Pay for Performance in the NFL Essay Example

Pay for Performance in the NFL Essay Example Pay for Performance in the NFL Paper Pay for Performance in the NFL Paper Statistics Project Pay for Performance in the NFL Introduction Pay for performance is a common theme throughout almost all organizations. Merit increases, performance bonuses for executives, and commissions for real estate salespeople are common examples of this concept. Even teachers’ pay in some states is linked to performance of their students. According to the Washington Post, the state of Florida instituted a policy that individual teacher’s raises and performance starting in 2007 will be tied directly to student’s scores on standardized tests. This pay for performance concept has generally been accepted by the new Obama administration and may make its way into more common usage across the United States. In corporate America, examples of pay for performance are quite common, especially for top executives. Most year end bonuses are based on individuals meeting certain criteria established by the board of directors. These bonuses can be quite substantial. According to the Proxy Statement for Meredith Corporation, the total executive bonuses for the year 2007 exceeded $2. 5 million dollars. While pay for performance seems a reasonable concept in general, it is not without its critics. In education, there are a number of critics that question the fairness of the standardized test score results as a measure of teacher performance. They worry about teaching towards the exam at the expense of the overall education of the student. The criticism from Congress and much of the population of the United States over the bonuses paid to AIG executives questions how performance is actually measured. This paper will attempt to partially address the issue of pay for performance in professional sport, specifically in the National Football League. Many different positions in football are difficult to obtain good performance measures. Offensive lineman, special teams players especially do not have good measures of individual performance that are tracked. This analysis will focuses on two groups of NFL players, quarterbacks and running backs where individual performance measures are readily available. Analytical Technique A correlation study will be done on a variety of performance measures and the salaries of both NFL quarterbacks and running backs to see which of the individual performance measures are most closely related to the individuals salaries. The assumption will be that the current salary is based on last year’s performance. In addition to the correlation study, a multiple regression model with the best performance measures will be used to explain the relationship between the measures and salaries. This could be potentially used as a basis of predicting next year’s salary for those players that are in contract discussions or are entering the market as free agents. The data for the study will be obtained from two primary sources, ESPN. com which tracks player performance measures for a number of years, and USATODAY. com for player salaries. Professional football players are compensated in a number of ways, base salary, signing bonus, and other bonuses. This study will be using base salary as the pay in the pay for performance analysis. Performance measures for quarterbacks will include: completion percentage, total passing yardage, touchdown completions, interceptions, and finally QB rating. Performance measures for running backs will include: total yards, yards per game, touchdowns, and fumbles lost. While other measures are collected it is felt that these are the most appropriate performance measures to use for both categories of NFL players. A sample of 22 NFL quarterbacks from the 2007 season was selected while a sample of 13 NFL running backs from 2007 was used. RESULTS NFL quarterbacks: Pearson’s correlation coefficients for all variables in the study were run and are presented in the table below: |   |PCT |YDS |TD |INT |RAT |Salary | |PCT |1 | | | | | | |YDS |0. 43677 |1 | | | | | |TD |0. 230412 |0. 843951 |1 | | | | |INT |-0. 31751 |0. 475031 |0. 247018 |1 | | | |RAT |0. 639073 |0. 45897 |0. 703364 |-0. 41675 |1 | | |2008 Salary |0. 211532 |0. 562896 |0. 428047 |0. 276031 |0. 265671 |1 | As can be seen in the above table the strongest correlation exists between salary and total yards passing (0. 562896) and the number of touchdowns (0. 428047). The other variables have very weak relationships between themselves and salary and will be excluded from further analysis. It seems that only total passing yards is an important variable in understanding the relationship between quarterback’s salary and on field performance. A second part of the study is to use a regression model to predict the next periods salary for free agents and other players whose contracts are up for negotiation. It could be a valuable tool in beginning negotiations between the player and team owner. Since only two variables had anything more than a very weak relationship with salary, two regressions will be run. The first is a simple linear regression with yards passing as the independent variable and the second is a multiple regression with number of touchdowns included. The regression analysis is presented below: Simple linear regression using yards: |Regression Statistics | | | | |Multiple R |0. 62896387 | | | | |R Square |0. 316852343 | | | | |Observations |22 | | | | | | | | | | |   |Coefficients |Standard Error |t Stat |P-value | |Intercept |-1267325. 07 |1976273. 783 |-0. 64127 |0. 528628 | |YDS |1839. 467659 |603. 9569583 |3. 045693 |0. 006383 | Multiple regression using yards and touchdowns: |Regression Statistics | | | | |Multiple R |0. 569677436 | | | | |R Square |0. 24532381 | | | | |Observations |22 | | | | | | | | | | |   |Coefficients |Standard Error |t Stat |P-value | |In tercept |-1596606. 7 |2137031. 816 |-0. 747114146 |0. 464141 | |YDS |2290. 32518 |1148. 639878 |1. 993690591 |0. 060741 | |TD |-50963. 9896 |109649. 6343 |0. 464789417 |0. 647365 | The multiple regression will be excluded from use because the sign of the coefficient is negative, implying that the more touchdowns thrown the lower the salary. This is not logical. The most likely cause is that relationship between total yards passing and touchdowns is stronger than the correlation between touchdowns and salary. This could cause the regression coefficient for touchdowns to be unreliable. The regression equation provides only marginal explanatory power, based on the R square this equation using total yards only explains 31. 68% of salary for an NFL quarterback leaving over 68% of salary unexplained. It usefulness as a tool in negotiation would seem to be very limited. NFL running backs: Pearson’s correlation coefficients for all variables in the study were run and are presented in the table below    |YDS |AVG |TD |FUM |Salary | |YDS |1 | | | | | |AVG |0. 196119 |1 | | | | |TD |0. 382323 |0. 466749 |1 | | | |FUM |0. 017765 |0. 069592 |-0. 31995 |1 | | |Salary |0. 571773 |0. 260196 |0. 38083 |-0. 05109 |1 | Only the total yards gained seem to have anything but a weak relationship with salary. The number of touchdowns being somewhat explanatory of salary and will be used in the multiple regression. Since only two variables had anything more than a very weak relationship with salary, two regressions will be run. The first is a simple linear regression with yards rushing as the independent variable and the second is a multiple regression with number of touchdowns included as well. The regression analysis is presented below: Simple linear regression using yards: Regression Statistics | | | | |Multiple R |0. 57177269 | | | | |R Square |0. 326924009 | | | | | | | | | | |   |Coefficients |Standard Error |t Stat |P-value | |Intercept |-1273523. 69 |1812128. 448 |-0. 702777759 |0. 496798 | |YDS |3659. 184626 |1583. 057254 |2. 311467016 |0. 041192 | Multiple regression using yards and touchdowns: |Regression Statistics | | | | |Multiple R |0. 598119739 | | | | |R Square |0. 57747222 | | | | |Ob servations |13 | | | | | | | | | | |   |Coefficients |Standard Error |t Stat |P-value | |Intercept |-1191870. 48 |1860286. 025 |-0. 64069 |0. 536128 | |YDS |3194. 299878 |1755. 207634 |1. 819899 |0. 098793 | |TD |64585. 6109 |93229. 10033 |0. 692765 |0. 504227 | The multiple regression will be used since it is marginally better in explanatory power than the simple regression model The regression equation provides only marginal explanatory power, based on the R square this equation using total yards only explains 35. 77% of salary for an NFL quarterback leaving over 64% of salary unexplained. It usefulness as a tool in negotiation would seem to be very limited. Conclusion While there seems to be a relationship between player salaries and total yardage for both quarterbacks and running backs, the relationship is not very strong. The use of individual statistics does not seem to explain the greatest proportion of player salaries. It does not seem as if trying to use individual performance measures provides much important information on the value of the player to the team as measured by salary. This could be due to a number of issues. Possibly base salary is not the appropriate measure for player compensation. Maybe the owners look at improvement in individual performance measures over time or the average of the performance measures over time. We also need to consider that qualitative factors play a role in player salaries. It could be the so called â€Å"star power† of the player as an entertainment value. Or maybe the owners do not look at the individual statistics but rather the ability of the player to improve overall team performance. Is the owner actually looking at numbers put up by the player or is the owner estimating how many more games can we win by having this player? Sample Data Quarteracks |NAME |PCT |YDS |TD |INT |RAT |salary | |Tom Brady QB, NWE |68. 9 |4806 |50 |8 |117. | |Tomlinson RB, SDG |1474 |4. 7 |15 |0 |$5,750,000 | | Peterson RB, MIN |1341 |5. 6 |12 |4 |$2,821,320 | |Willie Parker RB, PIT |1316 |4. 1 |2 |4 |$2,900,000 | |Jamal Lewis RB, CLE |1304 |4. 4 |9 |4 |$1,400,000 | |E. James RB, ARI |1222 |3. 8 |7 |4 |$5,000,000 | |Fred Taylor RB, JAC |1202 |5. 4 |5 |2 |$4,000,000 | |Thomas Jones RB, NYJ |1119 |3. |1 |2 |$2,000,000 | |M. Lynch RB, BUF |1115 |4 |7 |1 |$2,635,770 | |Frank Gore RB, SFO |1102 |4. 2 |5 |3 |$2,562,000 | |E. Graham RB, TAM |898 |4 |10 |0 |$1,500,000 | |D. Foster RB, CAR |876 |3. 5 |3 |5 |$1,903,120 | |C. Taylor RB, MIN |844 |5. 4 |7 |5 |$3,000,000 | |L. Maroney RB, NWE |835 |4. 5 |6 |0 |$1,571,720 |