A Markov Process Model of the Number of Years Spent in Major League Baseball
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Anthony C. Krautmann
We treat the number of years spent in major league baseball as a random variable and estimate probability distributions for this random variable through the use of recursive formulae. Distributional characteristics, including major league baseball worklife expectancies, are estimated for players by age and current activity status in the major leagues. Data from a recent time period (1977-2007) are used to calculate current characteristics of time spent in major league baseball. However, the contemporaneous nature of our data leads to censoring because many players in our data set had not completed their major league careers by the end of 2007. We deal with censoring through a Markov process model that captures transitions between activity and inactivity in major league baseball.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
- Article
- A Game-Theoretic Approach to Personnel Decisions in American Football
- Evaluation of Individual and Team Judo Strengths Using AHP Technique and Team Competition Data
- Relationship between the Opponent Block and the Hitter in Elite Male Volleyball
- Who Deserved the 2008-2009 Belgian Football Champion Title? A Semiparametric Answer
- From One-and-Done to Seasoned Veterans: A Demographic Analysis of Individual Career Length in Major League Soccer
- A Markov Process Model of the Number of Years Spent in Major League Baseball
- That's the Second-Biggest Hitting Streak I've Ever Seen! Verifying Simulated Historical Extremes in Baseball
- Using the Count to Measure Pitching Performance
- A Closer Look at the Relative Age Effect in the National Hockey League
- Structural Analysis of Action and Time in Sports: Judo
- A Simplified Analysis of Contemporary Defensive Performance in the National Football League
