Komar, John

In handball, the way the team organizes itself in defense can greatly impact the player’s activity and displacement during the play, therefore impacting the match demands. This paper aims (1) to develop an automatic tool to detect and classify the defensive organization of the team based on the local positioning system data and check its classification quality, and (2) to quantify the match demands per defensive organization, i.e., defining a somehow cost of specific defensive organizations. For this study, LPS positional data (X and Y location) of players from a team in the Spanish League were analyzed during 25 games. The algorithm quantified the physical demands of the game (distance stand, walk, jog, run and sprint) broken down by player role and by specific defensive organizations, which were automatically detected from the raw data. Results show that the different attacking and defending phases of a game can be automatically detected with high accuracy, the defensive organization can be classified between 1–5, 0–6, 2–4, and 3–3. Interestingly, due to the highly adaptive nature of handball, differences were found between what was the intended defensive organization at a start of a phase and the actual organization that can be observed during the full defensive phase, which consequently impacts the physical demands of the game. From there, quantifying for each player role the cost of each specific defensive organization is the first step into optimizing the use of the players in the team and their recovery time, but also at the team level, it allows to balance the cost (i.e., physical demand) and the benefit (i.e., the outcome of the defensive phase) of each type of defensive organization.

Recent technological advancements have allowed movements to be tracked ecologically via markerless motion capture (mocap). However, occlusions remain a major concern pertaining to markerless mocap. Within racket sports where the number of players involved are low and occlusions are minimal, there exists a unique opportunity to delve into and provide an overview on the utilisation of markerless mocap technology. Twenty studies were included after a systematic search. Several methods were applied to obtain 2D positional data. Most studies adopted some form of background subtraction or thresholding method (n = 12), the remaining relied on pose estimation algorithms (PEA; n = 3), Hawk-Eye (n = 2) and object recognition (n = 1). Conversely, only the visual hull method was found to obtain 3D joint kinematics (n = 2). Markerless mocap are conventionally used to extract joint kinematics, however, study results revealed that the predominant use of markerless mocap was to capture the movement of a player’s location on court, this finding was unexpected. Low sampling frequencies of input videos and unsuitability of model detection used in the included studies could have limited the ability for markerless mocap to accurately track movements in racket sports. While current evidence suggests that the use of PEA in racket sports to extract 3D kinematics is limited, perhaps a slightly different approach gearing towards performance analysis, specifically stroke classification with the amalgamation of player location data and joint kinematics may be worth exploring further.

The study aimed to 1) assess the risk of conceding a goal in an empty net; 2) characterise the context when using the empty goal (EG) rule (4:1) in handball and 3) identify the performance variables that influence the EG attacks’ efficacy. A total of 974 EG attacks were sampled from 65 matches of the 2022 European Men’s Handball Championship. An observational tool was developed to analyse all EG attacks. Frequency analysis and the chi-square test have been performed to analyse the use of EG according to the match context. The binomial logistic regression was used to identify the variables that influence the EG attacks’ effectiveness. The results demonstrated that the goalkeeper was replaced mainly to maintain numerical equality. To create offensive superiority, the teams mostly used EG during the last quarter of the match playing under a small score disadvantage. The risk of conceding a goal in the empty net was higher if a team failed to score in previous possession. No relationship was found between the team’s final classification and the frequency of EG use, nor the efficacy of EG situations. Two variables have been identified as significant for the 6 × 6 + GK possession outcome: 2nd pivot position and shot zone.

This study aimed to investigate individual trial-to-trial performance in three tests to define adaptive regulation as a key feature of expertise in nine-ball. Thirty-one male players were assigned into the low-skilled (n = 11), intermediate (n = 10), or high-skilled groups (n = 10). The power control, cue alignment, and angle tests were selected to assess participants’ ability to control the power applied in shots, strike the ball straight, and understand the ball paths, respectively. Error distance and correction of error distance were identified for each shot using 2D video analysis. Results of one-way analysis of variance showed that the high-skilled group performed better in two out of the three tests than the other two groups (p = .010 for the cue alignment test; p = .002 for the angle test). However, the adaptation effect represented by the decreased error distances across trials was not observed. Pearson correlation revealed only a few significant correlations between the error distance and its correction within each participant in all tests (p < .05), and hence, the hypothesis that “low correction happened after small error and vice versa” is not supported.

Short-term forecasting of performance in football is crucial in week-to-week decision making. The current study presented novel contributions regarding the considerations that should be accounted for in the prediction of match actions performed in competitive matches. First, the study examined whether the quantity and recency of training data used to build a prediction model significantly influenced predictive accuracy. Three prediction models were built with the exponential moving weighted average (EMWA) method, each differing in the quantity of training data used (three, five, and seven preceding match days). Next, the study examined if contextual constraints, such as type of match action being predicted, playing position, or player age, significantly influenced predictive accuracy. Match action data from players in the top five European leagues were collected from the 2014/2015 to the 2019/2020 seasons. The model trained using less but more recent data (three preceding match days) demonstrated the greatest accuracy. Next, within the offensive and defensive phases, match actions differed significantly in predictive accuracy. Lastly, significant differences were found in prediction accuracy between playing positions, whereby actions associated with the primary task of the playing position were more accurately predicted. These findings suggest that in the forecasting of individual match actions, practitioners should seek to train the prediction model using more recent data, instead of including as much data as possible. Furthermore, contextual constraints such as the type of action and playing position of the player must be keenly considered.

This study investigated how developmental ten-pin bowlers can better transit to a heavier ball by comparing differences in performance outcomes and movement execution between two ball weights. Eight pre-transited bowlers bowled 10 first-frame trials each in two ball weight conditions: normal (NB) and heavy (HB); of which 3 trials were analysed. Full body joint kinematics were recorded via a 3D motion capture system. Paired sample t-test on performance outcome, joint kinematics and kinetics was conducted. No differences in performance outcome and peak joint velocities were found (p>0.05). Bowling with the HB resulted in higher peak elbow extension moment and quicker third step, with differences in left shoulder rotation and ankles abduction during the movement (p<0.05). Results suggest strengthening of the musculature around the shoulder and arm to prepare for the transition.

This study aimed to quantify the break shot characteristics and identify their significance in predicting the game outcomes in 9-ball tournaments. The break shots of 275 frames (241 men’s, 34 women’s) of professional tournaments were analyzed from two aspects: (1) cue ball position, represented by the distance between the cue ball and the table center, and (2) ball distribution, indicated by the standard deviation of Voronoi cell areas determined from all remaining balls on the table. Spearman correlation and binary logistic regression were utilized to identify associations and to predict the frame outcomes, respectively. Results showed that the more balls falling into the pockets during the break, the more clustered the remaining balls (rs = 0.232, p < 0.001). The closer the cue ball ending toward the table center, the more balls potted in the visit immediately after the break (rs = −0.144, p = 0.027). Neither cue ball position nor ball distribution could predict table clearance or winning of a frame. In conclusion, pocketing more balls during the break is associated with more clustered balls remaining on the table. Parking the cue ball near the table center after the break can facilitate potting more balls immediately after.

(1) Background: Uncertainty in extreme sports performance environments, such as climbing, provides considerable psycho-emotional and physiological demands, notably due to the many different environments in which climbing can be performed. This variety of environments, conditions of practice and engagement would challenge the acquisition of perceptual-motor skills; (2) Methods: To better understand how perceptual-motor skills are controlled and acquired in climbing, we proposed a narrative review anchored in the ecological dynamics theoretical framework and showed how this theoretical framework would support a nonlinear pedagogy to skill acquisition and to design safe learning and training situations that are representative of extreme performance contexts; (3) Results: We explained three theoretical pillars and we provide examples for design intervention following nonlinear pedagogy, notably (i) to set a constraint-led approach (in particular task constraint), (ii) to implement conditions of practice (constant vs. variable, imposed vs. self-controlled), (iii) to promote adaptive and creative behavioral variability during practice; (4) Conclusions: The challenge for the extreme sport practitioner is how to set up conditions of practice for efficient exploration in a manner that manages the dangers of performing in uncertain environments. Representing uncertainty within the relative safety of indoor settings may be one approach for preparing climbers for performance in extreme environments.

Recent advancements in Inertial Measurement Units (IMUs) offers the possibility of its use as a cost effective and portable alternative to traditional optoelectronic motion capture systems in analyzing biomechanical performance. One such commercially available IMU is the Perception Neuron motion capture system (PNS). The accuracy of the PNS had been tested and was reported to be a valid method for assessing the upper body range of motion to within 5° RMSE. However, testing of the PNS was limited to upper body motion involving functional movement within a single plane. Therefore, the purpose of this study is to further validate the Perception Neuron system with reference to a conventional optoelectronic motion capture system (VICON) through the use of dynamic movements (e.g., walking, jogging and a multi-articular sports movement with object manipulation) and to determine its feasibility through full-body kinematic analysis. Validation was evaluated using Pearson’s R correlation, RMSE and Bland-Altman estimates. Present findings suggest that the PNS performed well against the VICON motion analysis system with most joint angles reporting a RMSE of < 4° and strong average Pearson’s R correlation of 0.85, with the exception of the shoulder abduction/adduction where RMSE was larger and Pearson’s R correlation at a moderate level. Bland-Altman analysis revealed that most joint angles across the different movements had a mean bias of less than 10°, except for the shoulder abduction/adduction and elbow flexion/extension measurements. It was concluded that the PNS may not be the best substitute for traditional motion analysis technology if there is a need to replicate raw joint angles. However, there was adequate sensitivity to measure changes in joint angles and would be suitable when normalized joint angles are compared and the focus of analysis is to identify changes in movement patterns.