Perceptual and Motor Skills, 2012, 114, 2, 1-17. © Perceptual and Motor Skills 2012 DOI 10.2466/05.10.PMS.114.2. ISSN 0031-5125 APPLICATION OF REGRESSION AND NEURAL MODELS TO PREDICT COMPETITIVE SWIMMING PERFORMANCE 1 ADAM MASZCZYK AND ROBERT ROCZNIOK Department of Sports Training Chair of Methodology and Statistics ZBIGNIEW WAŚKIEWICZ Department of Team Sport Games MIŁOSZ CZUBA Department of Sports Training KAZIMIERZ MIKOŁAJEC Department of Team Sport Games ADAM ZAJĄC Department of Sports Training ARKADIUSZ STANULA Department of Sports Training Chair of Methodology and Statistics Jerzy Kukuczka Academy of Physical Education Katowice, Poland Summary.—This research problem was indirectly but closely connected with the optimization of an athlete-selection process, based on predictions viewed as de- terminants of future successes. The research project involved a group of 249 com- petitive swimmers (age 12 yr., SD = 0.5) who trained and competed for four years. Measures involving ftness (e.g., lung capacity), strength (e.g., standing long jump), swimming technique (turn, glide, distance per stroke cycle), anthropometric vari- ables (e.g., hand and foot size), as well as specifc swimming measures (speeds in particular distances), were used. The participants (n = 189) trained from May 2008 to May 2009, which involved fve 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 fnal swimming performance in the 50 meter and 800 meter crawl events. Nonlin- ear 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 fnal swimming performance from initial training, strength, ftness, and body measurements. Diferences in the sum of absolute error values were 4:11.96 (n = 30 for 800m) and 20.39 (n = 30 for 50m), for models struc- tured 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 specifc styles and distances in swimming. A scientifc approach to sports, especially competitive sports, is multi- dimensional, incorporating such traditional sciences as pedagogy (Hamil- 1 Address correspondence to Adam Maszczyk, Department of Sports Training, Academy of Physical Education, Mikolowska 72A str., 40-065 Katowice, Poland or e-mail (a.maszczyk@ awf.katowice.pl).