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The use of performance analysis technology to monitor the coaching environment in soccer.
International Journal of Performance Analysis in Sport
Abstract
Performance analysis systems are well known for their use in enhancing the feedback process in coaching. These systems have also been developed to analyse the coaching environment and coach-athlete behaviour. This study used a computerised performance analysis system to monitor the coaching environment of two independent soccer coaching groups. The aims were to report on the use of performance analysis technology to monitor the coaching environment in soccer and investigate the reliability of the performance analysis system used. The two coaching groups were part of a larger study investigating the development of a new soccer coaching programme based on a preceding biomechanical analysis. Sessions were videoed and analysed using GameBreaker™ performance analysis software which was set up to log five events relevant to the focus of the study. A trained independent operator demonstrated the two events whole group on task (2.30% error) and whole group off task (3.12% error) were reliable events to analyse further. The other three events; small group off task, individual off task and other, were deemed to be unreliable. Possible reasons for this include limitations of equipment and training of the observer. Chi-square analysis revealed a non-significant difference between groups for whole group on task (p=0.91) and whole group off task (p=0.87), indicating both groups experienced similar amounts of practice time over six coaching sessions. This study suggests performance analysis technology can be used as an effective tool to monitor the coaching environment. However, care must be taken when setting up the operational definitions and training an independent operator to use the system in order to obtain objective, reliable data.
... En este sentido, las herramientas tecnológicas que ayudan a la labor diaria de los técnicos y deportistas, podemos dividirlas en software que facilitan el análisis estadístico y estratégico del entrenamiento y la competición 8,9 , y software que facilitan la planificación y el control del entrenamiento 10,11 . Dichos software pueden ofrecer información a nivel físico 12 , biomecánico 13 ...
El objetivo del presente estudio fue conocer la percepción del staff técnico de un equipo de voleibol de alto nivel de la viabilidad, contextualización y utilidad, de una herramienta tecnológica, con posibilidad de acceso desde dispositivos móviles, empleada para almacenar e intercambiar información. La muestra estuvo formada por el staff técnico de un equipo de voleibol femenino que compite en alto nivel, compuesto por un entrenador y un entrenador asistente con funciones de preparador físico. El uso de la herramienta tecnológica, por el equipo, se realizó durante una temporada (siete meses). La herramienta tecnológica consta de una página web y su aplicación con acceso directo a la información desde dispositivos móviles. Como técnica de recogida de datos, se empleó la entrevista semiestructurada al terminar la temporada. El análisis de los datos se realizó a través de la Grounded Theory. Nuestros resultados indican que el staff técnico considera dicha herramienta tecnológica como viable, contextualizada y útil, permitiendo organizar, estructurar e intercambiar información de forma sencilla. Además, los resultados destacan que la posibilidad de acceso mediante un dispositivo móvil tiene grandes ventajas (acceso desde cualquier lugar y en cualquier momento), y alguna desventaja como la posible pérdida de contacto humano.
The development of expertise in sport is the result of successful interaction of biological, psychological, and sociological constraints. This review examines the training and environmental factors that influence the acquisition of sport expertise. Research examining the quality and quantity of training indicate that these two elements are crucial predictors of attainment. In addition, the possession of resources such as parental support and adequate coaching are essential. Social factors such as cultural influences and the relative age effect are also considered as determinants of sport expertise. Although it is evident that environmental factors are essential to the acquisition of high levels of sport development, further research is clearly required.
Minimal measurement error (reliability) during the collection of interval- and ratio-type data is critically important to sports medicine research. The main components of measurement error are systematic bias (e.g. general learning or fatigue effects on the tests) and random error due to biological or mechanical variation. Both error components should be meaningfully quantified for the sports physician to relate the described error to judgements regarding 'analytical goals' (the requirements of the measurement tool for effective practical use) rather than the statistical significance of any reliability indicators. Methods based on correlation coefficients and regression provide an indication of 'relative reliability'. Since these methods are highly influenced by the range of measured values, researchers should be cautious in: (i) concluding acceptable relative reliability even if a correlation is above 0.9; (ii) extrapolating the results of a test-retest correlation to a new sample of individuals involved in an experiment; and (iii) comparing test-retest correlations between different reliability studies. Methods used to describe 'absolute reliability' include the standard error of measurements (SEM), coefficient of variation (CV) and limits of agreement (LOA). These statistics are more appropriate for comparing reliability between different measurement tools in different studies. They can be used in multiple retest studies from ANOVA procedures, help predict the magnitude of a 'real' change in individual athletes and be employed to estimate statistical power for a repeated-measures experiment. These methods vary considerably in the way they are calculated and their use also assumes the presence (CV) or absence (SEM) of heteroscedasticity. Most methods of calculating SEM and CV represent approximately 68% of the error that is actually present in the repeated measurements for the 'average' individual in the sample. LOA represent the test-retest differences for 95% of a population. The associated Bland-Altman plot shows the measurement error schematically and helps to identify the presence of heteroscedasticity. If there is evidence of heteroscedasticity or non-normality, one should logarithmically transform the data and quote the bias and random error as ratios. This allows simple comparisons of reliability across different measurement tools. It is recommended that sports clinicians and researchers should cite and interpret a number of statistical methods for assessing reliability. We encourage the inclusion of the LOA method, especially the exploration of heteroscedasticity that is inherent in this analysis. We also stress the importance of relating the results of any reliability statistic to 'analytical goals' in sports medicine.
A computerized coaching analysis system (CCAS) is described, consisting of a series of systematic observation instruments that allow the collection of behavioral data on both the coach and the athlete. This system is composed of three interactive computer programs that structure the acquisition, immediate analysis, and storage of pertinent observable behaviors displayed by coaches and athletes during a typical coaching practice. Three separate observation instruments are outlined in detail and the discussion of their use centers on the general application of this technology in the sporting milieu.
The purpose of this project was to assess the degree to which the most popular method of calculating reliability of interval data, the I-I method, served the author's scientific purposes. Data were collected and analyses performed to assess the adequacy of I-I reliability scores in serving three functions: (1) as an index of how precise, clear, objective and complete the definition is; (2) as an index of how competently the observer is recording; and (3) as an index of the believability of the experimental effect reported. Findings from the study indicate gross unreliability of I-I scores as an index of definition adequacy, observer competence or believability of experimental effects. This unreliability is attributed to the fact that I-I scores are highly subject to influence by the rate or duration of the behavior being recorded. Conclusions by the authors stress that, because the I-I reliability scores are clearly inadequate, it is likely that a significant body of applied behavior analysis has seriously misrepresented the relationships between certain environmental factors and certain significant human behaviors. (Author/PC)
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.
This study tested a computer‐aided coaching analysis instrument (CAI) as part of an intervention strategy designed to modify verbal coaching behaviour. Four coaches were observed and analysed over 12 practice sessions. Coaches A, B and C received intervention feedback through CAI data, where selected behaviours were highlighted for discussion, and videotape images were used to illustrate discussion points. Coach D was provided with videotapes of his own performance and told to formulate and implement any of his own recommendations. The CAI data are primarily quantitative, so target values were created for the different dimensions of verbal behaviour. This benefited the coaches in interpreting their effectiveness and provided a reference to evaluate the magnitude of change. Written journals and audiotape recordings were also used to promote insight into the complexity of verbal behaviour and the ‘human factors’ (e.g. relationship with players, attitude to researcher) that affect behaviour modification.
Instructional effectiveness was assessed by time‐series analysis. There was evidence from each behaviour dimension that change can occur and be maintained as a result of exposure to the CAI intervention strategy. However, this is clearly contingent upon the coach understanding what is asked of him or her, and remains focused and committed to changing these particular behaviours. The analysis of Coach D's behavioural change suggests there are limitations to the sensitivity of discretionary viewing, as only two dimensions of behaviour were identified for, and resulted in, positive change. The results of this study provide support for Locke's (1984) contention that behaviour modification can occur by using data as direct feedback, as reinforcement and as information in the form of recommendations. However, the study also illuminates several factors that can negate the modification and maintenance of verbal coaching behaviour.
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