Suchergebnisse - Intelligence

Vladimir, B. “… The modern goalkeeper plays a vital role in both phases of play, emphasizing the need for technical proficiency, game intelligence, and decision-making under pressure in elite youth football. …”

Popovych, I., Danko, D., Yakovleva, S., Haponenko, L., Shcherbyna, O., Kryzhanovskyi, O., Hoian, I. “… The aim of this study is to empirically investigate and theoretically substantiate the role of emotional intelligence (EQ) within the self-control structure of junior athletes.  …”

Rizvanova A. A. “… Gaming talent is an original systemically developing model of readiness for gaming activity, expressed in the totality and qualitative uniqueness of innate physical (speed of action), psychophysiological (perception, sensorimotor reactions, gaming intelligence) and acquired social (propensity for gaming interaction, emotional and social intelligence) qualities, which make it possible to compensate for the insufficiency of some functional qualities due to the priority of the evolution of others.  …”

Baker, J., Cattle, A., McAuley, A., Kelly, A., Johnston, K. “… Introduction The last decade has seen a rapid increase in the use of artificial intelligence (AI) approaches such as machine learning and deep learning in the sport sciences.  …”

Tróznai, Zs., Utczás, K., Pápai, J., Pálinkás, G., Szabó, T., Petridis, L. “… The developmental status was the strongest predictor in the selection, but it seems that the impact of relative age and maturation is not limited to body size and muscular development, but it may expand to other important aspects in handball, such as technical competence or game intelligence. …”

Gomez Souffront, S., Everett, E. R., Kostrna, J. “… The intervention and efficacy of this study support applied practitioners` integration of biofeedback and psychological skills training to improve adolescents` self-awareness, emotional intelligence, and decision making. …”

Samanipour, M. H., Azizi, M., Salehian, O., Ceylan, H. I., Mielgo-Ayuso, J. F., Coso, J. D., Muntean, R. I., Bragazzi, N. L., Herrera-Valenzuela, T. “… There were no gender differences in any psychological attributes associated with emotional intelligence, sport success perception, and mental toughness.  …”

Schürmann, R., Bandemer, R., Madauß, L., Wohak, O., Schwarzenbrunner, K., Danielsmeier, C. “… Die Menge und Präzision erhobener Daten sowie die Möglichkeit, diese intelligent zu verknüpfen und auszuwerten, haben sich spätestens mit dem Aufkommen großer Speicherkapazitäten und Künstlicher Intelligenz um Größenordnungen erhöht.  …”

Sieber, N., Walser, S., Weber, T., Gubler, R., Badertscher, H., Eggenberger, P. “… Methods: We developed the novel exergame training system using four synchronized cameras for video-based motion tracking of the athlete, in combination with two video projectors showing the game tasks on a wall-mounted screen and on the floor in front of the athlete. Artificial intelligence (i.e., machine learning) was applied to train and validate algorithms to accurately detect joint positions of the human body based on large open-source training and validation data sets.  …”

Retzepis, N.-O., Avloniti, A., Kokkotis, C., Protopapa, M., Stampoulis, T., Gkachtsou, A., Pantazis, D., Balampanos, D., Smilios, I., Chatzinikolaou, A. “… Hence, this study aims to deepen the understanding of the factors that affect maturity in 11-year-old Team Sports Athletes by utilizing explainable artificial intelligence (XAI) models. We utilized three established machine learning (ML) classifiers and applied the Sequential Forward Feature Selection (SFFS) algorithm to each.  …”

Reinhard, M. L., Mann, D. L., Höner, O. “… Results The generic Football Intelligence score showed a small but significant association with football-specific decision-making when controlling for age (p=0.006).  …”

Ren, Y., Sang, H., Huang, S., Qin, X. “… This paper aims to use artificial intelligence technology to reduce ineffective exercises and alleviate the training burden.  …”

Bozdech, M., Zhánel, J. “… The cleaned dataset (n = 2847) underwent statistical analyses, including Chi-square tests, Cramer`s V, Bayesian approaches, and Multinomial Logistic Regression (MLR). Artificial Intelligence (AI) models, using supervised learning classification, were applied.  …”

Söderström, T., Sandlund, S., Westerlund, R., Tervo, T. “… The physiological testing practice articulates action intelligibility through rules and structures which emphasizes tests as isolated quantified indicators of physical status.  …”

Saal, C., Helm, N., Prieske, O. Zeitschrift: “… PACSS 2021. Advances in Intelligent Systems and Computing, vol 1426 …”

Tróznai, Zs., Utczás, K., Pálinkás, G., Juhász, I., Szabó, T., Petridis, L. “… The significant differences in in-game performance scores between the relative age groups can be explained by the assumption that relatively older players have more training and competition possibilities developing in this way their game intelligence. In summary, it is not the relative age itself that affects the selection, but mostly advanced biological maturation of presumably above one-year differences between the players. …”

Cui, Y., Zhou, Y., Jozef, B., Shen, Y. “… The advancement of in micro-sensors and intelligent algorithms allow for the automatic and more tennis-specific quantification of the load, which enables the monitoring of multiple players in real-time.  …”

Kutz, M. R. “… One such meta-skill is contextual intelligence. Contextual intelligence is the capacity to recognize the convergence of different variables and respond to the emerging context as it is developing.  …”

Nagovitsyn, R. S., Gibadullin, I. G., Batsina, O. N., Mokrushina, I. A. “… Objective of the study was to develop a program for predicting the competitive performance of young athletes based on artificial intelligence technology. Methods and structure of the study.  …”

Kolman, N. “… METHODS: Using the Dutch Technical-Tactical Tennis Test (D4T), the Perceived Tactical Skills Scale in Tennis (PTSST) and the Self-Regulated Learning for Sport Practice (SRL-SP), this study aims to determine (i) whether there is a correlation between technical skills (i.e. ball speed, accuracy, percentage errors and spin rate) and perceived tactical skills, (ii) whether there is a correlation between technical skills in various tactical situations (offensive, defensive and neutral) and game situations (fixed and variable) and any of the four subscales of the PTSST (Anticipation and positioning, Game intelligence and adaptability, Decision-making and Recognizing game situations) (iii) which of the five domains of self-regulatory skills (Planning, Checking, Evaluating/Reflecting, Self-Efficacy for Challenges and Effort) is correlated with technical skills or perceived tactical skills and (iiii) whether technical skills, perceived tactical skills and self-regulatory skills differ according to gender or age category.  …”