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2019 Major League Baseball Annual Payroll by Position

Paper Type: Free Assignment Study Level: University / Undergraduate
Wordcount: 1867 words Published: 20th Apr 2020

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Purpose Statement

The purpose of this paper will be on the study of the relationship between the annual player’s salary and their performance in Major League Baseball. The dependent variable chosen is the player’s annual salary and independent variables will be the batting average, years of experience in the league, and on base percentage, and slugging average. The independent variable that will be first considerate on this relationship is the on base percentage and slugging, since is the one that is most catching the eye on the league lately. The on base percentage and slugging is a variable which use many factors such as on base, slugging, place, time on a simplified 100 average. So, for this reason the key independent variable on base and slugging percentage will be the most important to evaluate the player’s overall performance.

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Definition of variables

On Base Percentage and Slugging will be the first factor to consider how well a player performed compared to everyone else on the MLB. The second factor for our independent variable will be Batting Average, which is measured by the number of hits achieved to base divided by the times the player goes to the plate, if he goes eight times but arrived safe two times to base, it will be 2/8=.250. So, the more the player reach the base safely the better batting average will have. The batting average will provide us with a performance factor, the higher their batting score, the higher their award will be. The batting average has always been a good positive correlation to identify the player’s performance for the player’s annual salary. The On Base Percentage will be another measure of performance to be considered, which is identify by the number of times the player arrived to the home plate. The higher the number of On Base Percentage, the higher the player will be awarded for the overall performance to determine the annual salary.

The last independent variable that will be the years in the league.  On how long the player has been in the league will help on how reliable the player can be. The longer you are in the league the higher the pay should be. The more experience the player has, the more advantage will have when time comes to renew a contract agreement. It is expected that the number of years in the major league baseball will add value to the player’s salary as long as he stays consistent.

Data Description

The primary sources for the chosen data of 30 random players will be mainly from baseball-reference.com and MLB.com. The extracted data information will be for the chosen dependent variable and independent variables, which are the annual salary, BA, OPS, SGL, and years of experience at the end of 2019. The references contain various statistics for present and past major league players. The variables will be manually collected to determine the statistics of the 30 randomly chosen major league players.  The extracted data will give the advantage for filtering the most updated data according to various websites. The data can be also sorted out on a basis of the stats being tested. The five variables data will be collected from the summary of 2018 with salary information.  On the OPS, BA, SGL and YIL are values being recorded in this database from the mainly used sites. The years in the league would be based on the statistics of service time. On Appendix A; The general model is: Average Annual Salary pay= b0 + b1 + b2 + b3 + b4 + e


The model to predict Average Annual Salary from BA, OPS, SLG, and YIL did not violate any assumptions of multiple linear regression model. However, the insignificant ANOVA test display that the model was not a good fit for the data. Only the years in the league variable was statistically significant in predicting the salary. The batting average, On base percentage, and slugging, variables were not a significant factor to the prediction.

Appendix A (Sample Data)

Full Data Set (up to 2019 Season)

Number PLAYER AVG OPS SLG YIL AAS (thousands)
1 Christian Yelich 0.337 1.141 0.707 6 7,081,429
2 Jeff McNeil 0.336 0.905 0.507 1 567,714
3 DJ LeMahieu 0.332 0.898 0.517 8 12,000,000
4 Cody Bellinger 0.33 1.104 0.673 2 605,000
5 Rafael Devers 0.329 0.959 0.579 2 614,500
6 Michael Brantley 0.325 0.91 0.524 10 16,000,000
7 Ketel Marte 0.32 0.954 0.576 4 4,800,000
8 Charlie Blackmon 0.319 0.958 0.591 8 18,000,000
9 Anthony Rendon 0.318 1.02 0.616 6 18,800,000
10 Xander Bogaerts 0.316 0.974 0.575 6 12,000,000
11 Hanser Alberto 0.311 0.735 0.405 3 578,000
12 Freddie Freeman 0.308 0.962 0.564 9 16,875,000
13 Jorge Polanco 0.305 0.875 0.512 5 5,150,000
14 David Dahl 0.304 0.881 0.525 2 560,000
15 Nolan Arenado 0.303 0.898 0.531 6 26,000,000
16 Whit Merrifield 0.302 0.842 0.486 3 4,062,500
17 Francisco Lindor 0.302 0.876 0.524 4 10,550,000
18 Yoan Moncada 0.301 0.893 0.535 3 575,000
19 Yuli Gurriel 0.3 0.861 0.526 3 9,500,000
20 Mike Trout 0.298 1.098 0.657 8 24,083,333
21 Omar Narvaez 0.297 0.849 0.482 3 581,200
22 David Fletcher 0.297 0.772 0.414 1 561,500
23 George Springer 0.296 0.983 0.602 5 12,000,000
24 Kris Bryant 0.294 0.947 0.548 4 12,900,000
25 J.D. Martinez 0.293 0.895 0.532 8 22,000,000
26 Leury Garcia 0.292 0.714 0.387 6 1,550,000
27 Ronald Acuna Jr. 0.292 0.879 0.502 1 560,000
28 Gleyber Torres 0.291 0.862 0.504 1 605,200
29 Alex Verdugo 0.29 0.816 0.475 2 560,000
30 Miguel Rojas 0.289 0.726 0.382 5 3,155,000

Appendix B


  1. 2019 New York Yankees Statistics | Baseball-Reference.com, Retrieved July 4, 2019, from https://www.baseball-reference.com › Teams › Franchise Encyclopedia.
  2. Baseball Encyclopedia of MLB Players. (n.d.). Retrieved July 6, 2019 from https://www.baseball-reference.com/players.


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