Individual dynamics mathematical patterns of qualified basketball players competitive performance

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Background and purpose Since the goal in sport is to overcome one's limitations and take one's functional state to a qualitatively new level, forecasting is an integral part of managing any process, including the athlete's training process. Purpose: to develop an algorithm and identify patterns of individual dynamics in competitive performance of eligible basketball players. Materials and methods The main team players of the men's basketball team of H.S. Skovoroda Kharkiv National Pedagogical University. Twelve matches of the experimental team playing against national teams of other universities were analyzed. Observations were made on the players during the championships played by student teams from Kharkiv. For each player, the number of shots and hits from short, mid and long range, goal assists, shots and hits from the penalty line, and turnovers were recorded. To identify individual patterns of competitive performance dynamics, indicators such as 'total positive points in a match', which most accurately reflect a player's 'positive' contribution to the outcome of a match, were used. To identify individual regularities in competitive performance dynamics, indicators such as 'total positive points in the match', which most accurately reflect the 'positive' contribution of players to the outcome of the match, were used. Results It was determined that the process of change in competitive performance should be treated as a variable process. The most appropriate function to describe this model is the sine function. It was shown that the regression model of the individual dynamics of competitive activity of the players of the Ukrainian national basketball team has a sine dependence, which is defined by the regression equation S + = a + bsin ((2π / t) (Т-c)). Where the coefficient a is the average value of a player's match performance, b is the amplitude of the change in a player's match performance, t is the previous period of a player's match performance and c is the value period in the first analyzed match. Conclusion The application of the regression sinusoidal model proved to be effective in practical studies. This is because, using only data from technical reports, it is possible to very quickly predict periods of 'rise' and 'fall' in an individual's game performance, which is useful for adjusting training programs and identifying psychophysiological individual characteristics of players.