The application of discriminant analysis and logistical regression as methods of compilation in the prediction function in youth rugby

Abstract :

Talent identification is a process where potential sportsmen/women are identified and developed in a specific type of sport. One of the primary reasons for talent identification is to ensure the future survival and development of the particular sport in which the talent is identified and sought. Another very important reason for talent identification is to be of assistance to the individuals who are being tested for talent. Should these individuals possess talent, they can be further developed. Should they not possess talent for a particular sports-type, they can be redirected in responsible and sensitive manner. Worldwide there is a move toward the identification of talent, with certain countries ahead in the process, successfully applying it to their national sporting codes. Due to our exclusion from international competition for many decades, we find ourselves in the position of being behind in the field of talent identification, albeit catching up very fast. What has been noticed however, is the move towards the favoring of two primary methods of talent identification, namely discriminant analysis and logistical regression. Different countries of the world use either of these methods, with an open debate currently being fought as to the predictive abilities of these respective methods. This study compares the two main models of talent identification ie discriminant analysis and logistical regression in terms of their ability to predict talent and their prediction functions, hopefully assisting in resolving the debate as to which is the better model and why. These models were applied to two U/12 groups of rugby players. One group was talented, the other less so. The results were compared and then certain conclusions and recommendations were made about these models. A literature study was also done to give this study a philosophical foundation. This included evaluations of other models, the development of these models in history, as well as discussions on whether talent identification is needed or not. Honest discussions about the advantages, disadvantages and options of combining the two models as well as recommendations for future applications are also contained in this study. The hypothesis is made that there is a significant difference in the predictive function of the one model with relation to the other. This hypothesis has proved to be inaccurate, with the predictive function found to be exactly the same in both models. Other questions were asked and answered by this study. With the hypothesis being disproved, some of the questions were found to be irrelevant and there was no need to evaluate them further. One question that is investigated and evaluated is whether it is possible to combine these two models to form a more accurate model. Through various methods of statistical evaluation and substitutions, it is found that by combining the two models a less accurate predictor is formulated, negating the need to combine the two models. This then elicited the conclusion that each model, whilst both being 100% accurate, could be applied under different circumstances when different information is sought. Discriminant analysis provides an accurate view of the best discriminating factors involved in talent, while logistical regression provides a view of the relative impact of the various factors that determine talent. As can be seen, both of these models can and should be used effectively in the identification of talent. The proposition is made that when talent is identified, both models be used rather than following an “either-or” approach. The advantage with this is that a well-rounded view of the individuals under scrutiny after applying both models is formulated. Therefore, in summary it can be said that both models predict with the same accuracy, with each model having it’s own unique areas of application.



Author Booysen C
Date Accessioned 2016-09-26T06:11:08Z
Date Available 2016-09-26T06:11:08Z
Date Created
Identifier URL 2002
Language English
Subject Education
Subject 2 Education
Alternative Title
Degree Type Masters degree
Degree Description  MA