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The study objective is to test general- and sport-specific adaption during a single training on the Flexibility Trainer. The device is designed to trigger residual muscle tone decreases of the hip-joint muscles by providing (nearly) isokinetic resistance during a full range of motion strength training in adduction/abduction and flexion/extension direction. Static hip flexion and abduction as well as kinematics of double side kicks were analysed on 15 participants before and after training (or rest for controls) to assess general flexibility and sport-specific movement range. Tests were recorded by a Vicon® motion capturing system. Static hip flexion and abduction as well as leg vector spreading angles (VSA) at different nodes of the kick were selected to determine adaptions of active and passive flexibility. Normalised hip joint moments, movement velocities and VSA were calculated to evaluate the training with the device. ANOVAs with 4-repeated measures and Friedman tests were performed to identify time differences and Bonferroni post-hoc test to identify between-subject effects. Significant differences were found for both static flexibility tests (Flexion = 13.65%; Abduction = 9.94%) and the VSA at specific action phases (≤15.15%). Results indicate that short-term adaptions when training with the Flexibility Trainer are exceeding comparable literature showing improved flexibility and sport-specific performance. [ABSTRACT FROM AUTHOR]

With recent technological advancements, quantitative analysis has become an increasingly important area within professional sports. However, the manual process of collecting data on relevant match events like passes, goals and tacklings comes with considerable costs and limited consistency across providers, affecting both research and practice. In football, while automatic detection of events from positional data of the players and the ball could alleviate these issues, it is not entirely clear what accuracy current state-of-the-art methods realistically achieve because there is a lack of high-quality validations on realistic and diverse data sets. This paper adds context to existing research by validating a two-step rule-based pass and shot detection algorithm on four different data sets using a comprehensive validation routine that accounts for the temporal, hierarchical and imbalanced nature of the task. Our evaluation shows that pass and shot detection performance is highly dependent on the specifics of the data set. In accordance with previous studies, we achieve F-scores of up to 0.92 for passes, but only when there is an inherent dependency between event and positional data. We find a significantly lower accuracy with F-scores of 0.71 for passes and 0.65 for shots if event and positional data are independent. This result, together with a critical evaluation of existing methodologies, suggests that the accuracy of current football event detection algorithms operating on positional data is currently overestimated. Further analysis reveals that the temporal extraction of passes and shots from positional data poses the main challenge for rule-based approaches. Our results further indicate that the classification of plays into shots and passes is a relatively straightforward task, achieving F-scores between 0.83 to 0.91 ro rule-based classifiers and up to 0.95 for machine learning classifiers. We show that there exist simple classifiers that accurately differentiate shots from passes in different data sets using a low number of human-understandable rules. Operating on basic spatial features, our classifiers provide a simple, objective event definition that can be used as a foundation for more reliable event-based match analysis. [ABSTRACT FROM AUTHOR]

The work investigates the relationship between the leg spread and hip joint flexibility during kicking action of highly skilled martial artists. Vector-based spreading angles (VSAs), as a simplified way to assess kick execution patterns and movement ranges, are proposed. The first VSA measures the angle between the two femur vectors (i.e., upper leg spread), whereas the second describes the full-leg spread. The proposed measures were applied to 3D motion analysis performed on 33 participants at various skill levels who executed double side kicks and performed two static flexibility tests (hip joint flexion and abduction). Statistical analysis showed that the proposed parameters differentiate between skill levels. Both VSAs and VSA rate of change showed high Pearson correlation to the quality of execution (the average of 10 subjective kickboxing experts' evaluation scores) at critical instants of the kick execution , i.e., during the first leg elevation and kick (angles: ρ > 0.8; p < 0.001). In addition, they significantly correlated to maximum static hip abduction at any instant (ρ = 0.37–0.69; p < 0.05). The work established that both static and dynamic joint flexibility contribute to high-performance levels and could be used for initial selection and assessment of training effectiveness. [ABSTRACT FROM AUTHOR]

To identify the relationship between load and the number of repetitions performed to momentary failure in the pin press exercise, the present study compared different statistical model types and structures using a Bayesian approach. Thirty resistance-trained men and women were tested on two separate occasions. During the first visit, participants underwent assessment of their one-repetition maximum (1-RM) in the pin press exercise. On the second visit, they performed sets to momentary failure at 90%, 80% and 70% of their 1-RM in a fixed order during a single session. The relationship between relative load and repetitions performed to failure was fitted using linear regression, exponential regression and the critical load model. Each model was fitted according to the Bayesian framework in two ways: using an across-subjects pooled data structure and using a multilevel structure. Models were compared based on the variance explained (R2) and leave-one-out cross-validation information criterion (LOOIC). Multilevel models, which incorporate higher-level commonalities into individual relationships, demonstrated a substantially better fit (R2: 0.97–0.98) and better predictive accuracy compared to generalised pooled-data models (R2: 0.89–0.93). The multilevel 2-parameter exponential regression emerged as the best representation of data in terms of model fit, predictive accuracy and model simplicity. The relationship between load and repetitions performed to failure follows an individually expressed exponential trend in the pin press exercise. To accurately predict the load that is associated with a certain repetition maximum, the relationship should therefore be modelled on a subject-specific level. [ABSTRACT FROM AUTHOR]