The Effects of Roster Turnover on Demand in the National Basketball Association

Morse, Alan L.; Shapiro, Stephen L.; McEvoy, Chad D.; Rascher, Daniel A.

doi:10.2139/ssrn.1690885

Cited by 9 publications

(9 citation statements)

References 14 publications

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“…This means that, approximately, 13 athletes are substituted per team from a season to another. The player turnover rate found in this study is higher than the previous findings in baseball (27%) 6 and basketball teams (36%) 7 in national competitions. There are some differences between our methods and the previous ones which may explain the different findings.…”

Section: Discussioncontrasting

confidence: 87%

“…Previous studies in the sports area have explored the relationship between player turnover and match attendance in baseball 6 and basketball 7 national championships. In these studies, the authors showed an average turnover of 27% 6 and 36% 7 per team in a one-year season. Some questions may arise from these results, such as how was the turnover variability between the teams and if the turnover influenced team performance.…”

Section: Introductionmentioning

confidence: 97%

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Player turnover and team performance in FIFA Women’s World Cup

Barreira

Sousa

Galatti

2019

Motriz: rev. educ. fis.

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Section: Discussioncontrasting

confidence: 87%

Section: Introductionmentioning

confidence: 97%

Player turnover and team performance in FIFA Women’s World Cup

Barreira

Sousa

Galatti

2019

Motriz: rev. educ. fis.

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“…The most significant explanatory variables in his model included team performance and geographic factors. Since his research was published, several other authors have investigated demand within professional sports, using spectator attendance as the variable of interest in regression models and testing a wide array of predictor variables (Hanson and Gauthier, 1989;McDonald and Rascher, 2000;Mongeon and Winfree, 2012;Morse et al, 2008). A smaller contingent of sport economists and marketing researchers has focused on demand within collegiate sporting events, particularly college football games (Eddy et al, 2016;Flanagan, 2008;Griffith, 2010;Groza, 2010;Mirabile, 2015;Price and Sen, 2003).…”

Section: Literature Reviewmentioning

confidence: 99%

Maximizing visitors at college football bowl games

Popp

Jensen

Jackson

2017

IJEFM

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Purpose The purpose of this paper is to isolate factors predictive of event attendees, and assist tourism professionals such as members of host committees, in maximizing the number of out-of-town visitors to their region and optimizing tourism-related revenue when hosting college football bowl games. Design/methodology/approach A total of 16 demand variables were entered into a hierarchical regression model, including the stature of the event and market-related variables, as well as team-related variables reflecting team or program stature and current season performance. Findings A final model containing seven variables (bowl age, market population, conference affiliation, bowl game stature, season wins, home attendance, and distance traveled) predicted 77.5 percent of the variance in bowl game attendance. Research limitations/implications This paper illustrates the use of predictive modeling for major sport event attendance with a unique sample and variables explored. Future research may build off the model to explore attendance for other populations or events. Practical implications The applied nature of this study allows practitioners working in the tourism and event management field to incorporate a predictive model to best select participants in sporting events to maximize event attendees. Originality/value Understanding the variables which predict event attendees in the context of college football bowl games provide useful data to practitioners. This study advances this area of research by treating event participants as unique observations (something which has not been done in prior studies), and looking at a new data set which incorporates the College Football Playoff era.

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“…They claim that the knowledge of these differences could allow the coaches to increase the effectiveness of the player recruitment process. Morse et al (2008) examine through a regression equation the effects of roster turnover on season attendance in the NBA, over a five-year period (2000)(2001)(2002)(2003)(2004)(2005). Popp et al (2011) admit that player recruitment is both intense and expensive as coaches must recognize talent at an early age and from a broad geographic range.…”

Section: Literature Reviewmentioning

confidence: 99%

Building a basketball game strategy through statistical analysis of data

Nikolaidis

2013

Ann Oper Res

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Management practices may be based either on manager's intuition or on analytical and objective reasoning. Sports and, particularly, basketball can be considered as an indicative field where these differences in decision-making can be met. This paper has been motivated by the statistical analysis that the author conducted in the past in order to support the management of a Greek basketball team and specifically the decision-making process of its coach regarding the team's strategy during its games. The aim of the paper is on the one hand to present some indicative, simple ideas for the statistical analysis of basketball data, and on the other hand to show that any basketball team can improve significantly its decision-making process if it chooses to be statistically supported. Basketball data is numerous; consequently its elaboration can be extensive and fruitful.

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The Effects of Roster Turnover on Demand in the National Basketball Association

Cited by 9 publications

References 14 publications

Player turnover and team performance in FIFA Women’s World Cup

Player turnover and team performance in FIFA Women’s World Cup

Maximizing visitors at college football bowl games

Building a basketball game strategy through statistical analysis of data

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