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Analysis of Italian Serie A Players Salaries in Correlation to their Personal Performance

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Introduction

Analysis of Italian Serie A Players Salaries in Correlation to their Personal Performance Regression Analysis Tara Westfall December 6, 2009 TABLE OF CONTENTS 1. ABSTRACT 2. INTRODUCTION 3. BACKGROUND AND PURPOSE 4. DATA 5. EMPIRICAL RESULTS 6. CONCLUSION 7. REFERANCES 8. APPENDIX A - DATA 9. APPENDIX B - REGRESSION RESULTS AND TESTS RUN Analysis of Italian Serie A Players Salaries in Correlation to their Personal Performance Tara Westfall Longwood University tara.westfall@live.longwood.edu ABSTRACT Throughout history, men and women have competed against one another in sport and many have enjoyed watching these competitions for entertainment. In early history, most men competed for their lives. However, in modern times, men are paid large salaries to compete in competitions and sports. In the past fifty years, the salaries of athletes in every sport have risen to disproportionate amounts in comparison to the public per capita worldwide. The financial worth of these athletes exceeds the value that any one individuals actually worth. Athlete salaries in the sporting industry have grown extremely excessive and league officials need to control the outrageous payrolls by using salary caps, negotiations, and legal tactics. Such as the player's salaries of the NBA, which are determined under the regulations and requirements of the salary caps. However, salary caps are not present in every sport or league for example Italian Serie A. Therefore, what significant variables actually make up a player, in the Italian Serie A's, yearly salary? The purpose of this analysis is to analyze specific variable, of Italian Serie A's players, to help the general public understand player's yearly salaries. INTRODUCTION It is now widely recognized that the distribution of pay among athletes in not just a passive economic outcome, but an incentive to encourage performance that will positively impact revenue from fan sales. However, since salaries of athletes have grown drastically over the years, leagues are requiring salary caps to minimize outrageous salaries. ...read more.

Middle

And the third test was the Ramsey RESET test which is a general is a test that determines the likelihood of an omitted variable. The p value for the Ramsey test was 0.3924, concluding the regression does not have omitted variable. With the results from the three tests and the regression results it can be concluded that the field position striker, and the attendance of the club are significant. Therefore, each of the significant variables one unit increase will increase salary of players by the estimate coefficient amount holding all other variables constant. This regression suggest that if a player is a striker and the club has a high attendance record for the season, then that player will make more money than the other individuals on the team. Although running the two above regression gave some insight into what variables affect salary, I did find the result entirely satisfying. Therefore, I chose to run three more different regressions focusing on field positions. I did this by filtering out my data and running a regression on just defenders, midfielders and stickers. Inserted below is table 3 shows each regression, including coefficients and t-values with asterisks denoting levels of significance. TABLE 3 - REGRESSION RESULTS WITH POSITIONS SEPARATED Dependent Variable: Natural Log of Salaries Model A DEFENCE Model B MIDFIELD Model C STRIKER Explanatory Variables: NUMGST 0.001153 0.0514482 -.0106515 (0.09) (1.91)* (-0.14) NUMGSD .0904523 (0.70) NUMAST -.0406246 (-0.10) NUMFUL -0.000653 -0.0252633 (-0.08) (-1.88)* CLBATT 3.27E-06 1.80E-06 8.15E-06 (1.77)* (0.61) 0.97 PREGMW 0.6421211 0.9030625 (0.64) (0.58) AGE 0.0863807 0.1375364 .03412 (3.14)*** (2.69)*** 0.15 CONSTANT 9.395947 7.837642 11.43619 ADJ. R� 0.5964 0.3135 0.2700 *=t-Statistic significant at .10 level **=t-Statistic significant at .05 level ***=t-Statistic significant at .01 level Focusing on Model A, defense, there were only two significant variables in the regression. The club attendance had a ten percent significance level and the age had a one percent significance level therefore, both suggesting the estimated coefficients for these variable are reliable. ...read more.

Conclusion

estat vif Variable | VIF 1/VIF -------------+---------------------- numful | 3.26 0.306785 numgst | 3.11 0.321244 pregmw | 1.56 0.641090 clbatt | 1.55 0.645619 age | 1.08 0.926363 -------------+---------------------- Mean VIF | 2.11 . correlate Lnsal numgst numful clbatt pregmw age (obs=21) | Lnsal numgst numful clbatt pregmw age -------------+------------------------------------------------------ Lnsal | 1.0000 Numgst | 0.2113 1.0000 numful | 0.0258 0.8226 1.0000 clbatt | 0.2778 0.1626 0.2491 1.0000 pregmw | 0.2941 0.1736 0.2484 0.5791 1.0000 age | 0.5620 0.1140 0.1426 0.2196 0.2425 1.0000 . estat ovtest Ramsey RESET test using powers of the fitted values of Lnsal Ho: model has no omitted variables F(3, 12) = 0.12 Prob > F = 0.9466 Model C: . regress Lnsal numgst clbatt pregmw age Source | SS df MS Number of obs = 10 -------------+------------------------------ F( 4, 5) = 2.24 Model | 4.87241397 4 1.21810349 Prob > F = 0.2003 Residual | 2.72233457 5 .544466914 R-squared = 0.6416 -------------+------------------------------ Adj R-squared = 0.3548 Total | 7.59474854 9 .843860949 Root MSE = .73788 ------------------------------------------------------------------------------ Lnsal | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- Numgst | .0338424 .0262684 1.29 0.254 -.0336825 .1013674 clbatt | 9.55e-06 5.06e-06 1.89 0.118 -3.47e-06 .0000226 pregmw | .8372471 2.570203 0.33 0.758 -5.769669 7.444163 age | .1296185 .1432986 0.90 0.407 -.2387424 .4979793 _cons | 7.553542 4.770114 1.58 0.174 -4.708427 19.81551 ------------------------------------------------------------------------------ . estat vif Variable | VIF 1/VIF -------------+---------------------- clbatt | 2.22 0.451018 pregmw | 1.97 0.508396 age | 1.37 0.728427 numgst | 1.08 0.926009 -------------+---------------------- Mean VIF | 1.66 . correlate Lnsal numgst clbatt pregmw age (obs=10) | Lnsal numgst clbatt pregmw age -------------+--------------------------------------------- Lnsal | 1.0000 numgst | 0.2232 1.0000 clbatt | 0.6553 -0.1727 1.0000 pregmw | 0.5048 -0.2528 0.6219 1.0000 age | 0.0252 0.0897 -0.3899 0.0195 1.0000 . estat ovtest Ramsey RESET test using powers of the fitted values of Lnsal Ho: model has no omitted variables F(3, 2) = 2.81 Prob > F = 0.2737 ?? ?? ?? ?? ...read more.

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