Project Topic and Feasibility Paper

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Unit 2: Project Topic and Feasibility Paper

Amelia Maples

Purpose Statement

The purpose of this paper is to evaluate the relationship between the pay and performance

in Major League Sports in which I have chosen Major League Baseball (MLB). I will attempt to

identify that performance has a direct correlation to the players’ annual salary. The dependent

variable position players’ annual salary (AS) is determined by independent variables that help

determine a position players’ annual salary include the number of Runs Scored (RS), Batting

Average (AVG), On Base Percentage plus Slugging (OPS+), and Years in the League (YIL). The

most important independent variable in this relationship is the OPS+ because multiple factors

and bases it off a simplified average of 100.

Definition of Variables

I will draw a direct correlation between player salary and performance. I identified in the

“Purpose Statement,” the dependent variable will be the players’ salary. The dependent variable

is simply that, the amount of pay a player receives in a given year (Average Annual Salary -

AAS) for their participation as a member of the team.

The primary independent variable that I chose is the On Base Percentage Plus Slugging

(OPS+). Depending on the player, statisticians can/may consider other baseball statistics as their

point of primary value, however, the OPS+ appears to be on the rise. According to Eye on

Baseball, the OPS+ includes more than one single value, it considers the on base percentage, the

slugging percentage, and the adjustments for park and league conditions. (Eye On Baseball,

2012. Additionally, OPS+ is on the rise and Batting Average (AVG) should not be used as the

single source to determine performance. OPS+ is similar to OPS, however, it adds in league and

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park conditions. OPS+ “considers these factors by normalizing a player’s production based on

league and park factors. In other words, it looks at how well a player performed compared to

everyone else in the league, and how well he performed compared to everyone else who batted in

that park (cbssports).” Several articles and analyst identify that when you measure OPS+ in

addition to AVG, RS, etc., they become a major factor in establishing a player’s salary.

The primary independent variable, I have additional independent variables that add

weight to our measurement and decision as well. The first would be the Batting Average (AVG).

A batting average is the performance of a batter and is measured by the number of safe hits (1B,

2B, 3B, HR) they receive per at bat. As an example, if a player goes to the plate ten times and

reaches base safely (Hits Safely) three times, their batting average would be measured by three

hits divided by ten official at bats (3/10=.300). The more you hit the ball and reach base safely,

the higher the average will be. In this case AVG provides us with the players’ performance when

they go up to bat and as with anything, the better you perform, the more you are awarded.

Another independent variable is measurement for Runs Scored (RS). Runs Scored (RS)

is also considered a measure of performance as it identifies the number of times a player crosses

home plate. The player must successfully reach base (OBS). The higher the number of RS by an

individual player will add value to the overall performance of that player and add value to

identifying if performance determines player salary.

The final independent variable will be the Years In League (YIL). What this variable

helps identify is how long a player has been in the Major Leagues. The longer they are in the

MLB, the more experience they garner and the more they are able to take advantage of certain

aspects aligned with contracts and the collective bargaining agreement. As an example, when a

Rookie comes up from the minors, they make the league minimum. They may perform well and

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can be awarded certain titles (MVP, Gold Glove, CY Young), however, their pay remains

consistent with that of a Rookie. Now as that player puts in the time, continues to perform and

add value, contracts are renewed and their salaries are increased. The number of years in the

league will have a direct impact on the players’ overall annual salary.

Data Description

The MLB data that will be used in my research will be gathered primarily from Baseball

Reference and USA Today Sports, however, in the “Works Cited” section, I have listed other

sites that may be used to enhance the data set. I will select a Random Sample of 30 active

players from the overall list to create my baseline that will allow me to perform the calculations

and evaluate if performance has an impact towards annual salary. The specific data that will be

gathered and put into the data set for the 30 random players will include:

 Player Name

 Average (AVG)

 Runs Scored (RS)

 On Base Percentage Plus Slugging (OPS+)

 Years in the League (YIL)

 Average Annual Salary (AAS)

Numbe r

Player Name AVG RS OPS+ YIL AAS

1 Mike Trout 0.306 780 174 6 24,083,33

3 2 Jose Abreau 0.296 397 139 5 13,000,00

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0

3 Mookie Betts 0.301 458 132 4 10,000,00

0

4 Jose Altuve 0.318 630 127 6 23,357,14

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5 Miguel Cabrera 0.316 1388 151 15 29,200,00

0

6 Yoenis Cespedes 0.274 472 126 7 27,500,00

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7 Albert Pujols 0.302 1773 149 18 24,000,00

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8 Jason Heyward 0.263 631 103 9 23,000,00

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9 Joey Votto 0.311 921 156 11 22,500,00

0

10 Yadier Molina 0.283 649 99 14 20,666,66

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11 Bryce Harper 0.279 591 138 6 21,625,00

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12 Andrew McCutchen 0.288 1586 134 9 14,750,00

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13 George Springer 0.263 410 128 4 12,000,00

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14 Kris Bryant 0.286 365 138 3 10,850,00

0 15 Anthony Rizzo 0.269 559 128 6 5,857,143

16 Freddie Freeman 0.293 673 137 8 16,875,00

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17 Troy Tulowitzki 0.290 761 118 12 15,775,00

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18 Manny Machado 0.283 507 121 6 16,000,00

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19 Dustin Pedroia 0.300 921 114 12 13,750,00

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20 Anthony Rendon 0.282 430 118 5 12,300,00

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21 Justin Turner 0.291 378 125 8 16,000,00

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22 Adam Jones 0.278 893 108 11 14,250,00

0 23 Evan Gattis 0.249 297 113 6 6,700,000 24 Curtis Granderson 0.252 1160 115 13 5,000,000 25 Rougned Odor 0.25 327 94 4 8,250,000 26 David Peralta 0.295 272 117 4 3,300,000

27 Joe Mauer 0.306 1000 124 15 23,000,00

0

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28 Chris Young 0.235 668 95 12 2,000,000 29 DJ LeMahieu 0.299 481 93 6 8,500,000 30 Yasiel Puig 0.278 352 125 5 6,000,000

Works Cited

Baseball Prospectus. (n.d.). 2018 Compensation. Retrieved August 25, 2018, from https://legacy.baseballprospectus.com/compensation/?cyear=2018&team=&pos=

Eye on Baseball staff Nov 16. (2017, April 06). What is the best offensive stat? Retrieved August 25, 2018, from https://www.cbssports.com/mlb/news/what-is-the-best-offensive-stat/

MLB Advanced Media. (2018, January 20). Standard Stats | Glossary. Retrieved August 25, 2018, from http://m.mlb.com/glossary/standard-stats

Spotrac. (n.d.). MLB Rankings. Retrieved August 25, 2018, from https://www.spotrac.com/mlb/rankings/batters/

USA Today. (2018, April 1). MLB Salaries - MLB Baseball. Retrieved August 25, 2018, from https://www.usatoday.com/sports/mlb/salaries/2018/player/all/

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