judgement 0 0 1

4 Gade-Christensen Error 3 Smash 1 3 1 5 Permadi Winner 11 Kiil iiii:ii*liìi.. 3 1 6 Permadi Winner 5 Smash 5 þ 1

7 Permadi Enor 3 Block 6 7 1 8 Gade-Christensen Enor 21 Flick I 3 1 I Gade-Christensen Error I Net Return 9 7 1 10 Gade-Christensen Winner I Drop 2 4 1

11 Gade-Christensen Winner 17 Smash 4 4 1 12 Gade-Christensen Winner 5 Kiil I 2 1 13 Gade-Ghristensen Enor 3 Flick 7 1 'l

14 Permadi Enor 1 High serve 5 3 1 15 Gade-Christensen Enor 3 Drop 2 I 1

16 Permadi Enor 3 9 3 1

17 Gade-Christensen Enor 5 Flick 7 3 1

18 Gade-Christensen Winner 2 Smash 3 4 1

19 Permadi Enor I Net Retum 9 7 1 20 Permadi Winner I 1 4 1

21 Gade-Christensen Enor 2 Clear 3 3 1

22 Gade-Christensen Winner 2 Smash 2 4 1 23 Permadi Enor 2 Net Return I 7 1 24 Gade-Christensen Error 13 Net Return 5 9 1

25 Permadi Enor 13 Drive 5 3 1

26 Gade-Christensen Winner 7 Clear 3 1 1 27 Gade-Christensen Winner I Drop 3 I 1

28 Gade-Christensen Enor 5 Smash 3 6 1 29 4 Net Return 9 7 1 30 Permadi Winner Net Retum I 7 1 31 Gade-Christensen Enor 2 Smash 1 4 1

32 Gade-Christensen Winner 2 Smash 3 :::::::: 1 ii!! ! 33 Gade-Christensen Winner 3 Smash 1 5 1

34 Gade-Christensen Enor 5 Net Return 4 9 1

35 Gade-Christensen Winner 2 Smash 3 1 1 36 Gade-Christensen Enor 3 Lifr I 3 1 37 Gade-Christensen Error 8 Smash 2 4 1 38 Gade-Christensen Error 2 Flick I 1 1 39 Permadi Winner 5 Smash 5 4 1

40 Gade-Christensen Winner 5 Drive 5 5 1

41 Permadi Winner 26 Smash 1 3 1 42 Gade-Christensen Error 2 Flick I 1 1 o 43 Gade-Christensen Winner 16 K¡II 4 1

44 Gade-Christensen Enor 5 Smash 2 4 1

45 Permadi Winner 7 Smash 5 1 1

46 Permadi Enor 11 Smash 3 1 1 47 Gacle-Christensen Winner 11 Drop 2 I 1 48 Permadi Winner þ Net Retum 5 I 1 49 Gade-Christensen Winner 4 Net Retum I I 1 50 Gade-Christensen Enor 5 Clear 3 1 1 51 Gade-Christensen Enor 4 Net Return 7 I 1 52 Gade-Christensen Enor 14 Drop 3 Þ 1 53 Gacle-Christensen Winner 2 Smash 1 4 1 54 Permadi Winner I Kiil 7 1 Table x. lntra-observer reliability notation 2

55 Gade-Christensen Enor 2 Smash 2 4 1 56 Gade-Christensen Winner 2 Smash 3 1 1 57 Gade-Chrislensen Winner 3 Smash 3 iìiiiì i' 1 58 Gade-Christensen Enor 3 Net Retum 9 1 59 Gade-Christensen Enor 2 Drop 2 1 60 Gade-Christensen Winner 10 Net Retum 7 1 61 Gade-Christensen Winner 17 Smash 2 1 62 Gade-Christensen Enor 19 Kiil 7 1 63 Gade-Christensen Enor 6 Flick 7 1 64 Gade-Christensen Enor 10 Flick 9 2 65 Gade-Christensen Winner 2 Smash 3 2 66 Permadi Enor 2 Drop 1 2 67 Gade-Christensen Winner 3 Net Retum 4 2 68 Gade-Christensen Enor 3 Clear 3 2 69 Gade-Christensen Winner 6 Smash 3 2 70 Permadi Winner 4 Net Retum 7 2 71 Gade-Christensen Winner 4 Smasn 2 2 72 Gade-Christensen Winner 5 Smash 2 2 73 Permadi Error 6 Net Retum I 2 74 Gade-Christensen Winner 7 Smasn 2 2 75 Gade-Christensen Winner 5 Smash 1 2 76 Gade-Christensen Winner 3 Net Retum 9 2 77 Gade-Christensen Winner 7 Smash 1 2 78 Gade-Christensen Winner 15 Net Retum 7 2 79 Permadi Winner 4 Smash 1 2 80 Gade-Chrislensen Winner 4 Smash 3 2 81 Gade-Christensen Winner 3 Smash 3 2 82 Permadi Enor 12 Smash 2 2 83 Gade-Christensen Error 7 Drop 1 2 84 Gade-Christensen Winner 2 Net Retum I 2 85 Gade-Christensen Winner 11 Flick 9 2 86 Gade-Christensen Winner 3 Smash 1 2 ¡:ç:::ì::::::::¡:,¡:: à 87 Gade-Christensen Winner và .ili:nii.1irr: J !!!!!'!!t:!t!: 2 88 Gade-Christensen Winner 7 Smash 5 2 89 Gacle-Christensen Winner 7 Net Return 4 3 90 Permadi Error 4 Smash 5 3 91 Gade-Christensen Error 7 Smash 1 3 92 Gade-Christensen Winner I smash iü îiii 3 93 Gade-Christensen Enor 5 Smash 1 3 94 Gade-Christensen Winner 4Kiil7 3 95 Gade-Christensen Enor 3 Drop 6 3 96 Gade-Christensen Enor I Clear 3 3 97 Gade-Christensen Winner 12 Drive 4 3 98 Gade-Christensen Enor 3 Net Return 7 3 99 Permadi Enor 9 Net Return 7 3 100 Permadi Error 6 Flick 9 3 101 Gade-Christensen Winner 7 Smash 3 3 102 Permadi Winner 14 Smash 1 3 103 Gade-Christensen Error 144 3 104 Gade-Christensen Winner 14 Kiil I 3 105 Gade-Christensen Error 19 Net Return 7 3 106 Gade-Christensen Winner 2 Smasn 2 3 107 Permadi Enor I Block 6 3 108 Gade-Christensen Error 9 Smash 3 3 Table x. lntra-observer reliability notation 2

109 Gacle.Christensen Enor 6 Smash 3 6 3 110 Gade-Christensen Winner 2 Net Retum I I 3 111 Gade-Christensen Enor 5 Lin 7 3 3 112 Gade-Christensen Winner 10 Smash 2 4 3 113 Gade-Ghrilensen Winner 3 Smash 2 3 3 114 Permadi Winner 4 Net Retum I 7 3 115 Gacle-Christensen Enor 4 Smash 3 4 3 116 Gade-Christensen Enor 4 K¡II 8 4 3 117 Gade-Chrislensen Winner I Drive 7 1 3 118 Permadi Winner 10 Kiil 9 3 3 119 Gade-Christensen Enor 4 Net Retum 9 I 3 120 Gade-Christensen Winner 2 Smash 2 4 3 121 Permadi Enor 2 Flick I ó 3 122 Gade-Christensen Winner 3 Smash 6 1 3 123 Permadi Winner 4 Net Retum 7 I 3 124 Gade-Christensen Enor 10 Glear 2 1 3 125 Gade-Christensen Winner 10 Smash 2 3 3 126 Permadi Winner I 1 3 3 127 Permadi Error 5 Lift I 3 3 128 Gade-Christensen Enor 3 Net Retum 9 7 3 129 Gade-Christensen Winner 2 Net Retum E I 3 130 Gade-Christensen Enor 5 Kiil 5 2 3

131 Permadi Winner 1'l Smash 1 6 3 132 Gade-Christensen Error I Drop 2 I 3 133 Permadi Enor 5 Lift I 2 3 134 Permadi Error 2 Drop 1 7 3 135 Gade-Christensen Winner 3 Smash 1 3 3

136 7 L¡ft 7 1 3 137 Gade-Christensen Winner 2 Smash 3 1 3 138 Permadi Error 6 Smash 3 4 3 139 Permadi Winner 14 Smash 3 3 3 140 Gade-Christensen Winner 4 Push 7 3 3 141 Gade-Chrilensen Enor 7 Net Retum I 7 3 142 Permadi Enor 3 Flick 9 3 3 143 Gacte-Christensen Winner 5 Smash 3 3 3 144 Gade-Chrilensen Enor 7 Smash 3 1 3 145 Gade-Christensen Winner 6 Smash 3 1 3 146 Gade-Christensen Error 3 Lift 9 3 3 147 Gade-Ghristensen Winner 4 Smash 5 6 3 '148 Gade-Christensen Error 11 Smash 3 i üi*, 3 149 Gade-Christensen Winner 14 Smash 4 5 3 150 Permadi Winner 8 Smasn 2 1 3 151 Gade-Christensen Enor 2 Flick I 3 3 152 Gade-Christensen Enor 2 Flick I 1 3 153 Gade-Christensen Error 8 Smash 2 1 3 154 Permadi Winner 3 Flick I 1 3 155 Gade-Christensen Winner 4 Kill I I 3 156 Gade-Christensen Winner 3 Smash 2 1 3 157 Gade-Chrilensen Enor

159 Gade-Christensen Winner 2 Smash 1 4 3 160 Gacle-Christensen Winner 5 Smash 2 1 3 161 Gade-Christensen Enor g Net Return I I 3 162 Gade-Christensen Enor 2 Net Retum I 9 3 Table x. lnter-observer reliabilÍty observer 2

1 Gacle-Ghristensen Enor 13 Smash 5 3 1 2 Gade-Christensen Winner 16 Smash 3 3 1 3 Permadi Enor 3 Judgement 0 0 1 4 Gade-Christensen Enor 3 Smash 1

5 Permadi Winner 11 Kiil 1 6 Permadi Winner 5 Smash 1

7 Permadi Enor 3 Block 6 7 1 I Gade-Christensen Enor 21 Flick I 3 1 I Gade-Christensen Enor I Net Retum I 7 1 10 Gade-Christensen Winner I Drop 2 4 1 11 Gade-Chrislensen Winner 17 Smash 4 4 1 12 Gade-Christensen Winner 5 K¡II I 2 1 ll .: l.' 13 Gade-Christensen Error 3 i::: i: ::: 7 1 1

14 Permadi Error 1 High serve 5 1 15 Gade-Christensen Enor 3 2 I 1 16 Permadi Error 3 I 3 1 17 Gade-Christensen Enor 5 Flick 7 3 1

18 Gade-Christensen Winner 2 Smash 3 4 1 19 Permadi Error I Net Retum I 7 1 20 Permadi Winner I 1 4 1

21 Gade-Christensen Enor 2 Clear 3 3 1

22 Gade-Christensen Winner 2 Smash 2 4 1

23 Permadi Enor 2 Net Retum 5 7 1

24 Gade-Christensen Enor 13 Net Retum 5 9 1

25 Permadi Enor 13 Drive 5 1

26 7 Clear 3 1 1 27 Gade-Christensen Winner 9 Drop 3 I 1 28 Gade-Christensen Enor 5 Smash 3 1

29 5 L¡ft 7 1 1

30 Permadi Winner Net Retum 9 1

31 Gade-Christensen Enor 2 Smash 1 1

32 Gade-Christensen Winner 2 Smash 3 1

33 Gade-Christensen Winner 3 Smash 1 5 1

34 Gade-Christensen Error 5 Net Return 4 9 1

35 Gacle-Christensen Winner 2 Smash 3 1 36 Gade-Christensen Enor 3 Lift I 3 1 37 Gade-Christensen Error I Smash 2 4 1 38 Gacle-Christensen Error 2 Flick I 1 1 t 39 Permadi Winner 5 Smash 4 1

40 Gade-Christensen Winner 5 Drive 5 5 1

41 Permadi Winner 26 Smash 1 3 1 42 Gade-Christensen Error 2 Flick I 1 1 43 Gade-Christensen Winner 16 Kiil I 1 44 Gade-Christensen Enor 5 Smash 2 4 1

45 Permadi Winner 7 Smash 5 1 1

46 Permadi Enor 11 Smash 2 1 1

47 Gade-Ghristensen Winner 11 Drop 2 9 1 48 Permadi Winner 6 Net Return 5 I 1 49 Gade-Christensen Winner 4 Net Retum I 9 1 a 50 Gade-Christensen Enor 5 Clear 1 1 51 Gade-Christensen Enor 4 Net Return 7 I 1 52 Gade-Christensen Error 14 Drop J 6 1

53 Gade-Christensen Winner 2 Smash 1 4 1

54 Permadi Winner I Kiil 1 Table x. lnter-observer reliability observer 2

55 Gacte-Christensen Enor 2 Smash 2 4 1 5ô Gade-Christensen Winner 2 Smash 3 1 1 57 Gade-Ghristensen Winner 3 Smash 3 ""ï' 1 58 Gade-Christensen Enor 3 Net Retum I 1 59 Gade-Christensen Enor 2 Drop 2 o 1 60 Gade-Christensen Winner 10 Net Retum 7 7 1 61 Gade-Christensen Winner 17 Smash 2 4 1 62 Gade-Christensen Error 21 7 2 1 63 Gade-Christensen Enor 6 Flick 7 1 1 64 Gade-Christensen Enor 10 Flick 9 3 2 65 Gade-Christensen Winner 2 Smash 3 :i::::::::l.lf :ì::::::::::' 2 þtt Permadi Enor 2 Drop 1 7 2 67 Gade-Christensen Winner 3 Net Retum 4 I 2 68 Gade-Christensen Enor 3 Clear 3 3 2 69 Gade-Christensen Winner 6 Smash 3 1 2 70 Permadi Winner 4 Net Retum 7 I 2 71 Gade-Christensen Winner 4 Smash 2 6 2 72 Gade-Christensen Winner 5 Smash 2 o 2 73 Permadi Error 6 Net Retum I 7 2 74 Gade-Christensen Winner 7 Smash 2 4 2 75 Gade-Christensen Winner 5 Smash 1 4 2 76 Gade-Christensen Winner 3 Net Return 9 7 2 77 Gade-Chrislensen Winner 7 Smash 1 6 2 78 Gade-Christensen Winner 15 Net Retum 7 I 2 79 Gade-Christensen Error 5 Block 5 6 2 80 Gade-Christensen Winner 4 Smash 3 1 2 81 Gade-Christensen Winner 3 Smash 3 6 2 82 Permadi Enor 't2 Smash 2 4 2 83 Gade-Christensen Enor 7 Drop ii: 7 2 84 Gade-Christensen Winner 2 Net Return I 9 2 85 Gade-Christensen Winner 11 Flick 9 3 2 86 Gade-Christensen Winner 3 Smash 1 4 2 87 Gade-Christensen Winner 5 5 3 2 86 Gade-Christensen Winner 7 Smash 5 5 2 89 7 Net Retum 4 I 3 90 Permadi Error 4 Smash 6 3 91 Gade-Christensen Enor 7 Smash 3 3 92 Gade-Christensen Winner I Smash 3 3 93 Gade-Christensen Error 5 Smash 4 3

94 Gade-Christensen Winner 4 Kiil 7 1 3 95 Gade-Christensen Error 3 Drop 6 7 3 96 Gade-Christensen Enor I Clear 3 1 3 97 Gade-Christensen Winner 12 Drive 4 5 3 98 Gade-Christensen Enor 3 Net Return 7 I 3 99 Permadi Enor I Net Return 7 7 3 100 Permadi Enor 6 Flick I 3 3 101 Gade-Christensen Winner 7 Smash 3 6 3 102 Permadi Winner 14 Smash 1 4 3

103 Gade-Christensen Enor 14 4 1 3 '104 Gade-Christensen Winner 14 Kiil 8 I 3 105 Gade-Christensen Enor 19 Net Retum 7 I 3 106 Gade-Christensen Winner 2 Smash 2 Þ 3 107 Permadi Enor Block o 7 3 108 Gade-Christensen Error 9 Smash 3 4 3 Table x. lnter-observer reliability observer 2

109 Gade-Christensen Enor 6 Smash 3 6 3 110 Gacle-Christensen Winner 2 Net Retum I I Ja 111 Gade-Christensen Enor 5 Lifr 7 3 3 112 Gade-Christensen Winner 10 Smash 2 4 3 113 Gade-Christensen Winner 3 Smash 2 3 3 114 Permadi Winner 4 Net Retum 9 7 3 115 Gade-Christensen Enor 4 Smash 3 4 3 116 Gade-Christensen Enor 4 Kiil I 4 3 117 Gade-Christensen Winner I 7 1 3 118 Permadi Winner 10 Kiil I 3 3 119 Gacle-Christensen Enor 4 Net Retum I o 3 120 Gade-Christensen Winner 2 Smash 2 4 3 121 Permadi Error 2 Flick I 3 3 't22 Gade-Christensen Winner 3 Smash 1 3 123 4 Net Retum 7 9 3 124 Gade-Ghristensen Enor 10 Clear 2 2 3 125 Gade-Christensen Winner 10 Smash 2 3 "...... ,.'...'.....'....iii 126 Permadi Winner I tH$*ii#il\ìiiìì,i:;ffi 1 3 3 127 Permadi Enor 5 Lifr 8 3 3 128 Gade-Christensen Error 3 Net Retum I 7 3 129 Gade-Christensen Winner 2 Net Retum I 9 3 130 Gade-Christensen Enor 5 5 2 3

131 Permadi Winner 11 Smash 1 6 3 132 Gade-Chrislensen Error 8 Drop 2 I 3 133 Permadi Enor 5 L¡ft 8 2 3

134 Permadi Enor 2 Drop 1 iììii:'i'f :ì!'i:;:! !, 3

135 Gade-Christensen Winner 3 Smash 1 3 3

136 7 Lift 7 1 3 137 Gade-Christensen Winner 2 Smash 3 1 ó 138 Permadi Error 6 34 3 139 Permadi Winner 14 Smash 3 3 3 140 Gade-Christensen Winner 4 Push 7 3 3 141 Gade-Christensen Error 7 Net Return 5 7 3 142 Permadi Error 3 Flick 9 3 3 143 Gede-Christensen Winner 5 Smash 3 3 3 144 Gade-Christensen Error 7 Smash 3 1 3 145 Gade-Christensen Winner 6 Smash 3 1 3 146 Gade-Christensen Error 3 1ift93 3 '147 Gade-Christensen Winner 4 smash :äätiä;ititi 6 3 148 Gade-Christensen Enor 11 Smash 3 3 149 Gade-Christensen Winner 14 Smash 4 5 3 150 Permadi Winner I Smash 1 3 151 Gade-Christensen Enor 2 Flick I 3 3 152 Gade-Christensen Error 2 Flick I 1 3 .) 153 Gade-Christensen Enor I Smash 2 1 154 Permadi Winner 3 Flick I 1 3 155 Gade-Christensen Winner 4 Kiil I I 3 156 Gade-Christensen Winner 3 Smash 2 1 3 157 Gade-Christensen Enor 3 Smash 3 3 158 7 Smash 3 5 3 159 Winner 2 Smash 1 4 3

160 Gade-Christensen Winner 5 Smash 2 1 3 161 Gade-Christensen Error 9 Net Retum I I 3 162 Gade-Christensen Error 2 Net Retum I 9 3 Table x. Camera angle reliability - rear court view

1 Knowles Winner 6 Smash 3 6 1 2 Knowles Winner 3 Drop 3 Hall Error 6 Block 4 Hall Winner 16 Drop 5 Knowles Enor 4 Block ô Knowles Winner 10 Drop 7 Knowles Enor 11 Clear I Knowles Winner 4 Smash 9 Knowles Enor 13 Block 10 Knowles Winner 10 Drop 11 Hall Error 22 Drop 12 Hall Winner 6 Smash 13 Hall Enor 7 Drop 14 Knowles Winner 9 Smash 15 Hall Enor 16 Clear 16 Hall Enor 2 Net Retum 17 Hall Winner I Net Retum 18 Hall Enor 13 Drop l9 Knowles Enor 4 Judgement 20 Hall Winner 3 Drive 21 Hall Winner 7 Smash 22 Knowles Enor 2 Push 23 Hall Winner 13 Drop 24 Knowles Enor 4 Drive 25 Hall Enor 13 Drop 26 Knowles Winner 7 Drop 27 Knowles Error 15 Net Retum 2E Knowles Winner 2 Smash 29 Knowles Enor 9 Net Return 30 Knowles Enor I Smash 31 Knowles Winner 20 Drop 32 Knowles Winner 3 Net Retum 33 Knowles Error 7 Smash 34 Knowles Winner 10 Smash 35 Hall Winner 6 Net Return 36 Knowles Enor 4 Smash 37 Knowles Winner 4 Smash 38 Hall Enor 6 Net Return 39 Hall Error 10 Smash 40 Knowles Winner 3 Drop 41 Hall Error 2 Net Return 42 Hall Error 12 Drop 43 Knowles Error 15 Clear 44 Knowles Winner 6 Drop 45 Knowles Winner 3 Smash 46 Hall Winner 4 Smash 47 Hall Winner 7 Unclassified 48 Knowles Enor 2 Flick 49 Hall Error 2 Judgement 50 Knowles Winner 3 Drive 51 Hall Winner 20 Net Return 52 Knowles Error 6 Net Retum 53 Knowles Enor 6 Net Retum 54 Knowles Error 24 Net Return Table x. Camera angle reliability - rear court view

55 Knowles Winner 2 Flick I 1 2 56 Hall Enor Net Return 57 Knowles Winner Clear 58 Knowles Winner Flick 59 Knowles Winner Smash 60 Knowles Enor Drive 61 Hall Winner Smash 62 Knowles Winner Net Retum 63 Knowles Enor Smash 64 Hall Winner Smash 65 Knowles Winner Smash 66 Knowles Enor Drive 67 Knowles Winner Smash 68 Hall Winner Smash 69 Knowles Winner Drive 70 Knowles Error Net Retum 71 Knowles Error Flick 72 Knowles Winner Drop 73 Hall Winner Smash 74 Hall Winner Drop 75 Knowles Enor Drop 76 Knowles Winner Net Retum 77 Knowles Winner Smash 78 Hall Enor Net Return 79 Knowles Error Smash 80 Knowles Enor Drop 81 Hall Error Low serve 82 Hall Winner Smash 83 Hall Enor Smash 84 Hall Error Block 85 Hall Error Net Return 86 Hall Enor Smash 87 Hall Enor Smash 88 Knowles Winner Kiil 89 Hall Winner Flick 90 Hall Winner Drop 91 Knowles Winner Smash 92 Hall Enor Net Retum 93 Knowles Enor Clear 94 Hall Enor Net Return 95 Hall Winner Smash 96 Hall Enor Low serve 97 Knowles Winner Smash 98 Knowles Enor Smash 99 Knowles Enor Smash 100 Hall Enor Smash 101 Hall Enor Net Return 102 Hall Winner Net Retum 103 Hall Winner Smash 104 Hall Winner Unclassified 105 Knowles Winner Smash 106 Knowles Error Judgement 107 Knowles Enor Flick 108 Knowles Enor Net Return Table x. Camera angle reliability - rear court view

109 Hall Enor 11 Clear 3 1 z 110 Hall Enor 4 Kiil 9 7 2 11 1 Knowles Enor I Smash 4 5 2 112 Knowles Error o Lift 8 2 2 113 Knowles Enor I Net Retum I I 3 114 Knowles Winner 6 Kiil 9 2 3 115 Knowles Winner 17 Smash 3 3 3 116 Hall Enor 10 Net Retum 7 o 3 117 Hall Winner 2 Net Retum I I 3 118 Hall Enor 5 Smash 3 4 3 119 Hall Winner I Kiil 7 I 3 120 Knowles Error 6 Drop 6 9 3 121 Knowles Winner 6 Kiil 9 I 3 122 Hall Enor 10 Block 5 0 3 123 Knowles Winner 7 Drop 2 7 3 124 Knowles Winner 3 Net Retum I 7 3 125 Knowles Winner 5 K¡II 8 5 3 126 Knowles Error 5 Smash 3 4 3 127 Hall Enor 19 Smash 3 1 3 128 Knowles Winner 3 Smash 3 1 3 129 Hall Winner 6 Smash 2 4 3 130 Knowles Error 6 Smash 1 6 3 131 Knowles Winner 8 Smash 5 2 3 132 Knowles Error I Smash 1 6 3 133 Knowles Winner 12 Smash 1 4 3 134 Knowles Winner 3 Smash 3 1 3 135 Hall Enor 14 Block 5 I 3 136 Knowles Winner 3 Smash 4 3 3 137 Hall Error 6 Kiil 8 5 3 138 Knowles Winner 3 Drop 3 4 3 139 Knowles Enor 7 Net Retum I 7 3 140 Knowles Enor 4 Smash 1 6 3 141 Knowles Winner 12 Net Retum I 9 3 142 Knowles Winner 3 Smash 3 1 3 143 Hall Winner 4 Smash 1 4 3 144 Knowles Enor 13 Judgement 0 0 3 145 Knowles Winner I Kiil 5 5 3 146 Hall Error 12 Drop 3 o 3 147 Knowles Winner 7 Kilt I 3 3 Table x. Camera angle reliability - side court view

1 Knowles Winner 6 Smash 5 1

2 Knowles Winner 3 Drop 1 1 ó Hall Enor 6 Block 4 0 1 4 Hall Winner 16 Drop 1 4 1

5 Knowles Enor 4 Block 6 7 1

o Knowles Winner 10 Drop b 1

7 Knowles Enor 11 Clear 3 1

I Knowles Winner 4 Smash 3 1 I Knowles Enor 13 Block 4 6 1 10 Knowles Winner 10 Drop 1 7 1 11 Hall Enor 22 Drop 6 I 1 12 Hall Winner 6 Smash 5 1

13 Hall Error 7 Drop 3 4 1 14 Knowles Winner I Smash 3 6 1

15 Hall Error 16 Clear 3 3 1 16 Hall Enor 2 Net Retum I I 1 17 Hall Winner I Net Retum I I 1 18 Hall Enor 13 5 4 1

19 Knowles Error 4 Judgement 0 0 1

20 Hall Winner 3 Drive 5 2 1

21 Hall Winner 7 Smash 3 6 1 22 Knowles Enor 2 I 3 1 23 Hall Winner l3 Drop 3 b 1

24 Knowles Error 4 Drive 4 3 1

25 Hall Error 13 Drop 2 4 1

26 Knowles Winner 7 3 9 1

27 't4 Dro 1 7

28 Knowles Winner 2 Smash 1 3 1 29 Knowles Error I Net Retum I I 1 30 Knowles Enor I Smash 3 1 1 31 Knowles Winner 20 Drop 3 1 32 Knowles Winner 3 Net Return 8 I 1

33 Knowles Error 7 Smash 3 1 1

34 Knowles Winner 10 Smash 3 1 35 Hall Winner 6 Net Retum 7 I 1

36 Knowles Enor 4 Smash 1 1

37 Knowles Winner 4 Smash 3 1 1

38 Hall Enor 6 Net Return 7 9 1 il:ì:i::ri:::::":::: ,l .liigii: ...r: 1 39 Hall Error 10 Smash r!r1$!!!!rrrrr\ I

40 Knowles Winner 3 Drop ó 1

41 Hall Enor 2 Net Retum I I 1 42 Hall Error 12 Drop 6 I 1 43 Knowles Enor 15 Clear i ììü¡äìüi.'ì 1 1

44 Knowles Winner 6 Drop 3 6 1

45 Knowles Winner 3 Smash 3 1 1

46 Hall Winner 4 Smash 3 6 1

47 Hall Winner 7 Unclassified 9 8 1 48 Knowles Error 2 Flick I 3 1 49 Hall Enor 2 Judgement 0 0 1

50 Knowles Winner 3 Drive 7 5 1 51 Hall Winner 20 Net Return I I 2 52 Knowles Enor 6 Net Retum 7 I 2 53 Knowles Enor 6 Net Retum 7 I 2 54 Knowles Enor 24 Net Retum 9 7 2 Table x. Camera angle reliability - side court view

55 Knowles Winner 2 Flick I 1 2 56 Hall Enor 2 Net Retum I I 2 57 Knowles Winner 27 Clear 2 1 2 58 Knowles Winner 25 Flick 1 2 59 Knowles Winner 5 Smash 2 3 2 60 Knowles Enor 3 Drive 1 3 2 61 Hall Winner 5 Smash 3 o 2 62 Knowles Winner 6 Net Retum I I 2 63 Knowles Enor 5 Smash 3 1 2 64 Hall Winner 5 Smash 1 2 65 Knowles Winner 16 Smash 3 2 oo Knowles Error Drive 4 2 67 Knowles Winner 4 Smash ,| 2 68 Hall Winner 4 Smash 3 2 t i-i::1-i-i-::tì:f::t¡-:.: 69 Knowles Winner 3 Drive J i:i::::::::::Ë:i:l::::::: 2 70 Knowles Error 18 Net Retum rliüi1i;Iti!i:i!,r: I 2 71 Knowles Error 2 Flick I 1 2 72 Knowles Winner I Drop 1 4 2 73 Hall Winner I Smash 3 o 2 74 Hall Winner 19 Drop 3 I 2 75 Knowles Enor 4 Drop 2 7 2 76 Knowles Winner 2 Net Return I 7 2 77 Knowles Winner 1',| Smash 4 5 2 78 Hall Enor 8 Net Retum 9 9 2 79 Knowles Error 11 Smash 3 6 2 80 Knowles Error 6 Drop 1 2 81 Hall Enor 1 Low serve 5 8 2 82 Hall Winner 5 Smash 2 4 2 83 Hall Enor I Smash 5 3 2 84 Hall Enor 4 Block 4 I 2 85 Hall Error þ Net Return 7 I 2 86 Haii Enor 12 Smash 5 5 2

87 Hall Enor 22 Smash 3 1 2 88 Knowles Winner 17 Kiil 7 3 2 89 Hall Winner 2 Flick I 3 2 90 Hall Winner 3 Drop 3 2 91 Knowles Winner 14 Smash 3 2 92 HâII Enor 18 Net Return 4 2 93 Knowles Error 3 Clear 3 2 94 Hall Enor 5 Net Retum I 2 95 Hall Winner þ Smash 5 2 96 Hall Enor 1 Low serve 5 I 2 97 Knowles Winner 7 Smash 1 2 98 Knowles Enor 7 Smash 1 5 2 99 Knowles Enor 4 Smash 3 4 2 100 Hall Enor 3 Smash 3 2 101 Hall Enor 20 Net Return I 7 2 102 3 Net Retum I I 2 103 Hall Winner 3 Smash 2 3 2 't04 Hall Winner 7 Unclassified I I 2 105 Knowles Winner 24 Smash 3 b 2 106 Knowles Enor 6 Judgement 0 0 2 107 Knowles Enor 2 Flick I 3 2 108 Knowles Enor 4 Net Retum 9 7 2 Table x. Camera angle reliability - side court view

109 Hall Enor 11 Clear 1 110 Hall Enor 4 Kiil I 7 2 't 11 Knowles Enor 9 Smash 4 5 2 112 Knowles Enor þ Lift I 2 2 113 Knowles Enor 8 Net Retum 8 I 3 114 Knowles Winner 6 K¡II I 2 3 115 Knowles Winner 17 Smash 3 3 3 116 Hall Enor 10 Net Retum 7 I 3 117 3 Net Retum I I 3 118 Error Smash 3 4 3 119 Hall Winner I Kiil 7 I 3 't20 Knowles Enor 6 Drop 6 I 3 121 Knowles Winner 6 Kiil 9 I 3 122 Hall Enor 10 Block 5 0 3 't23 Knowles Winner 7 Drop 2 7 3 124 Knowles Winner 3 Net Return I 7 3 125 Knowles Winner 5 K¡II I 5 3 126 Knowles Enor 5 Smash 3 4 3 127 Hall Enor 19 Smash 3 1 3 128 Knowles Winner 3 Smash 3 1 3 129 Hall Winner 6 Smash 2 4 3 130 Knowles Enor 6 Smash 1 6 3 131 Knowles Winner I Smash 5 3 132 Knowles Enor 9 Smash 1 6 3 133 Knowles Winner 12 Smash 1 4 3 134 Knowles Winner 3 Smash 3 1 3 135 Hall Enor 14 Block 5 I 3 136 Knowles Winner 3 Smash 4 3 3 137 Hall Enor 6 Kiil I 5 3 138 Knowles Winner 3 Drop 3 4 3 139 Knowles Error 7 Net Retum I 7 3 140 Knowles Enor 4 Smash 1 6 3 141 Knowles Winner 12 Net Retum I 9 3 142 Knowles Winner 3 Smash 3 1 3 143 Hall Winner 4 Smash 1 4 3 144 Knowles Enor 13 Judgement 0 0 3 145 Knowles Winner I Kiil 5 3 't46 Hall Error 12 Drop 3 9 3 147 Knowles Winner 7 Kiil I 3 3 MATCH / GAME DETAILS :

Detail Game 1 Game 2 Game 3 Match Time No. of Shots -4? No. of Rallies ' - per JL Ave. Shots Rally 7,2- :! Standard Deviation Max. Shot Length i '--.ì,

WINNER / ERROR ANALYSIS : 7

Game I Game 1 Garne I Player Winners Enors W/E,Ratio Knowles Hall Total

Game 2 Game 2 Game 2 Plaver Winners Errors W/E Ratio Knowles Hall Total

Game 3 Game 3 Game 3 Plaver Winners Errors WE Ratio Knowles Hall Total

Match Match Match Player Winners Enors W/E Ratio Knowles Hall Total TYPE OF WINNING SHOT :

Shot Type Knowles Hall Total % age Unclassified Smash Æ-' Push Net retum Kilr Flick Drop Drive

Clear t Block

Flick Serve , Total

Shot'Tvpe :Kr¡owles 9/o ãQê Hall 016 aoe Unclassified Smash Push Net return Kiil Flick Drop Drive Clear Block Flick Serve Total TYPE OF LOSING SHOT:

Shot Type Knowles Hall Total 016 age Judoement Smash Push Net return Lift K¡II Flick Drop Drive Clear

Block L -,J High Serve Low,Serve Flick,Serve Total

Shot T-vpe Knowles % aqe oA age Judgement Smash Push Net retum Lift Kiil Flick Drop Drive Clear Block High Serve Low'Serve Flick Serue Total POSITION OF WINNERS AND ERRORS:

Winners / Errors Winners / Errors Knowles Hall

net net -.L

¡ /..

,t f

back court back court

Winner I E¡ror Ratio Winner I Enor Ratio

net net

back court back court RALLY LENGTH ANALYSIS :

Rally Length Rally End Knowles Hall Total % age 1-4 Shots Winners 1-4 Shots Errors 5€ Shots Winners 5€ Shots Errors 9-12 Shots Winners - 9:12 Shots Errors 13+ 5¡e1t Winners 13+ 5¡e1t Errors Total

o Rally Lensth Rally End : Knowles age Hall % age 1-4,Shots : ' Winners 14"Shots :: Errors 5€ Shots Winners 5€ Shots Errors 9-12 Shots Winners 9-12 Shots Errors 13+ $¡e1s Winners 13+,Shots Enors Total

Rallv Lenoth Rally End Knowles Hall 1-4 Shots W/E Ratio 5-S Shots WE'Ratio 9-12 Shsts W/E Ratio 13+'Shots WE Ratio TYPE OF ERROR:

Error Type Knowles Hall Total % age Judqement //y'' Hit net Out wide Out long Other Total

016 Error Tvpe , Knowles '7o age Hall age Judgernent,, Hit'net Out wide Out,lono Other Total MATCH / GAME DETAILS :

Detail Game 1 Game 2 Game 3 Match Time 0 0 0 0 No. of Shots 382 517 260 1 159 No. of Rallies 50 o¿ 35 147 Ave. Shots per Ralþ 7.6 8.3 7.4 7.9 Standard Devîation 5.0 6.9 4.1 5.7 Max. Shot Length 22 27 19 27

WINNER / ERROR ANALYSIS :

Garne I Game 1 Game l Flayer Winners , Errors W/E Ratio Knowles 16 13 1.2 I 12 0.8 Total 25 25 1.0

Game 2 Game 2 Game 2 Player Winners 'Errors ,WE Ratio Knowles 15 19 0.8 Hall 13 15 0.9 Total 28 34 0.8

Game 3 Game 3 Game 3 Player Winners Enors WE Ratio Knovyles 16 8 2.0 Hall 4 7 0.6 Total 20 15 1.3

Match Match Match Player 'Winners Errors WE Ratio Knowles 47 40 1.2 Hall 26 34 0.8 Total 73 74 1.0 TYPE OF WINNING HOT :

Shot Type Knowles Hall Total % age Unclassified 0 2 2 2.7 Smash 21 12 33 45.2 Push 0 0 0 0.0 Net retum 5 5 10 13.7 'Kill 6 1 7 9.6 'Flick 2 1 3 4.1 Drop 10 4 14 19.2 Drive 2 1 3 4.1 Clear 1 0 1 1.4 tslock ,, 0 0 0 0.0 :Flick,Serve 0 0 0 0.0 Total 47 zõ 73 100.0

Shot Type Knowles 16,?9ê Hall % age Unclassified 0 0.0 2 7.7 Smash 21 44.7 12 46.2 Fush 0 0.0 0 0.0 Net retum 5 10.6 5 19.2 Kilt 6 12.8 1 3.8 Flick 2 4.3 1 3.8 Drop 10 21.3 4 15.4 Drive 2 4.3 1 3.8 Clear 1 2.1 0 0.0 Block 0 0.0 0 0.0 Ffick Serve 0 0.0 0 0.0 Total 47 0.0 26 100.0 TYPE OF LOSING SHOT:

Shot Tlæe Knowles Hall Total o/o âcl€ Judgement 3 1 4 5.4 Smash 12 7 19 25.7 Push 1 0 1 1.4 'Net retum I 10 19 25.7 1 0 1 14 0 2 2 2.7 Fliok 3 0 3 4.1 Drop 3 6 I 12.2 3 0 3 4.1 Clear 3 2 5 6.8 tslock 2 4 6 8.1 ligh Serve 0 0 0 0.0 l-ow,Serve 0 2 2 2.7 Flick,'Eerue 0 0 0 0.0 Total 40 34 74 100.0

o/o Shot:Type Knowles ã9ê Hall % age Judoement 3 7.5 1 2.9 Smash 12 30.0 7 20.6 Push 1 2.5 0 0.0 o Net:return 22.5 10 29.4 Lifr 1 2.5 0 0.0 Kilr 0 0.0 2 5.9 Flick 3 7.5 0 0.0 Drop 3 7.5 6 17.6 Drive 3 7.5 0 0.0 Clear 3 7.5 2 5.9 Block 2 5.0 4 11.8 Hish Serve 0 0.0 0 0.0 Low Serve 0 0.0 2 5.9 Flick Serve 0 0.0 0 0.0 ïotal 40 100.0 34 100.0 POSITION OF WINNERS AND ERRORS:

Winners / Errors Winners / Errors Knowles Hall

net net 13 3 4 2 I

13 3 5 3 11 5 0 3

7 7 3 13 29 I 3 14

1 17 1 0 9 back court back court

21 15 11 11 10 5

15 814 14 13 6

Winner I Enor Ratio Winner / Error Ratio

net net

o7 1.0 t.0 0.8 0,7 0.8

#Drv/or o.7 0 0.0 0.2

1.7 1 3.0 #Dtv/ot 1.6

back court back court

1.4 1.9 0.8 0.8 0.8 0.8 o/o ãÇ!ê Winners / Errors o/o ãÇ!ê Winners / Errors Knowles Hall

net net 12 I 35

19 35 15 9 33 0 0 12

19 I 21 I 20 12 54

46 3 0 27 back court back court

45 32 23 42 38 19

41 22 38 42 39 18 RALLY LENGTH ANALYSIS :

Rally Length Rally End Knowles Hall Total % age 14 Shots Winners 18 9 27 184 1-4 ,Shots Errors 14 I 23 15.6 5-8,Shots Winners 14 13 27 18.4 5-8 Shots Errors 15 8 23 15.6 9-12 Shots Winners 7 0 7 4.8 9-12 Shots 5 'Errors I 13 8.8 13+,Shots Winners I 4 12 8.2 13+ Shots Errors 6 I 15 10.2 Total 87 60 147 100

Rallv:Length' Rally:End ,Knowles o/o ?8ê Hall % age 14 Shots Winners 18 20.7 I 15.0 1-4 Shots Errors 14 16.1 9 15.0

5-8'Shots : Winners 14 16.1 13 21.7 54 Shots Errors 15 17.2 I 13.3 9-12 Shots Winners 7 8.0 0 0.0 9-12 Shots Errors 5 5.7 I 13.3 13+ 36e1t Winners 8 9.2 4 6.7 :13+ Shots Errors 6 6.9 o 15.0 Total 87 100 60 100

Rallv Lenoth Rally End Knowles Hall 14 Shots W/E Ratio 1.3 1.0 5€,Shots W/E Ratio 0.9 1.6 9-12'Shots WE Ratio 14 0.0 13+ Shots WE Ratio 1.3 o4 TYPE OF ERROR:

Error Type Knowles Hall Total % age Judoement 3 1 4 5.4 Hít net 20 21 41 55.4 Out wide 12 I 20 270 Out long 5 1 6 8.1 Other 0 3 3 4.1 Total 40 34 74 100.0

Knowles 'o/o Age Hall '016 age Judgernent 3 7.5 1 2.9 Hit,net 20 50.0 21 61.8 Out,wide 12 30.0 I 23.5 Out lonq 5 12.5 1 2.9 Other 0 0.0 3 8.8 Total 40 100.0 34 100.0 Simple user guide to notate rally-ending variables into database created.

Open Access and select file Badminton9T

To Notate Data

Left Click (LC) Tables

2xLC Event

Input Event, Venue, Date

(Note Event ID for input in Match table)

Close

2xLC Player

If not already in data base:

Input Name, Surname, Country

(Note Player ID's for input in match and rally tables)

Close

2 xLC Match

Input Player ID À Player ID C, Event ID, Score AB, Score CD

(Note Match ID for input in Game table)

Close

2 xLC Game

Input Match ID for each game of match Q e,Ð or (l& 2 e3)

(Note Game ID's for input in Rally table) Close

2 xLC Rally

LC >* at bottom of database

Input Rally No. (i.e. l)

Input Time Hours :Mnutes : Seconds (00:00:00)

Play first rally on video

Input Number of shots in rally

Input Player ID of player who hit rally ending shot

Input Rally End (winner [w] or error [e])

Input Shot Type abbreviation (see table 5)

Input From (see figure l)

Input To (see figure 1)

Input Game number ID (remember to change it after the start of each

new game)

Play next rally on video etc.

Close at any time after completing a full rally entry or at end of match notated

To Analvse Data

LC Queries

2xLC any query and input any requested information Table x. Winners hit by Plaver A when Player A wins game

14.1 14.1 t4.t t4.l 15.6 12.7 14.9 134 14.3 14.0 13.8 27.9 13.9 l4.l 15.6 t2.7 t4.9 t3.4 14.3 14.0 18.2 46.1 15.4 14.1 15.ó 12.7 t4.9 134 14.3 140 17.7 63.8 15.9 141 15 6 12.7 14.9 t3 4 14.3 14.0 10.3 74.1 14.1 14.3 140 6 I 1.8 85.8 14.3 r4.t 15.6 t2.7 14.9 13.4 14.3 14.0 7 t4.9 100.7 r4.4 t4.t 156 12.7 14.9 t3.4 14.3 14.0 8 8.5 t09.2 13.7 14.1 15.6 12.7 14.9 134 143 r 4.0 9 14.7 t23.9 13.8 14.1 15.6 t2.7 14.9 13.4 14.3 14.0 16.7 140.6 14.1 l5.6 t2.7 t4.9 t3.4 l1 14.8 r55.4 14.1 l4.l 15.ó 12.7 14.9 13.4 14.3 14.0 t2 t6.4 171.8 14.3 14.1 15.6 12.7 14.9 13.4 14.3 14.0 l3 tt.4 183.2 t4.t 14.1 r 5.6 12.7 14.9 t3.4 14.3 14.0 14 14.9 198. 1 t4.t t4.l 15.6 12.7 14.9 134 14.3 14.0

Table x. Errors hit by Player A when Player A wins game

I 9.8 9.8 9.8 12.l 13.3 10.9 t2.7 I t.s t2.2 I1.9 2 7.5 t7.3 8.6 12.1 13.3 10.9 t2.7 I1.5 12.2 Il 9 3 10.6 27.9 9.3 12.1 13.3 10.9 12.7 I 1.5 12.2 I 1.9 4 18.8 46.6 11.7 t2.r r3.3 l0 9 12.7 I 1.5 t2.2 1 1.9 5 7.4 54.0 10.8 t2.1 13.3 10.9 r2.7 I1.5 t2.2 I1.9 6 7.4 61.3 10.2 t2.r 13.3 10.9 12.7 I 1.5 12.2 I L9 7 13.2 74.5 10.6 t2.1 13.3 10.9 12.7 I 1.5 12.2 I 1.9 8 9.4 83.9 10.5 t2.1 13.3 10.9 12.7 11.5 12.2 ll.9 9 12.9 96.9 10.8 12.1 13.3 10.9 t2.7 ll.5 12.2 ll.9 .t:i+tlsr^.firri: ääi::i[:tli:iii:!l 14.6 ll.1 12.1 t2.7 11.5 12.2 ll.9 t5.7 I 1.6 12.1 13.3 10.9 16.4 12.0 t2.1 13.3 10.9 l3 11.4 155.0 I 1.9 t2.t 13.3 10.9 t2.7 11.5 12.2 11.9 t4 138 I 68.8 t2.1 12.1 13.3 10.9 t2.7 I1.5 12.2 11.9 Winners hit by Opponent when Player A wins Table x. -qame

1 10.9 10.9 10.9 9.2 l0.l 8.3 9.7 8.7 9.3 9.1 2 I1.3 22.1 1l.t 9.2 10. I 8.3 9.7 8.7 9.3 9l J 12.1 34.2 11 4 9.2 l0 l 8.3 97 87 9.3 91 4.2 38.4 9.2 87 93 9r 5 8.8 47.2 9.4 9.2 10.1 8.3 9.7 8.7 9.3 9 l 6 8.8 5ó.1 9.3 9.2 l0.l 8.3 9.7 8.7 9.3 9.I 7 9.6 65.7 9.4 9.2 10. I 8.3 9.7 8.7 9.3 9.1 8 12.3 78.0 9.7 9.2 l0 I 83 9.7 8.7 9.3 9.I 9 tt.2 89.2 99 9.2 10.1 8.3 9.7 8.7 9.3 9.1 7.3 96.5 9.2 10.1 8.3 iiirii: 9.3 9.I 5.6 t02.0 9.2 10.1 8.3 9.7 8.7 t2 7.9 109.9 9.2 9.2 l0.l 8.3 9.7 87 9.3 9l 13 tt.4 121,.2 9.3 9.2 10.1 8.3 9.7 8.7 9.3 9.I 14 74 r28.7 9.2 9.2 10.1 8.3 9.7 8.7 9.3 9 I

Table x. Errors hit by Opponent when Player A wins game

I 15.2 15.2 t5.2 14.5 16.0 l3.l 15.3 t 3.8 t4.7 14.4 2 t7.5 32.7 16.4 14.5 16.0 13.1 15.3 13.8 14.7 14.4

3 9.1 41.8 ),3.9 14.5 16.0 13. 1 15.3 13.8 14.7 14.4 4 9.4 5r.2 12.8 14.5 16.0 13. r 15.3 13.8 14.7 14.4 5 23.5 74.7 14.9 14.5 16.0 l3.l 15.3 13.8 14.7 14.4 6 22.1 96.8 16.1 14.5 1ó.0 l3.l 15.3 13.8 14.7 t4.4

7 t2.3 109. r 15.6 14.5 16.0 13. 1 15.3 13.8 14.7 14.4 8 19.8 r28.9 l6.l 14.5 16.0 l3.l 15.3 138 14.7 14.4 tl.2 140. I 14.5 15.3 13.8 14.7 14.4 10.4 r 50.5 t4.5 16.0 l3.l 14.7 t4.4 11 13.9 t64.4 t49 14.5 16.0 13.1 15.3 13 8 14.7 t4 4 9.3 173.7 t4.5 16.0 l3.l 15.3 13 8 l3 15.9 189.6 14.6 t4.5 ló.0 13. I 15.3 13.8 14.7 t4.4 14 138 203.4 14.5 14.5 160 13.1 15.3 13.8 14.7 14.4 Table x. Winners hit by Plaver A when Player A loses _qame

I 4.5 4.5 4.5 8.4 9.2 7.5 8.8 7.9 8.4 8.3 2 J.Z 7.8 3.9 8.4 9.2 7.5 8.8 7.9 8.4 8.3 J 5.4 13.2 4.4 84 9.2 7.5 8.8 7.9 84 8.3 4 104 23.6 5.9 8.4 9.2 7.5 88 7.9 84 8.3 5 4.5 28.1 5.6 8.4 9.2 7.5 8.8 7.9 84 8.3 6 10.8 39.0 65 8.4 9.2 7.5 8.8 7.9 84 8.3 7 7.8 46.7 6.7 8.4 9.2 7.5 8.8 7.9 8.4 8.3 ló.0 62.7 8.4 8.8 7.9 8.4 8.3 9 7.8 70.5 7.8 8.4 9.2 7.5 8.8 7.9 84 8.3 l0 13.0 83.5 8.4 8.4 9.2 7.5 88 79 84 8.3

Table x. Errors hit by Player A when Player A loses game

I t8.2 t8.2 18.2 17.0 18.7 15.3 t7.9 t6.2 17.2 r 6.8 I 22.6 40.8 20.4 17.0 18.7 15.3 17.9 16.2 17.2 16.8 J 2t.6 62.4 20.8 17.0 18.7 15.3 17.9 16.2 t7.2 168 12.5 74.9 17.0 17.9 16.2 17.2 ló.8 5 16.7 91.6 18.3 17.0 t8 7 15.3 17.9 t6.2 17.2 16.8 6 rt.7 t03.2 17.2 t7.o 18.7 15.3 17.9 t6.2 17.? I ó.8 7 12.1 I 15.3 165 t7.o 18.7 15 3 17.9 16.2 t7.2 16.8 8 13.0 t28.3 16.0 17.0 18.7 r 5.3 17.9 162 t7.2 16.8 18.8 147.0 17.0 187 15.3 17.2 ló.8 t0 23.0 r 70.0 t7.0 17.0 t8 7 15.3 17 .9 t6.2 17.2 16 I Table x. Winners hit by opponent when Plaver A loses game

I tr.4 114 tl.4 14.0 15.4 12.6 t4.7 13 3 14 I 13.8 2 I 1.3 22.7 I 1.3 14.0 15.4 t2.6 t4.7 13 3 t4.t 13.8 J 1,2.2 34.8 116 140 154 12.6 t4.7 13.3 14 l 13.8 22.9 57.7 14.0 14.7 13.3 l4.l 13 8 5 t5.2 72.9 t4.6 14.0 15.4 t2.6 t4.7 13.3 14t 13.8 6 14.2 87.1 14.5 14.0 15.4 t2.6 t4.7 I J.J 14 l 13.8 7 16.4 103.4 14.8 14.0 t5.4 12.6 14.7 13.3 14.1 138 8 14.0 1t7.4 14.7 14.0 15.4 12.6 14.7 13.3 l4.l 13.8 9 17.2 134.6 15.0 14.0 15.4 12.6 14.7 13.3 14.1 13.8 10 50 t39.6 14.0 14.0 1,5.4 12.6 14.7 13.3 l4.l 13 8

Table x. Errors hit by opponent when Player A loses game

1 15.9 15.9 t5.9 t0.7 tt.7 9.6 tr.2 10.I 10.8 106 2 12.9 28.8 14.4 t0.7 tr.7 9.6 tt.2 10. l 10.8 t0.6 J 10.8 39.6 t3.2 t0.7 1r.7 9.6 tt.2 l0.l 10.8 106 4 4.2 43.8 10.9 to.7 tt.7 9.6 tr.2 l0.l 10.8 10.6 5 13.6 57.4 I 1.5 to.7 tt.7 9.6 rt.2 10. l 10.8 r0.6 6 13.3 70.8 I 1.8 r0.7 r1.7 9.6 tt.2 10.1 t 0.8 10.6 7 138 84.6 12.1 10.7 tt.7 9.6 1r.2 10. I r 0.8 10.6 7.0 91.6 10.7 71.2 l0.l l0 8 10.6 9 6.3 97.8 10.9 10.7 11.7 9.6 ll.2 10. I 10.8 10.6 l0 9.0 l0ó.8 107 10.7 tl.7 9.6 11.2 l0.l 10 8 10.6

no Table 4.x Player As smash winners per normalised match

I 10.6 10.6 10.6 9.4 104 8.5 9.9 89 ) 8.8 19.4 9.7 9.4 10.4 8.5 9.9 89 3 12.5 31.9 10.6 9.4 10.4 8.5 9.9 8.9 4 t4.2 46.1 I 1.5 9.4 10.4 8.5 9.9 89 5 t0.2 56.3 I 1.3 94 10.4 8.5 9.9 89 6 1.6 57.9 9.7 9.4 104 8.5 9.9 89 7 12.4 70.3 10.0 9.4 104 8.5 9.9 8.9 8 6.4 76.7 9.6 9.4 10.4 8.5 9.9 89 9 8.9 85.6 9.5 9.4 10.4 8.5 9.9 89 10 8.7 94.3 9.4 9.4 10.4 8.5 9.9 8.9 l1 9.2 103.5 94 9.4 10.4 8.5 99 89

Table 4.x Player A's smash errors per normalised match

I 1.0 1.0 1.0 4.2 4.6 3.8 4.4 4.0 2 1.5 2.5 1.3 4.2 4.6 3.8 4.4 4.0 J 2.3 4.8 1.6 4.2 4.6 3.8 44 4.0 aa 4 8.3 13. I J.J 4.2 4.6 3.8 4.4 4.0 5 t.7 14.8 3.0 4.2 4.6 3.8 4.4 4.0 6 7.8 22.6 3.8 4.2 4.6 3.8 4.4 4.0 7 4.1 26.7 3.8 4.2 4.6 3.8 4.4 4.0 8 7.7 34.4 4.3 4.2 4.6 38 4.4 4.0 9 4.8 39.2 4.4 4.2 4.6 38 4.4 4.0 10 1.9 41.1 4.1 4.2 4.6 38 4.4 4.0 11 5.2 46.3 4.2 4.2 46 3.8 44 4.0 Table 4.x Opponent's smash winners per normalised match

1 9.6 9.6 9.6 10. I I1.1 9.0 10.ó 9.6 2 22.1 31.7 15.9 10. I l1.l 90 106 9ó J 10.2 41.9 14.0 10.1 u.l 9.0 10.6 9.6 4 2.5 44.4 11.1 10.1 I1.1 9.0 10.6 9ó 5 7.6 52.0 10.4 10.1 ll.r 90 10.ó 9.6 6 t4.t 6ó. r I 1.0 10. I 1l.l 9.0 106 96 7 8.2 74.3 10.6 10.1 11.1 90 10.ó 96 8 3.8 78. l 9.8 10.1 l1.l 90 10.6 9ó 9 15.3 93.4 10.4 10.1 n.l 9.0 10.6 9.6 l0 8.7 102.1 10.2 l0 I 1l.l 9.0 10.ó 96

l1 8.5 110.6 10 1 10. I 11.1 9.0 10.ó 9.6

Table 4.x Opponent's smash errors per normalised match

I 2.9 2.9 2.9 4.5 5.0 4.t 4.8 4.3 2 2.9 5.8 2.9 4.5 5.0 4.1 4.8 4.3 J 1.1 6.9 2.3 4.5 5.0 4.1 4.8 4.3 4 2.5 9.4 2.4 4.5 5.0 4.1 4.8 4.3 5 8.5 17.9 3.ó 4.5 5.0 4.1 4.8 4.3 6 7.8 25.7 4.3 4.5 5.0 4.1 4.8 4.3 7 6.2 3 1.9 4.6 4.5 5.0 4.1 4.8 4.3 8 9.0 40.9 5.1 4.5 5.0 4.1 4.8 4.3 9 4.8 45.7 5.1 4.5 5.0 4.1 4.8 4.3 l0 2.9 48.6 4.9 4.5 5.0 4.1 4.8 4.3 l1 1.3 499 4.5 4.5 50 4.1 4,8 4.3 Table 4.x Plaver A's winners from position 3 per normalised match

I 3.8 3.8 3.8 ).1 40 J.J 39 3.5 ,) 4.4 8.2 4.1 J.l 4.0 J.J 3.9 3.5 3 6.8 15.0 5.0 3.7 4.0 J.J 3.9 3._s 4 5.0 20.0 5.0 3.7 4.0 J.J 39 3.5 5 2.5 22.5 4.5 3.7 40 J.J 39 35 6 0.0 22.5 3.8 3.7 4.0 J.J 3.9 3.5 7 2.1 24.6 3.5 3.7 4.0 3.3 3.9 3.-S 8 5.1 29.7 ).t 3.7 4.0 3.3 3.9 3.5 9 1.6 31.3 3.5 3.7 4.0 J.J 3.9 35 10 5.8 37.1 3.7 3.7 4.0 3.3 39 3.5 11 33 40.4 3.7 3.7 4.0 J.J 3.9 35

Table 4.x Player A's errors from position 3 per normalised match

I 1.0 1.0 1.0 4.6 5.1 4.1 4.8 4.4 2 to 3.9 2.0 4.6 5.1 4.1 4.8 4.4 J 2.3 6.2 2.1 4.6 5.1 4.t 4.8 4.4 4 5.8 12.0 3.0 4.6 5.1 4.1 4.8 4.4 5 5.1 17.r 3.4 4.6 5.1 4.1 4.8 4.4 6 12.s 29.6 4.9 4.6 5.1 4.t 4.8 4.4 7 2.1 31.7 4.5 4.6 5.1 4.1 4.8 4.4 8 5.1 3ó.8 4.6 4.6 5.1 4.1 4.8 4.4 9 1.6 38.4 4.3 4.6 5.1 4.t 4.8 4.4 t0 5.8 44.2 4.4 4.6 5.1 4.1 4.8 4.4 11 6.5 50.7 46 4.6 5.1 4.1 48 4.4 Table 4.x Opponent's winners from position 3 per normalised match

1 3.8 3.8 3.8 4.8 5.3 4,3 50 4.6 2 8.8 t2.6 6.3 4.8 5.3 4.3 5.0 4.6 3 8.0 20.6 69 4.8 5.3 4.3 50 4.6 4 3.3 23.9 ó.0 4.8 5.3 43 5.0 4.6 5 2.5 26.4 5.3 4.8 5.3 4.3 50 4.6 6 7.8 34.2 5.7 4.8 5.3 4.3 50 4.6 7 2.1 36.3 5.2 4.8 5.3 .+.J 50 4.6 8 3.8 40.1 5.0 4.8 5.3 4.3 50 4.6 9 8.1 48.2 5.4 4.8 5.3 4.3 50 4.6 l0 0.0 48.2 4.8 4.8 5.3 4.3 5.0 4.6 1t 4.6 52.8 4.8 4.8 5.3 4.3 5.0 46

Table 4.x Opponent's effors from position 3 per normalised match

I 4.8 4.8 4.8 4.0 4.4 3.ó 4.2 3.8 2 2.9 7.7 3.9 4.0 4.4 3.6 4.2 38 3 2.3 10.0 3.3 4.0 4.4 3.6 4.2 3.8 4 3.3 13.3 J.J 4.0 4.4 3.6 4.2 3.8 5 2.5 15.8 3.2 4.0 4.4 3.ó 4.2 3.8 6 6.3 22.1 3.7 4.0 4.4 3.ó 4.2 3.8 7 4.1 26.2 3.7 4.0 4.4 3.6 4.2 3.8 8 9.0 35.2 4.4 4.0 4.4 3.6 4.2 3.8 9 4.0 39.2 4.4 4.0 4.4 36 4.2 3.8 l0 3.8 43.0 4.3 4.0 4.4 3.6 4.2 3.8 ll 1.3 44.3 4.0 4.0 4.4 36 4.2 3.8 Table x. Positional frequencies by match of Constable's winners

7 4 4 2 3 0 4 3 J I 0 3 0 2 I 0 0 2

4 2 6 2 1 2 2 4 4 4 9 6 I 5 0 5 8 4 9 5 J I J I 7 4 7

0 0 0 I 1 0 I I I 4 4 2 i 2 2 I I 0 1 I 4 0 I 0 4 I 5 4 4 2 1 I 0 I 3 6 I 4 6 1 4 I 5 4 5 5 5 5 I 4 0 6 I ll 40 38 41 l1 25 7 36 30 48 3.6 3.5 3.7 l0 z.J 0.6 3.3 2.7 4.4 2.8 2.6 2.0 0.6 1.2 0.8 2.4 2.3 30 76 76 52 63 52 t27 72 84 70

Table x. Positional frequencies by match of Constable's errors

J 0 I 2 J 2 4 6 3 a 0 2 2 3 I 2 J J 2

I 3 2 1 I 4 1 2 4

2 5 7 I I 1 7 l0 4 2 1 6 3 6 2 I 6 7 J 4 8 I 0 2 0 3 I J 4 2 3 I J J 5 0

2 2 4 I 0 I 0 I 5

8 2 2 6 3 1 2 2 6 4 I 6 2 I J 2 2 5 5 3 l0 4 6 1 J 7 ll JJ 27 50 27 23 22 26 47 48 3.0 2.5 4.5 2.5 2.1 2.0 2.4 4.3 4.4 2.1 1.5 3.0 1.6 )') 1.0 2.0 2.8 3.0 7t 6t 66 64 104 50 85 65 70 Table x Summated positional frequencies by match of opponent's winners

4 5 4 I 3 0 2 I J

5 4 6 2 0 0 1 I 2 3 J 7 0 I 0 0 I I 2 I 4 I I I ? ? I

2 6 J 0 1 0 2 4 4 t J 2 5 2 0 0 I 2 2 2 5 2 I 4 0 4 6 2 I J J 0 0 I 4 2 2 4 7 l0 4 4 I J I 2 1 J 0 2 1 I I 5 2 J 2 7 4 5 I 3 -5 4 30 4T 51 17 20 5 23 30 25 2.7 J.t 4.6 1.5 1.8 0.5 2.1 2.7 /.J 1.3 1.8 2.8 14 18 0.5 1.3 t9 l0 47 50 60 93 l0l 115 62 70 44

Table x. Summated positional frequencies by match of opponent's errors

I 5 5 I 3 0 3 4 5

4 1 2 1 I 1 2 2 2 4 J 2 I 4 3 2 J I

1 1 4 3 1 0 0 5 6 4 5 3 I J I 3 4 2 I 0 4 2 0 2 I 4 3 5 2 4 0 2 4 J 0 4 4 2 7 J I J 2 I 5 6 4 5 2 I 2 6 4 4 2 4 4 I I 2 3 5 5

6 I 2 1 6 5 5 8 2 38 28 42 l6 23 23 30 40 39 3.5 2.5 3.8 1.5 2.t 2.1 2.7 3.6 3.5 t.9 1.8 1.5 0.9 1.8 1.6 t.7 2.2 1.6 55 69 40 64 84 75 62 59 46 MC_winning_shots

Match Shot Frequency Rally No. Freq./100 Sum Average St.Dev. Ave:SD o/o

1 Block 0 104 0.0 0.0 0.0 ,| 2 Block 68 1.5 1.5 0.7 0.7 96 3 Block 1 88 1.1 2.6 0.9 0.6 ô6 4 Block 2 120 1.7 4.3 1.1 0.8 76 5 Block 1 118 0.8 5.1 1.0 0.7 69 6 Block 0 64 0.0 5.1 0.9 0.8 88 7 Block 2 97 2.1 7.2 1.0 0.8 80 I Block 1 78 1.3 8.5 1.1 0.8 71 I Block 0 124 0.0 8.5 0.9 0.8 83 10 Block 1 104 1.0 9.4 0.9 0.7 78 11 Block 0 153 0.0 9.4 0.9 0.8 88

1 Drop E 104 7.7 7.7 7.7 2 Drop 0 68 0.0 7.7 3.8 5.7 147 3 Drop 4 88 4.5 12.2 4.1 4.0 98 4 Drop 6 120 5.0 17.2 4.3 3.4 79 5 Drop 2 118 1.7 18.9 3.E 3.2 84 6 Drop 0 64 0.0 18.9 3.2 3.3 'l04 7 Drop 0 97 0.0 18.9 2.7 3.2 120 I Drop 1 78 1.3 20.2 2.5 3.1 't21 9 Drop 0 124 0.0 20.2 2.2 3.0 134 10 Drop 6 104 5.8 26.0 2.6 3.1 118 11 Drop 4 153 2.6 28.6 2.6 2.9 113

1 Flick 1 104 1.0 1.0 1.0 2 Flick 0 68 0.0 1.0 0.5 0.7 147 3 Flick 0 E8 0.0 1.0 0.3 0.6 180 4 Flick 1 120 0.E 1.8 0.4 0.6 129 5 Flick 2 118 1.7 3.5 0.7 0.E 120 o Flick 1 64 1.6 5.1 0.8 0.8 89 7 Flick 1 97 1.0 6.1 0.9 0.7 79 I Flick 1 78 1.3 7.4 0.9 0.6 70 9 Flick 1 124 0.8 8.2 0.9 0.6 66 10 Flick 0 104 0.0 8.2 0.8 0.6 77 11 Flick 3 153 2.0 10.1 0.9 0.9 97

1 Kiil 5 104 4.8 4.8 4.8 2 K¡II 1 68 1.5 6.3 3.1 2.8 90 3 Kilt 3 88 3.4 9.7 3.2 2.0 62 4 Kiil 5 120 4.2 13.9 3.5 1.9 55 5 Kiil I 118 6.8 20.6 4.1 2.6 63 o Kiil 0 64 0.0 20.6 3.4 2.9 86 7 K¡II 0 97 0.0 20.6 2.9 3.0 103 I Kiil 3 78 3.8 24.5 3.1 2.8 91 I Kiil 4 124 3.2 27.7 3.1 2.6 86 10 Kilt 7 104 6.7 34.4 3.4 2.8 80 11 Kiil 9 153 5.9 40.3 3.7 3.1 84

Page I MC_winning_shots

Match Shot Frequency Rally No. Freq./100 Sum Average St.Dev. Ave:SD o/o

1 Net Retum 5 104 4.8 4.8 4.8 2 Net Retum 1 68 1.5 6.3 3.1 2.8 90 3 Net Retum 5 EE 5.7 12.0 4.0 2.3 5E 4 Net Retum 11 120 9.2 21.1 5.3 4.1 78 5 Net Retum 6 118 5.1 26.2 5.2 3.6 68 6 Net Retum 2 64 3.1 29.3 4.9 3.5 72 7 Net Return 2 97 2.1 31.4 4.5 3.4 76 I Net Retum tt 78 7.7 39.1 4.9 3.2 65 9 Net Retum 5 124 4.0 43.1 4.8 3.0 62 10 Net Retum 7 104 6.7 49.9 5.0 2.9 58 11 Net Return 6 153 3.9 53.8 4.9 2.8 57

1 Smash 11 104 10.6 10.6 10.6 2 Smash 6 68 8.8 19.4 9.7 3.5 36 3 Smash 11 E8 12.5 31.9 10.6 2.9 27 4 Smash 12 120 10.0 41.9 10.5 2.7 26 5 Smash '17 118 14.4 56.3 11.3 3.9 35 6 Smash 1 64 1.6 57.9 9.6 5.5 57 7 Smash 12 97 12.4 70.2 10.0 5.1 51 I Smash 5 7E 6.4 76.7 9.6 5.0 53 I Smash 11 124 E.9 85.5 9.5 4.7 50 10 Smash 9 104 E.7 94.2 9.4 4.5 48 11 Smash 14 153 9.2 103.3 9.4 4.5 47

,| Smash 11 104 10.6 10.6 10.6 2 Smash 6 68 8.8 19.4 9.7 3.5 36 3 Smash 11 88 12.5 31.9 10.6 2.9 27 4 Smash 12 120 10.0 41.9 10.5 2.7 26 5 Smash '17 118 14.4 56.3 11.3 3.9 35 t Smash 12 97 't2.4 68.7 11.4 3.5 31 7 Smash 5 7E 6.4 75.1 10.7 4.0 38 I Smash 11 124 8.9 84.0 f 0.5 3.7 36 I Smash 9 104 8.7 92.6 10.3 3.5 34 10 Smash 14 153 9.2 101 .8 10.2 3.5 35

Page 2 MC_enor_shots

Match Shot Frequency Rally No. Freq./100 Sum Average St.Dev. Ave:SD %

1 Block 2 104 1.9 1.9 1.9 2 Block 4 6E 5.9 7.8 3.9 1.4 36 3 Block 4 88 4.5 12.4 4.1 1.2 28 4 Block 4 120 3.3 '15.7 3.9 1.0 26 5 Block 2 118 1.7 17.4 3.5 1.1 32 6 Block 2 64 3.1 20.5 3.4 1.1 32 7 Block 3 97 3.1 23.6 3.4 1.0 30 I Block 1 78 1.3 24.9 3.1 1.2 37 I Block 6 124 4.8 29.7 3.3 1.5 47 10 Block 1 104 1.0 30.7 3.1 1.6 52 11 Block 5 153 3.3 33.9 3.1 1.6 53

1 Clear 0 104 0.0 0.0 0.0 2 Clear 1 68 1.5 1.5 o.7 0.7 96 3 Clear 0 88 0.0 1.5 0.5 0.6 118 4 Clear 0 120 0.0 1.5 o.4 0.5 136 5 Clear 2 118 1.7 3.2 0.6 0.9 141 6 Clear 4 64 6.3 9.4 1.6 1.6 102 7 Clear 1 97 1.0 10.4 1.5 1.5 98 I Clear 1 78 1.3 11.7 1.5 1.4 93 I Clear 1 124 0.8 12.5 1.4 1.3 91 10 Clear 2 104 1.9 14.5 1.4 '1.2 85 11 Clear 6 153 3.9 '18.4 1.7 1.9 111

1 Drop 4 104 3.8 3.8 3.8 2 Drop 3 68 4.4 E.3 4.'l 0.7 17 3 Drop 4 88 4.5 12.8 4.3 0.6 14 4 Drop 5 120 4.2 17.0 4.2 0.8 19 5 Drop 6 118 5.1 22.1 4.4 1.1 26 6 Drop E, 64 9.4 31.4 5.2 1.2 23 7 Drop 4 97 4.1 35.6 5.1 1.1 22 I Drop 1 78 1.3 36.E 4.6 1.6 36 I Drop I 124 6.5 43.3 4.8 2.0 42 10 Drop 9 104 8.7 51.9 5.2 2.4 45 11 Drop 7 153 4.6 56.5 5.1 2.3 45

1 Flick 2 104 1.9 1.9 1.9 2 Flick 3 68 4.4 6.3 3.2 0.7 22 3 Flick 1 88 1.1 7.5 2.5 1.0 40 4 Flick 2 120 1.7 9.1 2.3 0.8 36 5 Flick 4 118 3.4 12.5 2.5 1.1 46 6 Flick I 64 1.6 14.1 2.3 1.2 50 7 Flick 3 97 3.1 17.2 2.5 1.1 45 ,| I Flick 78 1.3 18.5 2.3 1.1 49 9 Flick 2 124 1.6 20.1 2.2 1.1 47 10 Flick 0 104 0.0 20.1 2.0 1.2 60 11 Flick 4 153 2.6 22.7 2.1 1.3 63

Page I MC error shots

Match Shot Frequency Rally No. Freq./100 Sum Average St.Dev. Ave:SD o/o

1 Judgement 1 'l04 1.0 1.0 1.0 2 Judgement 0 68 0.0 1.0 0.5 0.7 147 3 Judgement 0 88 0.0 1.0 0.3 0.6 180 4 Judgement 1 120 0.8 1.8 0.4 0.6 129 5 Judgement 0 118 0.0 1.8 0.4 0.5 153 6 Judgement 0 64 0.0 1.8 0.3 0.5 173 7 Judgement 1 97 1.0 2.8 0.4 0.5 132 I Judgement 2 78 2.6 5.4 0.7 0.7 110 I Judgement 1 124 0.E 6.2 0.7 0.7 103 10 Judgement 4 104 3.8 10.0 1.0 1.2 124 11 Judgement 1 153 0.7 10.7 1.0 1.2 122

1 Lift I 104 1.0 1.0 1.0 2 Lift 2 68 2.9 3.9 2.0 0.7 36 3 Lifr 0 88 0.0 3.9 1.3 1.0 77 4 Lift 0 120 0.0 3.9 1.0 1.0 98 5 Lift 1 118 0.8 4.8 1.0 0.8 88 6 Lifr 1 64 1.6 6.3 1.1 0.8 72 7 Lift 3 97 3.1 9.4 1.3 1.1 80 I Lifr 1 78 1.3 10.7 1.3 1.0 74 I Lift 2 124 1.6 12.3 1.4 1.0 71 10 Lifr 0 104 0.0 12.3 1.2 1.0 81 11 Lift 1 153 0.7 13.0 1.2 0.9 80

1 Net Return 11 104 10.6 10.6 10.6 2 Net Return 7 68 10.3 20.9 10.4 2.8 27 3 Net Return 7 88 8.0 28.8 9.6 2.3 24 4 Net Retum E 120 6.7 35.5 8.9 1.9 2'l 5 Net Retum 16 118 13.6 49.1 9.8 3.8 39 6 Net Retum 4 64 6.3 55.3 9.2 4.2 45 7 Net Retum I 97 8.2 63.5 9.1 3.8 42 E Net Return 3 78 3.8 67.4 8.4 4.1 48 9 Net Return 5 124 4.0 71.4 7.9 3.9 50 10 Net Return 7 104 6.7 78.2 7.8 3.7 48 11 Net Retum 13 153 8.5 86.7 7.9 3.9 49

'l Smash 'l 104 1.0 1.0 1.0 2 Smash 1 68 1.5 2.4 1.2 0.0 0 3 Smash 2 88 2.3 4.7 1.6 0.6 37 4 Smash 2 120 1.7 6.4 1.6 0.6 36 5 Smash 10 118 8.5 14.8 3.0 3.8 129 6 Smash 5 64 7.8 22.7 3.8 3.5 93 7 Smash 4 97 4.1 26.8 3.8 3.2 84 I Smash 6 78 7.7 34.5 4.3 3.1 72 I Smash 6 124 4.8 39.3 4.4 3.0 68 10 Smash 2 104 1.9 41.2 4.1 2.9 70 11 Smash I 153 5.2 46.5 4.2 3.0 71

Page 2 MC_EnorJype_by_match

I w Hit net l4 104 13.5 13.5 13.5 2 L Hit net 10 68 147 28.2 14.1 0.9 6 3 w Hit net l6 88 18.2 46.3 15.4 2.4 l6 4 w Hit net 20 r20 16.7 63.0 1s.8 2.1 l3 5 V/ Hit net 24 ll8 20.3 834 16.7 )1 l6 6 L Hit net l5 64 23.4 106.8 17.8 ).1 21 7 L Hit net l8 97 18.6 r25.3 t7.9 3.4 l9 8 w Hit net 9 78 I 1.5 r36.9 t7.r 3.9 23 9 L Hit net 1t r24 8.9 145.8 16.2 4.5 28 l0 w Hit net l6 104 15.4 l6l. I 16.1 4.3 27 l1 L Hit net 26 153 17.0 178.1 t6.2 4.1 25

TABLE x.

I w Hit net 14 104 13.5 13.5 13.5 2 w Hit net l6 88 t8.2 31.6 15.8 3.3 2t 3 w Hit net 20 t20 16.7 48.3 16. l 2.4 t5 4 w Hit net 24 lt8 20.3 68.6 17.2 2.9 17 5 w Hit net 9 78 I 1.5 80.2 16.0 3.5 22 6 w Hit net l6 t04 15.4 95.6 Is.9 3.2 20 ( TABLE x -

I L Hit net l0 68 t4.7 14.7 14.7 2 L Hit net l5 64 23.4 38.1 l9.l 6.2 32 3 L Hit net 18 97 18.ó 56.7 18.9 4.4 23 4 L Hit net l1 t24 8.9 65.6 16.4 6.2 38 5 L Hit net 26 153 17.0 82.6 16.5 5.3 32

TABLE x

Page 1 MC_Enorjype_by_metch

I \ry Out long 1 104 1.0 1.0 l0 2 L Out long 5 68 7.4 8.3 4.2 4.5 109 J w Out long 0 88 0.0 8.3 2.8 4.0 r44 4 w Out long I r20 0.8 9.1 2.3 3.4 149 5 w Out long 2 ll8 t.7 r0.8 2.2 30 136 6 L Out long 2 64 3.1 14.0 !.J 2.7 I l5 7 L Out long 2 97 2.r ló.0 2.3 2.4 107 8 w Out long 0 78 0.0 16.0 2.0 2.4 t20 9 L Out long 4 124 3.2 19.3 2.1 2.3 107 10 w Out long 0 104 0.0 19.3 1.9 2.3 tt7 l1 L Out long 5 r53 J.J 22.5 2.0 2.2 106

TABLE x

1 \ry Out long I 104 1.0 1.0 1.0 2 w Out long 0 88 0.0 1.0 0.5 0.7 t4l

3 w Out long 1 r20 0.8 1.8 0.6 0.5 87 4 w Out long 2 118 1.7 3.5 0.9 0.7 80 5 w Out long 0 78 0.0 3.5 0.7 0.7 103 6 w Out long 0 104 0.0 3.5 0.ó 0.7 t2t

TABLE x.

I L Out long 5 68 7.4 7.4 7.4 2 L Out long 2 64 3.1 10.5 5.2 3.0 57 J L Out long 2 97 2.t 12.5 4.2 2.8 67 4 L Out long 4 r24 3.2 15.8 3.9 2.3 59 5 L Out long 5 153 J.J 19.0 3.8 2.0 54

TABLE x.

Page 2 MC_EnorJype_by_match

I w Out wide 6 104 5.8 5.8 5.8 2 L Out wide 5 68 7.4 13.1 6.6 ll t7 J w Out wide I 88 1.1 14.3 4.8 3.2 68 4 w Out wide 6 t20 5.0 19.3 48 2.6 55 5 w Out wide 1l ll8 9.3 28.6 5.7 3.0 53 6 L Out wide 8 64 t2.5 4t.l 6.8 39 57 7 L Out wide 6 97 6.2 47.3 ó.8 3.6 53 8 \ry Out wide 4 78 5.1 52.4 6.5 3.3 51

9 L Out wide l0 t24 8.1 60.5 6.7 3 ..!. 47 l0 w Out wide 6 104 5.8 66.2 6.6 3.0 45 1l L Out wide lt 153 '7 1 73.4 6.7 2.9 43

TABLE x.

I w Out wide 6 t04 5.8 5.8 5.8 J w Out wide I 88 1.1 6.9 2.3 3.3 t42 4 w Out wide 6 t20 5.0 11.9 3.0 2.5 83 5 w Out wide ll ll8 9.3 2t.2 4.2 3.4 79 8 w Out wide 4 78 5.1 26.4 J.J 2.9 88 l0 w Out wide 6 t04 5.8 32.1 3.2 2.6 8l

TABLE x

2 L Out wide 5 68 7.4 7.4 3.7 6 L Out wide 8 64 t2.5 19.9 J.J 3.6 ll0 7 L Out wide 6 97 6.2 26.0 3.7 3.4 90 9 L Out wide l0 t24 8.1 34.1 3.8 2.8 73 11 L Out wide ll 153 7.2 41.3 3.8 2.5 66

Page 3 T 0't 04

I 6 34 5.9 5.9 5.9 2 5 l8 9.3 15.1 7.6 2.4 3? J 2 2l 3.2 18.3 6.1 3.0 50 4 9 4t 7.3 25.6 6.4 2.6 40 5 4 30 4.4 30.0 6.0 2.4 40 6 J 19 5.3 35.3 5.9 2.2 5t 7 10 3l 10.7 46.0 6.6 2.7 41 8 5 26 6.4 52.5 6.6 2.5 38 9 13 48 9.0 61.5 6.8 2.5 36 10 6 38 5.3 66.7 6.7 2.4 36 11 8 50 5.3 72.1 6.6 2.3 35

I t2 34 11.8 11.8 I 1.8 2 5 l8 9.3 2r.0 10.5 1.8 t7 J 7 2t l1.l 32.1 t0.7 1.3 t2 4 8 4t 6.5 38.ó 9.7 2.4 24 5 7 30 7.8 46.4 9.3 )') 24 6 I 19 14.0 60.4 10.1 2.8 27 7 7 3l 7.5 67.9 9.7 2.7 28 8 12 26 15.4 83.3 10.4 3.2 3l 9 t4 48 9.7 93.0 10.3 3.0 29 l0 8 38 7.0 100.0 10.0 3.0 30 l1 17 50 I 1.3 1 il.3 10. I 2.9 29

Page 1 T_01_04

1 6 34 5.9 5.9 5.9 2 5 l8 9.3 15.1 7.6 2.4 32 J 2 21 3.2 18.3 6.1 3.0 50 4 9 4l 7.3 2s.6 6.4 2.6 40 5 4 30 4.4 30.0 6.0 2.4 40 6 5 l9 5.3 3 5.3 5.9 ', ') 37 7 l0 31 10.7 460 6.6 2.7 41 ,)\ 8 5 26 6.4 52.5 6.6 38 9 13 48 9.0 61.5 6.8 )< 36 10 6 38 5.3 66.7 6.7 2.4 36 l1 8 50 5.3 72.1 6.6 2.3 35

1 t2 34 11.8 11.8 I 1.8 2 5 l8 9.3 2r.0 10.5 1.8 17 3 7 2l 11.1 32.t r0.7 1.3 l2 4 8 41 ó.5 38.6 9.7 2.4 24 5 7 30 7.8 46.4 9.3 2.2 24 6 8 t9 t4.0 60.4 10.1 2.8 27 7 7 31 7.5 67.9 9.7 2.7 28 8 t2 26 t5.4 83.3 10.4 3.2 3l 9 t4 48 9.7 93.0 10.3 3.0 29 10 8 38 7.0 100.0 10.0 3.0 30 11 17 50 I 1.3 11 1.3 10. l 2.9 29

Page 2 1 7 24 9.7 9.7 9.7 2 4 l7 7.8 17.5 8.8 1.3 l5 J 10 33 10.1 27.6 9.2 1.2 l3 4 l5 37 13.5 41.1 10.3 2.4 23 5 11 28 l3.l 54.2 10.8 2.4 22 6 2 l9 3.5 57.7 9.6 3.7 38 7 7 29 8.0 65.8 9.4 3.4 36 8 9 29 10.3 76.t 9.5 3.2 33 9 8 44 6.1 82.2 9.1 3.2 35 l0 l0 JJ 10.1 92.2 9.2 3.0 JJ l1 t4 44 10.6 102.8 9.3 2.9 31

I J 24 A) A) 4.2 2 5 t7 9.8 14.0 7.0 4.0 57 J 6 JJ 6.1 20.0 6.7 2.9 43 4 6 37 5.4 25.4 6.4 2.4 38 5 7 28 8.3 33.7 6.7 2.3 34 6 8 l9 14.0 47.8 8.0 3.6 45 7 9 29 10.3 58. I 8.3 3.4 41 8 5 29 5.7 63.8 8.0 3.3 4l 9 tl 44 8.3 72.2 8.0 3.1 38 l0 7 JJ 7.t 79.2 7.9 2.9 37 ll 13 44 9.8 89. I 8.1 2.8 35 I 6 24 8.3 8.3 8.3 2 6 17 r 1.8 20.1 10.0 2.4 24 J 9 33 9.1 29.2 9.7 1.8 l9 4 6 37 5.4 34.6 8.6 2.6 30 5 7 28 8.3 42.9 8.6 2.3 26 6 7 19 t2.3 55.2 o? 2.5 27 7 5 29 5.7 60.9 8.7 2.6 30 8 6 29 6.9 67.8 8.5 2.5 30 9 13 44 9.8 77.6 8.6 2.4 28 l0 8 JJ-a 8.1 85.7 8.6 2.3 27 1l 10 44 7.6 93.3 8.5 1,) 26

I 8 24 11.1 I l.l 11.1 2 I 17 3.9 15.0 7.5 5.1 68 3 8 JJ 8.1 23.7 7.7 3.6 47 4 l0 37 9.0 32.r 8.0 3.0 38 5 J 28 3.6 35.7 7.t J.J 46 6 2 19 3.5 39.2 6.5 J.J 50 7 8 29 9.2 48.3 6.9 3.2 46 8 9 29 10.3 58.7 7.3 3.2 43 9 t2 44 9.1 67.8 7.5 3.0 40 10 8 33 8.1 75.8 7.6 2.9 38 ll 7 44 5.3 81.1 7.4 28 38 I 7 24 5.8 5.8 5.8 2 J 20 3.0 88 4.4 2.0 45 3 8 21 7.6 16.5 5.5 2.3 42 4 6 18 6.7 23.1 5.8 2.0 34 5 t2 26 9.2 32.3 6.5 2.3 36 6 2 9 4.4 36.8 6.1 )') 36 7 4 l5 5.3 42.r 6.0 2.1 34 8 2 ll 3.6 45.8 5.7 2.1 36 9 6 l6 7.5 53.3 5.9 2.0 34 10 5 21 4.8 58.0 5.8 2.0 34 11 9 29 6.2 64.2 5.8 1.9 32

I I 24 6.7 6.7 6.7 2 7 20 7.0 13.7 6.8 0.2 J 3 4 2t 3.8 17.5 5.8 1.8 30 4 3 l8 J.J 20.8 5.2 1.9 36 5 9 26 6.9 27.7 5.5 1.8 JJ 6 5 9 l1.l 38.8 6.5 2.8 43 7 3 l5 4.0 42.8 6.1 2.7 44 8 2 lt 3.6 46.5 5.8 2.7 46 9 I t6 1.3 47.7 5.3 2.9 55 l0 4 2t 3.8 51.5 5.2 2.8 54 ll 10 29 6.9 58.4 5.3 2.7 5l I 5 24 4.2 4.2 4.2 ) 5 20 5.0 9.2 4.6 0.6 13 3 3 21 to 12.0 4.0 l.l 27 4 3 l8 J.J 15.4 3.8 0.9 ?5 5 0 26 0.0 15.4 3.1 1.9 62

6 1 9 )) 17.6 to 1.7 59 7 6 l5 8.0 25.6 3.7 2.5 68 8 2 ll 3.6 29.2 3.7 2.3 63 9 4 t6 5.0 34.2 3.8 )) 58 10 J 21 )a 37.1 3.7 2.r 57 ll 4 29 2.8 39.8 3.6 2.0 55

1 4 24 J.J J.J J.J 2 5 20 5.0 8.3 4.2 t.2 28 a J 6 2l 5.7 14.0 4.7 1.2 26 4 6 l8 6.7 20.7 5.2 1.4 27 5 5 26 3.8 24.6 4.9 r.4 28 6 I 9 ') ') 26.8 4.5 1.6 37 7 2 l5 2.7 29.4 4.2 1.6 39 8 5 ll 9.t 38.5 4.8 2.3 48 ,r) 9 5 l6 6.3 44.8 5.0 44 10 9 2t 8.6 53.4 5.3 2.4 44 11 6 29 4.1 57.5 5.2 2.3 44 1 l0 )') 9.1 9.1 9.1 2 0 l3 0.0 9.1 4.5 64 141 J 2 t2 J.J t2.4 4.1 4.6 111 4 ll 24 o? 21.6 5.4 4.5 84 5 8 34 4.7 26.3 5.3 3.9 75 6 0 I4 0.0 26.3 4.4 4.1 94 7 J 22 2.7 29.0 4.1 3.8 92 8 2 T2 J.J 32.4 4.0 3.5 87 9 4 t6 5.0 37.4 4.2 J.J 80 l0 6 I2 10.0 47.4 4.7 3.6 77 11 5 30 J,J 50.7 4.6 3.5 75

1 J ')) 2.7 2.7 2 4 l3 6.2 8.9 4.4 2.4 55 J 5 t2 8.3 17.2 5.7 2.8 49 4 6 24 5.0 '))') 5.6 2.3 42 5 15 34 8.8 31.0 6.2 2.5 40 6 5 t4 7.t 38.2 6.4 2.3 36 7 7 22 6.4 44.5 6.4 2.1 JJ 8 J t2 5.0 49.5 6.2 2.0 32 9 2 t6 2.5 52.0 5.8 ') ') 38 a l0 J 72 5.0 57.0 5.7 2.t 37 11 8 30 5.3 62.4 57 2.0 35 ll I 6 22 5.5 5.5 5.5

2 5 13 7.7 13. 1 6.6 1.6 24 J 2 T2 J.J 16.5 5.5 2.2 40 4 4 24 J.J 19.8 5.0 2.1 42 5 4 34 2.4 J)1 4.4 2.1 48 6 6 l4 8.6 30.7 5.1 2.6 50 7 5 22 4.5 3 5.3 5.0 2.3 47 ,, ., 8 J 12 5.0 40.3 5.0 43 9 6 l6 7.5 47.8 5.3 ') ') 41 10 I L2 t.7 49.5 4.9 2.4 48 1i l0 30 6.7 56. I 5.1 2.3 45

I J 22 2.7 2.7 2.7 2 4 l3 6.2 8.9 4.4 2.4 55 J J t2 5.0 13.9 4.6 1.7 38 4 J 24 2.5 t6.4 4.t 1.8 43 5 7 34 4.1 20.5 4.1 1.5 38 6 J t4 4.3 24.8 4.t 1.4 JJ 7 7 ), 6.4 31.1 4.4 1.5 34 8 4 t2 6.7 37.8 4.7 1.6 34 9 4 16 5.0 42.8 4.8 1.5 32 10 2 t2 J.J 46.1 4.6 1.5 32 1l 7 30 4.7 50.8 4.6 14 3l MATCH / GAME DETAILS :

Detail Game 1 Game 2 Game 3 Match Time 19 24 0 43 No. of:Shots 428 540 0 968 No,,olt:Rallies 46 58 0 104 Ave. Shots per Ralty 9.3 9.3 0 9.3 Standard, Deviation 8.0 8.1 0 8.0 Malc ,Shot'l-ength 26 15 0 26

WINNER / ERROR ANALYSIS :

,Game 1 Game I Flayer ,Winners ,:'Errors 'WE',Ratio .lonassen 10 14 0.7 Gonstable 13 9 1.4 Total 23 23 1.0

,,Garne 2 , Game 2 Game 2 Plaver Winners WE Ratio Jonassen 13 13 1.0 Constable 17 14 1.2 Total 30 27 1.1

Game 3 Game 3 Game 3 Player ,Winners Errors WE'Ratio Jonassen 0 0 #Dtv/o! Constable 0 0 #Dtv/ot Total 0 0 #Dtv/o!

'Match Match Match Plaver Winners Errors W/E Ratio Jonassen 23 27 0.9 Constable 30 23 1.3 Totâl 53 50 1.1 TYPE OF WINNING SHOT :

Shot Type Jonassen Constable Total % age Unclassrfied 1 0 1 1.9 Smash 10 11 21 39.6 Fush 0 0 0 0.0 Net return 1 5 6 11.3 4 5 9 17.O

Flick 0 1 1 1.9 6 8 14 26.4 0 0 0 0.0 CIear 0 0 0 0.0

1 0 1 1.9 ,Flick Serve ,' 0 0 0 0.0 Total,, 23 30 53 100.0

Shot Tvpe Jonassen :016,aoe Constable a/o âQê Unclassified 1 4.3 0 0.0 Smash 10 43.5 11 36.7 Push 0 0.0 0 0.0

Net,retum , 1 4.3 5 16.7 4 17.4 5 16.7 :Flick 0 0.0 1 3.3 Drop 6 26.1 8 26.7 Dnve 0 0.0 0 0.0 Clear 0 0.0 0 0.0 Block 1 4.3 0 0.0 Flick,Serue 0 0.0 0 0.0 Total 23 0.0 30 100.0 TYPE OF LOSING SHOT:

Shot Type Jonassen Constable Total % age Judqement 0 1 I 2.0 Smash 3 1 4 7.8 'Push 0 0 0 0.0 Net retum 11 11 22 43.1

l_ift 0 1 1 2.0 0 1 1 2.0 Flick 2 2 4 7.8 Drop 5 4 I 17.6 Drive 0 0 0 0.0 Clear 3 0 3 5.9

Block,, , 2 2 4 7.8

Hîgh,,Serve 1 0 1 2.0 l-ow,Serve 0 1 1 2.0 Flick Serve 0 0 0 0.0 Total 27 24 51 100.0

Shot Twe .Jonassen 'o/o à0,ê Constable % aoe Judgement 0 0.0 1 4.2 Smash 3 11.1 1 4.2 Push 0 0.0 0 0.0 Net retr,¡rn 11 40.7 11 45.8

Lifr 0 0.0 1 4.2 Kiil 0 0.0 1 4.2 Flick 2 7.4 2 8.3 DroP 5 ' 18.5 4 16.7 Drive 0 0.0 0 0.0 Clear 3 11 .1 0 0.0 Bloek 2 7.4 2 8.3 l'lioh'Serve 1 3.7 0 0.0 Low'Serve 0 0.0 1 4.2 Flick'Sen¡e 0 0.0 0 0.0 Total 27 100.0 24 100.0 POSITION OF WINNERS AND ERRORS:

Winners / Errors Winners / Errors Jonassen Constable

net net 3 10

12 6 l' 4 12 5

4 2 7 15

11 it 3 4 back court back court

7 I 7 7 10 13

10 12 5 5 II

Winner I Enor Ratio Winner I Error Ratio

net

0.5 0;5 0.8

1.0 0 1.0 1 0.7

:,0;8 '1.0 ,, 1.2 4 2.3 3.8

back court back court

0.7 0.8 1.4 1.4 1.1 1.4 o/o ?!ê Winners / Errors % age Winners / Errors Jonassen Constable

net net '13 26 13 33

44 26 17 52 17 0 7 17

15 t3 9 30 23 50

41 13 17 back court back court

30 39 30 23 33 43

37 44 19 22 39 39 RALLY LENGTH ANALYSIS :

Rally Length Rally End Jonassen constable Total % age 1-4 Shots Winners b 6 12 1'1.5 1-4,Shots Enors 12 10 22 21.2 5.8,Shots Winners 6 7 13 12.5 5-8 Shots Errors I 3 11 10.6 12 11.5 9-12'Shots , Winners' ,, 5 7 9-1'2 Shots 4 I 12 11.5 13+ Shots lAlinners 6 10 16 154 'l$+ $f¡sfs ., Errors 3 3 6 5.8 Total , 50 54 104 100

o/o Rally::End ,: : ,.Jonassen Yo a9e Constable â0,ê Winners .' 6 12.0 6 11 .1 12 24.0 10 18.5 5€,,Shots Winners 6 12.0 7 13.0 5€"Shots Errors I 16.0 3 5.6 9-12:Shots Winners 5 10.0 7 13.0 9-12,,Shots 4 8.0 I 14.8 13+'$hots Winners 6 12.O 10 18.5 13+:Slrots Enors 3 6.0 3 5.6 Total 50 100 54 100

Rallv,,Lenoth,l Rallv End Jonassen Constable 1-4 Shots IWE'Rat¡o 0.5 0.6 5€ Shots IW/E Ratio 0.8 2.3 9-12 Shots IWE Ratio 1.3 0.9 l3+ Shots IW/E Ratio 2.O 3.3 TYPE OF ERROR:

Error Type ,Jonassen Constable Total oó age

Judsement 0 1 1 2.O Hit net 15 14 29 58.0 Out wide I 6 14 28.0 Out'long 3 1 4 8.0 Other 1 1 2 4.0 Total , 27 23 50 100.0

Error,T.yoe , ,.lonagsen ,o/o age 'Constable % age .ludgernent 0 0.0 1 4.3 15 55.6 14 60.9 Out'wide , I 29.6 6 26.1 'Out lono 3 11.1 1 4.3 Other 1 3.7 1 4.3 Totál 27 100.0 23 100.0 A2 - Rally lnformation by game of match 15t07t99

1 1 9. 3 I 0l I 1 9 3 8 1l B0 - We by game and match 15t07t99

1 Winner 1 Jonassen

'l Enor 1 Jonassen 1 Winner 1 Constable 1 Enor Constable

Winner 1 eth Jonassen Enor eth Jonassen

Winner 1 Constable Enor 1 Gonstable 83 - w/e shots by match 15t07t99

Kenneth Jonassen Block 1 Kenneth Jonassen Kenneth Jonassen

Kenneth Jonassen Retum 1

Kenneth Jonassen 1 Kenneth Jonassen Kenneth Jonassen Kenneth Jonassen Kenneth Jonassen Kenneth Jonassen ck

Kenneth Jonassen Enor serve 1 Kenneth Jonassen Enor Return 11 Kenneth Jonassen Error Mark Constable nner Mark Gonstable nner I Mark Constable nner Kiil 5 Mark Constable nner Net Retum Mark Constable nner Smash 11 Mark Constable Block Mark Constable Dro Mark Constable Flick

Mark Constable 1

Mark Constable 1

Mark Constable 1

Mark Constable serye 1

[ilark Constable et Retum 'l 1

Mlark Constable 1 82 - wle position by match 15/07/99

Jonassen ner 1 4 neth Jonassen ner 2 5 neth Jonassen ner 3 4

neth Jonassen ner 4 1 neth Jonassen ner 5 3 neth Jonassen nner

neth Jonassen nner I 1 neth Jonassen nner I 3 neth Jonassen Enor 1 a neth Jonassen Error 2 5 neth Jonassen Error q 5 neth Jonassen rror t 1 neth Jonassen rror 5 3 neth Jonassen rror j 3 neth Jonassen rror s 4 neth Jonassen rror I 5

ark Constable nner 1 7 ark Constable nner 2 4 Constable nner 3 4 ark Constable nner 4 2 ark Constable 5 3 ark Constable 7 4 Mark Constable lwinner I 3 ark Constable I 3 ark Constable 0 1

Constable 1 3

Constable 3 1 Constable 4 2 Constable 5 3 Constable 6 2 Constable 7 4 Constable 8 6 Constable 9 2 cross tab 01-04 15t07t99

ner 6

12 1 cross tab 05-08 15t07t99

Winner I Error 3 cross tab 09-12 15t07t99

ner 7 cross tab 13-99 15t07t99

1 85 - Error type by match 15t07t99

ble net

ble nt 1

ble hit 1 ble ut long i ble wide 6 it net 5

hit 1 nassen onassen wide Decision Tree 2+ ANcovA f ind. -ffMANcovA 2* ,MANovA Covariate Ditferences Two Pearson's r DV's Chi-square frequency counts Multiple l{ominal regressron Two 2* ,MANovA gfoups f dep or DV's more variables Type lnterval or ratio 2+ of Analyze mean differences Between ANOVA ------>MANOVA dûta DV's- Three or between groups Spearman's rho more rankings Within 2* frinal ANOVA R.M rMANove Mann-Whitney U D\fs Two or more Differences among Two groups factors rankings 2+ Three or more groups ANOVAB.B. D¡IS-+MANOVA

lndependent Kruskal-Wallis ANOVA 2+ Covariate ANOVAw.w. Oç=-+MANOVA Dependent Wilcoxon matched

Two groups ANOVA MANOVA DV's Three or more groups 2+ ANCOVA :MANCOVA DV's Friedman's two-way ANOVA Table 4.3 Values for Student's f Distribution One -tailed test Two-tailed test l0 .05 .01 .01 df d.f t0 .05 6.-314 3 I .821 I 3.078 6.314 17.106 63.657 2.920 6.965 I 2 1.886 2.920 4.303 9.9?5 2.353 4.541 2 J I 638 2.353 3.182 5.841 2.r32 3;t47 3 4.ffi 4 1.533 4 2 132 2;t76 2.015 3.365 4032 5 1.476 2.015 2.571 1.943 3.143 5 6 t.440 2.44'l 3;101 2.998 6 t.943 'l I .415 1.895 1 ?.365 3 499 2.896 I r.895 1.397 1.860 3.355 8 r.860 2.306 1.833 2.821 8 9 L383 2.262 3.250 2;tu 9 t.833 t0 1.372 1.8t2 2.228 3.1 69 2.118 t0 t.812 1.363 t:196 2.201 3.106 il 2.681 il t.796 r.356 t;182 3.055 t2 1.782 2.1'19 I .771 2.650 t2 t3 t.350 2.1 60 3.012 2.624 l3 1.711 r.345 1.761 2.971 t4 t4 1.761 2.145 1.753 2.602 294'l t5 I .341 L?53 2.t11 1.746 2.583 l5 r6 1.337 2.t20 2.92t tó 1.146 t'l t.333 1.740 2.56'l 2.1 10 2.898 2.552 t7 1.740 t8 l.330 1.734 2.878 2.539 t8 1.134 2l0l 1.328 1.729 2 861 l9 t;t29 2.093 1.725 2.5f,8 l9 20 1.32s 2.086 2.845 2.518 20 t.725 ?l 1.323 t.721 2.080 2.831 2.508 2t l;'l?t 1.321 1.7 t7 2.819 22 t .711 2.074 L714 2.500 27 23 1.319 2.069 2.807 2 492 23 1.114 r.318 l.7ll 2;19'l 24 2.064 L708 2.485 24 l.7ll 25 t.316 |.J 2.060 ?.181 4'19 À 25 r.708 315 t.706 2 -) 2'719 26 l 26 r.706 2.056 ( r urtinued) t\) TableA3 (continued) æà

TVo.tailed test One-tailed test dr i.l0 .05 .0t df l0 .05 .01

27 1.703 2.052 2;t7l 2'l t.3t4 1.703 2.473 28 1.701 2.U8 2.763 28 1.313 1.701 2.467 29 1.699 2.M5 2.756 29 l.3l I 1.699 2.462 30 I.697 2.M2 2.750 30 1.310 t.697 2.45't 40 1.684 2.02t 2.7M 40 1.303 l.6M 2.423 ó0 t.67t 2.000 2.660 60 1.296 1.67t 2.390 t20 1.658 1.980 2.6t7 r20 1.289 1.658 2.358 æ t.&5 1.960 2.576 t.282 l.út5 2.326

sl2 l

-z 0 +Z +Z

ErcmBiottt¿trikaTablesforStatistúcr'ans (Vol. I) (3rd ed.) by E.S. Pearson and H.O. Hanley (Eds.), l9ó6, London: BiometrikaTrustees. Copyright 1966 by Biomeri.ka of thc Biomctrikc Trustccs. (l) Table A.l0 Values of lhe Chi'square Distribution

.0I dÍ l0 .05 3.84 ó.ó3 I 2.11 5.99 9.2t 2 4.61 7.81 I r.34 3 6.25 I 3.28 4 7;18 9.49 t.07 t 5.09 5 9.24 I I ó.81 ó t0.ó4 12.59 r4.07 18.48 1 12.02 t5.51 20.09 8 t4.36 2t.6'l 9 t 4.ó8 t6.92 23.21 l0 15.99 r8.31 21.73 ll 17.28 t 9.ó8 26.22 t2 18.55 2t.03 27.69 Il t3 t9 8l 22.36 29. r4 t4 2l.0ó 23.68 30.58 t5 22.31 25.00 ló 23.54 2ó.30 32.00 -r3.41 t1 21.77 21.59 3.1.81 t8 25.99 28.87 .16. l9 t9 27.20 30 14 37.51 20 28 4l 31.41 44.31 25 34.38 37.ó5 50.89 30 40.26 43.71 63.69 4() 5 t.81 55 76 76.1 5 50 ó3.1 7 67.50 tr8.80 ó0 74.40 79.08 I (X).43 10 85.53 90.53 r I2.33 80 9ó.58 101.88 t21.12 90 to7.57 I r3. t5 I 18.50 124.34 I 35.tr I

Hunley(Eds)' lìro¡¡rBir¡rerri/!uTuhlesforSt¿¡lisliciarts(Vol. l)(3rded.)byES'PearsonandH'O Rcprintetl by pernrissiott 1g66. London: Bit¡nretrikaTrustees Copyright l966by BionretrikaTlustees ùl rhc tsioructliliu Ttustees.

25s