BASKETBOLDA MAÇ İÇİ İSTATİSTİKLERİNİN MARKOV ZİNCİRLERİ İLE ANALİZİ: STEPHEN CURRY ÖRNEĞİ

Abstract

Basketbol, sürekli evrilen ve dinamikleri hızla değişen bir spor dalıdır. Bu değişimlerin anlaşılması ve oyuncuların performanslarının değerlendirilmesi, modern spor bilimlerinde önemli bir yer tutar. Basketbol istatistikleri, oyuncuların maç içindeki performanslarını çeşitli parametreler üzerinden değerlendirmek için kullanılır. Bu parametreler arasında atış başarısı, ribaund sayısı, asist miktarı ve diğer önemli performans göstergeleri bulunur. Bu tür detaylı istatistikler, oyuncuların güçlü ve zayıf yönlerini belirlemeye ve takım stratejilerini şekillendirmeye yardımcı olur. Markov zinciri analizi, bu istatistiksel verilerin modellemesinde kullanılan güçlü bir matematiksel araçtır. Bir Markov zinciri, bir sonraki durumun yalnızca mevcut duruma bağlı olduğu ve geçmiş tüm durumlardan bağımsız olduğu stokastik bir süreçtir. Basketbol gibi dinamik bir ortamda, Markov zincirleri, oyuncu performansının zaman içindeki değişimlerini ve olası gelecek senaryolarını tahmin etmek için idealdir. Özellikle, oyuncuların maç içi performans değişiklikleri gibi sürekli değişen koşullar altında, Markov zincirleri bu değişkenliği modelleyerek, daha bilinçli kararlar alınmasına imkan tanır. Bu çalışmada NBA yıldızı Stephen Curry'nin performansı üzerine analizler yapılmıştır. Bu analizde Stephen Curry’nin 2015-2016, 2016-2017, 2017-2018 yıllarındaki üçlük sayısı, üçlük yüzdesi, ribaund, asist ve toplam sayı değişkenleri kullanılarak Markov Zinciri analizi ile tahminleri yapılmıştır. Markov zinciri kullanılarak onun üçlük atışları, asistler ve diğer önemli istatistiklerinin nasıl değişebileceği öngörülmüştür. Bu tür analizler, Curry gibi oyuncuların oyun içi etkilerini daha iyi anlaşılmasına ve onların performansının takım başarısı üzerindeki etkilerini değerlendirmemize olanak sağlayacağı düşünülmektedir.

Keywords

• Markov zinciri
• spor istatistikleri
• stokastik süreçler

ANALYSIS OF IN-MATCH STATISTICS IN BASKETBALL WITH MARKOV CHAINS: THE STEPHEN CURRY EXAMPLE

Abstract

Basketball is a constantly evolving sport with rapidly changing dynamics. Understanding these changes and evaluating player performance hold significant importance in modern sports sciences. Basketball statistics are used to assess players' in-game performance across various parameters, including shooting success, rebound numbers, assist amounts, and other critical performance indicators. Such detailed statistics help identify players' strengths and weaknesses and aid in shaping team strategies. Markov chain analysis is a powerful mathematical tool used for modeling these statistical data. A Markov chain is a stochastic process in which the next state depends only on the current state and is independent of all past states. In the dynamic environment of basketball, Markov chains are ideal for predicting changes in player performance over time and possible future scenarios. Especially under continuously changing conditions like in-game performance shifts, Markov chains model this variability, allowing for more informed decision-making. This study has analyzed NBA star Stephen Curry's performance. Using Markov Chain analysis, predictions were made based on his three-point shooting, rebound, assist, and total point statistics from the 2015-2016, 2016-2017, and 2017-2018 seasons. The Markov chain was used to forecast how his three-point shots, assists, and other significant statistics might change. Such analyses are thought to better understand the in-game effects of players like Curry and assess the impact of their performance on team success.

Keywords

• Markov chain
• sports statistics
• stochastic processes

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