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Sport performance is the result of a complex and challenging blend of many factors. Sport coaches and National Goveming Bodies (NGBs) of sport have begun to recognise that the most efficacious way of preparing athletes for competition is one based upon proven scientific methods and not upon trial and error judgements. Such a response flies in the face of much of the coaching folklore that has been passed down through the generations. Indeed, it is not so long ago that most sport coaches would treat the idea of support from a sport scientist with abject cynicism. Today, however, it is far more commonplace for individual athletes and teams of athletes, who aspire towards achieving superior optimal performances, their coaches and NGB advisors, to seek an input from sport scientists so that these athletes can achieve their full potential. The complex blend of component factors necessary for successful sport performance are activity specific, and this has led to the demand for the provision of assessment batteries that have proven specificity within the context of a particular sport. In addition, sport scientists require testing protocols to be duplicated, and for comparable data to be obtained when athletes are tested in different laboratories by different scientists and at different times throughout a preparatory and competitive season (MacDougall and Wenger, l99l). Even when scientists revert to data collection in the field, mainly because of convenience, information gathered about athletes might be less consistent, but it might well be more specific and upon which some key decisions can often be made. Clearly, athletes, their coaches, their NGB advisers and the sport scientists that support them each have concerns over performance enhancement and optimisation. Additionally, sport scientists themselves might well have a personal research agenda. It has to be acknowledged, therefore, that all of these stakeholders have an interest in the quality of the data collected and that these data should be relevant, consistent and accurate.

The aims of this thesis were to: (i) increase understanding of the biomechanical and motor control processes that underpin proficient fast bowling performance using dynamical systems theory and 'complex' analyses; and (ii) demonstrate the application of dynamical systems theory and the utility of 'complex' analyses to performance-oriented sports biomechanics research using cricket fast bowling as a representative task vehicle. Prior to analysing within- and between-bowler differences in coordination patterns at different levels of analysis and their relationship to ball release speed, the suitability of manual coordinate digitising for analysing intra- and inter-individual variability was examined. Both the reliability of time-discrete and time-continuous kinematic variables was considered. Of the 33 time-discrete kinematic variables examined, 31 exhibited between-participant variances and re-digitisation variances that accounted for the largest and smallest portions of total variance, respectively. Furthermore, re-digitisation variance accounted for less than 5% of total variance in 29 of these variables with 15 of these exhibiting less than 1%. For the 45 time-continuous kinematic variables, measurement error accounted for 17.2% of movement variability (range 4.3-41.0%). When considered together, these results indicated that manual coordinate digitising was sufficiently sensitive to reliably measure differences in technique within and between bowlers. Kohonen Self-Organising Maps (SOMs) were used to analyse coordination patterns in cricket fast bowling at a global whole-body level of analysis. Qualitative differences in SOM trajectories between bowlers signified participant-specific coordination patterns, which were attributed to differences in organismic constraints and intrinsic dynamics. A theoretical argument against the common optimal movement pattern concept was constructed and the utility of SOMs was evaluated. Several issues currently limiting their practical application, including the difficulty in linking the SOM trajectory to aspects of technique and the inability of biomechanists to identify optimal sports techniques, were highlighted. A combination of 'complex' analytical techniques was then applied to quantify intersegmental coordination among key limb and torso segments. Cross-correlation functions showed that moderate (0.5+) to very strong (0.9+) coupling relationships existed for the four segment couplings (NBA vs. FL, BA vs. NBA, BA vs. FL, UT vs.P) with the majority of these moving in synchrony. Statistically significant mean differences in both cross-correlation coefficients and average coupling angle for the four segment couplings throughout (0-100%), and during different phases (0-24%, 25-49%, 50-74%, 75-99%) of, the delivery stride provided further evidence of participant-specific coordination patterns. However, no associations between coupling relationships and ball release speed could be identified either within or between bowlers. This study further highlighted the difficulties in making associations between technique and outcomes.It was concluded that, based on the reported research findings, dynamical systems theory and its associated 'complex' analyses could make a substantive contribution to the enhancement of knowledge of cricket fast bowling techniques and also advance applied sports biomechanics research more generally. Further investigations into cricket fast bowling performance, focusing on the link between technique and outcomes using a combination of kinetic, energetic and coordination analyses, were identified as a research priority.

From November 2001 through to March 2002, 100 boys and 108 girls, aged 12.0 ± 0.8 years were assessed for coronary heart disease (CHD) risk factors. Rural (school 1) and valley (school 2) comprehensive schools (age range 11 to 18 years) were selected to represent differing socio-economic status (SES). Informed written consent was sought from head teachers, parents, and schoolchildren. All measurements were made in the subjects' respective schools, with the author making a total of 60 visits to these schools. Tests were carried out at the same relative time intervals and by an identical team of researchers. Physical and physiological measurements included stature (m), body mass (kg), body mass index (BMI) (kg m'2), skinfold measurements (£ SKF) (mm), waist to hip ratio (WHR), aerobic fitness (20 MST) (shuttles), systolic (SBP) (mm Hg) and diastolic blood pressure (DBF) (mm Hg). Total cholesterol (TC) (mmol-L' 1 ), high density lipoprotein-cholesterol (HDL-C) (mmol-L" 1 ), low density lipoprotein-cholesterol (LDL-C) (mmol-L" 1 ), TC: HDL-C, triglyceride (TG) (mmol-L' 1 ), lipoprotein(a) (Lp(a)) (mg-dL'1), glucose (Glu) (mmol-L' 1), fibrinogen (Fg) (mg-dL" 1), folate (ng-mL"1 ), vitamin Bn (pmol-L' 1), and homocyst(e)ine (Hey) (umol-L" 1 ) were measured for haematological concentrations. Lifestyle assessments included dietary and physical activity analysis. Familial risk of coronary heart disease was also determined. Blood analyses were conducted at the Biochemistry Laboratories of the Royal Glamorgan Hospital, Llamtisant, and Llandough Hospital, Penarth.