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It has been demonstrated that, by varying the mechanical properties of footwear, the sprinting performance can be improved. It has been hypothesized that, for maximal performance, tuning the shoe stiffness to the requirements of the athlete is necessary. The aim of this study was to investigate the feasibility of using sprint shoes constructed with selective-laser-sintered Nylon 12 sole units for sprint-related jump tasks and to examine whether adaptations to the mechanical properties of the footwear were sufficient to elicit changes to lower-limb dynamics during athletic performance. An internationally competitive sprinter completed sprint-related jump metrics in various selective-laser-sintered shoes with bending stiffnesses of 9 N, 24.5 N, and 38 N in flexion and 7.4 N, 14.7 N, and 26.1 N in extension. The participant performed best in the medium-stiffness shoe for squat jumps and the maximum-stiffness shoe for bounce drop jumps. This investigation has demonstrated that selective laser sintering can produce high-integrity footwear with markedly different mechanical properties. Such footwear, coupled with an appropriate test method, has been shown to be suitable for investigating the relationship between lower-limb dynamics and shoe stiffness.

The use of machine learning to identify and classify offensive and defensive strategies in team sports through spatio-temporal tracking data has received significant interest recently in the literature and the global sport industry. This paper focuses on data-driven defensive strategy learning in basketball. Most research to date on basketball strategy learning has focused on offensive effectiveness and is based on the interaction between the on-ball player and principle on-ball defender, thereby ignoring the contribution of the remaining players. Furthermore, most sports analytical systems that provide play-by-play data is heavily biased towards offensive metrics such as passes, dribbles, and shots. The aim of the current study was to use machine learning to classify the different defensive strategies basketball players adopt when deviating from their initial defensive action. An analytical model was developed to recognise the one-on-one (matched) relationships of the players, which is utilised to automatically identify any change of defensive strategy. A classification model is developed based on a player and ball tracking dataset from National Basketball Association (NBA) game play to classify the adopted defensive strategy against pick-and-roll play. The methodology described is the first to analyse the defensive strategy of all in-game players (both on-ball players and off-ball players). The cross-validation results indicate that the proposed technique for automatic defensive strategy identification can achieve up to 69% accuracy of classification. Machine learning techniques, such as the one adopted here, have the potential to enable a deeper understanding of player decision making and defensive game strategies in basketball and other sports, by leveraging the player and ball tracking data.

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