You are here

Home » Content

ANALYZING MAJOR LEAGUE BASEBALL PLAYER’S PERFORMANCE BASED ON AGE AND EXPERIENCE

Journal Name:

  • Journal of Sports Economics & Management

Publication Year:

  • 2017

Key Words:

Keywords (Original Language):

Author NameUniversity of Author
Abstract (2. Language): 
Este estudio modela el rendimiento del jugador en función de su edad, experiencia y talento. El panel desequilibrado incluye 5.754 temporadas repartidas entre 562 bateadores y 4.767 temporadas repartidas entre 489 lanzadores. La edad pico para los bateadores y lanzadores es 26,6 años y 24,5 años, respectivamente, manteniendo constante la experiencia. Con una mayor experiencia, el pico de los bateadores está cerca de los 29 años, mientras que el pico de los lanzadores esta cerca de los 28 años. Además, los bateadores encuentran mayores fluctuaciones en el rendimiento en sus carreras que los lanzadores. Este modelo está diseñado para ser utilizado por equipos de MLB para predecir el rendimiento futuro basado en los primeros seis años de estadísticas de un jugador.
Bookmark/Search this post with
Abstract (Original Language): 
This study models player performance as a function of age, experience, and talent. The unbalanced panel includes 5,754 seasons spread among 562 batters and 4,767 seasons spread among 489 pitchers. Peak physical age for hitters and pitchers are 26.6 years and 24.5 years, respectively, when holding experience constant. With increased experience, batters peak near age 29, while pitchers peak near age 28. Also, batters encounter greater fluctuations in performance over their careers than pitchers. This model is designed for use by MLB teams to predict future performance based on a player’s first six years of statistics.
FULL TEXT (PDF): 
PDF icon arastirmax-analyzing-major-league-baseball-players-performance-based-age-and-experience.pdf
  • 2
78
100
  • English

REFERENCES

References: 

BABIP. (2015). Fangraphs. http://www.fangraphs.com/library/pitching/babip/
Baseball Payrolls History. (2015). The Baseball Cube.
http://www.thebaseballcube.com/extras/payrolls/
Baumer, B. S., Jensen, S. T., & Matthews, G. J. (2015). openWAR: An open source
system for evaluating overall player performance in major league baseball.
Journal of Quantitative Analysis in Sports, 11(2), 69–84.
Bradbury, J.C. (2009). Peak athletic performance and ageing: Evidence from baseball.
Journal of Sports Sciences, 27(6), 599-610.
Demiralp, B., Colburn, C., & Koch, J. (2010). The effects of age, experience and
managers upon baseball performance. Journal of Economics and Finance, 36(2),
481-498.
Fair, R. (2008). Estimated age effects in baseball. Journal of Quantitative Analysis in
Sports, 4(1), 1-39.
Furnald, N. A. (2012). The impact of age on baseball players’ performance.
http://cms.colgate.edu/portaldata/imagegallerywww/21c0d002-4098-4995-
941f-9ae8013632ee/ImageGallery/2012/the-impact-of-age-on-baseball-playersperformance.
pdf
Kahn, L. M. (1993). Managerial quality, team success, and individual player performance
in major league baseball. ILR Review, 46(3), 531–547.
Krautmann, A. C., & Oppenheimer, M. (2002). Contract length and the return to
performance in Major League Baseball. Journal of Sports Economics, 3(1), 6–17.
Krautmann, A. C., & Solow, J. L. (2009). The dynamics of performance over the duration
of Major League Baseball long-term contracts. Journal of Sports Economics,
10(1), 6–22.
Krohn, G. (1983). Measuring the experience-productivity relationship: The Case of
Major League Baseball. Journal of Business & Economic Statistics, 1(4), 273-279.
Mangine, G. T., Hoffman, J. R., Fragala, M. S., Vazquez, J., Krause, M. C., Gillett, J., &
Pichardo, N. (2013). Effect of age on anthropometric and physical performance
measures in professional baseball players. Journal of Strength and Conditioning
Research 27, 375–381.
Ng, K. (2017). Analyzing Major League Baseball player’s performance based on age and experience.
Journal of Sports Economics & Management, 7(2), 78-100.
2340-7425 © 2017 The Authors. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/3.0)
92
Minimum salary. (2014). Baseball-Reference.com. http://www.baseballreference.
com/bullpen/Minimum_salary.
Mondout, P. (2005). Major League Payrolls. Baseball Chronology.
http://www.baseballchronology.com/Baseball/Teams/Background/Payroll/defau
lt.asp.
Ozanian, M. (2015). MLB Worth $36 Billion as Team Values Hit Record $1.2 Billion
Average. Forbes. http://www.forbes.com/sites/mikeozanian/2015/03/25/mlbworth-
36-billion-as-team-values-hit-record-1-2-billion-average/.
Petchesky, B. (2015). 2015 Payrolls and Salaries for Every MLB Team. Deadspin.
http://deadspin.com/2015-payrolls-and-salaries-for-every-mlb-team-
1695040045.
Schulz, R., Musa, D., Staszewski, J., & Siegler, R. S. (1994). The relationship between age
and major league baseball performance: implications for development.
Psychology and Aging, 9(2), 274.
Scully, G. W. (1974). Pay and performance in major league baseball. The American
Economic Review, 64(6), 915–930.
Sims, J., & Addona, V. (2016). Hurdle Models and Age Effects in the Major League
Baseball Draft. Journal of Sports Economics, 17(7), 672–687.
Smith, S. (2013). WAR Explained, Converting Runs to Wins. Baseball-Reference.com.
http://www.baseball-reference.com/about/war_explained_runs_to_wins.shtml.
Society for American Baseball Research. (2016). Primary Salary Data. Baseball-
Reference.com. http://www.baseball-reference.com
Sports Reference, LLC. (2014). Major League Statistics and Information. Baseball-
Reference.com. http://www.baseball-reference.com/.
Sulloway, F. J., & Zweigenhaft, R. L. (2010). Birth order and risk taking in athletics: A
meta-analysis and study of major league baseball. Personality and Social
Psychology Review, 14(4), 402–416.

Thank you for copying data from http://www.arastirmax.com