Analyzing the Value of the Top-Twenty Annual NHL Unrestricted Free Agent Signings from 2012-2017

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Analyzing the Value of the Top-Twenty Annual NHL Unrestricted Free Agent Signings from 2012-2017 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 General Manager (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 National Hockey League (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 season 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 goal 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 Chicago 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 Stanley Cup. The surge in on-ice productivity from players such as Andrew Ladd, Dustin Byfuglien, 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 defenceman, and two goaltenders 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 Brent Seabrook, 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/ 1 Andrew Grossman November 27, 2018 1. Salary per player- contract length, average annual value (AAV), and total salary 2. Dollar per goal 3. Dollar per assist 4. Dollar per point 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 2 Andrew Grossman November 27, 2018 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 shot 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.
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