Central European Journal of Sport Sciences and Medicine


ISSN: 2300-9705     eISSN: 2353-2807    OAI    DOI: 10.18276/cej.2018.3-08
CC BY-SA   Open Access   DOAJ  DOAJ

Issue archive / Vol. 23, No. 3/2018
Principal Component Analysis in the Study of Structure of the Best Polish Decathlon Competitors from the Period between 1985–2015

Authors: Bartosz Dziadek ORCID
University of Rzeszow, Faculty of Physical Education, Poland

Janusz Iskra ORCID
Opole University of Technology, Faculty of Physical Education and Physiotherapy, Poland

Krzysztof Przednowek ORCID
University of Rzeszow, Faculty of Physical Education, Poland
Keywords: decathlon sport career principal component analysis
Whole issue publication date:2018-09
Page range:11 (77-87)
Cited-by (Crossref) ?:

Abstract

The modern decathlon is a sport consisting of ten different events held over two days, played by men. Depending on the complexity of combined events, variety of events (runs, throws, jumps), the multi-stage, time-consuming and difficult training process the sport is considered as one of the most difficult. The analysis of careers of the best decathlon participants and applying advanced data-mining methods can help define the patterns occurring between each decathlon event and the final result. The research material encompasses career data of the 25 top competitors from Poland in years 1985–2015. Principal component analysis (PCA) was used in the research in order to designate new uncorrelated variables (components), representing input data across a new plane. Data analysis involved appointment of correlations between the events, determining the number of main components taken into account in further studies, analysis of the weight of each variable in formation of main components as well as visualisation and interpretation of results in the new plane described by the determined main components. Through the implementation of PCA method in the process of analysis it was possible to designate over 69% of compound data volatility with the use of the first three components. The first component, comprised of seven variables, displays the largest share in the total variability. The study of the relationship between variables in the new plane displayed strong correlations between sprint events (100 m, 110 m hurdles) and long jump and pole vault. No correlations between the 1500 m run and other events were found.
Download file

Article file

Bibliography

1.Bilić, M. (2015). Determination of taxonomic type structures of top decathlon athlets. Acta Kinesiologica, 9 (1), 20–23.
2.Bishop, C.M. (2006). Pattern Recognition and Machine Learning. NY: Springer Science+Business Media, LLC.
3.Chen, C., Zhang, B. (2016). Development trend of world men decathlon scores BP neural network analysis. National Convention on Sports Science of China.
4.Daszykowski, M., Walczak, B. (2008) Analiza czynników głównych i inne metody eksploracji danych. Retrieved from: http://www. chemometria.us.edu.pl (27.04.2018).
5.Dziadek, B., Iskra, J., Przednowek, K. (2016). Running preparation and the final decathlon score in terms of sports career development. Physical Activity Review, 4, 115–123.
6.Ertel, S. (2011). Exploratory factor analysis revealing complex structure. Elsevier, 50 (2),196–200.
7.Furdal, S. (1986). Analizy modelowe wyników w dziesięcioboju. Warszawa: Raport Instytutu Sportu.
8.Hardle, W., Simar, L. (2007). Applied Multivariate Statistical Analysis. L.A.: Springer Science & Business Media.
9.IAAF – International Association of Athletics Federations (2018). Retrieved from: http://www.iaaf.org/home.
10.Iskra, J. (1990). Struktura czynnikowa dziesięcioboju. Wrocław: WSWF Wrocław.
11.Karvonen, M.J., Niemi, M. (1953). Factor analysis of performance in track and field events. European Journal of Applied Physiology, 2 (15), 127–133.
12.Kassambara, A. (2017). Practical Guide to Principal Component Methods in R. STHDA. Retrieved from: http://www.sthda.com.
13.Matthews, P. (2013). Athletics 2013. The International Track and Field Annual. Cheltenham: Sports Book Ltd.
14.Nowak, L. (1989). Optymalizacja osiągnięć w dziesięcioboju w kolejnych latach rozwoju zawodniczego. Kraków: AWF Kraków.
15.Park, J., Zatsiorsky, V.M. (2011). Multivariate Statistical Analysis of Decathlon Performance Results in Olympic Athletes (1988–2008). International Journal of Sport and Health Sciences, 5 (5), 779–782.
16.PZLA – Polish Athletics Association (2018). Retrieved from: http://www.pzla.pl (1.03.2018).
17.PZLA – Zestawienie tabel (1985–2006). Warszawa: Dział Sportowo-Techniczny.
18.Quercetani, R.L. (2000). Athletics. A history ofmodern track and field athletics – Men and Woman (1860–2000). Mediolan: SEP Editrice.
19.R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. Retrieved from: https://www.R-project.org.
20.Socha, S. (1977). Sterowanie treningiem i prognozowanie wyników w wielobojach. Katowice: WSWF Katowice.
21.Stemmler, M., Bäumler, G. (2005). The Detection of Types among Decathletes using Configural Frequency Analysis (CFA). Psychology Science, 47 (3/4), 447–466.
22.STHDA (2018). Principal component analysis. Retrieved from: http://www.sthda.com.
23.Vana, Z. (2003). The training of the best decathletes. New Studies in Athletics, 4, 15–30.
24.Walaszczyk, A. (1998). Wybrane uwarunkowania osobnicze osiągnięć sportowych kobiet i mężczyzn w wielobojach lekkoatletycznych. Katowice: AWF Katowice.
25.Zaciorski, W, Godik, M. (1963). Osnovnye faktory trenirovannosti v legkoatleticeskom desjatiborie (opyt faktornogo analiza). Teorijai Praktika Fiziczeskoj Kultury, 8, 27–30.