Exploring the influence of task and environmental constraints on batting and bowling performance in cricket: A systematic review

Cricket is an unique international sport where environmental and task constraints have shown to have a significant impact on batting and bowling performance. The aim of this systematic review was to determine the effect of task and environmental constraints on cricket performance. A systematic literature search was conducted across Scopus, PubMed, Web of Science, CINAHL, and SportDiscus. Studies were deemed eligible if they reported the effects of pitch type, pitch length, equipment (e.g. cricket bat, batting pads, ball type, etc.) on cricket performance. A total of 20 studies met the inclusion criteria with Kmet score ranging between 75% and 92%. The results from this study demonstrate that environmental constraints such as pitch-type and task constraints such as equipment modification (e.g. type of cricket bat, batting pads, ball) and pitch length can influence cricketer's batting and bowling performance. Scaling cricket bats and reducing pitch length were acutely beneficial to cricket batting, while ball type, pitch length and soil properties were impactful on bowling performance. Importantly though, the impact of constraint manipulation seemed to be influenced by the skill level of the performer. The findings from this study may help to inform coaches and practitioners improve skill acquisition, through constraint manipulation, to develop highly adaptive cricket batting and bowling skill

Model-based clustering and classification using mixtures of multivariate skewed power exponential distributions

Families of mixtures of multivariate power exponential (MPE) distributions have been previously introduced and shown to be competitive for cluster analysis in comparison to other elliptical mixtures including mixtures of Gaussian distributions. Herein, we propose a family of mixtures of multivariate skewed power exponential distributions to combine the flexibility of the MPE distribution with the ability to model skewness. These mixtures are more robust to variations from normality and can account for skewness, varying tail weight, and peakedness of data. A generalized expectation-maximization approach combining minorization-maximization and optimization based on accelerated line search algorithms on the Stiefel manifold is used for parameter estimation. These mixtures are implemented both in the model-based clustering and classification frameworks. Both simulated and benchmark data are used for illustration and comparison to other mixture families