Biology of Sport
eISSN: 2083-1862
ISSN: 0860-021X
Biology of Sport
Current Issue Manuscripts accepted About the journal Editorial board Abstracting and indexing Archive Ethical standards and procedures Contact Instructions for authors Journal's Reviewers Special Information
Editorial System
Submit your Manuscript
SCImago Journal & Country Rank
4/2016
vol. 33
 
Share:
Share:
abstract:
Original paper

Regression shrinkage and neural models in predicting the results of 400-metres hurdles races

K Przednowek
1
,
J Iskra
2
,
A Maszczyk
3
,
M Nawrocka
3

  1. Faculty of Physical Education, University of Rzeszow, Poland
  2. Faculty of Physical Education and Physiotherapy, Opole University of Technology, Opole, Poland
  3. Faculty of Physical Education, Academy of Physical Education in Katowice, Poland
Biol. Sport 2016;33:415-421
Online publish date: 2016/11/24
View full text Get citation
 
PlumX metrics:
This study presents the application of regression shrinkage and artificial neural networks in predicting the results of 400-metres hurdles races. The regression models predict the results for suggested training loads in the selected three-month training period. The material of the research was based on training data of 21 Polish hurdlers from the Polish National Athletics Team Association. The athletes were characterized by a high level of performance. To assess the predictive ability of the constructed models a method of leave-one-out cross-validation was used. The analysis showed that the method generating the smallest prediction error was the LASSO regression extended by quadratic terms. The optimal model generated the prediction error of 0.59 s. Otherwise the optimal set of input variables (by reducing 8 of the 27 predictors) was defined. The results obtained justify the use of regression shrinkage in predicting sports outcomes. The resulting model can be used as a tool to assist the coach in planning training loads in a selected training period.
keywords:

Predicting in sport, 400-metres hurdles, Regression shrinkage, Neural modelling

 
Quick links
© 2024 Termedia Sp. z o.o.
Developed by Bentus.