Quantification of individual rugby player performance through multivariate analysis and data mining : a thesis presented for the fulfilment of the requirements for the degree of Doctor of Philosophy at Massey University, Albany, New Zealand

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dc.contributor.authorBracewell, Paul J
dc.date.accessioned2010-11-05T00:04:26Z
dc.date.availableNO_RESTRICTIONen_US
dc.date.available2010-11-05T00:04:26Z
dc.date.issued2003
dc.description.abstractThis doctoral thesis examines the multivariate nature of performance to develop a contextual rating system for individual rugby players on a match-by-match basis. The data, provided by Eagle Sports, is a summary of the physical tasks completed by the individual in a match, such as the number of tackles, metres run and number of kicks made. More than 130 variables were available for analysis. Assuming that the successful completion of observed tasks are an expression of ability enables the extraction of the latent dimensionality of the data, or key performance indicators (KPI), which are the core components of an individual's skill-set. Multivariate techniques (factor analysis) and data mining techniques (self-organising maps and self-supervising feed-forward neural networks) are employed to reduce the dimensionality of match performance data and create KPI's. For this rating system to be meaningful, the underlying model must use suitable data, and the end model itself must be transparent, contextual and robust. The half-moon statistic was developed to promote transparency, understanding and interpretation of dimension reduction neural networks. This novel non-parametric multivariate method is a tool for determining the strength of a relationship between input variables and a single output variable, whilst not requiring prior knowledge of the relationship between the input and output variables. This resolves the issue of transparency, which is necessary to ensure the rating system is contextual. A hybrid methodology is developed to combine the most appropriate KPI's into a contextual, robust and transparent univariate measure for individual performance. The KPI's are collapsed to a single performance measure using an adaptation of quality control ideology where observations are compared with perfection rather than the average to suit the circumstances presented in sport. The use of this performance rating and the underlying key performance indicators is demonstrated in a coaching setting. Individual performance is monitored with the use of control charts enabling changes in form to be identified. This enables the detection of strengths/weakness in the individual's underlying skill-set (KPI's) and skills. This process is not restricted to rugby or sports data and is applicable in any field where a summary of multivariate data is required to understand performance.en_US
dc.identifier.urihttp://hdl.handle.net/10179/1823
dc.language.isoenen_US
dc.publisherMassey Universityen_US
dc.rightsThe Authoren_US
dc.subjectRugby football playersen_US
dc.subjectPerformance analysisen_US
dc.subjectMultivariate data analysisen_US
dc.subjectData miningen_US
dc.subjectStatisticsen_US
dc.subject.otherFields of Research::230000 Mathematical Sciences::230200 Statistics::230204 Applied statisticsen_US
dc.titleQuantification of individual rugby player performance through multivariate analysis and data mining : a thesis presented for the fulfilment of the requirements for the degree of Doctor of Philosophy at Massey University, Albany, New Zealanden_US
dc.typeThesisen_US
massey.contributor.authorBracewell, Paul J
thesis.degree.disciplineStatisticsen_US
thesis.degree.grantorMassey Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophy (Ph.D.)en_US
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