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Extent Wins Above Replacement and Po...

Candelaria, Javier R.,

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Extent Wins Above Replacement and Position Predict Salaries in Major League Baseball /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Extent Wins Above Replacement and Position Predict Salaries in Major League Baseball // Javier R Candelaria.
Author:	Candelaria, Javier R.,
Description:	1 electronic resource (235 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
Contained By:	Dissertations Abstracts International86-04B.
Subject:	Statistics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31563800
ISBN:	9798384469193

Extent Wins Above Replacement and Position Predict Salaries in Major League Baseball /
Candelaria, Javier R.,

Extent Wins Above Replacement and Position Predict Salaries in Major League Baseball /Javier R Candelaria. - 1 electronic resource (235 pages)

Source: Dissertations Abstracts International, Volume: 86-04, Section: B.

The purpose of this quantitative correlational-predictive study was to determine if, and to what extent, wins above replacement and player position, combined and individually, predict salaries for players on teams in Major League Baseball. The constructs of principal agency theory guided analysis between the relationship of player performance, position, and salaries in MLB. Data derived from publicly accessible databases and media sources, where subject matter experts grade every game. The total population included all position players (1,200) on the 40-man roster for each MLB team during the 2009-2019 season but excluded pitchers. The target population was 810 players, with a sample of 100 players. Results of multiple regression analysis indicated WAR and position, combined and individually, had a statistically significant impact on salary, but did not account for a material and/or a majority change. Combined, the multiple regression model predicted 14.4% of the variance and the model was statistically significant, [F(2, 91) = 7.663, p < .001]. Individually, the multiple regression analysis statistically predicted WAR [F(1, 92) = 9.844, p < .05, R2 = .097] and position, [F(1, 92) = 4.541, p < .05, R2 = .047]. Further, analysis results indicated WAR (β =.312, t = 3.21, p = .002) and position (β =.218, t = 2.25, p = .027) statistically significant predicted player salary. WAR and player position positively predicted salary with weak/moderate correlation suggesting that the MLB labor market, with respect to salaries, corrected to equilibrium to account for the competitive advantage identified by Oakland in 2002. Future research may identify other variables that have greater impact on salary.

English

ISBN: 9798384469193Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Data analytics

Extent Wins Above Replacement and Position Predict Salaries in Major League Baseball /
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245 1 0 $a Extent Wins Above Replacement and Position Predict Salaries in Major League Baseball / $c Javier R Candelaria.
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500 $a Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
500 $a Advisors: Ali, Mohamed A. Committee members: Bennett, Michele; Ritter-Williams, Debbie.
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520 $a The purpose of this quantitative correlational-predictive study was to determine if, and to what extent, wins above replacement and player position, combined and individually, predict salaries for players on teams in Major League Baseball. The constructs of principal agency theory guided analysis between the relationship of player performance, position, and salaries in MLB. Data derived from publicly accessible databases and media sources, where subject matter experts grade every game. The total population included all position players (1,200) on the 40-man roster for each MLB team during the 2009-2019 season but excluded pitchers. The target population was 810 players, with a sample of 100 players. Results of multiple regression analysis indicated WAR and position, combined and individually, had a statistically significant impact on salary, but did not account for a material and/or a majority change. Combined, the multiple regression model predicted 14.4% of the variance and the model was statistically significant, [F(2, 91) = 7.663, p < .001]. Individually, the multiple regression analysis statistically predicted WAR [F(1, 92) = 9.844, p < .05, R2 = .097] and position, [F(1, 92) = 4.541, p < .05, R2 = .047]. Further, analysis results indicated WAR (β =.312, t = 3.21, p = .002) and position (β =.218, t = 2.25, p = .027) statistically significant predicted player salary. WAR and player position positively predicted salary with weak/moderate correlation suggesting that the MLB labor market, with respect to salaries, corrected to equilibrium to account for the competitive advantage identified by Oakland in 2002. Future research may identify other variables that have greater impact on salary.
546 $a English
590 $a School code: 1582
650 4 $a Statistics. $3 517247
650 4 $a Sports management. $3 3423935
650 4 $a Information science. $3 554358
653 $a Data analytics
653 $a Major League Baseball
653 $a Player salaries
653 $a Predictive analytics
653 $a Professional sports
653 $a Wins above replacement
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690 $a 0723
690 $a 0463
690 $a 0430
710 2 $a Grand Canyon University. $b College of Doctoral Studies. $e degree granting institution. $3 3759077
720 1 $a Ali, Mohamed A. $e degree supervisor.
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