The birth of data analytics in hockey occurred in the 1950s when Roger Neilson, coach of the Vancouver Canucks, began measuring which players were on the ice while a goal was scored (known as plus-minus today). 15 years ago, the Hockey Information and Tracking System (HITS) was run by a team of 20 people, who would manually input basic analytic stats such as time on ice, shots, face-offs, in addition to other stats., More recently, hockey analytics have been tracked with automated data tracking systems including InStat and Statstrack; tracking in-depth analytics, such as puck battles and zone entries providing an enhanced post-game report that ultimately benefits players, as well as coaches and managers.
Newly-introduced artificial intelligence (AI) software has further instituted technology into hockey. AI has been programmed to recognize specific plays and provide a breakdown on the success/failure of that play. Perhaps the most critical feature of AI is that all data is processed without bias or human error. It is important to note, however, artificial intelligence can only lead to so many strategic opportunities, but ultimately it is up to coaches and managers to utilize the analytics provided in a manner that is appropriate and most beneficial.
The outlook on interpreting hockey analytics is so astronomical that analytics departments in the NHL consist of more than 100 people solely for data-driven roles. These roles may consist of interpreting the data produced by AI for understanding strategy, maximizing player usage, and uncovering hidden free-agent gems. The game is now progressing from an era of coaches going off of gut feeling into one of utilizing analytics to enhance coaching strategies. For example, with the use of analytics, teams can review opponents’ strategies and identify any areas of their game that are flawed. This allows them to develop strategies to exploit the opposition’s shortcomings. It is also important to note that the analytical system today is almost instant, thus allowing team staff to manipulate strategies during the game.
Furthermore, the game has become faster with the use of analytics and changes in technology as it has modified scoring strategy and gameplay. Additionally, analytics has significantly affected building of teams in regards to both the salary cap and the draft. Player development has also been benefited with the use of analytics and technological advancements, through post-game analysis and sport-testing. This paper will explore the ways in which analytics have progressed the gameplay through in-game strategy, player development, and player evaluation which will strongly impact the future generations of hockey.
Building of Teams
As the game of hockey progresses into a more skill-based, rather than a physical game, the need for analytics is crucial. Teams have now created strategies to develop more skillful players, and recognized that a player’s time could be better spent on the ice than sitting 5 minutes in the penalty box. The role analytics plays in this scenario is through evaluating specific players, thus leading to better building of teams.
Building a hockey team is an arduous process that can potentially last years. Therefore, teams try to maximize their opportunity of becoming a superior team through drafting up-and-coming players. The NHL draft occurs every summer and teams are given one pick in each of the seven rounds. It is important to note that this varies depending on teams’ willingness to trade picks prior to the start of the draft for a variety of reasons. These include receiving players, clearing salary space or even trading higher-valued picks for multiple lower-valued picks. Regardless, the draft exists for teams to acquire who they believe will be most beneficial to their roster.
Analytics in the draft is utilized by taking into account which potential draftees would play best with current players on the roster, the role they would best fill, and how much the player might improve going forward. This is done through qualitative information that looks at player attributes and which of those best translates to success. Point equivalencies were introduced in the early 2000s to help compare players who play in different leagues. This was done through analysis of player performances in their respective leagues and comparison to their first few years in the NHL. Value by draft position is another factor that has been taken into consideration over recent years. Scouts can understand fluctuations from specific picks based on players drafted at each position while also taking into account the number of games played.
After the draft is when teams begin to focus on the salary cap. Players are analyzed to ensure a team effectively utilizes its salary cap by looking at player impact and performance statistics. Ignoring hockey analytics has led to poor contracts, thus affecting teams’ payroll, as some believe that player value cannot be statistically quantified. Although some teams remain hesitant about relying solely on statistics to justify player demand, it is still necessary to take into account some aspects such as Corsi. Corsi for percentage tracks how much a player controls the puck. If an individual’s stats are relatively low to his teammates, it can help distinguish between the thought that they are low simply because they’re on a bad team. The chart below shows the roles within a hockey team:
How specific teams value these types of players vary from team to team, but in general, skilled forwards and offensive defensemen are paid more than two-way forwards and defensive defensemen. Goaltenders’ salaries may range from high to low depending on their ranking on the depth chart. Thus, teams may value a particular kind of player more than others; therefore, they pay them a higher salary than they are traditionally expected to receive. To test the scoring correlation between the salary cap chart and goals per game, the 2012-2013 Chicago Blackhawks “core” players and their salaries were analyzed:
Below is a figure of the 2012-2013 Chicago Blackhawks “core” players, along with their salaries:
The chart below illustrates the correlation between the point production of the Blackhawks’ “core” players and the typical salary received by the player in their respective role.
In the lockout-shortened season, Patrick Kane and Jonathan Toews had both the two highest point totals on the team and the highest and second highest salaries on the team, respectively. Both of these players are characterized as skilled forwards. Moreover, there is a strong correlation between the typical salary and the results they produce. Other players who also fall into this same category are Patrick Sharp and Marian Hossa, each with substantial point-per-game totals. The two-way core forwards in this list, Dave Bolland and Bryan Bickell, also match up with the typical salary received, with Bolland receiving a moderate one, and Bickell receiving a low one. There is also a correlation with the offensive defensemen on this roster, Duncan Kieth and Brent Seabrook, both with high salaries and a solid point contribution. Finally, there is a correlation in the defensive defenceman category with Nicklas Hjalmarrson, as he received a moderate salary while also putting up relatively average numbers for a defenceman. Teams can use stats like this to ensure that players are appropriately paid, based on their expected production.
The significant change in technology has facilitated the collection of advanced data to be used for analytics. One such way is the aforementioned artificial intelligence (AI) tracking system, which is trained to recognize all moving entities on the ice. These computer algorithms put together can also match individual player coordinates, giving them a complete game report based on their performance. It is also significant because better technology simplifies the modeling process for analysts. For example, AI enhances player identification, enabling individuals to solely watch their own ice time. Previously, clipping every single minute of a sixty-minute game divided up into three periods would take hours. Not only can AI single out players, but it can also go so far as recognizing the skill in which they’re performing, such as a certain skating movement or utilizing a specific skill move. The final benefit is the advancement of sensors. This enables players and coaches to see the progression through particular skills including skating and shooting, as sensors measuring speed and other metrics are embedded in the equipment itself.
These techniques enable teams to enhance their decision-making processes and allow coaches and management staff to interpret the data and make informed decisions regarding player selection and strategy., Player selection is especially important as team needs vary. This is similar to that of the roles within building a team’s salary cap. The need and role for detailed statistics becomes more apparent when looking at it in a scenario comparing a two-way forward against WAR (Wins Above Replacement). A two-way forward’s primary role is to defend opponents from scoring and WAR is a model to estimate the individual impact a player has on his team. Considering the two-way forward occasionally scores, it may seem as if they have a minimal impact on the game. However, by analyzing the detailed stats and looking at the puck-battles-won or stick tie-up categories, it becomes more recognizable the desired need for that specific type of player.
In-Game Strategy
The evolution of hockey is widely known to be tied to the progression of the pace of the game. Previously, it was mentioned that the game has become a more skillful game rather than a physical one. In addition, analytics have enabled teams to modify their scoring approaches and systems, thereby enhancing their scoring probability by attempting to move up the ice as quickly as possible. The recent changes in equipment technology have allowed teams to execute such strategies.
Analytics can be used to evaluate team performance within games. The analytic teams analyze current tactics and may modify them based on in-game shortcomings. In one instance, if a team is conceding too many goals, analytics with built-in AI can identify the problem and make suggestions from there. The analytical data can enhance tactics, make recommendations on player selection, and even go so far as suggesting a training regime if a player is lacking a particular skill that could assist in combating their shortcomings. A substantial benefit from the analytical data is the ability for it to recommend formation strategies. Continuing from the example, the team may switch to a 1-1-3 approach, a more conservative and defensive neutral-zone approach, tempting the opposing team to give up and fight back for possession. It is important to note that this would be appropriate if the team is conceding goals as a result of the opposing team’s break-ins. Additionally, analytical data has the ability to recommend where players are to position themselves on the ice. A variable that influences systematic play is physicality. With many players, they often get caught up in a hitting mindset, believing that their role is to take every opportunity possible to deliver a check. However, if they’re so clear cut on finishing checks, they can repeatedly be left out of position, consequently opening a lane for the opposing player to take, thus creating a scoring opportunity. This same factor, in addition to analytical data, can be applied to a team that is struggling with scoring. Data analytics can recommend offensive zone positioning and formations, increasing the team’s odds of scoring. The physicality factor comes into play as players may be left out of position after delivering a hit. This makes their presence up ice much more delayed. Avoiding unnecessary hitting allows teams to advance up the ice quicker, both generating higher-quality scoring chances and contributing to the increased pace of the game. There is a time and place for physicality to be correctly utilized within the game. Smart physicality occurs when a player uses his body to separate the opposing player from the puck in order to regain possession.
Increased scoring has occurred largely in part due to analytically driven scoring approaches and systems. Analytics have indicated the elimination of the so-called “enforcer” type of player, whose job is solely to play physically. Scoring in the NHL has steadily increased, with teams averaging over three goals per game, which has not been done since the early 1990s. Soon after this spike in goals, the team’s line-ups were suited for an era of big fights, massive hits, and bench-clearing brawls. Teams today have three or four solid lines that can all contribute offensively, with some teams consisting of multiple twenty-goal scorers on a fourth line. The game has shifted from the physicality aspect, into one that increases their chances of generating scoring opportunities, from a more stylistic point of view. Fighting has also decreased over time. For example, there were 65% fewer fights per game in 2018-19 than in 2010-11. Teams have now focused on developing more skillful players, realizing a player’s time could be better spent than sitting 5 minutes in the penalty box. Teams today have willingly gone to eliminate the so-called “enforcer” player, to substitute in a player that can better help generate scoring opportunities.
The playoffs are ultimately left with the best teams, consisting of the best players. These players compete with an increased intensity, especially when it comes to hitting. This is evident as analytics have recognized that hitting in playoffs increased nearly fifty percent from what it was in the regular season. This in turn leads to a quicker pace of the game as players are tempted to move quickly enough up the ice to avoid getting hit.
Perhaps the largest contributor to increasing the pace of the game has been through technological advancements. Changes in technology have allowed the pace of the game to become quicker, as equipment has become much more lightweight and facilitates the transfer of energy much quicker than in the past. This theory that lighter equipment is more beneficial, is supported by the continuous innovations of weight decreases in equipment, which is also created with improvements in wearability and resilience. New advancements such as 3D technology have allowed for the scanning of individual bodies and have created equipment to better fit each player’s dimensions. This 3D data is able to produce nearly all protective equipment which is fit into precise measurements in a manner that is sleek and aerodynamic. Within this broad topic of equipment, a subcategory of hockey skates alone has resulted in the growth of the pace of the game. Today, there is a substantial number of players who can reach a higher speed than in previous hockey eras. Speedy hockey players have always existed in the past but the overall number of them at a given time is higher than ever before. An important factor is the ease of the use of the technology within the skate. Average skaters can sacrifice form, as they can be bailed out with BladeTech hockey blades. These blades are widely available at the professional level and benefit the skater as they “boast increased speed due to their lighter construction.” The new technology is a spring-loaded blade, which reduces the impact on the skater’s joints, while also enabling a transition smoothly from a flat-footed stance to one of a forward angle. Altogether, the BladeTech blades provide a great advantage as a result of injury prevention and increased acceleration through the propulsion force.
Player Development
The game of hockey has progressed largely in part due to player development. With recent advancements in analytics widely available to hockey players of all ages, both up-and-coming and current players can use hockey analytics to maximize their performance. The ways in which players are able to utilize analytics for development are through the analysis of in-game advanced statistics. Off the ice, Sport Testing analytics has allowed players to better see progression over time, thus altering their training based on the results.
Players can improve their on-ice performance by analyzing their in-game advanced stats to get a better understanding of their strengths and weaknesses, further allowing them to make more informed decisions on the ice. One of the more commonly used advanced analytical tools is the Expected Goals metric. This metric is a measure of the probability of a given shot, based on where it is taken on the ice. This number is measured based on historical data, but also based on personal statistics. The probability, measured from values 0 to 1, is based on a number of factors, including shot angle, distance from the net, and shot location. The trend tends to be the closer you are to the net, both horizontally and vertically, the closer the expected goals value is to 1, and vice versa. It is important to note that the positioning of the defenders has a substantial effect on the expected goals value. Furthermore, by combining multiple advanced statistics with one another, players can better strategize their approaches to the game. One such example can be analyzing the strength of an opponent with your own team’s zone entries and zone exits. Analytical data has the ability to identify opponents’ strengths and weaknesses. This data may include tactics imposed by the other team, leaving it up to players to interpret and counter their strategy. Zone Entries and Zone Exits are tracked and deemed either controlled (when the team enters or exits the zone with possession of the puck), or uncontrolled (when the team enters or exits the zone without possession of the puck). These categories are also broken down into more specific statistics, which analyze the formations the team enters or exits the zone in. Additionally, if they enter or exit uncontrolled, it is also categorized if they regain possession of the puck. When these statistics are previously analyzed against an opponent’s team, it becomes more apparent on which strategies are most beneficial to maintaining possession of the puck and increasing chances of scoring.
A significant way in which players have developed in training both off-ice and on-ice has been through the company Sport Testing. This creation has solved the challenge of having the ability of data analytics to track an athlete’s performance over time and from there identify enhancement opportunities. Sport Testing analytics has become important in the development of individuals, as they can benchmark their own performance over time, in addition to comparing themselves to others. Sport Testing specifically works with the use of photoelectric timing gates, and radio-frequency identification sensors. These connect through a wireless network to assess a player’s performance in a specific drill. In simpler terms, two gates made up of these sensors are positioned directly across from each other at a given distance based on the test. The player will either run or skate (depending on the environment of the test), through the first set of gates. Here, the sensors recognize a break in the laser reflection and begin a timer. The timer ends when the player hits the second set of gates and initiates a break in the second gate. The designs of the tests vary depending on the intended measurement. For example, a player may want to improve their agility speed so they may set up several cones to weave around between the gates. Another example may be a test of straight speed, so there are no obstacles and the player is simply tasked with reaching the opposite gates as quickly as possible. The results are immediate, allowing for quick analysis. From here, Sport Testing recommends exercises targeted at developing the skill performed in the test. Furthermore, Sport Testing keeps a track record of previous efforts and allows the player to clearly see improvements in their testing. Perhaps the largest benefit is being able to do game-like scenarios on the ice, such as carrying the puck or turns.
Technological advancements have transcended the game of hockey, by implementing a unique style of training, allowing for better development of hockey players. The technological advancement of virtual reality testing provides players the opportunity to practice both mental and physical skills off-ice, further leading to the improvement of their game. The virtual reality platform was designed with the assistance of professional hockey players, where they inputted hockey drills to improve consumer’s hockey mental skills. The virtual reality training system can benefit both skaters and goalies in areas such as reaction time, decision-making, and anticipation and tracking of objects. Virtual Reality systems also have the ability to undergo game-like reps quicker, without facing real-world risks of injury. Virtual Reality training systems are being used at all levels, with the Vegas Golden Knights being the first NHL team to take up the technology. They believe that this immersive training platform will give them a competitive advantage, assisting players to improve their game beyond their time on the ice. There are many iterations of Virtual Reality training, some of which have transcended the original versions. Specifically, the Sense Arena training system has substantial data tracking and analysis capabilities, as it measures every move made by the player. The system then provides specific metrics of the player’s overall performance and compares the metrics to other users of the training system to create age-appropriate comparable data. In addition, Sense Arena also has an individual approach, beginning with baseline diagnostic tests, which generate training plans to improve areas of deficiency identified by the technology. Once again, a breakdown of the results are provided to compare the metrics of other players in the same age group. Sense Arena also has the ability of personalized coaching, for an enhancement of the already provided training plans. This allows for the ability to even further maximize performance, while also having access to tips and tricks from professionals.
Conclusion
In summary, analytics has an astronomical effect on the game of hockey. Analytics is utilized in player evaluation, which has allowed for better building of teams. Analytics is used in the NHL entry draft to compare point equivalence between leagues and compare player value by position. Players are also analyzed to ensure a team effectively utilizes its salary cap by looking at statistics aimed at player impact and performance. In addition, technological advancements including advanced analytical techniques enable teams to enhance their decision-making processes, specifically by looking at player participation and scheduling.
Analytics have also had an effect on the evolution of hockey, including being a large factor in the progression of in-game strategy. Analytics have indicated that a stylistic game approach is more beneficial to team success and worth sacrificing the unnecessary physicality that does not occur during gameplay. This has resulted in the analytical indication of eliminating the so-called “enforcer” type of player. In terms of the pace of the game, changes in technology have quickened the game, as equipment has become much more lightweight and facilitates the transfer of energy much quicker than in the past.
Moreover, analytics has assisted in the development of both up-and-coming and current players. This has been done by allowing players to improve their on-ice performance by analyzing their in-game advanced stats to get a better understanding of their strengths and weaknesses. This further allows them to make more informed decisions on the ice. Sport Testing analytics has also become important in development for individuals, as they can benchmark their own performance over time, in addition to comparing themselves to others. Lastly, the technological advancement of virtual reality testing provides players the opportunity to practice both mental and physical skills off-ice, further leading to the improvement of their game.
In conclusion, after thoroughly analyzing analytics’ and technology’s effect on the game of hockey, I understand the changes it has had in hockey gameplay and the potential it has for the future. In the past few years, data and statistics have begun revolutionizing team strategies and individual player development. Even though there have been considerable technological advancements in recent years, data analytics will have an even stronger role in shaping hockey as technology continues to improve. Teams can work to the best of their abilities to fully grasp the benefits of data analytics, in order to gain a competitive advantage, thus leading to increased team success. Individuals can embrace the data analytics available to them and work on developing themselves both on and off the ice. This, in addition to technological advancements, will lead to a faster pace of the game. Ultimately, with constant analytic revolutions, the future of hockey looks to be exciting and innovative.
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By: Jay Feldberg
The Blue and White 