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Estimating Baseball Salary Equations from 1961-2005: A look at Changes in Major League Compensation Jared Grobels Sports Finance 2/6/2014.

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Presentation on theme: "Estimating Baseball Salary Equations from 1961-2005: A look at Changes in Major League Compensation Jared Grobels Sports Finance 2/6/2014."— Presentation transcript:

1 Estimating Baseball Salary Equations from : A look at Changes in Major League Compensation Jared Grobels Sports Finance 2/6/2014

2 Summary Paper was written by Gary Stone and Louis J. Pantuosco and published in International Journal of Sport Finance, 2008, Estimates Player Salaries in Major League Baseball (MLB) over three time Periods Salary productivity elasticity's have increased over time for slugging average, player durability, and player consistency; this result indicates that over time owners have placed more value on these measures of player productivity. Teams located in more densely populated areas are better able to compensate their players, particularly their pitchers.

3 Introduction “The current system (of escalating player salaries and lack of revenue sharing) is a prescription for disaster. But whether the disaster is just around the corner or will take place 10 years from now, I don’t know” Steve Greenberg (former deputy commissioner of the MLB)

4 Average Salary $29, $597, $3,150, MLB paid double the average salary 2005 MLB paid 70 times the average salary

5 Collection of Data information gathered from questionnaires sent in 1974 to players from this time period gathered from issues of The Sporting News gathered from USA Today Online.

6 Key Factors Growth of the labor union has gone hand- in-hand with increased financial compensation for players for their on-field productivity. Agents and long-term contracts have led to increased financial stability for players. Hitters’ slugging averages and pitchers’ strikeout-to- walk ratios are positively correlated with salaries in the 1960s and in the early 2000s. Teams located in more densely populated areas are better able to compensate their players.

7 A Shift in Power

8 Time Period The period represents the last years in which the highly restrictive reserve clause was fully in effect. This clause permitted owners to renew a player’s contract year after year, thus preventing the player from moving to another team and receiving a higher salary. During this period multi-year contracts were unusual, and the player’s union was often ineffective in player- owner contract negotiations. Owners had significant monopolistic control over players.

9 Time Period In 1972 the MLB had its first strike in their History, which led to policy changes that dramatically impacted the sport – Salary Arbitration and Free Agency. When a player and owner cannot reach a contract agreement. Either party can file for arbitration. The arbitrator, an outside party, hears the case of both parties, and then chooses the salary figure of one side, with no compromise figure allowed. In 1975, Free Agency opened the gates to a more player- focused labor market. Players began to challenge the rights of owners to use the reserve clause to limit their mobility and earnings, and the restrictive clause.

10 Time Period Season-ending strike of 1994 derails the possibility of data consistency. MLB has seen increased reliance on player agents, escalation of television revenue, and increased player mobility. Through a series of labor negotiations and adjustments, the modern market for baseball players has evolved into a system with long-term contracts, a powerful union presence, and revenue sharing across teams, and highly compensated players.

11 Description of Model

12 Model Early literature in this area verified that salaries are based on players’ contributions to their teams’ success, revenue, and profitability (Scully, 1974; Medoff, 1976). The salary equations used in this paper related the salary earned by a particular player to selected measures of his career performance and the size of the market where he performs.

13 Selected Measures For hitters, the primary variable chosen to represent the player’s marginal productivity was his slugging average. Slugging average distinguishes between singles and extra base hits, making it a better indicator of the player’s value to the team than his batting average. Speed contributes both to a team’s offensive and defensive success. It is assumed that the faster players will attempt more stolen bases and have a higher success rate than slower players. Quality of Pitching is measured by the ratio of strikeouts to walk.

14 Selected Measures (Continued) The length of a player’s career also contributes positively to his marginal productivity and salary. A higher salary might be due to the popularity of players whose names are recognizable to fans regardless of the players’ recent output. Salary equations after regression results showed a consistently stronger relationship between a player’s salary and his career statistics than between his salary and his performance statistics of just the preceding season. The size of the metropolitan area in which a player performs is an important factor in the determination of a player’s value to his team

15 Salary Equation (1) LSALCPI (Hitter i) = α0 + α1 LSA + α2 LSBPG + α3 LABPCT + α4 LYRM + α5 LPOP + α6 NL + ε1 (2) LSALCPI (Pitcher i) = β0 + β1 LSW + β2 LIPPCT + β3 LYRM + β4 LPOP + β5 NL + ε2

16 Variable LSALCPI – annual salary of an individual player expressed in 1967 dollars LSA – Slugging Average LSBPG – Stolen Bases per Game LSW – Strikeout-to-walk Ratio LABPCT – Career % of a hitters teams official at bats LIPPCT – Pitchers teams innings pitched IPPCT – Pitchers Career innings / (years played * # of games played * 9 innings LRYM – Years appeared in MLB LPOP – the Log of the population of the SMSA the team is located in NL – Dummy Variable to distinguish if team is in the National League SMSA – The population of the standard metropolitan statistical area (1) LSALCPI (Hitter i) = α0 + α1 LSA + α2 LSBPG + α3 LABPCT + α4 LYRM + α5 LPOP + α6 NL + ε1 (2) LSALCPI (Pitcher i) = β0 + β1 LSW + β2 LIPPCT + β3 LYRM + β4 LPOP + β5 NL + ε2

17 Description of Data Sets

18 Data Set Statistics The salary data sets for each of the three periods include pitchers who had appeared in at least eight major league games the preceding season, and hitters who had played in at least 15 games the previous season. In order to obtain a more representative salary sample for the period, in 1974 one of the authors distributed a questionnaire mailed to about 1,100 players who were active in the majors during the period.

19 Empirical Results

20 The slugging average salary elasticity is significant in all three periods, and has increased nine-fold from 1961 to 2005 from to Stolen Bases was only significantly correlated to salary in This could be related to artificial turf fields. In the era, the NL dummy variable had a negative salary elasticity for hitters. This result indicates that the salary of a given quality hitter was higher in the American League than it was in the National League.

21 The experience variable, years in the major leagues, also was positively and significantly correlated with a hitter’s salary in each of the three sample periods. There was a significant, positive relationship between hitters’ salaries and population in the and periods. Not so much during the middle period due to Free Agency and arbitration forcing smaller teams to compete against the larger teams and failing. Franchises located in the larger cities have an advantage over franchises located in smaller markets by offering higher salaries to players.

22 Consistent with the pooled regression estimates, the fixed panel results for hitters indicate a positive and significant relationship between years in the majors, slugging average, and at-bat percentage. The positively significant sign for stolen bases per game suggests that when player and time variations are controlled, speed does matter to team owners. The population variable is insignificant. This result supports the earlier assumption that either large market players receive longer contracts or high profile players are more likely to work in larger markets

23 To put the results into perspective, Mickey Mantle, the New York Yankees’ All-Star, earned $90,000 in If salaries were estimated in the 1960s using the productivity elasticity's of the modern era, Mantle would have been paid $1.4 million in 1961, and $10.5 million in Although these salaries tower over the one received by Mantle in 1961, they are dwarfed by Derek Jeter’s current Yankee salary of over $20 million a year.

24 All three of the productivity variables for pitchers had statistically significant positive impacts on salaries in each period. In the modern era, the salary elasticity of the strikeout- to-walk ratio (+0.693) is much higher than in the two previous periods. It is interesting to note that the NL variable had opposite effects on the salaries of pitchers and hitters for the period Due to the NL rejecting the designated hitter ruler. Years of experience also were positively correlated with pitcher salaries. Population was a significant contributor to pitchers’ salaries in the modern period but not in the two earlier ones.

25 In the modern era, the increase in information and analysis of productivity data available to owners, agents, and players has provided a more efficient market for negotiations. Small market owners often gave up the rights to a player because they knew his market value was beyond the financial means of the club. The players’ union contributed to this process by negotiating terms that benefited all players, such as higher minimum salaries, revenue sharing among teams, and player contract amendments.

26 Conclusion Model reveals an increase in the productivity elasticity’s in the hitters and pitchers equations over time. The lack of competition among teams during period kept the salaries of players well below their marginal revenue products. During the early years of free agency and arbitration, the link between marginal revenue product and player salaries weakened. In the modern era, the flow of information between players and their agents and team owners improved. The increase in salaries over time can be explained by increased competition for players, an increasingly powerful players’ union, and a growing fan base. As the players’ union continues to chip away at the owner’s revenue fortress, the players will continue to obtain a higher share of the profits

27 Thoughts on the Paper I found the article to be very fascinating and a good read. I thought it was interesting how the authors had to take different time periods ranging over 40 years in order to accurately derive salaries of MLB Players. I also liked the point they made how astro turf had led to speed being a significant variable towards the salary of a player when it really was not.

28 Q & A


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