title: Application of regression and neural models to predict competitive swimming performance.
abstract: This research problem was indirectly but closely connected with the optimization of an athlete-selection process, based on predictions viewed as determinants of future successes. The research project involved a group of 249 competitive swimmers (age 12 yr., SD = 0.5) who trained and competed for four years. Measures involving fitness (e.g., lung capacity), strength (e.g., standing long jump), swimming technique (turn, glide, distance per stroke cycle), anthropometric variables (e.g., hand and foot size), as well as specific swimming measures (speeds in particular distances), were used. The participants (n = 189) trained from May 2008 to May 2009, which involved five days of swimming workouts per week, and three additional 45-min. sessions devoted to measurements necessary for this study. In June 2009, data from two groups of 30 swimmers each (n = 60) were used to identify predictor variables. Models were then constructed from these variables to predict final swimming performance in the 50 meter and 800 meter crawl events. Nonlinear regression models and neural models were built for the dependent variable of sport results (performance at 50m and 800m). In May 2010, the swimmers' actual race times for these events were compared to the predictions created a year prior to the beginning of the experiment. Results for the nonlinear regression models and perceptron networks structured as 8-4-1 and 4-3-1 indicated that the neural models overall more accurately predicted final swimming performance from initial training, strength, fitness, and body measurements. Differences in the sum of absolute error values were 4:11.96 (n = 30 for 800m) and 20.39 (n = 30 for 50m), for models structured as 8-4-1 and 4-3-1, respectively, with the neural models being more accurate. It seems possible that such models can be used to predict future performance, as well as in the process of recruiting athletes for specific styles and distances in swimming.
authors: Department of Sports Training Chair of Methodology and Statistics, Jerzy Kukuczka Academy of Physical Education, Katowice, Poland. a.maszczyk@awf.katowice.pl
authors: Adam
authors: A
authors: Maszczyk
authors: Robert
authors: R
authors: Roczniok
authors: Zbigniew
authors: Z
authors: Waśkiewicz
authors: Miłosz
authors: M
authors: Czuba
authors: Kazimierz
authors: K
authors: Mikołajec
authors: Adam
authors: A
authors: Zajac
authors: Arkadiusz
authors: A
authors: Stanula
mesh_terms: D000886:Anthropometry; D054874:Athletic Performance / Q000502:physiology*; D002648:Child; D005544:Forecasting / Q000379:methods*; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D010809:Physical Fitness / Q000502:physiology; D012044:Regression Analysis; D013550:Swimming / Q000502:physiology*