Report On Pay and Performance in Major League Sports

There has been growing and large disparity in recent years among the payrolls of salaries of major league sports team. In order to acquire the players, very high prices are being paid by many teams so that team could be made up of best players. It hasbeen seen that high budget teams are often successful such as New York Yankees; this raises the question that either tag of high prices worth the improvement in players’ performance or not. Many statistics such as on base percentage plus slugging, stolen base, and batting average can be examined to evaluate their impact on the salariesof players.

The higher salaries are expected to be correlated to good players’ performance. OPS takes on base percentage plus slugging of players and normalize the number around whole league and accounts for ballparks kind of external factors. It is a solid tool for players’ performance evaluation at the place and it is a good measure to rank players switching the teams. The number of tears of experience refers to time period between a player joined the team till present working time and it also matters to evaluate the performance of the players.This project aims to identify the relationship between salary of player and their performance in major league sports and predicting the salary of baseball based on the performance as well in the field. The model for this project is explained as:

Wins = number of games won = dependent variable

Errors = number of errors committed = independent variable

ERA = team ERA = independent variable

HR = number of homes runs = independent variable

League =whether the team places in the National League or the American League. League code variable is added by using 1 for the American League and 0 for the National League.

Payroll = Players Pay = Independent variable

SB = Stolen base = independent variable

BA = Batting average = independent variable

On base percentage plus slugging is major independent variable because this variable plays an important role to identify the performance of player. Following is the general form of variables:

Definition of Variables of Pay and Performance in Major League Sports

Wins: Wins refer to the number of games that league has won.

League: League refers to two sports league i.e. National League and the American eague.

HR: It refers to the number of home runs. The expected result of this variable is positive.

Payroll: It refers to players’ salary that is what players are being paid for their work in a team. The expected result of this variable is positive.

Stolen base:In major league sports, stolen base occurs when players is not entitled to some base and advances to that base and it is ruled by the official scorer that action of the runner is credited by advances. It often occurs when ball is being pitched by pitcher to home plate and runner advances to a next base. The expected result of this variable on dependent variable is negative.

Batting average:the average score of a player is called batting average. For example, in cricket, runs scored by a batsman on one completed innings and in baseball, the safe hits of batter at bat per official times is knowns as batting average. The expected result of this variable on dependent variable is positive.

Data Description

This project is based on data of above mentionedvariables collected form “Bovée, C.L., Thill, J.V. and Raina, R.L., 2016. Business communication today. Pearson Education India.” The data is presented in following table:

Presentation and Interpretation of Results of Pay and Performance in Major League Sports

 Correlation Analysis of Major League Sports

The above table represents the correlation analysis. It can be seen that wins variable is positively correlated with league, ERA, andBA. While the variable is negatively correlated with HR, SB, and errors. The wins are highly correlated with league. From the total 7 variables only two variables are negatively correlated with the Wins these are; ERA and errors which have values roundabout the -0.681075006 and -0.634078486 respectively. Meanwhile the remaining five variables as; League, BA, HR, SB and payrolls are positively related with the Wins. The negative values shows that there is negative relationship among the dependent and independent variables and the positive values shows positive relationship.

Summary Statistics of Major League Sports

 The summary statistics of variables is given in the above table containing means, standard error, standard deviation, median, and mode etc. The above data shows that HR has highest mean value followed by error, SB, and payroll while the BA has lowest mean value. The summarystatics analysis used to represent the minimum and maximum values of the variables and it also can calculate the ranges along with the mean mode medianfor each variable individually. This tables is also represents the values of the Skewness andKurtosis that is commonly used to measure the relative size of the two tails and combined sizes of the two tails respectively. The probability of the two tails also can be measure by the analysis.The value of the leagues is the best match for the Kurtosis.

Regression Analysis of Major League Sports

Interpretations of Major League Sports

 The above given tables shows the regression analysis which is usually represent the effects of the independent variable on the dependent variables. In the coefficients table the value of the coefficient for all variablesconsidered for applying the equation.

Y= a+ bx

Y=a+ League x₁+ ERA x₂+ BA x₃+ HR x₄+ SB x₅+ Errors x₆+ Payroll x₇

Y=39.7199497+-0.061828574x₁+-16.80095049 x₂₊385.2606269x₃₊0.113088294x₄₊0.021301062x₅₊-0.097128388x₆₊0.010435676x₇

The above table is showing that there is postiche relationship among all of these variables except three variables as; Errors, League and ERA. Because these three variables have negative relationship with the wins.  It shows the winning power of the supports can be decrees by increasing the errors leagues and ERA. All the variables has significant values less than 0.05 but only league, ERA and payroll are not significant for the Wins.

In the table of the model summary the value of the adjusted R square is 0.824685691 which shows wins has 82% influence on the independents variables.

Conclusion Major League Sports

It is concluded that salaries is major key that enforce the person to continue to his jobs. The higher salaries are expected to be correlated to good players’ performance. OPS takes on base percentage plus slugging of players and normalize the number around whole league and accounts for ballparks kind of external factors. It has been concluded in this paper that there is the positive relationship between salary of player and their performance in major league sports and predicting the salary of baseball based on the performance as well in the field. By increasing the salaries the performance of the players will be enhance as well. It has been observed by applying the regression and correlation that there is the significant positive relationship among the salaries and good players’ performance.