Suchergebnisse - Big data

Schaugaard, B. T., Hinkle, L. J, Davidson, G., Mortensen, B. B., Johnson, A. W. “… METHODS: Preexisting ultrasound images and data from 133 Div. 1 college football players (Ht 189 ± 6.1 cm; Wt 105 ± 19.8 kg) were analyzed for PDI intensity using a previously validated clinical scale of 0-5.  …”

Hacking, T. K., Wilwand, M., Johnson, A. W., Mortensen, B. B. “… The average max force exerted from the right and left leg from 3 trials was recorded for data analysis. RESULTS: Overall, we noted a weak correlation between players BW and their max eccentric HS (r = 0.34, p = <0.0008, Force: 451.6 ± 83.5 N).  …”

D. Memmert “… Durch die Integration von Big Data können Spielergebnisse, Fitnessparameter und individuelle Leistungen eingehend analysiert werden, was zu neuen Entwicklungen in der Forschung führt.  …”

Wilwand, M., Sponbeck, J., Allen, S. P., Snow, G., Hunter, I., Johnson, A. W. “… The study included NCAA Division I AF players aged 18 and older who were active on the roster and not injured at the time of data collection. Positions were grouped as follows: the combo group (linebackers and tight ends), the big group (offensive and defensive linemen), and the skill group (defensive backs, kickers, wide receivers, quarterbacks, and running backs).  …”

Karcher, C., Font, R., Marcos-Jorquera, D., Gilart-Iglesias, V., Manchado, C. “… Auswertung externer Belastungsreaktionen auf kumulative Spielzeit und Position bei der Handball-Europameisterschaft der Frauen 2022 durch einen IoT- und Big-Data-Architekturansatz …”

Lev, A., Maymon, Y. K., Zion, T. B., Tenenbaum, G. “… Spanning two decades (2000-2021), this study examines the frequency and timing of technical fouls (TFs) committed by NBA coaches, and their relationship with momentum shifts throughout the quarters of a basketball game. A big data of 4,196TFs calls of NBA coaches was used to elucidate TFs association with momentum shifts, considering location, scoring position, and quarter.  …”

Redaktion Zeitschrift LEISTUNGSSPORT “… Erfahren Sie, wie innovative Technologien helfen, Spielstrategien zu optimieren sowie die Performance der Spieler zu verbessern und welche Rolle Big Data dabei spielt. …”

Manchado, C., Tortosa-Martinez, J., Marcos-Jorquera, D., Gilart-Iglesias, V., Pueo, B., Chirosa-Rios, L. J. “… Überwachung der externen Belastung während des realen Wettkampfs bei männlichen Handballspielern durch Big-Data-Analytik: Unterschiede nach Spielpositionen …”

Foteinakis, P., Pavlidou, S. “… Therefore, the study aimed to identify play type actions during end-of-game possessions across player positions (guard, forward, and big) that directly influence the possession`s outcome.  …”

Bautiste, F. J. S., Brunner, D., Koch, J., Laborie, T., Yang, L., El-Assady, M.

Agbozo, E., Pandya, K., Jovanovic, P., Suvorova, E. A. “… Big Data …”

Scott, G. A., Bezodis, N. E., Bennett, M., Church, S., Waldron, M., Kilduff, L. P., Brown, M. R. “… Wie siegreiche Teams im Rugby Union erfolgreich kicken: Ein Big-Data-Ansatz …”

Cartón-Llorente, A., Lozano, D., Iglisias, V. G., Jorquera, D. M., Manchado, C. “… Worst-Case-Szenario-Analyse der körperlichen Beanspruchung von Elite-Handballspielern nach Spielposition durch Big-Data-Analytik …”

Dodt, M., Fasold, F., Memmert, D. “… This study, therefore, examines the personality profile of amateur handball referees (n = 582) for the first time using the German version of the Big Five Inventory 2 (BFI-2). Current data from German handball referees at the expert level and the German general population were used to compare and discuss the results.  …”

Venzke, J., Schäfer, R., Niederer, D., Manchado, C., Platen, P. “… During 65 matches of the EURO 2020, local positioning system data (Kinexon Precision Technologies) were collected (16.6 Hz), yielding 1853 datasets.  …”

Siemon, D., Wessels, J. “… Design/methodology/approach: Automated personality mining and robotic process automation were used to gather data (player statistics and big five personality traits) of n = 185 professional basketball players.  …”

Parrucci, A. “… The system combines physical biomarkers with player input in a multi-layered, systematic approach and to consider the potential of AI and "big data" gathered from the wearable technology athletic equipment to design the future for elite women`s sports. …”

Nogueira da Gama, D. R., Máximo de Souza, C. A., Praxedes dos Santos, J. L., do Espírito Santo, W. R., Brandão Pinto de Castro, J., Gomes de Souza Vale, R. “… The data were treated according to descriptive and correlational statistics.  …”

Javadpour, L., Blakeslee, J., Khazaeli, M., Schroeder, P. “… Deep learning is a branch of machine learning that finds patterns within big data and can predict future decisions. The process relies on a raw dataset for training purposes.  …”

Zhao, C., Jia, C., Zhu, Y., Zhao, T. “… Therefore, the emerging application of flexible sensors have huge long-term prospects in sport big data collection and Internet of Things (IoT). …”