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Assessing the intensity characteristics of specific soccer drills (matches, small-side game, and match-based exercises) could help practitioners to plan training sessions by providing the optimal stimulus for every player. In this paper, we propose a data analytics framework to assess the neuromuscular or metabolic characteristics of a soccer-specific exercise in relation with the expected match intensity. GPS data describing the physical tasks' external intensity during an entire season of twenty-eight semi-professional soccer players competing at the fourth Italian division were used in this study. A supervised machine-learning approach was tested in order to detect difference in playing positions in different sport-specific drills. Moreover, a non-supervised machine-learning model was used to profile the match neuromuscular and metabolic characteristics. Players' playing positions during matches and match-based exercises are characterised by specific metabolic and neuromuscular characteristics related to tactical demands, while in the small-side game these differences are not detected. Additionally, our framework permits to evaluate if the match performance request is mirrored during training drills. Practitioners could evaluate the type of stimulus performed by a player in a specific training drill in order to assess if they reflect the matches characteristics of their specific playing position. [ABSTRACT FROM AUTHOR]

This study presents a method for analysing badminton matches based on the concept of perturbations. We transfer this principle to a badminton rally and describe the decisive shot, which turns a balanced situation into an advantage for one team or one player. Our paper proposes an observational system, which models the decisive shots by using four consecutive actions: impulse (the perturbation), follow-up, survival, and convert. To test the objectivity of the operationalization, independent raters analysed six matches in the singles disciplines of the 2022 World Championships. To evaluate rater agreement, Jaccard coefficient and Cohen's kappa were used. Results show an agreement in identifying impulses of J(R1, R2) =.80, while the agreement in classifying the impulse type (positive/negative) reached κ =.70. A comparison of this perturbation-based analysis and last shot analyses shows significantly different results. Direct errors usually occur in the midcourt (56.4%), whereas most negative perturbations originate from the backcourt (40.0%). In contrast to direct winners, mostly originating from a smash (45.5%), most positive perturbations are created by net shots (30.1%). We argue that our method can be complementary to common last shot analyses and provides a possibility to describe players' strengths and weaknesses in more detail. [ABSTRACT FROM AUTHOR]