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Modeliranje sloga igre košarkarske ekipe iz prostorskih podatkov
ID Mežnar, Sebastian (Author), ID Robnik Šikonja, Marko (Mentor) More about this mentor... This link opens in a new window, ID Vračar, Petar (Comentor)

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Abstract
V diplomskem delu se ukvarjamo z iskanjem sloga ekip v košarki s pomočjo prostorskih podatkov. Pri tem se omejimo na klasifikacijo ter gručenje ekip glede na premikanje žoge v njihovem napadu. Iz začetnih podatkov smo sestavili vektorje in slike, ki smo jih uporabljali pri klasifikaciji ter za predelovanje v boljše predstavitve. Za klasifikacijo smo uporabili naključni gozd ter nevronske mreže, za iskanje latentnega prostora podatkov pa avtomatske kodirnike. Z razvitimi metodami dosežemo napovedno točnost 7,8% ter dobimo predstavitev ekip, s katero lahko opišemo slog. Taka predstavitev je uporabna pri iskanju trenerjev in igralcev, ki bi koristili ekipi, uporabili pa bi jo lahko tudi kot dodatni atribut pri napovedovanju zmagovalca.

Language:Slovenian
Keywords:nevronska mreža, prostorski podatki, klasifikacija, košarka, športna analitika
Work type:Bachelor thesis/paper
Organization:FRI - Faculty of Computer and Information Science
Year:2019
PID:20.500.12556/RUL-110531 This link opens in a new window
COBISS.SI-ID:1538362307 This link opens in a new window
Publication date in RUL:16.09.2019
Views:1364
Downloads:260
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Secondary language

Language:English
Title:Modeling the playstyle of a basketball team from spatial data
Abstract:
In this thesis, we work on finding the style of play in basketball using spatial data. We focus on the classification and grouping of teams based on the movement of the ball in their attack. Original data is transformed into vectors and images and used in the classification and for producing a better representation of the attack. We used random forest and neural networks for the classification and autoencoders for finding the latent data space. With the developed methods, we achieve the classification accuracy of 7.8% and get a representation with which we can describe the style of play. This representation is useful in the search for coaches and players to improve the team, and can also be used as an additional attribute for prediction of the winner.

Keywords:neural network, spatial data, classification, basketball, sports analytics

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