DEPLOYMENT

Whenever you analyze a player, whether you are using the eye test or advanced analytics, it is important to understand the role in which the player is being used by the coach. Is the player a top-6 scoring wing, whom the coach throws onto the ice for as many offensive zone faceoffs as possible? Is he a shutdown center that the coach throws onto the ice against the opposing team’s top line? Is he a third-paring defenseman that gets sheltered by the coach, or in layman’s terms, is the player primarily used in the offensive zone and is only on the ice against weaker opposition lines?

Understanding a player’s role on the team and how he is deployed by the coach, are critical to understanding how well he is actually playing. If a forward is commonly used in a defensive role, and his primary responsibility is to prevent the opposing top line from scoring, it would be irresponsible for us as fans to expect him to put up lofty point totals. Conversely, a defenseman who is primarily used against weaker opponents and in the offensive zone (i.e. sheltered) is likely to put up inflated shot metrics and point totals compared to a defenseman tasked with shutting down the opposition’s best players.

There are a number of components that must be considered to fully understand how a player is being deployed:

Zone Starts – Simply put, a zone start is any time a player is on the ice for a faceoff. We all know hockey is a fluid sport, with line changes happening all the time during play. This makes it difficult to track how exactly a player is deployed, particularly due to the fact that line changes that occur during an active play often are only partial changes, so the lines get temporarily scrambled and a player might be out in a situation that the coach didn’t necessarily plan. Because of this, and the general chaotic nature of line changes at times, it isn’t all that valuable to track player deployment in terms of the situation every time they touch the ice.

The reason analysts use zone start to measure deployment is because, with the exception of icings following faceoffs, a coach has the ability to specify exactly which players he wants on the ice for the faceoff. Coaches take a number of variables into account when deciding who to deploy on a faceoff, including the state of the game and the zone the puck is in (offensive, neutral or defensive). Further, the home team has the benefit of “last change,” which means they can wait for the away team to choose who to deploy before they choose, allowing the home team to also consider the opponents on the ice when choosing who to deploy.

When analyzing zone starts, data sites such as Corsica provide statistics that show the number of zone starts or percentage of zone starts a player takes in each of the three zones: offensive, neutral or defensive. Typically, if a player has a higher percentage of offensive zone starts than defensive, it means the coach views him as a player he wants on the ice in offensive situations, most likely because he has a relatively strong ability to score or playmake compared to his other teammates. Conversely, if a player has an disproportionally large amount of defensive zone starts, it means the coach views that player as a defensive-oriented player, who he trusts to prevent the opposition from scoring after a defensive zone faceoff.

Using the 2016-2017 Rangers as an example, when looking at the zone starts, we see that head coach Alain Vigneualt viewed Brady Skjei and Chris Kreider as players he wanted on the ice in more offensive situations, as they were the only two players with a minimum of 500 minutes played with over 35% of their zone starts coming in the offensive zone. Conversely, Josh Jooris, Kevin Hayes and Nick Holden were all viewed by the coach as defensive-first players, as each had over 37% of their zone starts in the defensive end of the ice.

Quality of Teammates – The reason why looking at the quality of teammates that a player shares large amounts of ice time with is simple: good players make those around them better. This is a very simple concept that we hear across all team sports. There is much debate within analytical communities across all sports regarding how to quantify just how much a player can improve the play of those around him, but nearly all analysts agree that the quality of the players that an individual plays with will certainly have an impact on his play. In fact, Connor Tompkins wrote a Hockey-Graphs article a few years back that concluded that “quality of teammate effects are observable in a full season sample size.” In layman’s terms, his study concluded beyond a reasonable doubt that the data shows that the quality of teammates an individual plays with has a measurable impact on his play and production.

WOWY analysis is one way you can view this impact. Using the Mika Zibanejad example from the WOWY Analysis section below, you can clearly see Mika’s impact on his teammates. Mika Zibanejad is no doubt a high quality player for the Rangers, and the chart shows that nearly every single player he shares considerable ice time with benefits from his presence on the ice. Because of these measurable impacts, we can conclude that teammate quality is certainly important, and should be considered when discussing player analysis and deployment.

Quality of Competition – There is considerably more debate within the analytics community regarding the impact that quality of competition has compared to quality of teammates. The Connor Tompkins article I shared above offers a second conclusion, with Tompkins stating that he “did not find evidence that coaches can choose the quality of competition their players face over a full season of play.” He goes on to discuss numerous reasons for why he did not find any evidence of this, most notably sample size issues.

He also explicitly states that, just because that the data shows no evidence of the impact, doesn’t mean that quality of competition doesn’t have an impact, and he went on to share that Garret Hohl (whom is the co-founder of both Hockey-Graphs and a data company that professional hockey teams pay for information) has research showing that quality of competition has a greater impact on an individual game or playoff series than across an entire season.

There have been numerous other studies on the topic, but long story short, while many agree that quality of competition does matter, there is little consensus over exactly how it matters and how to measure its impact. Various sites, including Corsica, provide statistics that are weighted to demonstrate the quality of competition a skater plays against, and the quality of teammates he plays with, which I will discuss in greater detail within their dedicated sections below.

One thing that is important to note is that most of these models use an opposing player’s ice time as the weight used to account for quality. By this I mean that a metric such as Corsi Quality of Competition uses the time on ice the opponents received as the weight, with the methodology being that better players receive more ice time. All Ranger fans know the relatively limited ice time received by Pavel Buchnevich to this point in his career is a perfect example that this is not necessarily the best barometer of the quality of a player. However, it is still a step in the right direction, and more often than not, the best players on a team receive the most ice time.

Now that we’ve defined all of the important components behind deployment, let’s look at a test case from the 2016-2017 season to see how deployment can influence a player’s statistics: Brady Skjei. During the 2016-2017 season, Skjei was most certainly sheltered by head coach Alain Vigneault, which is a common tactic used by coaches looking to develop young defenseman.

Of the seven NYR defenseman that logged at least 500 minutes last year, Skjei comfortably had the largest offensive zone start rate at 38.37%. Skjei also had the lowest defensive zone start rate at only 27.6%. For context, Kevin Klein had the second highest offensive zone start rate at 33.77% and the second lowest defensive zone start rate at 30.09%. Further, when you look at the time on ice numbers weighted for quality of competition, Skjei received the easiest minutes of any NYR defender with respect to the average competition faced.

We all know the point production Skjei accumulated last year (5 goals and a 33 assists). He also finished the season first amongst defenseman with at least 500 minutes in 5v5 Corsi for % (50.38%) and second only to Ryan McDonagh in 5v5 expected goals for % (49.47%). While I believe Brady Skjei is an excellent player and will progress to be a top pairing defenseman, I think it is important to recognize that some of Skjei’s impressive production and analytics from the 2016-2017 season were, in part, due to the fact that he was clearly sheltered by AV. When you start an abnormally high percentage of shifts in the offensive zone, and primarily play against weaker opposition, it’s easier to put up more impressive figures compared to a player with comparable ability in a less sheltered role.

Player Usage Charts – Rob Vollman recently added a dynamic Player Usage Charts tool to his website which is an excellent resource for those looking to understand how teams deploy their players and how well the players perform in these roles. Individuals can customize the parameters listed on the left (season, games played TOI, position and team) in order to view the exact information they are looking for.

The chart on the right displays the results contains five critical elements to interpreting it. The x-axis charts the offensive deployment of each player; the further to the right the player appears, the higher their offensive zone start percentage is. The y-axis charts the quality of competition the player faces on average; the higher a player appears, the stronger the competition he typically faces is. In this tool, the benchmark used to assess quality of competition is the metric relative Corsi quality of competition, which is the weighted average relative Corsi for percentage of the opponents that an individual faces over a specified period of time. In layman’s terms, if a player is high on the chart, it means they often are on the ice against opponents who have a large positive impact on their team’s ability to control the shot attempt battle.

You will notice in addition to the axis placement of players, that each player has a circle of varying size and color. The size of the circle represents the time on ice per-game that each player receives; the larger the circle, the more ice time the player receives. The color designates the player’s relative Corsi for percentage, with dark blue indicating a strong relative Corsi for percentage, and dark red indicating a very poor relative Corsi for %. In layman’s terms, if a player’s circle is dark blue, that means that they have a strong positive impact on the amount of shot attempts his team takes when he is one the ice, compared to when he is off, and dark red means the player has a strong negative impact.

The last element of the chart are little data flyout windows that appear if you hover-over a particular player, which contain the exact data applicable to the chart. Each data flyout contains the following information: games played, total minutes, minutes per-game, quality of competition relative Corsi for %, offensive zone start %, Corsi for %, Corsi for per-60, Corsi against per-60, goals for %, goals for per-60 and goals against per-60. All of the Corsi and goals data is in relative terms (which compares how the team does when the player is on vs. off the ice), and it is all also listed in a sortable table beneath the chart.

Lastly, each flyout also lists the player’s role based off of the data. So, for example, Tony DeAngelo is listed as an “effective sheltered defenseman” at the time of writing this (February 6, 2018). This is because he has a very high offensive zone start rate (indicated by his placement on the far right side of the graph) and low average quality of competition faced (indicated by his placement low on the graph), which combined mean that DeAngelo is “sheltered” by head coach Alain Vignealut. Sheltering young defenseman is a common tactic to aide development, and it is something we saw from the Rangers last year with Brady Skjei. DeAngelo also has a deep blue but small circle, indicating he has a strong relative Corsi for %, but minimal ice time. So, when yo combine all of this, you come to the conclusion that DeAngelo has performed well in a sheltered role, AKA an “effective sheltered defenseman.”