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).
Keywords: artificial neural networks; sports results; time series; track and field; women sport.