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Soccer is the world's most popular sport, with individuals of different standards and ages competing. The identification and development of youth soccer players with the potential to reach professional status has come more into focus over recent decades. Maturity presents a compelling paradox for young soccer players. During adolescence, individuals mature at different rates; the timing and tempo of specific maturational events can vary significantly with some individuals entering puberty well in advance or delay of their same age peers. Additionally, both physical performance parameters and match running metrics can fluctuate during this period. The aim of this thesis was to investigate longitudinally the consequences of biological maturity on various physical performance parameters and match running metrics amongst a total of 84 elite youth soccer players aged 11.3 - 18.0 years of age. Initially, two common non-invasive methods for assessing maturity status, and a simple age-based strategy were compared to determine the accuracy in differentiating youths of varying maturity status. Longitudinal research was conducted over the course of five consecutive years, on a total of 28 elite youth soccer players. The study demonstrated the utility of the percentage height window determined using the Khamis-Roche equation, with maturity status estimated through this method showing a statistically significant improvement over chance (χ 2 = 19.17), whereas Mirwald maturity offset method did not (χ 2 = 1.09). Having established the most reliable and valid method for determining maturation status in elite academy soccer players, the main and interactive effects of biological maturity and relative age upon physical performance metrics (5 m and 20 m sprint, change of direction, countermovement jump height and reactive strength index) were investigated for one competitive playing season. Maturation was associated with performance on all but one of the physical fitness tests, reactive strength index (5 m - p < 0.01; 20 m - p < 0.01; change of direction - p < 0.01; countermovement jump - p < 0.01 and reactive strength index - p > 0.05). In contrast, relative age only served as a significant predictor of fitness on the countermovement jump (5 m - p > 0.05; 20 m - p > 0.05; change of direction - p > 0.05; countermovement jump - p < 0.05 and reactive strength index - p > 0.05). The interaction between these two constructs was also tests, however showed no statistical significance. Having found that maturation effects training data, the extent to which variance in biological maturation also contributed to in-match running performance (total distance covered, distance at high speed, distance at very high speed, maximum speed and the number of accelerations made from zone 4 to zone 6) across age groups (U14s, n = 21, U15/16s, n =16) over one competitive playing season was examined. There was a suggestion that maturation does have an impact on high speed match running metrics within the U14 age group (p < 0.05), however some of this variance may be attributed to the same individuals under / over performing consistently in matches on the match running metrics. Furthermore, within the U15/16s age group, the influence of maturation on match running metrics appeared to have less of an impact. Finally, to better understand the associations between biological maturation and fitness performance, Chapter 7 aimed to identify the overall dynamics of fitness performance relative to the timing of the adolescent growth spurt in 30 elite youth soccer players measured for six consecutive years. Measured velocities for fitness performance metrics did not express themselves at the same time relative to age at peak height velocity; however, these ii fitness metrics reached a peak around the time of maximal growth in height (12 months pre- to 12 months post-peak height velocity), with peaks in performance occurring for 5 m ~ 24 months pre and 12 months pre-peak height velocity; 20 m ~ 18 months pre-peak height velocity; COD ~ 12 months pre-peak height velocity, at moment of peak height velocity and 6 months post-peak height velocity; countermovement jump ~ 6 months pre-peak height velocity and RSI at moment of peak height velocity, respectively. In all the physical performance metrics measured, physical performance continued to show improvements following peak height velocity. The overall findings of this thesis have demonstrated the effects of maturity status on match running and fitness performance in elite youth male soccer players. Being able to correctly identify the interval of the adolescent growth spurt is of use to practitioners working in this area. During adolescence, maturity had a positive influence on high speed match running, and on tests of short and long sprint time, change of direction and explosive leg power.

Two of the most common forms of arterial disease are stenosis and aneurysm, estimated to affect between 1% and 20% of the population. Ruptured abdominal aortic aneurysms alone are estimated to be the cause of between 6,000 and 8,000 deaths a year within the United Kingdom. Patients with stenosis have been shown to have a mortality hazard ratio of 1.42 compared to a control population, and an unadjusted death rate of 3.35 per 100 person-years compared to 1.23 per 100 person-years in a control population. Current methods for the detection of arterial disease are generally impractical for large scale screening, expensive, or both. If an inexpensive method for the detection of both stenosis and aneurysm is created, that minimises the need for invasive measurements, the cost effectiveness of large scale screening could be improved making both continuous monitoring and screening feasible. One such method is to use easily acquirable haemodynamic measurements at accessible peripheral locations within the circulatory system for diagnosis. Within this thesis an initial exploratory study into the potential of using machine learning classification algorithms to detect arterial disease from such measurements is presented. It is likely that the indicative biomarkers of arterial disease held within pressure and flow-rate profiles consist of micro inter- and intra- measurement details. To facilitate the use of a data driven approach to the discovery of any biomarkers a framework for the creation of virtual patients, through the employment of a mathematical model of blood flow, is presented. This framework is utilised to create a series of virtual patient databases, as the balance between simplicity and realism progresses through the thesis. The most realistic of these databases is made publicly available (https://doi.org/10.5281/zenodo.4549764). The aforementioned framework for the creation of virtual patients is a major contribution of this thesis, and can be applied to a wide range of biological systems given a mathematical description. The synthetic data sets are used to train and subsequently test a series of machine learning classifiers, to predict the presence of both stenosis and aneurysm, using various combinations of pressure and flow-rate measurements. It is shown that the inclusion of a diseased vessel (either stenosis or aneurysm) produces consistent and significant biomarkers in haemodynamic profiles, irrespective of a patient's unique underlying arterial network. These biomarkers are found to be differentiable from the natural variability present across a large cohort of patients, showing that arterial disease has a clear and unique effect on pressure and flow-rate profiles. This suggests strong potential in the use of haemodynamic measurements to detect arterial disease.