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1. Jones, Benjamin. 'Game changers' : discriminating features within the microstructure of practice and developmental histories of super-elite cricketers : a pattern recognition approach.

Degree: PhD, 2019, Bangor University

This thesis advances understanding of expertise development by addressing notable methodological issues, to become the first in field to quantitatively measure the influence of the microstructure of practice in the development of expertise in a sample of truly elite (super-elite) sportsmen, using machine learning techniques. The research protocol provides a means of bridging the existing gap between expertise development theory and research, and its application for talent identification and development (Baker, Schorer & Wattie, 2018; Holt et al., 2018). The thesis contains six chapters, including three research papers. Chapter 1 critically reviews the research on expertise development in sport to date and presents the rationale for the research programme, which aimed to overcome the theoretical and empirical limitations of this research, namely: (i) restricting investigation to comparisons of practice quantity; (ii) one-dimensional studies of individual expertise domains, disregarding the multifaceted nature of expertise; (iii) reliance on linear analysis techniques in identifying isolated precursors of expertise; (iv) assumptions of homogeneity within sports; and (v) inconsistent benchmarking measures for classifying expertise (Coutinho, Mesquita & Fonseca, 2016; Jones, Lawrence & Hardy, 2018; Schorer & Elferink-Gemser, 2013). Chapter 2 presents two studies to determine whether the relative age effect (RAE) observed in youth sport, extends into 'super elite' performers (Cobley, Baker, Wattie & Mckenna, 2009). The findings provide new evidence of RAEs at the super-elite level, presenting both inter and intra-sport differences (Jones et al., 2018). The research developed and applied a set of stringent criteria to benchmark super-elite expertise, and considered inter and intra-sport differences, by assessing RAE prevalence across the disciplines/positions of cricket and rugby union separately. Potential explanations for the findings are explored, owing to the survival and evolution of the fittest concepts, which suggest that RAE is a contributing factor in the efficient turnover of performers who do excel in sport. Chapter 3 applies non-linear machine learning (pattern recognition) analysis to a set of 93 developmental features (variables) obtained from a sample of sub-elite and elite cricket spin bowlers. The analysis produced a holistic predictive model consisting of 12 developmental features, from 93 measured, that discriminated between the elite and sub-elite groups, with very good accuracy (85%). The 12-feature model highlights elite spin bowlers' greater quantity of domain-specific practice. The external validity of this new multidimensional non-linear model is also tested. Qualitative data obtained was subsequently analysed to achieve a deeper understanding of the discriminating features. A working group of England and Wales Cricket Board (ECB) pathway coaches and practitioners were invited to scrutinise the interpretation of findings, producing recommendations for the wider game. Chapter 4 examines the…

Subjects/Keywords: Talent Identification; Talent Development; Expertise Development; Microstructure of Practice; Skill Acquisition; Machine Learning; Pattern Recognition; Cricket; Rugby Union; Super-Elite

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Jones, B. (2019). 'Game changers' : discriminating features within the microstructure of practice and developmental histories of super-elite cricketers : a pattern recognition approach. (Doctoral Dissertation). Bangor University. Retrieved from https://research.bangor.ac.uk/portal/en/theses/game-changers-discriminating-features-within-the-microstructure-of-practice-and-developmental-histories-of-superelite-cricketers – a-pattern-recognition-approach(4e70c7f8-679a-4b0a-a858-36e9b2fb6ce5).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782095

Chicago Manual of Style (16th Edition):

Jones, Benjamin. “'Game changers' : discriminating features within the microstructure of practice and developmental histories of super-elite cricketers : a pattern recognition approach.” 2019. Doctoral Dissertation, Bangor University. Accessed October 31, 2020. https://research.bangor.ac.uk/portal/en/theses/game-changers-discriminating-features-within-the-microstructure-of-practice-and-developmental-histories-of-superelite-cricketers – a-pattern-recognition-approach(4e70c7f8-679a-4b0a-a858-36e9b2fb6ce5).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782095.

MLA Handbook (7th Edition):

Jones, Benjamin. “'Game changers' : discriminating features within the microstructure of practice and developmental histories of super-elite cricketers : a pattern recognition approach.” 2019. Web. 31 Oct 2020.

Vancouver:

Jones B. 'Game changers' : discriminating features within the microstructure of practice and developmental histories of super-elite cricketers : a pattern recognition approach. [Internet] [Doctoral dissertation]. Bangor University; 2019. [cited 2020 Oct 31]. Available from: https://research.bangor.ac.uk/portal/en/theses/game-changers-discriminating-features-within-the-microstructure-of-practice-and-developmental-histories-of-superelite-cricketers – a-pattern-recognition-approach(4e70c7f8-679a-4b0a-a858-36e9b2fb6ce5).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782095.

Council of Science Editors:

Jones B. 'Game changers' : discriminating features within the microstructure of practice and developmental histories of super-elite cricketers : a pattern recognition approach. [Doctoral Dissertation]. Bangor University; 2019. Available from: https://research.bangor.ac.uk/portal/en/theses/game-changers-discriminating-features-within-the-microstructure-of-practice-and-developmental-histories-of-superelite-cricketers – a-pattern-recognition-approach(4e70c7f8-679a-4b0a-a858-36e9b2fb6ce5).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782095


Delft University of Technology

2. Bosboom, J. Quantifying the quality of coastal morphological predictions.

Degree: 2020, Delft University of Technology

<p class="MsoNormal" style="mso-pagination:none;mso-layout-grid-align:none; text-autospace:none">This thesis investigates the behaviour of the often used point-wise skill score, the MSESSini a.k.a. BSS, and develops new error metrics that, as opposed to point-wise metrics, take the spatial structure of morphological patterns into account. The MSESSini measures the relative accuracy of a morphological prediction over a prediction of zero morphological change, using the mean-squared error (MSE) as the accuracy measure. The main findings about the MSESSini are: 1) a generic ranking, based on values for MSESSini, has limited validity, since the zero change reference model fails to make model performance comparable across different prediction situations; 2) the combination of larger, persistent and smaller, intermittent scales of cumulative change may lead to an increase of skill with time, without the prediction on either of these scales becoming more skilful with time; 3) in the presence of inevitable location errors, the MSESSini favours predictions that underestimate the variance of cumulative bed changes and 4) existing methods to correct for measurement error are inconsistent in either their skill formulation or their suggested classification scheme. In order to overcome the inherent limitations of point-wise metrics, three novel diagnostic tools for the spatial validation of 2D morphological predictions are developed. First, a field deformation or warping method deforms the predictions towards the observations, minimizing the squared point-wise error. Error measures are formulated based on both the smooth displacement field between predictions and observations and the residual point-wise error field after the deformation. In contrast with the RMSE, the method captures the visual closeness of morphological patterns. Second, an optimal transport method defines the distance between predicted and observed morphological fields in terms of an optimal sediment transport field. The optimal corrective transport field moves the misplaced sediment from the predicted to the observed morphology at the lowest quadratic transportation cost. The root-mean-squared value of the optimal transport field, the RMSTE, is proposed as a new error metric. As opposed to the field deformation method, the optimal transport method is mass-conserving, parameter-free and symmetric. The RMSTE, unlike the RMSE, is able to discriminate between predictions that differ in the misplacement distance of predicted morphological features. It also avoids the consistent reward of the underestimation of morphological variability that the RMSE is prone to. Third, a scale-selective validation approach allows any metric to selectively address multiple spatial scales. It employs a smoothing filter in such a way that, in addition to the domain-averaged statistics, localized validation statistics and maps of prediction quality are obtained per scale (geographic extent or areal size of focus). The employed skill score weights how well the morphological structure and… Advisors/Committee Members: Reniers, A.J.H.M., Stive, M.J.F., Delft University of Technology.

Subjects/Keywords: (root)-mean-squared error; model accuracy; morphodynamic modelling; model validation; optimal transport; Monge–Kantorovich; root-mean-squared transport error; effective transport difference; image warping; image matching; scale-selective validation; optical flow; Brier skill score; model skill; zero change model; measurement error; location error; pattern skill

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Bosboom, J. (2020). Quantifying the quality of coastal morphological predictions. (Doctoral Dissertation). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; urn:NBN:nl:ui:24-uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; 10.4233/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; urn:isbn:978-94-6384-091-0 ; urn:NBN:nl:ui:24-uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; http://resolver.tudelft.nl/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3

Chicago Manual of Style (16th Edition):

Bosboom, J. “Quantifying the quality of coastal morphological predictions.” 2020. Doctoral Dissertation, Delft University of Technology. Accessed October 31, 2020. http://resolver.tudelft.nl/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; urn:NBN:nl:ui:24-uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; 10.4233/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; urn:isbn:978-94-6384-091-0 ; urn:NBN:nl:ui:24-uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; http://resolver.tudelft.nl/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3.

MLA Handbook (7th Edition):

Bosboom, J. “Quantifying the quality of coastal morphological predictions.” 2020. Web. 31 Oct 2020.

Vancouver:

Bosboom J. Quantifying the quality of coastal morphological predictions. [Internet] [Doctoral dissertation]. Delft University of Technology; 2020. [cited 2020 Oct 31]. Available from: http://resolver.tudelft.nl/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; urn:NBN:nl:ui:24-uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; 10.4233/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; urn:isbn:978-94-6384-091-0 ; urn:NBN:nl:ui:24-uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; http://resolver.tudelft.nl/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3.

Council of Science Editors:

Bosboom J. Quantifying the quality of coastal morphological predictions. [Doctoral Dissertation]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; urn:NBN:nl:ui:24-uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; 10.4233/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; urn:isbn:978-94-6384-091-0 ; urn:NBN:nl:ui:24-uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3 ; http://resolver.tudelft.nl/uuid:e4dc2dfc-6c9c-4849-8aa9-befa3001e2a3

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