magistrsko delo
Jakob Košir (Author), Erik Štrumbelj (Mentor), Samo Rauter (Co-mentor)

Abstract

Na področju športnega treniranja je za izboljšanje posameznikove športne zmogljivosti izredno pomembno predpisovanje učinkovitih načrtov treningov. V želji po boljšem razumevanju spreminjanja športne zmogljivosti so odnos med treningi in zmogljivostjo poskušali opisati z matematičnimi modeli, vendar so napovedi teh modelov slabe. Menimo, da je glavni razlog v predprocesiranju podatkov, saj vse trenutno obstoječe metode za vsak trening določijo le eno številsko oceno obremenitve treninga, zaradi česar napovedni modeli ne morejo razlikovati med različnimi tipi treningov. Predstavimo nov način predprocesiranja podatkov o treningih in napovedni model, ki lahko take podatke upošteva. Kvaliteta napovedi novega modela je boljša. Z modelom, ki se najbolj pogosto pojavlja v literaturi, dosežemo povprečno absolutno napako napovedi 50.1W, povprečna absolutna napaka napovedi novega modela pa je 19.1W.

Keywords

športno treniranje;matematično modeliranje;napovedovanje;analiza signalov;cestno kolesarstvo;računalništvo;računalništvo in informatika;magisteriji;

Data

Language: Slovenian
Year of publishing:
Typology: 2.09 - Master's Thesis
Organization: UL FRI - Faculty of Computer and Information Science
Publisher: [J. Košir]
UDC: 004:796.61(043.2)
COBISS: 1538399427 Link will open in a new window
Views: 809
Downloads: 174
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Other data

Secondary language: English
Secondary title: Modeling the relationship between specific training and performance in road cycling
Secondary abstract: Exercise regimen is an integral part of sports coaching, the goal of which is to improve an individual's athletic performance. In an effort to better understand and optimize athletic performance, exercise physiologists have developed mathematical models of physical training and performance, but the forecasting ability of such models has been proven to be poor. We argue that the main reason for poor forecasting ability is in training load quantification methods, as all that currently appear in scientific literature quantify load with a single numeric value and thus models cannot distinguish between different types of training. We present a new method for quantifying training load with which we determine the load for each type of training independently, we also present a new prediction model. The prediction ability of the new model is better. The average absolute prediction error of the most commonly used model is 50.1W, while the average absolute prediction error of our model is 19.1W.
Secondary keywords: sports coaching;mathematical modeling;forecasting;signal analysis;road cycling;computer science;computer and information science;master's degree;
Type (COBISS): Master's thesis/paper
Study programme: 1000471
Embargo end date (OpenAIRE): 1970-01-01
Thesis comment: Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages: 46 str.
ID: 11238162