Dynamic Effort, Sustainability, Myopia, and 110% Effort
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Stephen Shmanske
By definition, giving 100 percent effort all of the time is sustainable, but begs the question of how to define 100 percent effort. As a corollary, once a benchmark for defining 100 percent effort is chosen, it may be possible, even optimal, to give a greater amount of effort for a short period of time, while recognizing that this level of effort is not sustainable. This dynamic effort provision problem is analyzed in the context of effort and performance by National Basketball Association (NBA) players over the course of a season. Within this context, several benchmarks for sustainable effort are considered, but these are rejected by the data. Meanwhile, the data are consistent with the proposition that NBA players put forth optimal effort, even if such effort is not always sustainable.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
Articles in the same Issue
- Conference Paper
- Uncovering Europe's Best Goalscorers from the 2009-2010 Season
- Dynamic Effort, Sustainability, Myopia, and 110% Effort
- The Intra-Match Home Advantage in Australian Rules Football
- The Relationship between Leader Experience and Team Performance in Cross-Sectional and Longitudinal Designs
- Stratified Odds Ratios for Evaluating NBA Players Based on their Plus/Minus Statistics
- Dependence Relationships between On Field Performance, Wins, and Payroll in Major League Baseball
- Optimal Dynamic Clustering Through Relegation and Promotion: How to Design a Competitive Sports League
- Perception ? Reality: Analyzing Specific Allegations of NBA Referee Bias
- NFL Prediction using Committees of Artificial Neural Networks
- An Alternative to the NFL Draft Pick Value Chart Based upon Player Performance
- Monte Carlo Simulation for High School Football Playoff Seed Projection
- Defining the Performance Coefficient in Golf: A Case Study at the 2009 Masters
- Reconsideration of the Best Batting Order in Baseball: Is the Order to Maximize the Expected Number of Runs Really the Best?
- Never Too Late to Win
- An Extension of the Pythagorean Expectation for Association Football
- Pitcher Accuracy Through Catcher Spotting: Assessing Rater Reliability
- Valuing Nostalgia: The Case of the Topps 1957 Baseball Cards
Articles in the same Issue
- Conference Paper
- Uncovering Europe's Best Goalscorers from the 2009-2010 Season
- Dynamic Effort, Sustainability, Myopia, and 110% Effort
- The Intra-Match Home Advantage in Australian Rules Football
- The Relationship between Leader Experience and Team Performance in Cross-Sectional and Longitudinal Designs
- Stratified Odds Ratios for Evaluating NBA Players Based on their Plus/Minus Statistics
- Dependence Relationships between On Field Performance, Wins, and Payroll in Major League Baseball
- Optimal Dynamic Clustering Through Relegation and Promotion: How to Design a Competitive Sports League
- Perception ? Reality: Analyzing Specific Allegations of NBA Referee Bias
- NFL Prediction using Committees of Artificial Neural Networks
- An Alternative to the NFL Draft Pick Value Chart Based upon Player Performance
- Monte Carlo Simulation for High School Football Playoff Seed Projection
- Defining the Performance Coefficient in Golf: A Case Study at the 2009 Masters
- Reconsideration of the Best Batting Order in Baseball: Is the Order to Maximize the Expected Number of Runs Really the Best?
- Never Too Late to Win
- An Extension of the Pythagorean Expectation for Association Football
- Pitcher Accuracy Through Catcher Spotting: Assessing Rater Reliability
- Valuing Nostalgia: The Case of the Topps 1957 Baseball Cards
