Selection biases within an English football academy: implications of the Elite Player Performance Plan

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.The Elite Player Performance Plan (EPPP) was introduced in 2011 in order to enhance the youth football academy system in England. Previous literature demonstrates that relative age and biological maturation are responsible for selection biases within youth football, where both factors exert an influence on anthropometry and physical performances. However, there is limited research that has examined the aforementioned factors over a prolonged period of time, and especially within academies operating under the EPPP. Therefore, the general aim of this thesis was to investigate relative age, biological maturity, anthropometric and physical performance characteristics of male youth players from an English football club, as they progressed through the developmental pathway, under the EPPP framework. The findings from Chapter 3 revealed that selection within the investigated club was heavily overrepresented by relatively older and earlier maturing players, and this persisted since the EPPP was introduced. Subsequently, Chapter 4 identified that biological maturity, anthropometry and physical performances distinguished players that were retained across the developmental pathway, in an age group dependent manner. Chapter 5 provided estimates for when the development of anthropometric and physical performance characteristics initiate, peak and plateau, according to somatic maturity. Finally, Chapter 6 demonstrated that a bio-banding intervention may influence the decision-making process adopted by academy coaches’ regarding player selection and retention. In summary, the investigations conducted within this thesis provide novel and contemporary knowledge that can be used to enhance practice within the current club. Specifically, the findings from this thesis highlight that relative age, biological maturity, anthropometry and physical performances influence player selection and retention within this academy, suggesting that policies (e.g. the EPPP) require careful evaluation so that inappropriate selection biases can be nullified. Further studies are required to corroborate and extend these findings on a wider scale through robust methodological approaches

Monitoring physiological responses to training and match play in adolescent footballers

Introduction: Recently, there has been a growing interest into the monitoring of training and match load and subsequent physiological responses adolescent footballers experience (Malone, 2014). Before a physical performance test can used as a monitoring tool, its reliability must be quantified (Thorpe et al., 2015). Therefore, the aims of this thesis are two-fold: 1) quantify the reliability of a number of physical performance tests and 2) using the same physical performance tests quantify physiological responses to load over acute and chronic training periods. Methodogly: First the reliability of eccentric hamstring strength, isometric adductor strength and linear sprint tests were quantified, in a cohort of adolescent footballers (n = 37). Secondly training and match load was recorded over a 4-week period in another group of adolescent footballers (n = 10). Measures of lower body strength and speed were recorded prior to the start of every training session and match. Results: Acceptable levels of reliability were found for at least one metric of the three physical performance tests. An increase greater than the typical error of the test in eccentric hamstring strength was found after a 4-week training period but despite variations in load, no changes in lower body strength and speed were recorded between training sessions and matches. Discussion: Eccentric hamstring strength, long lever isometric adductor strength and 30-metre sprint performance are reliable tests to assess adolescent footballers. However, these measures are not be sensitive enough to detect true changes in performance in relation to variations in training and match load. Alternative methods must be established that quantify the physiological responses to load experienced by adolescent footballers

The Dose-Response Effects of Dissociation Training on Measures of Neuromuscular Control during Performance Screening in Male Youth Footballers

AIMS: Movement screens purportedly identify compensatory kinematics that predispose athletes to injury (Kiesel et al., 2011). The efficacy of assessing select competencies and prescribing remedial training based on screen outcomes however remains equivocal. The Foundation Performance Matrix Screen© (FPMS) supposedly profiles injury risk, subsequently directing its independent motor control Dissociation Training (DT) (Mottram and Comerford, 2008). However, there appears to be no research evidencing that DT can improve FPMS score or reduce injury. Therefore this study aimed to investigate the doseresponse of DT on kinematic and kinetic measures of neuromuscular control in male elite academy footballers. METHOD: The dose-response to DT therefore remains to be established. With institutional ethics approval, elite U15/16 and U17/18 male academy footballers comprised group one (n = 6) (G1) and group two (n = 8) (G2) respectively. G1 performed DT 1x week while G2 performed DT 3x week over eight weeks. Centre of pressure (CoP) total, anterior-posterior (X) and medial-lateral (Y) displacements (cm), sway velocity (cm∙s -1) and ellipse area (cm2) were recorded from participants’ non-dominant leg during a single leg stance test (SLST) and Y balance test™ (YBT). Force platform time to stabilisation (TTS), peak vertical ground reaction force (PVGRF) and loading rates were recorded from a 20cm bilateral drop jump landing (DJL). The FPMS and YBT were scored according to respective guidelines. All tests were performed barefoot. Cohen’s d effect size (ES) was calculated from differences in means. RESULTS: Small ES for G1 (ES -0.180; 95% CI, −1.94 - 0.60) and G2 (ES −0.136; 95% CI, −0.12 - 1.62) FPMS scores were observed. Large ES for DJL loading rates (ES -1.89, 95% CI, 0.046 - 0.079) and YBT normalised anterior reach (ES 1.416, 95% CI, 66.30 - 73.29) were observed for G1 compared to G2 where trivial (ES 0.072, 95% CI, 0.067 - 0.095) and moderate effects (ES 1.104, 95% CI, 66.84 - 72.90) respectively, were observed. The magnitude of change for G1 was consistently greater for all DJL and YBT measures. Furthermore, SLST performance for G1 improved for all CoP measures whereas G2 decreased. CONCLUSION: The measures used to assess neuromuscular function indicate eight weeks DT had meaningful effects on neuromuscular control, however, the magnitude of effects were greater for G1 than G2. As SLST, YBT and DJL indicated greater effects and are all proposed to predict injury, they could be a suitable surrogate marker for assessing the effects of DT. These findings also suggest that a lower dose of DT is sufficient provided training is individualised

Conception et réalisation d'une variante parallèle de C basée sur la création paresseuse de tâche

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal

FPGA-based high-performance neural network acceleration

In the last ten years, Artificial Intelligence through Deep Neural Networks (DNNs) has penetrated virtually every aspect of science, technology, and business. Advances are rapid with thousands of papers being published annually. Many types of DNNs have been and continue to be developed -- in this thesis, we address Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Neural Networks (GNNs) -- each with a different set of target applications and implementation challenges. The overall problem for all of these Neural Networks (NNs) is that their target applications generally pose stringent constraints on latency and throughput, but also have strict accuracy requirements. Much research has therefore gone into all aspects of improving NN quality and performance: algorithms, code optimization, acceleration with GPUs, and acceleration with hardware, both dedicated ASICs and off-the-shelf FPGAs. In this thesis, we concentrate on the last of these approaches. There have been many previous efforts in creating hardware to accelerate NNs. The problem designers face is that optimal NN models typically have significant irregularities, making them hardware unfriendly. One commonly used approach is to train NN models to follow regular computation and data patterns. This approach, however, can hurt the models' accuracy or lead to models with non-negligible redundancies. This dissertation takes a different approach. Instead of regularizing the model, we create architectures friendly to irregular models. Our thesis is that high-accuracy and high-performance NN inference and training can be achieved by creating a series of novel irregularity-aware architectures for Field-Programmable Gate Arrays (FPGAs). In four different studies on four different NN types, we find that this approach results in speedups of 2.1x to 3255x compared with carefully selected prior art; for inference, there is no change in accuracy. The bulk of this dissertation revolves around these studies, the various workload balancing techniques, and the resulting NN acceleration architectures. In particular, we propose four different architectures to handle, respectively, data structure level, operation level, bit level, and model level irregularities. At the data structure level, we propose AWB-GCN, which uses runtime workload rebalancing to handle Sparse Matrices Multiplications (SpMM) on extremely sparse and unbalanced input. With GNN inference as a case study, AWB-GCN achieves over 90% system efficiency, guarantees efficient off-chip memory access, and provides considerable speedups over CPUs (3255x), GPUs (80x), and a prior ASIC accelerator (5.1x). At the operation level, we propose O3BNN-R, which can detect redundant operations and prune them at run time. This works even for those that are highly data-dependent and unpredictable. With Binarized NNs (BNNs) as a case study, O3BNN-R can prune over 30% of the operations, without any accuracy loss, yielding speedups over state-of-the-art implementations on CPUs (1122x), GPUs (2.3x), and FPGAs (2.1x). At the bit level, we propose CQNN. CQNN embeds a Coarse-Grained Reconfigurable Architecture (CGRA) which can be programmed at runtime to support NN functions with various data-width requirements. Results show that CQNN can deliver us-level Quantized NN (QNN) inference. At the model level, we propose FPDeep, especially for training. In order to address model-level irregularity, FPDeep uses a novel model partitioning schemes to balance workload and storage among nodes. By using a hybrid of model and layer parallelism to train DNNs, FPDeep avoids the large gap that commonly occurs between training and testing accuracy due to the improper convergence to sharp minimizers (caused by large training batches). Results show that FPDeep provides scalable, fast, and accurate training and leads to 6.6x higher energy efficiency than GPUs