Search Results - Big data

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  1. 1

    Predicting future stars: Probability and performance corridors for elite swimmers

    Born, D.-P., Stöggl, T., Lorentzen, J., Romann, M., Björklund, G. “…Objectives To evaluate the new age groups of the World Junior Championships in swimming from a scientific perspective, establish benchmarks and performance corridors that predict success at peak performance age and compare performance corridors between men and women and short-, middle-, and long-distance freestyle races. Design Longitudinal big data analysis. Methods In total, 347,186 annual best times of male (n = 3360, 561 ± 177 Swimming Points) and female freestyle swimmers (n = 2570, 553 ± 183 Swimming Points) were collected across all race distances at peak performance age and retrospectively analyzed throughout adolescence. …”
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  2. 2

    Olympic coaching excellence: A quantitative study of Olympic swimmers` perceptions of their coaches

    Cook, G. M., Fletcher, D., Peyrebrun, M.
    Published in Journal of Sports Sciences (2022)
    “…The questionnaires assessed perceptions of 12 variables within the Big Five personality traits, the dark triad, and emotional intelligence, and the data was analyzed using three one-way multivariate analysis of variance and follow-up univariate F-tests. …”
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  3. 3

    Pain processing in elite and high-level athletes compared to non-athletes

    Pettersen, S. D., Aslaksen, P. M., Pettersen, S. A.
    Published in Frontiers in Psychology (2020)
    “…Background: Previous studies shows that elite and high-level athletes possess consistently higher pain tolerance to ischemic and cold pain stimulation compared to recreationally active. However, the data previously obtained within this field is sparse and with low consistency. …”
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  4. 4

    Olympic coaching excellence: A quantitative study of psychological aspects of Olympic swimming coaches

    Cook, G. M., Fletcher, D., Peyrebrune, M.
    Published in Psychology of Sport and Exercise (2021)
    “…The questionnaires assessed 12 variables within the Big Five personality traits, the dark triad, and emotional intelligence, and the data was analyzed using three one-way multivariate analysis of variance and follow-up univariate F-tests. …”
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  5. 5

    Human running performance from real-world big data

    Emig, T., Peltonen, J.
    Published in Nature Communications (2021)
    “…Menschliche Laufleistung aus Big Data der tatsächlichen Welt…”
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  6. 6

    Study on optimization and innovation of swimming technique

    Xin, Z. W.
    Published 2018
    “…With the correction action in technical movements and new system training programs to increase the speed of the swim, the study has a big breakthrough in the future of the swim team.This study has carried out data analysis of the swimming team of a primary school in Guangzhou and national team of China, so as to study the training program of the most suitable members to improve the performance of the team members of the swimming team. …”
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  7. 7

    Towards machine learning on data from professional cyclists

    Hilmkil, A., Ivarsson, O., Johansson, M., Kuylenstierna, D., van Erp, T.
    Published 2018
    “…Big Data…”
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  8. 8

    About the role of physical trainers, sport science, powermeters and "big data" in professional road cycling

    Zabala, M., Javaloyes, A., Mateo-March, M.
    Published in Journal of Science and Cycling (2018)
    “…Zur Rolle von Trainern, Sportwissenschaft, Powermetern und "Big Data" im professionellen Straßenradsport…”
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  9. 9

    Towards machine learning on data from professional cyclists

    Hilmkil, A., Ivarsson, O., Johansson, M., Kuylenstierna, D., van Erp, T. “…Big Data…”
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  10. 10

    Effects of pacing properties on performance in long-distance running

    de Leeuw, A.-W., Meerhoff, L. A., Knobbe, A.
    Published in Big data (2018)
    “…Big data…”
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  11. 11

    Modelling of cycling power data and its application for anti-doping

    Hopker, J., Passfield, L., Faiss, R., Saugy, M.
    Published in Journal of Science and Cycling (2016)
    “…The application of `big data` analysis techniques would then enable better insight into the nature of variability of rider training and race performance, provide potential interpretation solutions, and enhance the effectiveness and impact of interventions. …”
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  12. 12

    Development of a worldwide network for the purpose of hypothesis-driven research through data mining

    Ferber, R. “…We also know that this goal of extracting and analysing big data via high-performance computing is only possible through a collaborative and international community: one we continue to build.…”
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  13. 13

    Understanding and analyzing a large collection of archived swimming videos

    Sha, L., Lucey, P., Morgan, S., Sridharan, S., Pease, D. “…Big Data…”
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  14. 14

    Case study on use of cloud computing, IoT, big data, machine learning, DevOps in Tour de France

    Sagar Nangare
    Published 2017
    “…Fallstudie zur Nutzung von Cloudcomputing, IoT, Big Data, maschinellem Lernen, DevOps bei der Tour de France…”
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  15. 15

    Power-velocity curve: relevance of the SRM Ergometer for simulated cycling performance and constant duration tests

    Smit, A., Wolbert, F., Hettinga , F. J.
    Published in Journal of Science and Cycling (2015)
    “…The SRM Ergometer has a free mode with a gear box and a big flywheel, so requirements 1 and 2 are fulfilled. …”
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  16. 16

    Relationship between isokinetic muscle strength and 100 meters finswimming time

    Kunitson, V., Port, K., Pedak, K.
    Published in Journal of Human Sport & Exercise (2015)
    “…Finswimming is a sport where athlete uses one big monofin to produce propulsion. The purpose of this study was to describe relationship between isokinetic strength of different muscle groups and 100 meters finswimming time. …”
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  17. 17

    Advancing swimming through big data

    Lehari, T.
    Published 2016
    “…Fortschritte im Schwimmen durch Big Data…”
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  18. 18

    How we use data to think in multiple time scales

    Harrison, A.
    Published 2016
    “…Big Data…”
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  19. 19

    How the British Cycling Team dominated using Big Data

    Hill, G.
    Published in Sports Performance & Tech (2016)
    “…Wie die Mannschaft von British Cycling mit Big Data dominierte…”
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  20. 20

    Musculoskeletal modelling in sports - evaluation of different software tools with focus on swimming

    Langholz, J. B., Westman, G., Karlsteen, M.
    Published in Procedia Engineering (2016)
    “…Therefore it would be valuable to find an easily applicable tool to visualize biomechanical data. Feedback to athletes based on scientifically measured variables would then ideally be more efficient and effective. …”
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