NHL SALARY DETERMINATION AND DISTRIBUTION A THESIS Presented to The Faculty of the Department of Economics and Business The Colorado College In Partial Fulfillment of the Requirements for the Degree Bachelor of Arts By Ian Young February 2015
NHL Salary Determination and Distribution Ian Young February 2015 Economics Abstract There have been many debates and studies on how to efficiently compensate players and build winning teams in professional sports. The National Hockey League instituted a salary cap following the 2003-2004 season that changed the way general managers could pay players both individually and as a team. The study uses player data from the 2009-2010 season and team data from the 2007-2008 season to the 2013-2014 season. The study found that the largest determinants of forwards salaries are points per game and average time on ice. The largest salary determinants for defensemen are points per game, average time on ice, and fights per season. The Gini coefficient did not turn out to be a significant predictor of team winning percentage, while power play and penalty kill percentage did. KEYWORDS: (National Hockey League, Salary, Gini Coefficient) JEL CODES: (L83, D31)
ON MY HONOR, I HAVE NEITHER GIVEN NOR RECEIVED UNAUTHORIZED AID ON THIS THESIS Signature
ACKOWLEDGEMENTS I would like to thank all of my economics professors, my thesis advisor Rich Fullerton, and my family for helping me get to this point in my academic career.
TABLE OF CONTENTS ABSTRACT ii ACKOWLEDGEMENTS... iv 1 INTRODUCTION... 1 2 LITERATURE REVIEW... 7 3 CONCEPTUAL FRAMEWORK 10 4 RESULTS 13 5 CONCLUSION 18 REFRENCES... 19
INTRODUCTION Every sports franchise typically has two distinct goals: to win a championship and to earn a profit. The obvious method to accomplish this is to collect the most desirable players possible in order to field the most successful and exciting team. Unfortunately, this is much more complicated than it sounds. In professional sports there have long been debates of how much money can be spent and how much can be paid to individual players. Hockey involves many different statistics that can determine how much a player is paid; from points to fights, every player contributes in a different way and seeks to be paid accordingly. There is also a question of income distribution; a large distribution, compared to an even distribution, could potentially affect the success of a team. Spending too much money on a few individual players can result in a lack of depth, while spending equally on all players may also result in a poor team. Most of these decisions are left in the hands of the general manager. General managers must find a balance between paying players the appropriate salaries and fielding a winning team. In recent years these economic issues have become more debated and discussed. Books like Moneyball, by Michael Lewis, have attempted to statistically determine how to win the most games while spending the least amount of money in baseball. There are debates as to whether or not sports should be analyzed statistically and economically rather than by the traditional method of scouting and determining talent based on instinct. There have not yet been techniques similar to those described in Moneyball introduced in hockey. These issues are important to general managers building teams, players and agents negotiating contracts, fantasy league fanatics building internet teams, and even fans just wanting to see their favorite team succeed. I believe that player salaries are primarily based on 1
player point production, plus/minus, ice time, and experience and that teams with a more even income distribution will be more successful. A National Hockey League (NHL) team is comprised of 23 players that play different roles for the team. Obviously, like other sports, hockey contains different positions that play different roles on a team: forwards, defensemen, and goaltenders. Within each of these positions are unique roles that players must fill. There are forwards that are relied upon to score goals and make plays, while other forwards must play defensively, block shots, and provide physical play. There are defensemen that are expected to play an aggressive and offensive style, while other defensemen must play a reserved game and must prevent the other team from creating offense. There are goaltenders whose sole role is to keep pucks out of the net. It is crucial to build a diverse team with players that can fill all of the different roles needed. If a team contains too many offensive players, it will suffer defensively; if a team has too many defensive players, it will not be able to produce enough offense. A hockey team also needs to be diverse with respect to size and physical toughness. Hockey is a rough, physical game that requires a substantial amount of grit to play. Offensive players are often quicker, more skilled, and smaller than other players. A team needs enough large and physical players, that may not be as skilled, to protect the smaller players thereby preventing injuries. Without different players to fill various roles, teams can become lopsided in certain categories and suffer. General managers must not only build a team with all the essential roles filled but must also decide how much to play the players in those different roles. They must decide which players are more superior in their respective roles, which players are able to fill multiple roles, and for how long the players will be productive. 2
After the 2003-2004 NHL season, the league s Collective Bargaining Agreement (CBA) expired; the league entered a lockout as the National Hockey League Players Association (NHLPA) and the owners underwent a dispute. The 2004-2005 season was eventually completely cancelled. The NHL and owners claimed that salaries were rising faster than team revenues were, making it more difficult for teams to earn profits. It was also difficult for teams in smaller markets to compete with larger-market teams for higher-end players because of financial limits. Though the NHLPA was in disagreement, a salary cap was eventually agreed upon. The only salary cap that had ever previously been instituted was during the Great Depression when teams were undergoing financial troubles. The cap then was set at $62,500 per team with maximum of $7,000 per player. The increase in player salaries and team revenues since then is amazing. The new cap was a hard cap with an upper and lower limit that could change from year to year based on team revenues. The team cap for the 2005-2006 season was set at $39 million for the upper limit and $21.5 million for the lower limit with a player maximum of $7.8 million. The lower limit was originally 20% below the salary cap but changed to a fixed $16 million below the cap in the 2006-2007 season. The individual player cap was set at 20% of the team salary cap. This new cap was slightly lower than the average team payroll of $44.4 million in 2003-2004. After the 2011-2012 season the CBA expired and another dispute began. Half of the 2012-2013 season was cancelled before a new CBA and lowered salary cap was agreed upon. Since the 2005-2006 season team revenues have increased along with the salary cap substantially increasing to a current cap of $69 million for the 2014-2015 season (nhl.com, 2014). 3
TABLE 1.1 NHL Salary Cap Changes Year Salary Cap (millions) Player Cap (millions) 2005-2006 39 7.8 2006-2007 44 8.8 2007-2008 50.3 10.06 2008-2009 56.7 11.34 2009-2010 56.8 11.36 2010-2011 59.4 11.88 2011-2012 64.3 12.86 2012-2013 60* 12* 2013-2014 64.3 12.86 2014-2015 69 13.8 *=Prorated for shortened season Source: Capgeek.com The salary cap changed the game for general managers. General managers were typically given a payroll range from the owner for the amount of money they could spend to compile a roster. The hard cap restricts the general managers to field a team with a payroll below a specific figure. This creates the dilemma of how to construct a team of appropriately paid players for each necessary role. General managers must decide how much a player is worth based on what that player can bring to the team. There are many factors that can determine a player s contract: how long he has played, how many points he has, where he previously played, how old he is, what round he was drafted in, and many other factors. Income distribution is also a factor. Many teams attempt to sign and draft big name players that are expected to put up a large number of points. This is not only to increase team success but also to draw fan interest. Fans typically will not want to come watch a team play that consists of a variety of average players. The Pittsburg 4
Penguins have players like Sidney Crosby and Evgeni Malkin that consistently finish in the top ten of league scorers. Both Pittsburg and the cities the Penguins visit sellout their buildings with fans that want to see those two exciting players. Less excitement means fewer fans, but if a team does have one or more high-profile players signed, it must make sure it has enough supporting players. Superstars cannot play the entire game, so it is necessary for teams to have depth in their lineup; without depth, teams have failed miserably in the standings and will continue to do so. There is also the possibility of injuries. Should a star player that a team relies on get hurt, the team will need other capable players to step up and fill the void. A team must find the right balance of players to draw fans and to be successful. How even that balance is becomes the question. Should a team pay a few superstars the majority of the payroll or keep it evenly distributed amongst all the players? As stated earlier, I believe that after determining the proper salaries for players and filling all the required roles, a more balanced payroll will result in a more successful team in the NHL. Although the salary cap may have a limiting effect on all players contracts, there may be certain players that are affected more than others. For instance, high-scoring forwards are typically the highest-paid players in the league. A recent example is when the Chicago Blackhawks re-signed their top two forwards, Patrick Kane and Jonathan Toews, to matching eight-year, $84 million contract extensions. The players will each provide an annual cap hit of $10.5 million per season. That is over 30% of the Blackhawks payroll for the next eight years for only two players (nhl.com, 2014). Kane and Toews are both well-established players with two Stanley Cups each; so many believe that these contracts are justifiable. The Blackhawks were very successful with 5
these two players but were paying the two almost half of what they are now going to be paid. Some general managers and analysts may argue this and claim that now the Blackhawks will not be able to provide a supporting cast for these two. I will be analyzing issues like this to determine the most appropriate and efficient method of salary distribution. This study will examine the determinants of player salaries for both forwards and defensemen in the NHL using variables such as points, games played, hits, birthplace, draft round, and other variables. Subsequently I will analyze the salary distributions throughout teams and between positions related to how successful the teams are. This is an important factor because as stated beforehand, every fan, general manager, coach, player, and owner wants his team to win. I want to delve more in depth regarding why teams are successful and how other teams can model themselves properly to reciprocate this success. I will find the appropriate determinants of how players are paid and how to properly construct a winning payroll and team. 6
LITERATURE REVIEW The purpose of this study is to find the major determinants of player salaries as well as the proper salary structure and distribution of a team. If players are played as efficiently as possible and the team is comprised of the proper salary structure, then it is more likely to be a winning team. Many players around the NHL are perceived as overpaid or not playing up to their expected potential. Many teams are also viewed as having highly-paid superstars without the depth to support them. Claude Vincent and Byron Eastman examine the determinants of player salaries in the NHL in their thesis, Determinants of Pay in the NHL. They perform quantile regressions for forwards and defensemen using player salary as the independent variable. The dependent variables examined are: career points per game, career games played, allstar appearances, draft round, height and weight, previous league, career penalty minutes per game, career plus/minus, and team revenue. The results for forwards and defensemen were very similar. Their results found that career points per game, career games played, draft round, and all-star appearances all had significant positive effects on salary. Their results also indicated that while all other variables were insignificant, weight had a negative effect on salary. Their study was very interesting and produced significant results, but I found some of the variables and results to be very predictable. I would like to test other variables that may produce results that aren t quite as expected. I will add variables such as shot (right or left), average time on ice per game, and fights per season. I will also collect the average annual salaries along with player statistics from their season prior to signing their contract rather than their current and career statistics. 7
Jonathan Fullard examines the second aspect of my study in his thesis paper, Investigating Player Salaries and Performance in the National Hockey League. Fullard investigates how salary structure affects team success; he discusses what is known as the salary theory. The salary theory states that there are two types of salary structures, hierarchal and compressed. Hierarchical pay structures are highly dispersed; a small number of individuals earn a significant amount of money, while the other employees make a fraction of that amount. This type of pay structure is often used to create a competitive work environment to reward success. Conversely, a compressed salary structure is condensed; all employees earn similar wages. The rationale for using a compressed pay structure is the promotion of cohesion among employees. (Fullard, 2012). In both cases, individuals are aware of their salaries in comparison to others and are motivated to succeed in the organization. Fullard also describes that these structures can be determined using a Gini coefficient. A Gini coefficient is a measure of the distribution of a set of numbers; in this case it measures salaries. The Gini coefficient is a number between one and zero. The closer to one the number is, the more hierarchal the organization is. The closer to zero the number is, the more compressed the organization is. Fullard also describes what is known as the tournament theory. This theory explains that hierarchal organizations are more successful because the players are more motivated to increase their salaries with harder work and increased performance. Fullard performed a regression of the Gini coefficient against winning percentage. He used statistics over the course of six NHL seasons. Fullard found that there was a very weak, linear relationship between the Gini coefficient and winning percentage - the higher the Gini coefficient, the higher the winning percentage. I would like to make the regression more 8
complex by adding in variables that measure team performance. I will add power play percentage, penalty kill percentage, and save percentage, all of which will provide a better comparison of how efficient teams are both offensively and defensively. In my model I will examine unstudied variables to find if any other variables affect player salary. I will also determine what type of salary structure is the most efficient and how a winning team can be built. These previous studies will enable me to construct an efficient model and determine the proper ways to compensate players and construct a successful NHL team. 9
CONCEPTUAL FRAMEWORK My model consists of two parts to properly determine how to pay players in the NHL and how to properly construct a successful team based on salary structure. The first part tests different variables of individual statistics against salary data for each player in the NHL. This will hopefully allow me to determine how to optimally pay players. The second part of my model tests team Gini coefficients and performance measures against winning percentage. I collected data from different sources on the Internet. The NHL player statistics were collected from nhl.com and dropyourgloves.com, while the salary data and information was collected from capgeek.com. The NHL team statistics were also collected from nhl.com. In order to analyze my data I used Ordinary Least Squares (OLS) Regressions. The first part of my model will establish the salary determinants of both forwards and defensemen in the NHL. I collected the average annual salary value for all 360 forwards and all 180 defensemen in the NHL during the 2009-2010 season. Subsequently I researched each player and collected their individual data and statistics from the season prior to signing that contract. I chose to collect statistics that I felt might have a strong correlation with how much each player was paid. The independent variables chosen were: points per game, plus/minus (difference between goals scored for and scored against when a player is on the ice), career games played, shot (left or right), draft round, fights per season, and average time on ice per game. These variables help define how skilled, tough, and experienced the players are. I will also be able to see how the variables affect salary differently between positions. The regression equation for forwards and defensemen will be: 10
Y = A + β1ppg(points per game) + β2plus/minus + β3careergamesplayed + β4shot + β5draftround + β6fights + β7avgtimeonice Here are summary statistics collected from 360 forwards and 180 defensemen during the 2009-2010 NHL season: TABLE 3.1 Forward Summary Statistics Variable Observations Mean Std. Dev. Min Max Salary (millions) 360 2.500801 1.931741 0.487 9.538 Points Per Game 360 0.5507639 0.2689017 0.07 1.51 Plus/Minus 360 0.6722222 11.1402-35 47 Draft Round 360 3.477778 2.910849 1 11 Shot 360 0.6138889 0.4875342 0 1 Career Games Played 360 359.9417 292.1518 1 1472 Fights 360 1.783333 4.20409 0 36 Average Time on Ice 360 15.93333 3.069483 7 22 Source: Author s Calculations TABLE 3.2 Defensemen Summary Statistics Variable Observations Mean Std. Dev. Min Max Salary (Millions) 180 2.643661 1.825976 0.483 7.5 Points Per Game 180 0.3439167 0.1710706 0.02 1.01 Plus/Minus 180 1.611111 11.34004-20 50 Draft Round 180 4.15 3.047987 1 10 Shot (Left/Right) 180 0.7 0.4595358 0 1 Career Games Played 180 345.7722 280.6073 5 1330 Fights 180 1.338889 2.106558 0 11 Average Time on Ice 180 20.45 3.440272 7 27 Source: Authors Calculations The second part of the model compares team salary structure and performance measures with team success. I collected data from NHL teams over seven seasons from 2007-2008 to 2013-2014. For each team in each season I determined: regular season 11
winning percentage, Gini coefficient, power play percentage, penalty kill percentage, and save percentage. The regression will be tested with winning percentage on the left side as the dependent variable and the others as independent variables. The Gini coefficient will be calculated from the salaries of the top twelve forwards, top six defensemen, and top two goalies from each team. As explained earlier, the Gini coefficient is a measure of a team s salary distribution. This statistic will be compared to winning percentage in order to find the optimal salary distribution for a team. I also added power play, penalty kill, and save percentage as variables to act as measures of team performance. Power play percentage is a measure of a team s offensive ability while penalty kill and save percentage are measures of a team s defensive ability. The regression equation will be: Y = A + β1ginicoefficient + β2pp% + β3pk% + β4save% Here are summary statistics from 30 NHL teams from the 2007-2008 to 2013-2014 seasons: TABLE 3.3 Team Performance Summary Statistics Variable Observations Mean Std. Dev. Min Max Winning % 210 0.559281 0.0814701 0.317 0.802 Gini Coefficient 210 0.3350524 0.0437479 0.224 0.471 Power Play % 210 18.06286 2.744643 10 26.8 Penalty Kill % 210 81.9319 2.907387 74.2 89.6 Save % 210 5.166324 61.69816 0.885 895 Source: Author s Calculations 12
follows: TABLE 4.1 RESULTS The results of the regression of 360 forwards in the NHL in 2009-2010 are as Forward Regression Results Salary (millions) Coefficient Standard Error t P>t Points Per Game 3.358199 0.4753941 7.06 0 Plus/Minus -0.0058394 0.0069678-0.84 0.403 Draft Round -0.0249528 0.0239416-1.04 0.298 Shot (Left/Right) 0.2236938 0.136992 1.63 0.103 Career Games Played 0.0013768 0.0002334 5.9 0 Fights 0.0291441 0.016682 1.75 0.082 Average Time on Ice 0.1766346 0.0383169 4.61 0 _cons -2.757315 0.4812771-5.73 0 Source: Author s Calculations As expected, points per game was strongly correlated with salary; each additional point per game creates a $3.35 million salary increase. It makes sense that the more goals and assists a player can contribute, the more valuable he is to a team. I was surprised to see that plus/minus and draft round were not nearly as significant as I thought they would be. Plus/minus is typically known as a measure of how responsible a player is on the ice, while draft round can represent a player s initial talent. Career games played was significant but only caused a slight increase in salary. This may not have as great an effect as expected, because as a player becomes older and approaches the end of his career his production may drop off. Average time on ice was a significant positive effect on salary; this is expected because the more a player plays during each game, the more valuable he can be. One more minute played per game causes an increase of almost $200,000 in salary. Which way a player shoots and number of fights per season both had 13
positive effects on salary but neither one was statistically significant. Fights may not be a determining factor for forwards because many forwards are smaller and faster and are relied on more for their prowess than toughness. Below is the correlation matrix for the forwards: TABLE 4.2 Forward Correlation Matrix Salary Salary (millions) 1 Points Per Gm Points Per Game 0.7097 1 Plus/ Minus Plus/Minus 0.2419 0.4633 1 Draft Round Draft Round -0.2723-0.313-0.1133 1 Shot Career Gms Played Shot (Left/Right) 0.024-0.0737-0.0623-0.0875 1 Career Games Played 0.3718 0.1993-0.0127-0.0924 0.0721 1 Fights Avg Time on Ice Fights -0.1786-0.2547-0.0203 0.1248-0.0735-0.0992 1 Average Time on Ice 0.6849 0.8014 0.2217-0.2705-0.0489 0.2449-0.3353 1 Source: Author s Calculations It is clear that there is a decent correlation between points per game and plus/minus. I was surprised to see that plus/minus was insignificant in the results. The amount of points a player scores has a large impact on their plus/minus, which could explain why there is such a strong correlation. 14
Below is the regression of 180 defensemen in the NHL in 2009-2010: TABLE 4.3 Defensemen Regression Results Variable Coefficient Standard Error t P>t Points Per Game 2.74519 0.685673 4 0 Plus/Minus -0.0060084 0.0078977-0.76 0.448 Draft Round -0.0723275 0.02983-2.42 0.016 Shot (Left/Right) 0.2642165 0.1976675 1.34 0.183 Career Games Played 0.003222 0.0003362 9.58 0 Fights 0.1001455 0.0447058 2.24 0.026 Average Time on Ice 0.1267529 0.0353235 3.59 0 _cons -2.015824 0.6618103-3.05 0.003 Source: Author s Calculations The results shown in the defensemen regression were very similar to those of the forward regression. Points per game again was very significant. A unit increase in points per game created a $2.75 million increase in salary. Points per game was less significant for defensemen because defensemen are obviously relied on less for offensive contributions than forwards are. Average time on ice was significant and had a larger impact on salary than forwards. Playing one more minute per game caused a $130,000 increase in salary. Fights did not prove to be a significant determinant. Draft round and career games played were also significant but had very small positive effects on salary. It was surprising to see that plus/minus was also insignificant for defensemen. This may be a result of many teams matching their best defensemen against the opposing team s most skilled forwards. Shot was also insignificant. Shooting right or left will not have an effect on a defenseman s salary. 15
Below is the correlation matrix for defensemen: TABLE 4.4 Defensemen Correlation Matrix Salary Salary (Millions) 1 Points Per Gm Points Per Game 0.4731 1 Plus/Minus 0.0421 0.1718 1 Plus/ Minus Draft Shot Draft Round -0.2573-0.0882 0.0216 1 Career Gms Played Shot (Left/Right) 0.1208-0.056 0.0697-0.0993 1 Career Games Played 0.6402 0.2027 0.0383-0.093 0.1099 1 Fights Avg Time on Ice Fights -0.0739-0.2916 0.0081-0.0767-0.1425-0.105 1 Average Time on Ice 0.5813 0.6225 0.0556-0.2159 0.0894 0.3684-0.2594 1 Source: Author s Calculations There are no variables that are too strongly correlated and led to insignificant results. Similar to the forwards, points per game is slightly correlated with plus/minus. This could again explain why plus/minus appeared to be insignificant in the results. Below is the winning percentage regression of all 30 NHL teams from 2007-2008 to 2013-2014 seasons: TABLE 4.5 Team Performance Regression Results Variable Coefficient Standard Error t P>t Gini Coefficient 0.0605834 0.1047344 0.58 0.564 Power Play % 0.0126752 0.001662 7.63 0 Penalty Kill % 0.0121043 0.0015669 7.72 0 Save % 0.0000245 0.0000739 0.33 0.74 _cons -0.6818257 0.1385517-4.92 0 Source: Author s Calculations It seems that the Gini coefficient for a team s salaries is not a significant predictor of team winning percentage when other team performance variables are included. This is 16
surprising to me because I hypothesized that a team s salary structure could be a major predictor. I thought that a hierarchal or compressed structure could either help or hurt a team. The coefficient was.06 so it could be argued that spending highly on one superstar could be worthwhile for the team; otherwise, Gini coefficient should not be considered a major aspect of building a team. Both power play and penalty kill percentages had significant effects on winning percentage; a unit increase in either caused a.01% increase in winning percentage. Save percentage was not significant; this could be a result of teams sacrificing defense to be stronger offensively or vice versa. Here is the correlation matrix for winning percentage: TABLE 4.6 Team Performance Correlation Matrix Winning % Gini Coefficient Power Play % Penalty Kill % Save % Winning % 1 Gini Coefficent 0.0459 1 Power Play % 0.4208 0.0975 1 Penalty Kill % 0.4208-0.0685-0.023 1 Save % 0.0535 0.0712 0.0287 0.047 1 Source: Author s Calculations As expected, winning percentage appears to be strongly correlated with both power play and penalty kill percentage; otherwise, none of the variables are correlated to the point of potentially affecting the results. 17
CONCLUSION After completing my data and running my regressions I believe that I have discovered some important influences on an NHL player s salary. Forwards salaries are typically based strongly on points per game and average ice time. Forwards are also paid based on career games played with more experienced players making more money. The same factors were important for defensemen; however, points per game had less of an impact. Average time on ice was a larger salary determinant than for defensemen than for forwards. Teams carry fewer defensemen than forwards, and the defensemen are typically expected to play more during games. General managers consider all of these factors when contemplating how much money to sign a specific player for. Gini coefficient turned out not to be a significant determinant of winning percentage; therefore, general managers should not place a large weight on the salary distribution of their team. However, since having more superstars on a team will create more exciting hockey and in turn draw more fans, general managers should not worry about salary inequality when signing stars. Given these tests and results it is clearer what general managers pay players for in their contracts. A player who wants to make more money can review the results to determine the deficiencies in his game. It is obvious that consistent scoring will always trump all other considerations. Owners, general managers, and players all want to win games, make money, and draw excited crowds, all while enjoying the game they love, hockey. 18
REFRENCES Fullard, J. (2012). Investigating Player Salaries and Performance in the National Hockey League. Faculty of Applied Health Sciences. Hockey Fights, Stats, Reviews and Trading. (n.d.). Retrieved December 20, 2015, from http://dropyourgloves.com/ NHL.com - The National Hockey League. (n.d.). Retrieved December 20, 2015, from http://www.nhl.com/ice/statshome.htm?navid=nav-sts-main# Wuest, M. (n.d.). Salary Chart History. Retrieved December 20, 2015, from http://www.capgeek.com/charts.php?team=9 Vincent, C., & Eastman, B. (2009). Determinants of Pay in the NHL A Quantile Regression Approach. Journal of Sports Economics. Retrieved from http://jse.sagepub.com/content/early/2009/01/07/1527002508327519.full.pdf 19