Development of A Database and Modelling
Teams and performers often demonstrate a stereotypical way of playing and these are idiosyncratic models, which include positive and negative aspects of performance. Patterns of play will begin to establish over a period of time but the greater the database then the more accurate the model. An established model provides for the opportunity to compare single performance against it. The modelling of competitive sport is an informative analytic technique because it directs the attention of the modeller to the critical aspects of data that delineate successful performance. The modeller searches for an underlying signature of sport performance, which is a reliable predictor of future sport behaviour. Stochastic models have not yet, to our knowledge, been used further to investigate sport at the behavioural level of analysis. However, the modelling procedure is readily applicable to other sports and could lead to useful and interesting results.
Once notational analysis systems are used to collect amounts of data that are sufficiently large enough to define 'norms' of behaviour, then all the ensuing outcomes of the work are based upon the principles of modelling. 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.
These problems have very serious direct outcomes for the analyst working with coaches and athletes, both in practical and theoretical applications. It is vital that when analysts are presenting profiles of performance that they are definitely stable otherwise any statement about that performance is spurious. The whole process of analysis and feedback of performance has many practical difficulties. 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. The need then is to provide coaches with accurate information on as many of the likely opposition players, or teams, in the amount of time available. This may be achieved by the instigation of a library of team and/or player analysis files, which can be extended over time and receive frequent updating. Player files must be regularly updated by adding analyses from recent matches to the database held on each player.
Finally, some scientists have considered the use of a number of sophisticated techniques, such as neural networks, chaos theory, fuzzy logic and catastrophe theory, for recognizing structures, or processes, within sports contests. Each of these system descriptions, while incomplete, may assist in our understanding of the behaviours that form sports contests. Furthermore, these descriptions for sports contests need not be exclusive of each other, and a hybrid type of description (or model) may be appropriate in the future, a suggestion that remains only a point of conjecture at this time.
Read more about this topic: Notational Analysis
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