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Title:Napovedovanje športnih rezultatov v nogometu s pomočjo strojnega učenja : diplomsko delo
Authors:ID Simičak, Jakob (Author)
ID Fister, Iztok (Mentor) More about this mentor... New window
ID Vrbančič, Grega (Comentor)
Files:.pdf VS_Simicak_Jakob_2024.pdf (3,58 MB)
MD5: 298B49FD91A4B83351FBFA0B1A85D0A2
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V zaključnem delu smo se osredotočili na statistiko pričakovanih zadetkov v nogometu. S programom Figma smo ustvarili izgled programa, ki smo ga poimenovali Footstat. Program Footstat uporablja podatke podjetja Statsbomb, ki je med vodilnimi podjetji v zbiranju in obdelovanju nogometnih podatkov. Z uporabo njihovega API-ja smo lahko dostopali do podatkov preko Python knjižnice. Omejili smo se na brezplačne podatke, ki jih je podjetje namenilo za raziskovalne in študijske namene. Nato smo ustvarili metriko s pomočjo logistične regresije, ki smo jo implementirali s pomočjo knjižnice za obdelavo podatkov v Pythonu Scikit-learn. Končni rezultat je postal program Footstat, ki je s pomočjo metrike izračunal pričakovane zadetke za izbrane tekme glede na omejene podatke podjetja Statsbomb . Izračunane pričakovane zadetke smo na koncu primerjali z dejanskimi zadetki na tekmah in analizirali morebitna odstopanja.
Keywords:pričakovani zadetki, nogomet, logistična regresija, Statsbomb, Footstat
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[J. Simičak]
Year of publishing:2024
Number of pages:1 spletni vir (1 datoteka PDF (X, [47] f.))
PID:20.500.12556/DKUM-90161 New window
UDC:004.85:796.332.093(043.2)
COBISS.SI-ID:221065475 New window
Publication date in DKUM:08.10.2024
Views:157
Downloads:63
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:23.08.2024

Secondary language

Language:English
Title:Expected results in football with the help of machine learning
Abstract:In my final project we focused on calculating Expected goals statistics in Football. With Figma, a software for designing prototypes, we created how we wanted our software, which we named Footstat, to look. Footstat uses data from a company named Statsbomb, one of the leading companies in analysing football data. With the use of their API, we could access the data through their Python library. We limited ourselves to using their free to use data, which were “gifted” for use in academic and research purposes. Then we made our metric with the help of logistic regression, which we implemented with the help of the Python library scikit-learn. This all resulted in the final software Footstat, which, with the help of our metric, calculated the expected goals for the chosen matches in our limited data from the Statsbomb company. At the end, the calculated expected goals were compared to the actual goals scored in matches.
Keywords:expected goals, football, logistic regression, Footstat, Statsbomb


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