Measuring the NBA Teams’ Cross-Efficiency by DEA Game

Aizemberg, Luiz; Roboredo, Marcos Costa; Ramos, Thiago Graça; Mello, João Carlos Correia Baptista Soares de; Meza, Lídia Ângulo; Alves, Alessandro Martins

doi:10.4236/ajor.2014.43010

Cited by 9 publications

(6 citation statements)

References 11 publications

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“…All the variables are symmetric and their purpose is to explain the distribution of frequencies. When the aim is to analyze the strength of associations between a dependent variable and a series of independent variables, other techniques, such as logistic regression analysis 19,39 or data envelopment analysis (DEA) 45,46 are more appropriate.…”

Section: Discussionmentioning

confidence: 99%

A log-linear analysis of efficiency in elite basketball applied to observational methodology

Alsasua

Ajamil

Idiakez

et al. 2019

International Journal of Sports Science & Coaching

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Although the full potential of observational methodology is realized through diachronic analyses, synchronic analyses can be used to investigate associations between categorical variables. Log-linear modeling is an appropriate method for investigating associations between three or more dimensions using multidimensional contingency tables. We provide a practical example of how we used log-linear analysis to study efficiency in a men’s basketball competition played by Spain’s top teams using a model containing three dimensions (and their respective categories): position of last pass before a shot, position of shot, and result of shot. The best-fit and most parsimonious model (i.e., the model that provided the best explanation of the observed frequencies in the contingency table and that contained the fewest effects) was a conditional independence model in which last pass position and shot position were associated independently of the categories in the shot result dimension and the interaction between shot position and shot result was not affected by the categories in the last pass dimension. Estimation and subsequent interpretation of the significant parameters in the selected model showed how log-linear modeling can provide basketball coaches with practical insights within an observational methodology study.

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Section: Discussionmentioning

confidence: 99%

A log-linear analysis of efficiency in elite basketball applied to observational methodology

Alsasua

Ajamil

Idiakez

et al. 2019

International Journal of Sports Science & Coaching

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“…One of the first to deal with this issue was Dean Smith, who noted that the number of rebounds itself is not a reliable indicator, but instead that it is the efficiency of such rebounding (1999). All of this lead to the practice of not using relative statistical indicators as is, but trying to mathematically model different formulas which should aid in making conclusions on game performance (Aizemberg et al, 2014;Csataljay et al, 2012;Kubatko et al, 2007;Mikołajec et al, VOLUME --| ISSUE -| 2020 | 7 2013). Jose Martinez may have best described the current situation in the field when he said that "To find best measure to valuate a basketball player is becoming like the search of the 'Holy Grail'" (2012, p. 21).…”

Section: Discussionmentioning

confidence: 99%

Difference-based analysis of the impact of observed game parameters on the final score at the FIBA Eurobasket Women 2019

Simović

Komić

Guzina

et al. 2020

jhse

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Evaluation in women's basketball is keeping up with developments in evaluation in men's basketball, and although the number of studies in women's basketball has seen a positive trend in the past decade, it is still at a low level. This paper observed 38 games and sixteen variables of standard efficiency during the FIBA EuroBasket Women 2019. Two regression models were obtained, a set of relative percentage and relative rating variables, which are used in the NBA league, where the dependent variable was the number of points scored. The obtained results show that in the first model, the difference between winning and losing teams was made by three variables: true shooting percentage, turnover percentage of inefficiency and efficiency percentage of defensive rebounds, which explain 97.3%, while for the second model, the distinguishing variables was offensive efficiency, explaining for 96.1% of the observed phenomenon. There is a continuity of the obtained results with the previous championship, played in 2017. Of all the technical elements of basketball, it is still the shots made, assists and defensive rebounds that have the most significant impact on the final score in European women's basketball. It can be noted that, unlike with the previous championship, inside play is no longer dominant, but there is a balance between inside and outside play, which has already been established as a developing trend in men's basketball. The emergence of the offensive efficiency variable indicates that it is becoming significant in top-tier competitions as well but is still a challenge for coaches to grasp the causes of this multicomplex issue based on this indicator.

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“…The issue of basketball player efficiency has been gradually shifting from the field of sport to the field of economy, and other sciences as well. As a result, there are volumes of research on efficiency in basketball in the popular Data Envelopment Analysis [40][41][42][43].…”

Section: A Moll Compiled Individual Efficiency At Games (Ieg)mentioning

confidence: 99%

Comparative values, correlation and classification of basketball players based on the efficiency index and expert evaluation by coaches

Simović

Matković

Mijanović

et al. 2019

Int. J. Phys. Educ. Fit. Sports

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Measuring the efficiency of athletes during competition has been a subject of interest both for experts and scientists in sports for more than a hundred years. Basketball has recognized in the 1940s how important it is to analyze efficiency indicators because these procedures allow coaches to increase their knowledge. There are two basic methods – objective and subjective – for evaluating the efficiency, or real quality of basketball players. The aim of this research is to establish the level of correlation between these two methods and to identify clusters, i.e. player hierarchy based on the results of both methods of efficiency evaluation. The sample of variables consisted of 12 basketball players who participated in the 2010 FIBA World Championships in Turkey. The subjective evaluation, also called expert evaluation, was performed by coaches of seven national teams that participated in the Championship. The objective evaluation was performed using the EEF efficiency index. The data was processed using z-scoring, the Pearson coefficient, and hierarchical cluster analysis. The Pearson coefficients of linear correlation between the efficiency index and the expert evaluation is r = 0.859 with a statistical significance of p ? 0.01. The cluster analysis distinguished two groups of players, which were named quality and super quality. The variance analysis showed that the probability of the clusters being equal is less than p ? 0.00. The research has shown that the evaluation by coaches is relevant and is fully consistent with the efficiency index formula. Also, the distinction of two groups of players by clustering is not uncommon in the basketball practice and is linked with efficiency at the given time.

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Measuring the NBA Teams’ Cross-Efficiency by DEA Game

Abstract: In this paper, we use DEA to measure the NBA basketball teams' efficiency in

Cited by 9 publications

References 11 publications

A log-linear analysis of efficiency in elite basketball applied to observational methodology

A log-linear analysis of efficiency in elite basketball applied to observational methodology

Difference-based analysis of the impact of observed game parameters on the final score at the FIBA Eurobasket Women 2019

Comparative values, correlation and classification of basketball players based on the efficiency index and expert evaluation by coaches

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