Elite Rugby Union Players Perceptions of Performance Analysis

This study provides an in depth insight into the current views and opinions of elite level rugby union players regarding the use of performance analysis as a tool for improving their own playing performance and in highlighting the strengths and weaknesses of upcoming opponents. A total of seventy-three elite level rugby union players from two clubs in Great Britain completed a semi-structured questionnaire. Additionally, four players completed a semi-structured interview and following inductive content analysis, four key themes emerged: (1) the use of video for player development, (2) preparing for a match, (3) using video for player reflection in addition to other psychological tools and (4) players suggestions for improvements to the clubs current performance analysis programme. The main finding of the study concludes that players viewed performance analysis as a beneficial and useful tool to support their development and preparation. As a result the study provides an insight into the use of performance analysis within professional rugby union, enabling rugby coaches and practitioners to gain an understanding and appreciation of the players views towards the clubs current provision. Additionally, the findings help build and strengthen the on-going knowledge coaches, analysts and researchers currently have regarding how players perceive performance analysis.

A Parameter Estimation and Identifiability Analysis Methodology Applied to a Street Canyon Air Pollution Model

Mathematical models are increasingly used in environmental science thus increasing the importance of uncertainty and sensitivity analyses. In the present study, an iterative parameter estimation and identifiability analysis methodology is applied to an atmospheric model – the Operational Street Pollution Model (OSPMr). To assess the predictive validity of the model, the data is split into an estimation and a prediction data set using two data splitting approaches and data preparation techniques (clustering and outlier detection) are analysed. The sensitivity analysis, being part of the identifiability analysis, showed that some model parameters were significantly more sensitive than others. The application of the determined optimal parameter values was shown to succesfully equilibrate the model biases among the individual streets and species. It was as well shown that the frequentist approach applied for the uncertainty calculations underestimated the parameter uncertainties. The model parameter uncertainty was qualitatively assessed to be significant, and reduction strategies were identified.