diplomsko delo
Maja Razinger (Author), Petar Vračar (Mentor)

Abstract

Razvili smo model za napovedovanje končnega vrstnega reda tekmovalcev v alpskem smučanju z uporabo dvostopenjskega pristopa. Prva stopnja je namenjena napovedi standardne deviacije časov na posamezni tekmi, kar ustreza oceni težavnosti tekme in variabilnosti med rezultati. Druga stopnja modela napoveduje odmike časov posameznih tekmovalcev od standardiziranega povprečja, kar omogoča napoved posameznih rezultatov. Obe napovedi skupaj oblikujeta normalno porazdelitev, iz katere vzorčimo predvideni čas za vsakega tekmovalca. Ta pristop omogoča generiranje širokega spektra možnih razvrstitev in izračun verjetnosti za različne scenarije, kot so uvrstitve na stopničke ali natančna napoved končnega vrstnega reda. Eksperimentalna evalvacija, izvedena na podatkih iz treh sezon (2020/21–2022/23), je pokazala, da obstaja korelacija med napovedanimi in dejanskimi rezultati, pri čemer model pogosto pravilno napoveduje, kateri smučar bo pred drugim, čeprav ne določi vedno natančnega končnega vrstnega reda.

Keywords

športna analitika;napovedovanje vrstnega reda;univerzitetni študij;diplomske naloge;

Data

Language: Slovenian
Year of publishing:
Typology: 2.11 - Undergraduate Thesis
Organization: UL FRI - Faculty of Computer and Information Science
Publisher: [M. Razinger]
UDC: 004.94:796.926(043.2)
COBISS: 229023235 Link will open in a new window
Views: 137
Downloads: 29
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Other data

Secondary language: English
Secondary title: Modeling results in alpine ski racing
Secondary abstract: We developed a model for predicting the final rankings of competitors in alpine skiing using a two-stage approach. The first stage focuses on predicting the standard deviation of times in an individual race, which corresponds to an estimate of the race's difficulty and the variability of results. The second stage of the model predicts the deviations of individual competitors' times from the standardized average, enabling the prediction of individual results. Together, these predictions form a normal distribution, from which the predicted time for each competitor is sampled. This approach allows for the generation of a wide range of possible rankings and the calculation of probabilities for various scenarios, such as podium finishes or exact predictions of final rankings. Experimental evaluation conducted on data from three seasons (2020/21–2022/23) demonstrated a correlation between the predicted and actual results, with the model often correctly predicting which skier will outperform another, even if it does not always determine the exact order.
Secondary keywords: sports analytics;data modeling;alpine skiing;ranking prediction;computer science;diploma;Alpsko smučanje;Modeliranje podatkov (računalništvo);Univerzitetna in visokošolska dela;
Type (COBISS): Bachelor thesis/paper
Study programme: 1000468
Embargo end date (OpenAIRE): 1970-01-01
Thesis comment: Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages: 1 spletni vir (1 datoteka PDF (55 str.))
ID: 26010930