Andrew Grossman November 27, 2018

A Number$ Game: Analyzing the Value of the Top-Twenty Annual NHL Unrestricted Free Agent Signings from 2012-2017

Andrew Grossman November 27, 2018

Introduction A fundamental task for the (GM) and front office of any professional sports team is their ability to efficiently sign Unrestricted Free Agents (UFA) on the open market as a complement to their existing roster without creating long-term financial burdens. In the (NHL), a mandated hard salary cap prevents the top earning teams from leveraging their financial position to stockpile the league’s top earning player and drastically outspend the competition. As a result, NHL GMs are required to precisely allocate valuable salary cap space each while simultaneously considering the long-term financial structure of their roster to ensure consistent completive performance. Teams have focused on assembling analytical departments and hiring salary cap specialists to navigate the spending celling by utilizing data analyzation to find undervalued players and increase spending efficiency. The of this paper is to evaluate whether NHL teams should invest valuable cap space on premium top 20 UFAs because they traditionally collect inflated contracts from teams bidding against each other.

Allocating Contracts Allocating a team’s precious dollars during the offseason free agency window is significant in building a well-rounded roster that is not deficient in any area from over spending on a single premium UFA. The dynamics of constructing an ideal NHL roster completely changed following the post-2005 salary cap era. The Blackhawks, under their Hall Of Fame GM-Scotty Bowman, brilliantly assembled a collection of undervalued players and players on entry-level contacts to capture the 2010 . The surge in on-ice productivity from players such as , , Brian Campbell, and Antti Niemi increased their financial worth and forced the Blackhawks to part way with several key players. After losing many key contributors to their Stanley Cup run, the Blackhawks had just eight forwards, four , and two under contract for a combined $65 million leaving them with just $6 million to fill out eight roster spots.1 Despite this challenge, Bowman focused on signing rookie players on entry-level contracts and veteran free agents to complement the team’s core players of Jonathon Toews, Patrick Kane, Duncan Keith, and , which helped them recapture the Stanley Cup in 2013 and 2015. After analyzing player and team data from 2012-2017, a direct correlation will be presented between investments in the top 20 UFAs with the UFA spending patterns of the Stanley Cup Champions from 2012-2017. Using the analytical data and interpretations found in this report, a recommendation on how to allocate UFA spending to improve signing efficiency and reduce the overall number of poor UFA signings will be presented. It should be noted that goaltenders were excluded from this report because there was not enough data to provide a sound analysis.

Data Analysis The data collected in this report derives from several sources, which are highly regarded amongst hockey statisticians. The following salary cap related data originates from the databases of CapFriendly2 and Spotrac3;

1 Vollman, Rob. “StatShot”, ECE Press (Toronto, Canada, 2016), Pp 12-13. 2 CapFriendly “NHL Salary Caps,” November 17, 2018. www.capfriendly.com, 3 Allen, Scott. Ginnitti, Michael. Sportrac “NHL Team Salary Tracker” November 17, 2018. https://www.spotrac.com/nhl/cap/

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1. Salary per player- contract length, average annual value (AAV), and total salary 2. Dollar per goal 3. Dollar per assist 4. Dollar per

Numerous players in each of the dollar per statistical categories were missing from the above databases and required a manual calculation by consulting Rob Vollman book Statshot. These salary-related statistics were calculated for the year following the players’ new contract to reflect the players’ value to their new teams. In addition, the salary figures are relative to the league mandated salary cap, which determines the maximum roster salary per season. The league salary cap ceilings are as follows;

1. 2017-2018: $75,000,000 2. 2016-2017: $73,000,000 3. 2015-2016: $71,400,000 4. 2014-2015: $69,000,000 5. 2013-2014: $64,300,000 6. 2012-2013: $60,000,000

The salary cap info derived from the NHL’s own website, which publishes its annual salary celling that teams must comply with derived primarily from hockey related revenue. Finally, it should be noted that because of an NHL lockout, there were only 48 regular season games during the 2012-2013 regular season. Despite this, individual player statistics and salaries were unaffected because they were analyzed by summing the difference of their statistics during their contract year with statistics produced the year after signing their new contract. The statistical information found in this paper derived from three sources; Evolving Hockey4, Corsica5, and NHL.com6. Each site contained data that was developed by consulting StatShot’s methods, which offered an in-depth analysis on salary-cap formulas. The player stats are from the 2012-2017 NHL seasons and were measured using data from the players’ contract year and the year after signing their new contract to compare production level difference. The stats included are as following;

1. Goals, Assists, Points 2. Games Played 3. GAR (Goals Above Average) using Evolving Hockey’s threshold level 4. WAR (Wins Above Average) using Evolving Hockey’s threshold level

The data pool consists of 120 players and 181 total team stats of NHL regular season standings. Data pertaining to NHL yearly standings was pulled directly from NHL.com.

4 Evolving-Hockey, “Goals Above Average Skater Tables”, November 18, 2018. https://www.evolving-hockey.com 5 Corsica Hockey 2.0 “Skaters-WAR”, November 18, 2018. http://corsica.hockey/war/ 6 National Hockey League “Statistics-Players” November 18, 2018. http://www.nhl.com/stats/player?reportType=season&seasonFrom=20182019&seasonTo=20182019&gameType=2 &filter=gamesPlayed,gte,1&sort=points,goals,assists

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Statistical Calculations

A) Comparing the Statistics of UFAs 1-10 with UFAs 11-20 per Free Agency Class The first step in conducting statistical analysis was grouping the data to reflect the top 20 UFAs per free agency class year. After sorting the data into the top 20 UFAs by total contract value per season, I conducted a comparison by measuring the statistics from the player’s contract year to the year after signing their new contract to produce a net result. On the whole, UFAs statistics dropped the year after a new contract; Games Played was down 1.137, GAR was down 3.458, Goals were down 2.049, Assist were down 3.210, Points were down 5.2411, and WAR was down 0.6112. Next, the statistics were broken down into two categories, one for UFAs 1-10 and the second for UFAs 11-20. The statistics in every category showed that UFAs 11-20 performed better when comparing statistics from the contract year to the year after signing their new contract. Specifically, they outperformed in goals by 1.42, in assists by 0.17, and in points by 1.58 (Figure 1). While line combinations are a factor when evaluating player statistics amongst different teams, there was no advantage or disadvantage for either UFA grouping because both are affected by changing line combinations. This data shows that there is inherently better value in signing second-tier UFAs because their salaries are not as inflated as the first-tier, which reduces an expectation match their previous statistics, often inflated during a contract year. Furthermore, the second-tier of UFAs consists of older players who are established consistent point producers and often require shorter-term contracts, which removes the risk of having a burdensome long-term deal embedded in a team’s salary cap structure for future seasons.

B) Analyzing Salary by position- Team Rank Correlation with GF & GA Next, spending on the top 20 UFA classes form 2012-2017 was broken down by each position to determine if teams favoured investing in a specific player position. Out of the top 20 UFAs from 2012-2017, 24 centres received new NHL contracts, 36 defenceman (consisting of both left defence and right defenceman) received new NHL contracts, 28 left wingers received new NHL contracts and 32 right wingers received new NHL contracts. Elite centres and defenceman are more sought-after assets because on average they take longer to develop and have a higher likelihood of becoming franchise players. As a result, they less frequently hit the open market and, if they are elite franchise players, they are usually re-signed by their original team. In terms of dollar spending per position however, wingers received the largest average total salaries compared to forwards and defenceman. Centres were paid a total of $329,500,000 with an average total contract value of $13,279,167, right wingers were paid a total of $453,475,000 with an average total contract value of $14,171,094, left wingers were paid a total of $519,300,000 with an average total contract value of $18,546,429, and defenceman (both left

7 National Hockey League “Statistics-Players” November 18, 2018. http://www.nhl.com/stats/player?reportType=season&seasonFrom=20182019&seasonTo=20182019&gameType= 2&filter=gamesPlayed,gte,1&sort=points,goals,assists 8 Evolving-Hockey, “Goals Above Average Skater Tables”, November 18, 2018. https://www.evolving-hockey.com 9 National Hockey League “Statistics-Players” November 18, 2018. http://www.nhl.com/stats/player?reportType=season&seasonFrom=20182019&seasonTo=20182019&gameType= 2&filter=gamesPlayed,gte,1&sort=points,goals,assists 10 Ibid 11 Ibid 12 Corsica Hockey 2.0 “Skaters-WAR”, November 18, 2018. http://corsica.hockey/war/

3 Andrew Grossman November 27, 2018 and right) were paid a total of $598,625,000 with an average total contract value of $16,628,472 (Figure 2). Since there are more wingers on the open market, teams can often pay a lower price because of oversupply, however the top wingers are paid a premium compared to their counterparts because they are coveted for their goal scoring ability, which produces a higher average salary. In addition, the goals for (GF) statistic has a higher correlation than goals against (GA) in relation to a better overall team record, which suggests why teams place an emphasis on allocated more dollars towards forwards over defenceman. After running a correlation between team standings and team statistics, GF had an 89% correlation to points in the regular season standings and 87% correlation to wins, while GA had a 32% correlation on points in the regular season standings and a 81% correlation to loses (Figure 3).13 As a result of these correlations, teams are allocating dollars to bolster their goal scoring rather than investing in improving their goals surrendered to increase regular season points in the NHL standings.

C) Jim Nill Case Study- Correlation model Jim Nill, the GM of the Stars since 2015, has been outspoken about the advantages of analytics and the benefits of implementing it into a front office strategy. Immediately upon being named the GM in 2015, Jim Nill revamped the Stars front office by placing an emphasis on the implementation of analytics to gain a competitive edge in a tight, salary cap league. Nill noted, “We use it, we’re in it [analytics]. We’re analyzing it all the time. There is some good things to it, we’re trying to find trends, it’s not people doing it, it’s all computerized.”14 During his tenure as GM, Nill’s primary signing in the top 20 UFA category has been Russian forward Alex Radulov to a five-year deal worth just over $31 million in 2017.15 This signing provided goal-scoring value for Nill as Radulov significantly out preformed the other top 20 free agents in his class (Figure 4). Radulov’s contract also provided great value in comparison to other members of the top 20 2017 free agency class as his salary only represented 3% of total contract values (Figure 5).16 Similarly, from a team perspective, the addition of Radulov’s added an offensive spark to the Stars lineup and only represented 8% of the entire team cap (Figure 6).17 Under Nill, the Stars finished 24/30 during the 2016-2017 NHL season and 25/31 during the 2017-2018 season (Figure 7).18 After these findings, I ran a correlation test between team rank in the regular season and team spending and found a 74% significance correlation (Figure 8). While Radulov preformed above his contract value for the Dallas Stars the team still finished towards the bottom of the league because his WAR was only 0.4119, which doesn’t provide any significant impact on the Stars overall team ranking. However, if Nill can add players who offer positive WAR ratings on salary-friendly contracts, the team will gain wins and improve its position in the standings. Nill also described how analytics are being

13 National Hockey League “Statistics-Players” November 18, 2018. http://www.nhl.com/stats/team?reportType=season&seasonFrom=20122013&seasonTo=20182019&gameType=2 &filter=gamesPlayed,gte,1&sort=points,wins 14 Shapiro, Sean. “How the Dallas Stars Use Analytics?”, The Upset-Sports, December 14, 2017. https://theupsetsports.com/dallas-stars-use-analytics/ 15 CapFriendly “NHL Salary Caps,” November 19, 2018. www.capfriendly.com 16 Ibid 17 Ibid 18 National Hockey League “Statistics-Players” November 18, 2018. http://www.nhl.com/stats/team?reportType=season&seasonFrom=20122013&seasonTo=20182019&gameType=2 &filter=gamesPlayed,gte,1&sort=points,wins 19 Corsica Hockey 2.0 “Skaters-WAR”, November 18, 2018. http://corsica.hockey/war/

4 Andrew Grossman November 27, 2018 implemented into contract negations but have faced challenges from NHL players and agents. Nill noted, “The big hurdle is for players and agents. Is this going to be used against me in negotiations? It might help me, or it might hurt me, so I think there is a hesitation there.”20 Overall, teams that prioritize analytics and economic efficiency into their UFA strategy by focus the WAR metric will be able to avoid long-term underperforming investments and instead collect salary-friendly contracts, which increase a team’s winning percentage.

D) Examining Historical Team Success with Signing Top 20 UFAs A great indicator to determine if teams should invest in top 20 UFAs is by analyzing patterns of UFA signings from teams who had successful seasons. As a result, I focused my evaluation by comparing the top 10 teams in the regular seasons from 2012-2017. During that sample size teams spent on average $2,013,295 on the top 20 UFAs, which is a minimal amount considering the salary cap’s annual increase (Figure 9).21 Furthermore, between the same period the were the only Stanley Cup champion to invest in a top 20 UFA, which was a $2,000,000 contract for defenceman Matt Hunwick, the year prior to winning the Stanley Cup Win (Figure 10).22 Rather than splurging on top 20 UFAs, Stanley Cup champions are successful in drafting and developing talent internally, which allows them to maximize rookie contracts to fill the holes of departing players who hit the open market after a successful season. In addition, the risk is higher when signing a top 20 UFA because they are expected to live up to their previous successful contract year and teams use a small sample size to invest in long-term contracts (Figure 11). To corroborate this theory, the ages of the top valued forwards (measured in dollars per goal from 2012-2017) was compared to the worst valued contracts. The top valued forwards consisting of 37-year-old , 37-year-old Jason Chimera, and 25-year-old Tyler Pitlick combined for an average age of 33 years-old23. The worst valued forwards consisting of 28-year-old Nathan Horton, 28-year-old Benoit Pouliot, and 31- year-old combined for an average age of 29-years-old.24 As a result, the best valued lineup of UFAs from 2012-2017 using dollar per goal and dollar per point would consist of right winger Jarome Iginla, centre Eric Stall, left winger Jason Chimera, defencemen John Moore and Francois Beauchemin. Committing significant portions of salary to younger unproven players seeking their first UFA contract is riskier because if the player underperformers the team is left with a hard-to-move salary burden. Furthermore, since the best teams are often situated at the maximum end of the salary cap, they often sign older more-established players to short-term contracts, which the data shows are the best-valued contracts. It remains common for teams facing cap issues to re-invest internally by signing restricted free agents (RFA), categorized as players under the age of 27 or have played fewer then 7 professional seasons, which are prevented from negotiating a contract on the open market. Resigning a UFA provides the team with leverage to negotiate exclusively with the or to trade the player’s exclusive negotiation rights for an asset that can contribute to a playoff run immediately.

20 Shapiro, Sean. “How the Dallas Stars Use Analytics?”, The Upset-Sports, December 14, 2017. https://theupsetsports.com/dallas-stars-use-analytics/ 21 CapFriendly “NHL Salary Caps,” November 17, 2018. www.capfriendly.com 22 Ibid 23 Allen, Scott. Ginnitti, Michael. Sportrac “NHL Team Salary Tracker” November 17, 2018. https://www.spotrac.com/nhl/cap/ 24 Allen, Scott. Ginnitti, Michael. Sportrac “NHL Team Salary Tracker” November 17, 2018. https://www.spotrac.com/nhl/cap/

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Conclusion The data and analysis discussed in this report reinforces the necessity for NHL teams to operate with salary-cap efficiency during the UFA window. This report concludes that teams should refrain from signing the top 20 annual UFAs to long-term contracts because they are ineffective in increasing a team’s collective WAR , which is a direct correlation to winning percentage. Despite data in this report showing that after signing new contacts the top 20 UFAs from 2012-2017 collectively regressed in points, goals, GAR, WAR , assists, and games played NHL teams generally overvalue contract year statistics, which produces inflated long-term contracts. Furthermore, avoiding investments in top 20 UFAs is a consistent variable amongst the Stanley Cup champions whom instead opt to sign established veteran players on short-term contracts. Drafting and developing young talent is a significant element of the modern NHL era because quality players on entry-level contracts generate a high GAR and WAR. Overall, this report concludes that NHL front offices should resist investing cap space in the top 20 annual UFAs and instead look to invest in RFA’s or less-valued veteran UFAs as complimentary roster pieces because they are predictable to replicate previous career statistics.

Works Cited 1. Allen, Scott. Ginnitti, Michael. Sportrac “NHL Team Salary Tracker” November 17, 2018. https://www.spotrac.com/nhl/cap/ 2. CapFriendly “NHL Salary Caps,” November 17, 2018. www.capfriendly.com 3. Corsica Hockey 2.0 “Skaters-WAR”, November 18, 2018. http://corsica.hockey/war/ 4. Evolving-Hockey, “Goals Above Average Skater Tables”, November 18, 2018. https://www.evolving-hockey.com 5. Gabriel Desjardins “How Much Do Wins Cost?” Artic , October 12, 2011 https://originalsixanalytics.com/2016/03/10/negotiation-leverage-and-the-massive-hidden- value-in-elcrfa-deals/ 6. National Hockey League “Statistics-Players” November 18, 2018. http://www.nhl.com/stats/player?reportType=season&seasonFrom=20182019&seasonTo=20 182019&gameType=2&filter=gamesPlayed,gte,1&sort=points,goals,assists 7. Original Six Analytics “Negotiations Leverage and the Massive Hidden Value in ELC/RFA Deals”, March 10, 2016. https://originalsixanalytics.com/2016/03/10/negotiation-leverage- and-the-massive-hidden-value-in-elcrfa-deals/ 8. Shapiro, Sean. “How the Dallas Stars Use Analytics?”, The Upset-Sports, December 14, 2017. https://theupsetsports.com/dallas-stars-use-analytics/ 9. Vollman, Rob. “StatShot”, ECE Press (Toronto, Canada, 2016), Pp 12-13.

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Appendix Figure 1

Figure 2

Figure 3

Figure 4

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Figure 5

Radulov Signing- Macro Lens-League

$31,250,000 , 3%

$989,875,000 , 97%

Radulov Contract Total Salary Spent 2017

Figure 6

Radulov Signing 2017- Micro Lens- Team

$6,250,000 , 8%

$74,724,071, 92%

Alexander Radulov's AAV Dallas Stars Team Salary

Figure 7

Dallas Stars Record 2015-2017 Under Jim Nill

120

100

80

60 102 40 76 20 48

0 2/30 24/30 25/31 2015-2016 2016-2017 2017-2018 8 Points 102 48 76 Andrew Grossman November 27, 2018

Figure 8

Figure 9

Figure 10

Figure 11

9