The short run demand for major league baseball

1982 ◽  
Vol 10 (2) ◽  
pp. 31-35 ◽  
Author(s):  
James Richard Hill ◽  
Jeff Madura ◽  
Richard A. Zuber
Keyword(s):  
Major League Baseball ◽  
Major League ◽  
Short Run

Short-Run Demand and Uncertainty of Outcome in Major League Baseball

2010 ◽  
Vol 37 (3) ◽  
pp. 197-214 ◽  
Author(s):  
Scott Tainsky ◽  
Jason A. Winfree
Keyword(s):  
Major League Baseball ◽  
Major League ◽  
Short Run ◽  
Uncertainty Of Outcome

scholarly journals Lower Extremity Biomechanics Predicts Major League Baseball Player Performance

2021 ◽  
Vol 9 (7) ◽  
pp. 232596712110152
Author(s):  
Lucas G. Teske ◽  
Edward C. Beck ◽  
Garrett S. Bullock ◽  
Kristen F. Nicholson ◽  
Brian R. Waterman
Keyword(s):  
Lower Extremity ◽  
Major League Baseball ◽  
Reaction Force ◽  
Regression Trees ◽  
Minimal Detectable Change ◽  
Vertical Force ◽  
Baseball Players ◽  
Major League ◽  
Lower Extremity Biomechanics ◽  
Statistical Metrics

Background: Although lower extremity biomechanics has been correlated with traditional metrics among baseball players, its association with advanced statistical metrics has not been evaluated. Purpose: To establish normative biomechanical parameters during the countermovement jump (CMJ) among Major League Baseball (MLB) players and evaluate the relationship between CMJ-developed algorithms and advanced statistical metrics. Study Design: Cohort study; Level of evidence, 3. Methods: MLB players in 2 professional organizations performed the CMJ at the beginning of each baseball season from 2013 to 2017. We collected ground-reaction force data including the eccentric rate of force development (“load”), concentric vertical force (“explode”), and concentric vertical impulse (“drive”) as well as the Sparta Score. The advanced statistical metrics from each baseball season (eg, fielding independent pitching [FIP], weighted stolen base runs [wSB], and weighted on-base average) were also gathered for the study participants. The minimal detectable change (MDC) was calculated for each CMJ variable to establish normative parameters. Pearson coefficient analysis and regression trees were used to evaluate associations between CMJ data and advanced statistical metrics for the players. Results: A total of 151 pitchers and 138 batters were included in the final analysis. The MDC for “load,” “explode,” “drive,” and the Sparta Score was 10.3, 8.1, 8.7, and 4.6, respectively, and all demonstrated good reliability (intraclass correlation coefficient > 0.75). There was a weak but statistically significant correlation between the Sparta Score and wSB ( r = 0.23; P = .007); however, there were no significant correlations with any other advanced metrics. Regression trees demonstrated superior FIP with higher Sparta Scores in older pitchers compared with younger pitchers. Conclusion: There was a positive but weak correlation between the Sparta Score and base-stealing performance among professional baseball players. Additionally, older pitchers with a higher Sparta Score had statistically superior FIP compared with younger pitchers with a similar Sparta Score after adjusting for age.

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