Mike D. Hughes

The use of performance indicators in performance analysis

The aims of this paper are to examine the application of performance indicators in different sports and, using the different structural definitions of games, to make general recommendations about the use and application of these indicators. Formal games are classified into three categories: net and wall games, invasion games, and striking and fielding games. The different types of sports are also sub-categorized by the rules of scoring and ending the respective matches. These classes are analysed further, to enable definition of useful performance indicators and to examine similarities and differences in the analysis of the different categories of game. The indices of performance are sub-categorized into general match indicators, tactical indicators, technical indicators and biomechanical indicators. Different research examples and the accuracy of their presentation are discussed. We conclude that, to enable a full and objective interpretation of the data from the analysis of a performance, comparisons of data are vital. In addition, any analysis of the distribution of actions across the playing surface should also be presented normalized, or non-dimensionalized, to the total distribution of actions across the area. Other normalizations of performance indicators should also be used more widely in conjunction with the accepted forms of data analysis. Finally, we recommend that biomechanists should pay more attention to games to enrich the analysis of performance in these sports.

Sport competition as a dynamical self-organizing system

The existence of structure in sport competition is implicated in the widespread practice of using the information gathered from a past contest to prepare for a future contest. Based on this reasoning, we previously analysed squash match-play for evidence of signature traits from among the stochastic relations between the various types of shot. The mixed findings from these analyses led us to re-analyse squash match-play as a dynamical system. Here, we extend this line of investigation with some suggestions as to how various sports might be described further within this theoretical framework. We offer some examples of dynamical interactions in dyadic (i.e. one vs one) and team (e.g. many vs many) sports, as well as some predictions from a dynamical systems analysis for these types of sports contests. This paper should serve to initiate further research into the complex interactions that occur in sport competition.

Analysis of passing sequences, shots and goals in soccer

Early research into how goals were scored in association football (Reep and Benjamin, 1968) may have shaped the tactics of British football. Most coaches have been affected, to a greater or lesser extent, by the tactics referred to as the “long-ball game” or “direct play”, which was a tactic employed as a consequence of this research. Data from these studies, published in the late 1960s, have been reconfirmed by analyses of different FIFA World Cup tournaments by several different research groups. In the present study, the number of passes that led to goals scored in two FIFA World Cup finals were analysed. The results conform to that of previous research, but when these data were normalized with respect to the frequency of the respective lengths of passing sequences, there were more goals scored from longer passing sequences than from shorter passing sequences. Teams produced significantly more shots per possession for these longer passing sequences, but the strike ratio of goals from shots is better for “direct play” than for “possession play”. Finally, an analysis of the shooting data for successful and unsuccessful teams for different lengths of passing sequences in the 1990 FIFA World Cup finals indicated that, for successful teams, longer passing sequences produced more goals per possession than shorter passing sequences. For unsuccessful teams, neither tactic had a clear advantage. It was further concluded that the original work of Reep and Benjamin (1968), although a key landmark in football analysis, led only to a partial understanding of the phenomenon that was investigated.

Advances in the application of information technology to sport performance

This paper overviews the diverse information technologies that are used to provide athletes with relevant feedback. Examples taken from various sports are used to illustrate selected applications of technology-based feedback. Several feedback systems are discussed, including vision, audition and proprioception. Each technology described here is based on the assumption that feedback would eventually enhance skill acquisition and sport performance and, as such, its usefulness to athletes and coaches in training is critically evaluated.

Twenty-five years of sport performance research in the Journal of Sports Sciences

Abstract In this historical review covering the past 25 years, we reflect on the content of manuscripts relevant to the Sport Performance section of the Journal of Sports Sciences. Due to the wide diversity of sport performance research, the remit of the Sport Performance section has been broad and includes mathematical and statistical evaluation of competitive sports performances, match- and notation-analysis, talent identification, training and selection or team organization. In addition, due to the academic interests of its section editors, they adopted a quality-assurance role for the Sport Performance section, invariably communicated through key editorials that subsequently shaped the editorial policy of the Journal. Key high-impact manuscripts are discussed, providing readers with some insight into what might lead an article to become a citation “classic”. Finally, landmark articles in the areas of “science and football” and “notation analysis” are highlighted, providing further insight into how such articles have contributed to the development of sport performance research in general and the Journal of Sports Sciences in particular.

Establishing normative profiles in performance analysis.

It is an implicit assumption in notational analysis that in presenting a performance profile of a team or an individual that a ‘normative profile‘ has been achieved. Inherently this implies that all the variables that are to be analysed and compared have all stabilised. Most researchers assume that this will have happened if they analyse enough performances. But how many is enough? In the literature there are large differences in sample sizes. Just trawling through some of the analyses in soccer shows the differences (Table 1). Table 1 Some examples of sample sizes for profiling in sport.ResearchSportN (matches for profile)Reep & Benjamin (1969)Soccer3,216Eniseler et al., (2000)Soccer4Larsen et al., (2000)Soccer4Hughes et al., (1988)Soccer8 (16 teams)Tyryaky et al., (2000)Soccer4 and 3 (2 groups)Hughes (1986)Squash12, 9 & 6 – 3 groupsHughes & Knight (1993)Squash400 ralliesHughes & Williams (1987)Rugby Union5Smyth et al., (2001)Rugby Union5 and 5Blomqvist et al., (1998)Badminton5O’Donoghue (2001)Badminton16, 17, 17, 16, 15Hughes & Clarke (1995)Tennis400 ralliesO’Donoghue & Ingram (2001)Tennis1328<rallies<4300 (8 groups) There must be some way of assessing how data within a study is stabilising. The nature of the data itself will also effect how many performances are required - 5 matches may be enough to analyse passing in field hockey, would you need 10 to analyse crossing or perhaps 30 for shooting? The way in which the data is analysed also will effect the stabilisation of performance means - data that is analysed across a multi-cell representation of the playing area will require far more performances to stabilise than data that is analysed on overall performance descriptors (e.g. shots per match). It is misleading to test the latter and then go on to analyse the data in further detail. This study aimed to explore strategies in solving these problems in two sports, squash and badminton, in depth and then present further examples from a multiplicity of types of sports. A computerised notation system (Brown and Hughes, 1995) was used to record and analyse play, post event, for elite (N=20), county (N=20) and recreational (N=20) players. T-tests were used to examine the inter- and intra-reliability of the data collection processes. In addition, to establish that a normative profile had been reached, the profiles of 8 matches were compared with those of 9 and 10 matches, using dependent t-tests, for each of the categories of players. This method clearly demonstrated that those studies assuming that 5, 6 or 8 matches or performances were enough for a normative profile, without resorting to this sort of test, are clearly subject to possible flaws. The number of matches required for a normal profile of a subject population to be reached is dependent upon the narure of the data and, in particular, the nature of the performers. A notation system, designed to record rally-end variables in Badminton, was shown to be both valid and reliable. Inter and intra reliability ranged from 98.6% (Rally length) to 91.3% (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 quantifying 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 11 matches of a player. A template, at match N (E), was established when these means became stable within set limits of error (LE). T-tests on the variable means in games won, and games lost established the existence of winning and losing templates for winners and errors. Match descriptors (rallies, shots and shots per rally) were independent of match outcome. General values of N(E) established for data types, (10% LE), were 3 matches (descriptive variables), 4 (winners/errors (w/e), 6 (smash + w/e), 7 (position + w/e). Respective values at 5% LE were 7, 5, 8 and 10. There was little difference in the values of N (E) 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 these 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. Further examples will be presented from different sports. 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.

The essentials of performance analysis : an introduction

Introduction 1. The Need for Feedback 2. What is Performance Analysis? 3. The Provision of Information 4. Video-Feedback and Information Technologies 5. An Overview of the Development of Notation Analysis 6. Sports Analysis 7. How do we Design Simple Systems? - How to Develop a Notation System 8. Examples of Notation Systems 9. Analysis of Notation Data: Reliability 10. Qualitative Biomechanical Analysis of Technique 11. Time-Motion Analysis 12. Probability Analysis of Notated Events in Sport Contests: Skill and Chance 13. Rule Changes in Sport and the Role of Notation 14. Performance Analysis in the Media 15. Notational Analysis of Coaching Behaviour

Analysis procedures for non-parametric data from performance analysis

It is vital that the reliability of a data gathering system is demonstrated clearly and in a way that is compatible with the intended analyses of the data. The data must be tested in the same way and to the same depth in which it will be processed in the subsequent analyses. In general, the work of Bland and Altman (1986) has transformed the attitude of sport scientists to testing reliability; can similar techniques be applied to the non-parametric data that most notational analysis studies generate? There are also a number of questions that inherently re-occur in these forms of data-gathering – this study aims to demonstrate practical answers to some of these questions. The most common form of data analysis in notation studies is to record frequencies of actions and their respective positions on the performance area, these are then presented as sums or totals in each respective area. What are the effects of cumulative errors nullifying each other, so that the overall totals appear less incorrect than they actually are? The application of parametric statistical techniques is often misused in notational analysis - how does this affect the confidence of the conclusions to say something about the data, with respect to more appropriate non-parametric tests? Analysing 72 research papers recently published under the banner of notational analysis, it was found that 70% did not report any reliability study and a large proportion of the remaining used questionable processes given the recent ideas in reliability testing in sports science (Atkinson, G. and Nevill, A. (1998) Statistical methods for assessing measurement error (reliability) in variables relevant to sports medicine. Sports Med., 26, 217–238). In some cases the reliability studies were executed on summary data, and the system was then assumed to be reliable for all of the other types of more detailed data analyses that were produced. By using practical examples from recent research and consultancy projects, undergraduate and postgraduate studies, this research investigated these issues associated with reliability studies and subsequent analyses in performance analysis, in order to give practical guidelines to enable establishing simple and reliable comparisons of non-parametric sets of data.

Patterns of play of international rugby union teams before and after the introduction of professional status.

Many methods of assessing game intensity that are appropriate to the research scientist- including heart rate analysis (Ali and Farrally1), blood lactates (Deutsch, Maw, Jenkins and Reaburn2) and time-motion analysis (Reilly and Thomas3 and Withers, Maricic, Wasilewski and Kelly4) - may have limited application in the ‘real world’. Hence in game analysis, the relative simplicity of videotape recordings may be considered the most appropriate tool for the rugby coach in terms of applicability and reproducibility. Anecdotal evidence would suggest that since the introduction of professional rugby union, the game has become more intense due to increases in total activity duration and speed of play. Analysis using a modified time/motion approach specifically relating to activity times may provide detailed information on the relative intensity of the game. The purpose of this study was to compare the time and frequency of activity and ball re-cycling (using videotaped recordings) of International Rugby Union teams in four discrete periods spanning the inception of professional rugby in the mid 1990’s with particular reference to periods spanning the inception of professional status in 1995/6. Individual activity times (initial possession to completed tackle) were recorded for twelve pre-recorded matches (1988-2002) taken from the Five and Six Nations Championships; using a standard lap split time stopwatch and hand notation system. Intra-observer reliability analysis of ruck time indicated 0.11sec difference between observations. This represented 96% level of agreement. For activity time, intra-observer reliability was calculated as 0.18 sec difference between observations. This represented an agreement of 97%. Repeated measure ANOVA indicated highly significant differences between periods activity times (F = 10.16, df = 3 12, p = .001). Post hoc analysis (Tukey HSD) revealed differences to be between period 1 compared to period 3 and period 1 compared with period 4. No main effect was established for ruck time. Analysis of ruck frequency revealed significant differences (F = 13.87, df = 3, 12, p < .0005) between games periods. Post hoc analysis indicated these to be between periods 4 and 2 (p = .003) and 4 and 1 (p = .0005). Repeated measures analysis for frequency of activity revealed significant main effects for period (F = 5.39, df = 3, 12, p <. 01). Results of post hoc analysis revealed differences in activity instances between periods 1 and 4 (p = .01). Analysis of ball in play time comparing pre and post 1995 revealed a significant main effect for period (F = 12.97, df = 1, 14, p = .003) with ball in play 26.5% of the time in the period pre-1995 compared to 32.1% of the time in the period post-1995. This represents a mean time difference of 4 minutes and 45 seconds of play. It was concluded that since the inception of professional status in rugby union, the mean time players spend in game activity has been significantly reduced, whilst total game activity has been increased. Similarly the frequency of rucks has significantly increased in the post professional era, although the speed of ball recycling has been shown to be relatively consistent during 1988-2002. This indicates that the game activity patterns may have shifted towards a faster ruck dominated game which includes more phases of play. Consequently, game activity time has increased indicating a positive shift in game activity duration. Accordingly, such changes in the game need to be considered in designing training schedules for rugby union.

The consequences of the introduction of professional playing status on game action variables in international northern hemisphere rugby union football

The aims of this study were to examine the consequences of the introduction of professional playing status in 1995 on game variables in international rugby union football, and secondly to provide a longitudinal game map for use in future research. Twelve ‘Five Nations’ Championship games between 1988 and 1995 (pre-professional Era), sub-divided into two Periods (1988-92 and 1995-97) and twelve ‘Five’ and ‘Six Nations’ Championship games between 1997 and 2002 (professional Era), sub-divided into two Periods (1997-99 and 2000-02) were analysed using a sequential data gathering hand notation system. Initial intra- observer reliability analysis established that the level of observer agreement exceeded 97% for all game variables. Normalised profiles were also constructed for each variable. These profiles demonstrated that the data for all variables reached stables means within 6 full games. Frequency data and frequency data per unit time were assessed with the Mann-Whitney U statistic to examine Era differences and the Kruskal Wallis H tests to identify Period differences. Where Period differences were indicated, the Mann-Whitney U test was used as the appropriate post-hoc procedure. Analysis identified significant increases (P < 0.005) in the frequency of rucks, dummy/scrum half passes, open play passes and total passes, and significant decreases (P < 0.005) in the frequency of lineouts, kicks out of play, total game kicks, mauls, set possessions and activity possessions from the pre- to the professional era. No significant difference was identified for either scrums or kicks in play frequencies. Analysis of these variables normalised to ball in play time resulted in significant increases (P < 0.005) in the frequency of dummy/scrum half passes, rucks, lineouts, and a significant decrease (P < 0.005) in the frequency of mauls, scrums, total game kicks, kicks out of play, kicks in play, and set possessions across the Eras. Significant (P < 0.005) Period main effects were identified for the frequency of lineouts, kicks out of play, scrums, (trend decreasing across Periods) total game passes, passes from the dummy/scrum half position, rucks, activities/phases, and set possession (P = 0.01) and total game kicks (P = 0.006) (trend increasing across Periods). Analyses of these variables normalised to ball in play time resulted a significant (P < 0.05) Period main effects being identified for total game kicks, kicks out of play, kicks in play, mauls, scrums, set possessions,(trend decreasing across Periods) passes from the dummy/scrum half position (trend increasing across Periods) and offloads (no identifiable trend across Periods). It was concluded that the introduction of professional playing status in rugby union had had a marked effect on game action variables and, as a consequence the playing pattern of the game is significantly different in the professional Era and Periods compared to the pre-professional Era and Periods.