Modelling analysis and prediction of women javelin throw results in the years 1946 - 2013.

Original Paper Modelling analysis and prediction of sports results DOI: 10.5604/20831862.1189201 Biol. Sport 2015;32:345-350 Modelling analysis and prediction of women javelin throw results in the years 1946 – 2013 AUTHORS: Grycmann P1, Maszczyk A2, Socha T3, Gołaś A2, Wilk M2, Zając T4, Przednowek K5 1 D  epartment of Team Sports, The Jerzy Kukuczka Academy of Physical Education in Katowice, Katowice, Poland  epartment of Sports Theory, The Jerzy Kukuczka Academy of Physical Education in Katowice, Katowice, Poland D 3 Department of Individual Sports, The Jerzy Kukuczka Academy of Physical Education in Katowice, Katowice, Poland 4 L aboratory of Functional Studies, The Jerzy Kukuczka Academy of Physical Education in Katowice, Katowice, Poland 5 Department of Physical Education, University in Rzeszow, Poland 2 Corresponding author: Artur Gołaś The Jerzy Kukuczka Academy of Physical Education in Katowice, Department of Theory and Practice of Sport; Address: Mikolowska Str.72A, 40-065, Katowice, Poland E-mail: ABSTRACT: The main goals of our study of the women’s javelin throw were twofold:. first, to analyse the dynamics of female javelin throw results variability as a function of time (time period 1946-2014), second, to create a predictive model of the results during the upcoming 4 years. The study material consisted of databases covering the female track and field events obtained from the International Association of Athletics Federations. Prior to predicting the magnitude of results change dynamics in the time to follow, the adjustment of trend function to empirical data was tested using the coefficients of convergence. Phase II of the investigation consisted of the construction of predictive models. The greatest decreases in result indexes were noted in 2000 (9.4%), 2005-2006 (8.7%) and 2009 (7.4%). The trend increase was only noted in the years 2006-2008. In general, until 1998 the mean result improved by 54.6% (100% - results of 1946) whereas from 1999 through 2011 the result only increased by 1.3%. Based on data and results variability analysis it might be presumed that, in the nearest future (2015-2018), results variability will increase by approximately 9.7%. Percent improvement of javelin throw distance calculated on the basis of the 1999 raw input data is 1.4% (end of 2014). CITATION: Grycmann P, Maszczyk A, Socha T, Gołaś A, Wilk M, Zając T, Przednowek K. Modelling analysis and prediction of women javelin throw results in the years 1946 – 2013. Biol Sport. 2015;32(4):345–350. Received: 2014-12-10; Reviewed: 2015-03-21; Re-submitted: 2015-03-31; Accepted: 2015-04-04; Published: 2015-12-29. Key words: women sport track and field artificial neural networks time series sports results INTRODUCTION Athletics encompasses four sporting events including competitive resulted from exogenous processes related to advances in technol- running, jumping, throwing, and walking. In the modern era, its roots ogy. Sport, initially understood as play and competition, has been th can be traced back to the second half of the XIX century. Origi- redefined as a result of technological innovations and broadening of nally, athletics comprised a very limited number of events and was the knowledge in this rapidly growing field. Changes have mainly meant for men only. In subsequent years several new events were affected sports equipment, upgrading of which continuously increas- added and female athletes were allowed to participate. In the 1980s, es the level of sports achievements, thus altering sport itself. All five new events were introduced and one was withdrawn, while in technological innovations have had a significant and direct influence the 1990s, one was added and one withdrawn. The women’s 20 on sports results and variability thereof. Sports equipment, devices kilometre race-walk became an Olympic event at the end of the XXth and facilities have evolved noticeably through the several hundred century. This delay in the introduction of women’s events in the years of athletics history, which remains an ongoing process. athletics program has significantly affected the dynamics of women’s The javelin throw is an event which places both physical and athletics development. It has also caused difficulties in the analysis technical demands on the athlete. It was first time held for women of sports results over time and in predicting the outcome of particu- at the 1932 Olympic Games. The distance of the javelin throw lar events [1, 2]. mainly depends on the initial velocity and angle of release; it is Technological progress is another factor that has an impact on calculated using the following formula: the dynamics of sports results variability. Technology can easily be perceived as a set of instruments and associated rules, and it should (1) be deemed an important factor to drive the developments in sport. Considering the evolution in sport, a hypothesis could also be for- The average length of the run-up for female athletes is 20 to 25 mulated that the overwhelming majority (if not all) of changes has meters. A fast and well-coordinated run-up, exactly along the throwBiology of Sport, Vol. 32 No4, 2015 345 Grycmann P et al. ing line, guarantees a good result. Correct work of the legs, trunk top athletes and their results. Starting from 1953 all events are and arms is of considerable importance at the delivery. The optimum listed year by year with the names and results of 100 best com- body position of a thrower is the position of a tense arch. The explo- petitors in each event. Hence our analysis is based on the arithmetic sive power-velocity effect of this system ensures maximum javelin means of 8 best athletes’ results in the years 1946-1952 and 10 acceleration at delivery. Since that moment, throw distance depends best athletes’ results starting from 1953. Scoring tables and data- on the initial javelin velocity and optimum angle of release [3, 4, 5]. bases of sports records prompted us to consider annual statistics. Javelin female and male throwers belong to the mesomorphic so- Prediction models were verified using mean values of sports results matotype. achieved during the 2012 Summer Olympics. The main goals of our study of the women’s javelin throw were The enormous amount of data resulted in the so called ‘informa- twofold:. first, to analyse the dynamics of female javelin throw results tion surplus’ and hampered correct graph reading. We therefore variability as a function of time (time period 1946-2014), second, decided to present comparisons of empirical data with linear or to create a predictive model of the results during the upcoming broken trends using 10-year intervals. 4 years. The most important component of the first part of the investigation was the determination of the strength and direction of Statistics results variability in the above mentioned time period. It should be The basi (...truncated)