Efficiency, RTS, and marginal returns from salary on the performance of the NBA players: A parallel DEA network with shared inputs

Abstract

National Basketball Association (NBA) is one of the popular sports leagues worldwide and is also a business source that generates enormous financial resources. Generally, the salary of sports players is associated with their performance in the field. However, the NBA players' performance in the game is related to specific technical features in the offensive and defensive activities. This paper aims to measure the impact of NBA players' salary on their efficiency levels using a big data set of eleven seasons (2604 players from 2005 to 2016) by considering the players' performance in offensive and defensive activities. First, we propose models to measure players' overall, offensive, and defensive efficiencies based on a non-homogeneous parallel data envelopment analysis (DEA) network. Then, we introduce input-output oriented network models to estimate the marginal returns from salary on the outcomes of both offensive and defensive activities. Results indicated that all players' average overall efficiency is low (63.5%), with 17 efficient players. The offensive efficiency is 12.8% higher than the defensive efficiency. When the impact of salary on offensive (defensive) activity is considered, about 73% (47%) of the players' observations indicate increasing marginal returns, respectively.

Evaluating the operational efficiency of NBA teams on franchise value: An assessment of data envelopment analysis

“…Output variables align with the goals of sports franchises, which vary based on the team, sport, and country. Despite these diverse goals, most efficiency studies in the sports domain do not consider both financial and performance aspects [8,28,29,41,47,48]. This research aims to contribute to the efficiency literature in the sports domain by estimating efficiency with more comprehensive input and output variables reflective of sports franchise characteristics.…”

Entegre Edilmiş Bulanık SWARA ve Bulanık TOPSIS Yöntemleri ile Spor Okulu Seçimi

“…Spor okulu seçiminde olmasa bile ÇKKV tekniklerinin spor alanında kullanımları literatürde görülmektedir. Bireysel oyuncu performansını etkileyen birçok faktör olduğu için hem oyuncu performansını değerlendirmede hem de oyuncu seçiminde ÇKKV tekniklerinden yararlanılmıştır [12][13][14][15][16][17][18][19][20][21][22][23][24][25]. Aynı şekilde takımların performanslarını değerlendirmede de yine ÇKKV teknikleri kullanılmıştır [26][27][28][29][30][31][32][33][34][35].…”

A cluster analysis of basketball players for each of the five traditionally defined positions

“…[26][27][28][29] Data envelopment analysis was one of the most preferred techniques. [30][31][32][33] In these performance evaluation studies, the playing positions of the players were generally not taken into account. Since basketball is a game played with five players on the court, the players are traditionally listed under five different groups (i.e.…”