THE EFFECTIVENESS OF DIFFERENT MACHINE LEARNING ALGORITHMS ON BASKETBALL PLAYERS’ SHOOTING PERFORMANCE

Year 2019, , 256 - 269, 16.12.2019

Serpil Kılıç Depren

https://doi.org/10.17155/omuspd.507797

Cited By: 1

Abstract

Bu çalışmanın
temel amacı, National Basketball Association (NBA) oyuncularının atış isabeti
üzerinde hangi faktörlerin önemli bir rolü olduğunu belirlemektir. Bu amaca
ulaşmak için, çalışmada 2014-2015 NBA sezonunda oynanan her bir maç için oyuncu
bazlı ham veri seti kullanılmıştır. Yedi farklı makine öğrenme algoritması
uygulanmış ve aynı zamanda aşırı uyum problemini önlemek için 10 kat çapraz
geçerlilik prosedürü 10 defa tekrar edilmiştir. Analizde dokuz adet bağımsız
değişken ve bir ikili bağımlı değişken kullanılmıştır. Bir basketbol
oyuncusunun başarılı bir atış yapıp yapamayacağını tahmin etmek için kullanılan
algoritmalar arasında en başarılı makine öğrenme algoritması k-en yakın komşu
algoritmasıdır. Atış Mesafesi, en yakın savunma oyuncusunun mesafesi ve temas
süresi oyuncunun başarılı bir atış yapmasını etkileyen en önemli faktörler
olarak tanımlanır. Oyuncuların atış performansı oyunu kazanmada çok etkili
olduğu için, bu çalışmanın sonuçları basketbol oyuncularına ve takım koçlarına
antrenman programları için bir rehber olarak kullanılabilir.

Keywords

Basketbol oyunu, makine öğrenmesi, performans analizi, sınıflama teknikleri

THE EFFECTIVENESS OF DIFFERENT MACHINE LEARNING ALGORITHMS ON BASKETBALL PLAYERS’ SHOOTING PERFORMANCE

Abstract

The main
purpose of this study is to determine which factors have an important role in National
Basketball Association (NBA) players’ shooting accuracy. To achieve this purpose,
player-based raw-dataset for each match
on the 2014-2015 NBA season is used in
this study. Seven different machine learning algorithms are applied and also
10-fold cross-validation with 10-repeat process is performed to avoid the overfitting problem. Nine independent variables
and one binary dependent variable are included in the analysis. According to
the results of the analysis, k-nearest neighbor
algorithm is the best machine learning algorithm among other algorithms that
are used in the analysis in order to predict whether basketball player can make
a shot or not. Shot Distance, distance of
closest defense player and touch time are
identified as the most important factors affecting player’s successful field
goal accuracy. Since the successful field goal
performance is very influential in winning the game, the results of this study
can be used as a guide for training programs to basketball players and team coaches. 

Keywords

Basketball games, classification techniques, machine learning algorithms, performance analysis

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