Journals / Spor ve Performans Araştırmaları Dergisi / 2019 / Cilt: 10 - Sayı: 3

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

FARKLI MAKİNE ÖĞRENMESİ ALGORİTMALARININ BASKETBOL OYUNCULARININ ATIŞ PERFORMANSI ÜZERİNDEKİ ETKİNLİĞİ

Pages
256–269
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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 basketballplayer can make a shot or not.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The main purpose of this study is to determine which factors have an important role in National Basketball Association (NBA) players’ shooting accuracy.
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Key Findings & Data:To achieve this purpose, player-based raw-dataset for each match on the 2014-2015 NBAseason is used in this study.
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Conclusion & Impact: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 basketballplayer can make a shot or not.
🔬Design:Modeling & Simulation
📍Scope:2014-2015 Verileri
⚙️Variables:Domain Parameters

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 NBAseason 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 dependentvariable 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 basketballplayer 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 veryinfluential in winning the game, the results of this study can be used as a guide for training programs to basketball players and team coaches.

Özet

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.

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Cite This Article

DEPREN, S. K. (2022). THE EFFECTIVENESS OF DIFFERENT MACHINE LEARNING ALGORITHMS ON BASKETBALL PLAYERS’ SHOOTING PERFORMANCE. Spor ve Performans Araştırmaları Dergisi, Cilt: 10 - Sayı: 3, 256–269. https://ajindex.com/articles/593354

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