Major League Baseball Managers: Do They Matter?

2008 ◽  
Vol 22 (3) ◽  
pp. 303-321 ◽  
Author(s):  
Dennis Smart ◽  
Jason Winfree ◽  
Richard Wolfe
Keyword(s):  
Human Resources ◽  
Team Performance ◽  
Major League Baseball ◽  
Study Finding ◽  
Production Frontier ◽  
Limited Effect ◽  
Future Studies ◽  
Winning Percentage ◽  
Major League ◽  
Manager Compensation

Smart and Wolfe (2003) assessed the concurrent contribution of leadership and human resources to Major League Baseball (MLB) team performance. They found that player resources (defense/pitching and offence/batting) explained 67% of the variance in winning percentage, whereas leadership explained very little (slightly more than 1%) of the variance. In discussing the minimal contribution of leadership to their results, the authors suggested that future studies expand their operationalization of leadership. That is what is done in this study. Finding that the expanded operationalization has limited effect in explaining the contribution of leadership, we take an alternative tack in attempting to understand leadership in MLB. In addition, we estimate a production frontier (based on offensive and defensive resources), determine the efficiency of MLB managers relative to that frontier, and investigate the extent to which manager efficiency can be explained by manager characteristics. Finally, manager characteristics are related to manager compensation.

Measuring Circadian Advantage in Major League Baseball: A 10-Year Retrospective Study

2009 ◽  
Vol 4 (3) ◽  
pp. 394-401 ◽  
Author(s):  
W. Christopher Winter ◽  
William R. Hammond ◽  
Noah H. Green ◽  
Zhiyong Zhang ◽  
Donald L. Bliwise
Keyword(s):  
Retrospective Study ◽  
Athletic Performance ◽  
Major League Baseball ◽  
Time Zone ◽  
Winning Percentage ◽  
Circadian Time ◽  
Game Outcome ◽  
Time Zones ◽  
Home Field Advantage ◽  
Major League

Purpose:The effect of travel on athletic performance has been investigated in previous studies. The purpose of this study was to investigate this effect on game outcome over 10 Major League Baseball (MLB) seasons.Methods:Using the convention that for every time zone crossed, synchronization requires 1 d, teams were assigned a daily number indicating the number of days away from circadian resynchronization. With these values, wins and losses for all games could be analyzed based on circadian values.Results:19,079 of the 24,121 games (79.1%) were played between teams at an equal circadian time. The remaining 5,042 games consisted of teams playing at different circadian times. The team with the circadian advantage won 2,620 games (52.0%, P = .005), a winning percentage that exceeded chance but was a smaller effect than home field advantage (53.7%, P < .0001). When teams held a 1-h circadian advantage, winning percentage was 51.7% (1,903–1,781). Winning percentage with a 2-h advantage was 51.8% (620–578) but increased to 60.6% (97–63) with a 3-h advantage (3-h advantage > 2-hadvantage = 1-h advantage, P = .036). Direction of advantage showed teams traveling from Western time zones to Eastern time zones were more likely to win (winning percentage = .530) than teams traveling from Eastern time zones to Western time zones (winning percentage = .509) with a winning odds 1.14 (P = .027).Conclusion:These results suggest that in the same way home field advantage influences likelihood of success, so too does the magnitude and direction of circadian advantage. Teams with greater circadian advantage were more likely to win.

Professional Playing Experience Does Not Lead to Professional Coaching Success

2010 ◽  
Vol 3 (3) ◽  
pp. 72-82 ◽  
Author(s):  
Paul G. Schempp ◽  
Bryan A. McCullick ◽  
Matthew A. Grant ◽  
Cornell Foo ◽  
Kelly Wieser
Keyword(s):  
Significant Relationship ◽  
National Football League ◽  
Major League Baseball ◽  
National Basketball Association ◽  
Sources Of Knowledge ◽  
Winning Percentage ◽  
Professional Coaching ◽  
Major League ◽  
The Relationship

The purpose of this study was to analyze the relationship between coaches’ professional playing experience and their professional coaching success. The sample (n = 134) included coaches who had the equivalent of three full seasons of head coaching experience in either Major League Baseball (MLB) (n = 46), the National Basketball Association (NBA) (n = 38) or the National Football League (NFL) (n = 50) as determined by the total number of games coached between the years 1997-2007. ANOVAs revealed no significant differences between coaches with more or less professional playing experience and professional coaching success as determined by professional winning percentage. Further, no significant relationship was found between professional playing experience and professional coaching success in MLB (r = -0.16), NBA (r = -0.05) or NFL (r = 0.00). It was concluded that professional playing experience was not a predictor of professional level coaching success. These findings support the notion that sources of knowledge other than playing experience may be necessary and useful in developing coaching expertise.

Salary distribution and team performance in Major League Baseball

2011 ◽  
Vol 14 (2) ◽  
pp. 167-175 ◽  
Author(s):  
Christopher N. Annala ◽  
Jason Winfree
Keyword(s):  
Team Performance ◽  
Major League Baseball ◽  
Major League ◽  
Salary Distribution

scholarly journals Team payroll, pitcher and hitter payrolls and team performance: Evidence from the U.S. Major League Baseball

2018 ◽  
Vol 7 (2) ◽  
pp. 62
Author(s):  
Xing Lu ◽  
Jason Matthews ◽  
Miao Wang ◽  
Hong Zhuang
Keyword(s):  
Team Performance ◽  
Major League Baseball ◽  
Curvilinear Relationship ◽  
Level Data ◽  
Major League ◽  
Baseball Team ◽  
The U.S

We use the U.S. Major League Baseball team level data in 1985-2015 and find an inverse U-shaped relationship between team payrolls and winning percentages. Furthermore, when investigating the winning effects of pitcher and hitter payrolls, we find the similar curvilinear relationship between pitcher/hitter salaries and team performance.

scholarly journals Of Bricks and Bats: New Stadiums, Talent Supply, and Team Performance in Major League Baseball

2017 ◽  
Vol 20 (1) ◽  
pp. 3-24 ◽  
Author(s):  
Duane W. Rockerbie ◽  
Stephen T. Easton
Keyword(s):  
Team Performance ◽  
Major League Baseball ◽  
Professional Sports ◽  
Revenue Sharing ◽  
Empirical Results ◽  
Major League ◽  
Sports Team ◽  
Budget Decision ◽  
New Facility

This article considers whether publicly financed new facility investments encourage professional sports team owners to increase their investments in costly talent. We develop a model of a sports league that incorporates publicly financed facility investments, the unique characteristics of the talent market, and revenue sharing to explore the complementarity between new facility amenities, the team budget decision, and team performance. Our empirical results suggest that publicly financed new stadiums do little to improve team performance, not due to restrictions in the talent market, but rather due to a lack of fan response.

scholarly journals COVID-19 countermeasures, Major League Baseball, and the home field advantage: Simulating the 2020 season using logit regression and a neural network

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 414 ◽  
Author(s):  
Justin Ehrlich ◽  
Shankar Ghimire
Keyword(s):  
Neural Network ◽  
Team Performance ◽  
Major League Baseball ◽  
Sporting Events ◽  
Sports Industry ◽  
Logit Regression ◽  
Home Field Advantage ◽  
Major League ◽  
Using Data ◽  
Baseball Team

Background: In the wake of COVID-19, almost all major league sports have been either cancelled or postponed. The sports industry suffered a major blow with the uncertainty of sporting events being held in the near future. Various scenarios of how and when sports might recommence have been discussed. This paper examines various scenarios of how Major League Baseball team performance is going to be impacted by the presence of fans, or the lack thereof, in the context of physical distancing and other COVID-19 countermeasures Methods: The paper simulates, using a neural network and a logit regression model, the win-loss probabilities for various scenarios under consideration and also estimates the home effect for each team using data for the 2017-2019 seasons. Results: The model demonstrates that individual team home effect is symmetric between home and away and teams will not necessarily have a win or loss of any additional games in neutral stadiums, as teams with a high home field effect will lose more neutral games that would have been at home but will win more neutral games that would have been away. However, the result of individual games will be different since home effect is asymmetric between teams. Our simulation demonstrates that these individual game differences may lead to a slight difference in Play-Off Berths between a full season, a half season, or a full season without fans. Conclusions: Without fans, any advantage (or disadvantage) from home field advantage is removed. Our models and simulation demonstrate that this will reduce the variance. This stabilizes the outcome based upon true team talent, which we estimate will cause a larger divide between the best and worst teams. This estimation helps decision makers understand how individual team performance will be impacted as they prepare for the 2020 season under the new circumstances.

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