The Overwhelming Statistical Case for Chris Kreider

Chris Kreider is easily amongst the most polarizing players on the New York Rangers roster, and has been at the heart of many fan arguments since entering the league. A quick scan of fan pages on any social media platform will reveal opinions ranging from outrage over the fact that he was not included on NHL.com’s Top 20 NHL Wings list, to fans including Kreider in discussions of the most overrated players in the league. In preparation for the upcoming season and some articles I plan to write, I compiled my own database that houses roughly 200 different statistics related to how the Rangers performed throughout the 2016-2017 season, ranging from simple counting stats such as goals and assists, to the complex WAR-like statistics created by hockey statisticians Dawson Sprigings and Luke Solberg (better known as DTM About Heart and EvolvingWild on Twitter, respectively). When I was creating the database, one thing practically jumped right out of the Excel workbook at me: despite missing seven games last season, Chris Kreider was the best forward on the Rangers during the 2016-2017 season, statistically speaking at least.

Personally, I have always enjoyed watching Kreider play, and I have valued him as one of the better forwards on the Rangers for a couple of years. I was never in the camp that expected him to be the next prime Rick Nash, a perennial 30-goal scoring power forward with 40 goal upside. Instead, I have viewed Kreider as a player that could be an effective first line power forward, who could score 20-30 goals a season with speed to burn to create space for his linemates. Sure, his inconsistent play and lack of discipline at times can be frustrating, but at the end of the day, the list of NHL players that consistently drive play and production without peaks and valleys is a very short one. Despite my generally positive opinions of Kreider, I was still surprised to see just how much his 2016-2017 stats jumped off the screen, and it caused me to want to dig deeper, and eventually write this article. Below, I present the overwhelming statistical case that shows that Chris Kreider was undoubtedly the best forward on the team last year, and should be in the conversation as a player who could be the best forward for the foreseeable future.

Before we begin, I should note that all data provided in this piece is from the 2016-2017 regular season only. While playoff performance is very important, it represents a very small sample size and gets slightly skewed due to more difficult competition and different styles of play and officiating. We can all acknowledge that Chris Kreider did not have the best post-season last year to say the least, but considering the fact that he is only 26 years old and already seventh on the Rangers’ all-time playoff scoring list, it would be ridiculous to suggest that his historical playoff performance is something that should downgrade his value as a player. Further, despite his disappointing performance in more recent playoffs, he still had three goals, which tied for fourth on the team.

This article references a variety of advanced statistics that may be foreign or confusing to some. While I provide some definitions and explanations, I encourage you to check out Corsica Hockey’s Glossary if you are unsure what any stats mean, or you can reach out to me on Twitter and I will be happy to answer any questions you have.

Standard Statistics

First, let’s start with standard statistics, the box score numbers that get bandied about in many typical hockey conversations. The table below shows Kreider’s overall rankings on the team and among NYR forwards for the 2016-2017 season, separated into two categories: all situations and 5v5 play. Despite missing seven games, Kreider ranked first on the team in total goals and 5v5 goals scored, and was first among forwards in total and 5v5 hits. He also led the team in total rebounds (second in 5v5 play), and was top-5 among forwards in both total and 5v5 shots, total assists, primary assists and total points. However, the table also shows the component of Kreider’s game that drives many fans mad, myself included: Kreider ranked first on the entire team in total and 5v5 penalties. Kreider was also middle of the pack in giveaways, which is actually fairly impressive given how often the puck is on his stick relative to other forwards. He was towards the bottom in terms of taking the puck from the opponent and blocking shots. Personally, I don’t want my top line forwards anywhere near the top of the rankings in blocked shots, so I consider that low ranking a positive.

	All Situation Statistics		5v5 Statistics
Stat	Team Rank	Forward Rank	Team Rank	Forward Rank
Total Season TOI	8	4	8	4
Goals	1	1	1	1
Total Assists	7	5	6	4
Primary Assists	6	5	5	4
Secondary Assists	10	6	7	5
Total Points	4	4	2	2
Shots	4	4	3	3
Rebounds	1	1	2	2
Giveaways	13th most	7th most	13th most	7th most
Takeaways	15	11	14	11
Hits	4	1	4	1
Shots Blocked	15	9	13	7
PIMs	1	1	1	1
Total Penalties	1	1	1	1

*All data from naturalstattrick.com*

Conclusion: Kreider was the leading goal scorer on the team last year, while also racking up the most hits of any forward, and ranking top-5 on the team in assists. He also had the most rebounds, showing his ability to drive to the net and clean up loose pucks near the goalie. This is a strong foundation for the argument that Kreider was the best forward last year, and should continue to be among the best going forward. However, as many fans already know, he needs to improve his discipline and learn to play a cleaner game and commit less penalties if he wishes to truly take the next step.

Standard Rate (per-60) Statistics

For the next few sections, all of the statistics and ranks referenced are in terms of 5v5 production only. Hockey analysts don’t agree on much, but one thing that most tend to acknowledge is the fact that 5v5 production is much more representative of a player’s true ability than total statistics, which can become bloated (or depressed) based on a player’s usage on the power play and penalty kill. Further, we specify 5v5 as opposed to all even strength play in order to strip away empty net goals and situations where both teams are a man down, which can pad a player’s statistics. This is not to say that you should just throw away non-5v5 data; any good analyst will tell you that you never throw away good data. However, for the purposes of player analysis, particularly when discussing shot quality and possession metrics, it is best to attempt to remove any factors that may be artificially skewing the data one way or another.

Most of the data-driven analysts in both the basketball and hockey worlds also agree that rate statistics, or per-60-minute production stats, provide a more accurate depiction of a player’s talent than raw stat totals. In addition to the obvious advantage of adjusting for games missed due to injuries, a prominent reason for using rate statistics is that they help account for the effects of a coach that makes terrible deployment decisions (such as Alain Vigneault playing Dan Girardi on the top line for much of the 2016-2017 season). While cumulative stats are easier to digest for most people, they can severely overrate or underrate players’ true value due to them getting more or less ice time than deserved. One thing to note when discussing rate stats is that sample size is critical. A player who is called up for one game and puts up a nice performance in only a few minutes of ice time will have tremendous rate stats, so it is important to eliminate players that haven’t garnered enough ice time.

In the analysis provided within the rest of this article, 350 total minutes of ice time in a Rangers uniform is the threshold for inclusion. I specifically chose this number to remove players like Marek Hrivik, Nicklas Jensen and Matt Puempel from the equation, all of whom put up some relatively bloated rate stats in their limited ice time last year; however, it is low enough to include Brendan Smith’s time with the Rangers a player we can all agree played a significant role on the team. This leaves us with 21 total qualified players, comprised of 13 forwards and 8 defenseman.

With all that out of the way, let’s discuss how Kreider fared in the 2016-2017 season in terms of standard rate statistics. Kreider received the second most per-game ice time among Rangers forwards, so you would expect his number to fall a bit overall when adjusting his numbers to rate statistics. The table shows that Kreider falls to second on the team and among forwards in goals per-60, and falls outside of the top-5 among forwards in all assist categories; however, he maintains his status as second on the team in points. Kreider also falls a spot or two among forwards in shots, rebounds, hits and takeaways, maintains his status as the most undisciplined forward in terms of penalties taken, and improves in giveaways to rank among the best players on the team at not coughing up the puck.

	Individual Player Statistics
Stat	Team Rank	Forward Rank
TOI Per Game	10	2
Goals/60	2	2
Total Assists/60	9	7
Primary Assists/60	6	6
Secondary Assists/60	10	7
Total Points/60	2	2
Shots/60	4	4
Rebounds/60	4	3
Giveaways /60	18th most	10th most
Takeaways/60	14	12
Hits/60	3	2
Shots Blocked/60	17	9
PIMs/60	2	1
Total Penalties/60	2	1

*All data from naturalstattrick.com*

Conclusion: Kreider understandably falls a spot or two in most standard statistical categories when adjusted for per-60-minute production, as he was leapfrogged by players who also had impressive seasons last year but in less playing time, such as Michael Grabner and Pavel Buchnevich. However, he does rise towards the top of the rankings in giveaways, a testament to his ability to control the puck when it is on his stick. Overall, Kreider still ranks top-three among forwards in multiple critical rate statistics, including goals and total points, furthering the case that he is a top-tier Rangers player.

Advanced Statistics

Now it’s time to dig deeper, and look into the advanced statistics that illustrate specific details regarding Kreider’s impact on a variety of key elements of the game, such as possession, scoring chances and shot suppression. As previously stated, all data in this section specific to 5v5 play, and only rate-based statistics are used. While there is some value in calculating an individual player’s Corsi or high danger scoring chance count, it pales in comparison to the importance of rate-based numbers. Along these same lines, all data will also be in terms of team numbers while the player is on the ice, as this serves as a much more accurate representation of a player’s impact on the team than his individual stat counts.

In this section, I also introduce the concept of relative statistics, also known as WOWY (With or Without You) analysis. These relative statistics help demonstrate a player’s impact on their team, and mitigate the impact that the team has on the player. A quick example of this is that a generally poor possession team (such as the Rangers) will generally have a large number of players below 50% in Corsi For Percentage (shot attempts for/shot attempts against) and Fenwick For Percentage (unblocked shot attempts for/unblocked shot attempts against). Of the 21 qualified Rangers in my 2016-2017 statistical database, only five finished with a CF% above 50%: Adam Clendening, Chris Kreider, Mats Zuccarello, Derek Stepan and Brady Skjei. However, using WOWY analysis removes the impact of a poor overall possession team on a player’s numbers, and instead displays his numbers in terms of how much better the team was with him on the ice, compared to when he was off the ice. It isn’t perfect, as players who play the majority of their minutes with the same players (such as Girardi being anchored to McDonagh for most of the season) will still be impacted by those teammates, but it is a significant step in the right direction of isolating a player’s impact on his team.

The table a few paragraphs below illustrates how Kreider led the Rangers in a number of key rate and relative statistics during the 2016-2017 season. In terms of per-60-minute production, Kreider led the entire team in Corsi For, Fenwick For, Shots For, and Shots For %. In other words, per-hour of playing time, Kreider was the best player on the team at generating shots and shot attempts. When you narrow the focus to only Ranger forwards, the picture is even more impressive. Kreider led all forwards in 13 of the 21 rate statistics in the table below, including both Corsi For % and Fenwick For %, two of the best metrics at evaluating a player’s impact on possession among all rate statistics. Kreider also led the forward group in Scoring Changes For % and High Danger Chances Against, illustrating his abilities to generate scoring chances and to prevent the opponent from obtaining high danger chances.

Kreider’s numbers become even more notable when examining the relative metrics. He led the entire team in six of the 14 total relative statistics, and led Ranger forwards in 10. Kreider had the greatest impact on the team in Corsi, Fenwick, Shots and Scoring Chances For, as well as Fenwick For % and Shots For % and High Danger For %. He also led the forwards in all shot and chance suppression metrics—Corsi Against, Fenwick Against Shots Against, Scoring Chances Against and High Danger Chances Against—as well as Corsi For % and Scoring Chances For %. In other words, not only did Kreider have the largest relative impact among forwards on driving shot attempts, scoring chances and high danger scoring chances, but he also had the largest relative impact on preventing shots, shot attempts and scoring chances. The impressive defensive numbers Kreider put up last season were among the more surprising things I discovered while compiling my database, and they undoubtedly show that he is a much more defensively responsible player than most give him credit for.

Despite his extremely impressive impact on Rangers’ possession and scoring chances, Kreider ranked about average in what some would argue are the most important statistics in the table, the goal-related metrics. Kreider fared admirably in Goals For, ranking 6^th on the team in Goals For per 60 and 5^th in relative Goals For per 60. However, despite his strong defensive metrics discussed in the previous paragraph, he ranks 13^th in relative Goals Against per 60 and only 7^th in relative Goals For %. How is it that Kreider had the single greatest impact in relative shots, shot attempts and scoring chances against but was middling on impacting goals against? Here is where we take a look at the team’s shooting percentage, save percentage and PDO when Kreider was on the ice, which, for lack of a better term, help to illustrate how lucky (or unlucky) a team was when a certain player was on the ice. For whatever reason, Kreider ranks near the bottom in all three measurements, which strongly indicates that the team was flat-out unlucky when he was on the ice. This helps somewhat to explain how Kreider could have such a dominating effect on driving possession and scoring chances as well as preventing them relative to his teammates, yet have only average overall effects on goal scoring and prevention.

	Rate Statistics		Relative Statistics (WOWY)
Stat	Team Rank	Forward Rank	Team Rank	Forward Rank
Corsi For/60	1	1	1	1
Corsi Against/60	2	1	3	1
Corsi For %	2	1	2	1
Fenwick For/60	1	1	1	1
Fenwick Against/60	2	1	3	1
Fenwick For %	2	1	1	1
Shots For/60	1	1	1	1
Shots Against/60	2	1	2	1
Shots For %	1	1	1	1
Goals For/60	6	5	5	3
Goals Against/60	14	8	13	7
Goals For %	13	8	7	5
On-Ice Shooting %	18	12	N/A
On-Ice Save %	19	12
PDO	20	12
Scoring Chances For/60	3	3	1	1
Scoring Chances Against/60	2	1	3	1
Scoring Chances For %	2	1	2	1
High Danger Chances For	6	5	4	3
High Danger Chances Against	2	1	3	1
High Danger Chances For %	2	1	1	1

*All data from naturalstattrick.com*

Conclusion: Kreider was overwhelmingly the best Rangers forward throughout the 2016-2017 season in terms of driving possession and scoring changes as well as preventing them. No forward on the Rangers matched Kreider’s per-60 minute or relative production in over 60% of the key metrics identified in the table above. Despite this, Kreider had a relatively average impact on goal scoring and prevention, much of which can be explained by the unsustainably low team shooting and save percentages while he was on the ice, resulting in one of the highest PDOs on the team; in layman’s terms, the team was very unlucky while Kreider was on the ice. In this upcoming season, it is reasonable to expect the team’s shooting and save percentages to regress to the mean a bit, which would result in Kreider having similarly impressive goal-driving metrics to his relatively dominating possession-driving abilities.

Above Replacement Production

In recent years, a few leading hockey statisticians have created models that attempt to capture the overall value of a player in one single stat, similar to baseball’s now popularized WAR statistic. Two of the recent models include Dawson Spriging’s Goals Above Replacement (GAR), which has gained a lot of popularity (and criticism) in the hockey world since its introduction in 2016, and the most recent model, Luke Solberg’s Weighted Points Above Replacement (wPAR). While neither of these stats are perfect, and both statisticians openly admit they are always looking for ways to improve upon the models, they both provide valuable insight into a player’s overall value. In fact, earlier this year, noted hockey statistician Matt Cane wrote a piece for Hockey Graphs discussing how he is a skeptic of WAR-like stats, and feels they have limitations and issues, particularly with how they value defensive contributions. However, despite these limitations, he goes on to discuss that they are still valuable resources for hockey analysis, particularly when analyzing the individual components of the catch-all statistic. He closes his piece with a concise summary of the value of WAR-like statistics, which I feel should be iterated here before I go on to discuss how they value Chris Kreider: “Again, none of this is to say that GAR/WAR are the best metric we have available today or should be the only thing we use, but there’s clearly some strong arguments for using it to start a conversation, or as a sanity check on subjective evaluations.”

GAR and wPAR both feature individual components that represent various aspects of a player’s value, that when combined form the overall value statistic. Both models have similar components, such as attempts at measuring offensive and defensive values, penalties drawn and taken, and faceoffs; however, they take different approaches, particularly with how they account for offensive and defensive value. GAR separates even strength offense and power play offense, and has a dedicated even strength defense component, while wPAR does not separate strength states (which Luke specifically references in his methodology), and instead focuses on counting stats and differential stats, the latter of which features relative Corsi differential as a way to account for defensive value. It should also be noted that there are differences in the way both stats calculate the player impacts of even the similar components, such as penalties taken, penalties drawn, and faceoffs.

Chris Kreider ranks among the best Rangers in both the GAR and wPAR models, but wPAR favors Kreider more. He ranks fifth on the NYR in overall GAR, and third among forwards, behind Mats Zuccarello and Mika Zibanejad. He ranks first on the entire team in wPAR and wPAR per 60, and 21^st and 27^th in the entire NHL in wPAR per 60 and total wPAR, respectively. In layman’s terms, Dawson Spriging’s Model ranked Kreider as the third best forward on the Rangers throughout the 2016-2017 season, while Luke Solberg’s model had him as the best skater on the team, and among the top-30 in the entire NHL. When you look at the individual components, GAR ranks Kreider as the best Rangers player in terms of even strength offense and second best in power play offense, while wPAR has Kreider as the best forward in counting stats and differential stats; all sentiments that many of the previous stats discussed in this article align with well. The biggest detriment to Kreider’s value in the eyes of both models should come as no surprise to Ranger fans: penalties taken, where Kreider ranks in the bottom two on the entire team for both models. You can see the value drained from Kreider due to penalties in GAR’s Pure Overall Value component, which consists of only the even strength offense, defense and power play offense components. After removing penalties and faceoffs from the equation, Kreider ranks third on the entire team (up from 5^th in overall GAR), behind only Ryan McDonagh and Mats Zuccarello.

GAR – Goals Above Replacement (Dawson Spriging’s Model)
Stat/Component	Team Rank	Forward Rank
Even Strength Offense	1	1
Even Strength Defense	6	4
Power Play Offense	2	2
Drawing Penalties	8	4
Taking Penalties	21	13
Faceoffs	N/A
Even Strength Overall Value	3	2
Pure Overall Value	3	2
Overall GAR	5	3
wPAR – Weighted Points Above Replacement (Luke Solberg’s Model)
Counting Stats	2	1
Differential Stats	1	1
Penalties Taken	20	13
Penalties Drawn	6	3
Faceoffs	N/A
Weighted Points Above Average (wPAA)	1	1
wPAA Per 60	1	1
Replacement Points	4	3
Weighted Points Above Replacement (wPAR)	1	1
wPAR Per 60	1	1

Conclusion: Both WAR-like statistics rated Kreider as a top-three forward and top-five skater on the Rangers roster during the 2016-2017 season, with wPAR ranking Kreider number one overall. When examining the components, both stats show that Kreider is an elite offensive player and a better than average defensive player who also draws a decent amount of penalties. However, both stats clearly illuminate the biggest flaw in his game: his lack of discipline which leads to far too many penalties taken. If Kreider simply broke even in terms of his penalties taken in the wPAR model last season, he would’ve risen three spots in the total wPAR rankings to 24^th in the NHL, residing between Nicklas Backstrom at 25 and Mark Scheifele at 23.

Additional Data: Zone Entries, Redirected Goals, Clutch Play and HERO Charts

To this point, I have now spent over 3,500 words and provided an overwhelming number of statistics to show that Chris Kreider was easily among not only the best forwards on the Rangers, but best players throughout the 2016-2017 season. However, if you still need more convincing, or just want even more ammo for your hockey debates, let’s examine some more specialized and nuanced data.

First, we have an abundance of zone entry data from last year, courtesy of Corey Sznajder, who scraped and compiled all of the data for the entire season himself as part of his All Three Zones Project, and Sean Tierney, who used the data to create intuitive visualizations to help us all better analyze and interpret it. According to Corey’s data, Chris Kreider ranked as the best Rangers player in both recovered dump-ins and recovered dump-in %, a stat that should come as no surprise as his big body and speed are great assets for recovering pucks in the offensive zone. However, Kreider ranked top-6 on the team in every single zone entry microstat, and top-three in total zone entries, carry-ins, dump-ins, entry passes, shots off carries and shots off entries. In layman’s terms, Kreider was easily among the top players on the team in terms of helping the Rangers effectively enter the offensive zone.

Another interesting stat was illuminated by Travis Yost’s early-August article that examined which players are the best in the NHL at redirecting pucks on net. Yost introduced the article by discussing the importance of redirecting pucks, and showed that the shooting percentage of tipped shots was over 20%, and approximately double the second most effective shot type: backhand shots. The meat of the article was a table that listed the NHL leaders in terms of number of goals scored off of tipped or redirected shots from 2014-2017. Chris Kreider led the entire NHL, with 19 such goals, one ahead of Joe Pavelski, a man who is often considered among the very best in the NHL at tipping shots. This is particularly impressive when you consider that Pavelski plays on a line with the great Joe Thornton, easily one of the best passers of his generation who can “pass” the puck into a deflected shot better than pretty much anyone in the NHL.

Moving on to another nuanced conversation, this offseason I witnessed multiple people on social media and in person debate whether Chris Kreider is “clutch.” Fortunately for this discussion, Rob Vollman recently published the most recent version of his Hockey Abstract (which I highly recommend for anyone interested in learning about hockey), which includes a chapter titled, “Who is the Best Clutch Scorer.” In it, Vollman lays out multiple different models that attempt to measure clutch scoring ability before arriving at his final model. To summarize, the final model weighs all goals scored based on the win probability of the leading team and time remaining in the game in an attempt to measure who is scoring the most “clutch” goals. Vollman considers data over the prior three seasons in his final table (14-15 to 16-17), and draws comparisons to overall NHL rank in total points (unweighted) and goals compared to his adjusted Weighted Goal Points (WGP) model, and published the results in a Clutch Performance Results table. While Kreider is tied for 39^th in the NHL in total goals over the previous three seasons with 70, he rises all the way to 25^th in adjusted WGP. Further, among all 30 players listed on Vollman’s Clutch Performance Results table, Kreider had the third highest rise in adjusted WPG compared to simple goals scored, with a 17.91% change, behind only Jeff Skinner (26.37%) and Anders Lee (19.64%). Translation: a very large relative portion of the goals that Kreider scored over the prior three seasons came in the most crucial moments of close games, and he should be considered among one of the more clutch goal scorers in the NHL.

Finally, let’s take a look at some HERO chart comparisons between Chris Kreider and some forwards listed on NHL.com’s recent Top 20 NHL Wings article (which I personally have many disagreements with, but it is fine for our purposes here). For those who are unfamiliar, the HERO charts tool was created by hockey statistician Domenic Galamini, and its comparisons serve as a great, intuitive resource for benchmarking skaters against their peers and to skater archetypes. The charts compare players on five key metrics: ice time, goals, primary assists, shot generation and shot suppression. The tool uses only rate statistics, and for all categories other than ice time considers on 5 on 5 production (for the same reasons I outlined above), and standardizes all statistics and scales them from 0-10, with 5 as the average (mean). The charts also encompass the last three NHL seasons, which are weighted based on temporal proximity (recent seasons are valued more highly). For more information on the tool’s methodology, you can read this article by Domenic.

In this first HERO chart, we see how Kreider stacks up against the prototypical first line winger. Kreider got slightly less ice time than the average first line winger, was right on par in terms of goal scoring, and was above average in primary assists, shot generation and shot suppression. In other words, Chris Kreider is undoubtedly a first line winger in today’s NHL.

Despite not being on the NHL.com’s list of top-20 wingers, Kreider compares very favorably to multiple players on the list, including Phil Kessel and Cam Atkinson, whom NHL.com ranked 12^th and 16^th, respectively. Kreider features similar levels of goal production to both players, but beats them in all other categories (other than ice time). Kreider also stacks up equivalently to multiple additional players on the top-20 list, including Johnny Gaudreau (10) and TJ Oshie (19). Long story short, based on these HERO charts at least, Kreider has a strong argument for inclusion on anyone’s list of top-20 NHL wingers.

Conclusion: In this section, I threw a lot of nuanced and specialized data at you all, so here is a very brief summary. Chris Kreider was one of the most effective Rangers forwards at successfully entering the offensive zone in the 2016-2017 season. He is easily among the best players in the NHL at scoring deflection goals, which have by far the highest shooting percentage of any shot type, and he is also among the most clutch goal scorers in the league. Finally, his production last year stacks up favorably to multiple players listed in the NHL.com’s top-20 NHL wingers list, and he should clearly be in the conversation as a top-20 winger himself.

Player Usage

Now that I’ve presented a massive statistical case for Chris Kreider, there is one last key factor that must be analyzed: player usage. The manner in which a coach deploys a player can greatly affect that player’s production. To accurately analyze a player’s deployment, you must consider much more than simple ice time totals. You must examine zone deployment (offensive, neutral and defensive zone usage) as well as the quality of competition a player faces. If a coach “shelters” a player, and therefor typically plays them in only offensive zone situations against the opponents’ bottom lines, it can greatly inflate the player’s production, and vice versa. So, to truly put all of the data I presented earlier into perspective, we must understand how Kreider was deployed throughout the season.

The table below shows Kreider’s team and forward ranks in terms of zone faceoffs, which are used to help us understand which situations a coach likes using a player, since he can switch his lines to dictate which players he wants on the ice prior to most faceoffs. Kreider received the third most offensive zone faceoffs among forwards per-60 minutes last year, and ranked 6^th and 9^th for neutral zone and defensive zone faceoffs, respectively. This ended up with Kreider having the highest offensive zone start percentage among forwards, and third overall on the team, behind Adam Clendening and Brady Skjei. However, it must be noted that Kreider’s offensive zone start percentage was 53.72%, while Derek Stepan had the 9^th highest percentage on the team at 52.01%, a mere 1.71% less. Translation: Alain Vigneault definitely preferred to have Kreider play a bit more in the offensive zone, which is typical deployment of a top-6 forward and could impact his numbers, but it was not a severe shelter by any stretch of the imagination, and so the impact was likely minimal. For additional context, Clendening started in the offensive zone a whopping 67.63% of the time, while Kevin Hayes inexplicably had the lowest offensive zone start percentage at 41.17%.

	Usage Statistics
Stat	Team Rank	Forward Rank
Offensive Zone Faceoffs/60	5	3
Neutral Zone Faceoffs/60	9	6
Defensive Zone Faceoffs/60	14	9
Offensive Zone Start %	3	1

*All data from naturalstattrick.com*

Rob Vollman’s Hockey Abstract 2017 also includes player usage charts for each team, which are valuable resources for evaluating player deployment. The charts display data based on a weighted three-year average, so the offensive zone start percentages are more comprehensive than the data in the table above, which only considers last year. The vertical axis shows the quality of competition faced by the player; the higher a player is on the chart, the stronger the competition they typically faced. The horizontal axis depicts a player’s offensive zone start percentage; the further to the right a player is, the more often they started in the offensive zone. Lastly, the circles indicate a player’s impact on possession, or more specifically, a team’s per-minute Corsi differential when the player is on the ice. The size of the circle indicates the level of impact the player has while the color indicates whether the impact was positive or negative; a large shaded circle means the player had a strong positive impact, while a small white circle means the player had a small negative impact. By this point it should come as no surprise that Kreider has the biggest shaded circle on the team. Kreider is still among the leading forwards in terms of offensive start percentages, and he is third behind Rick Nash and Ryan McDonagh in terms of the quality of competition faced. Translation: While Kreider often played in the offensive zone, he also typically faced the best players on the opposing team and still put up fantastic possession numbers.

Conclusion: Alain Vigneault likes to use Chris Kreider in a more offensive-oriented role, which is typical of a top-6 forward and can have a positive impact on his overall numbers. However, Kreider’s role in terms of zone starts is on par with many of the team’s other top forwards, and he is often on the ice against the stiffest of competition. So, all in all, AV’s deployment of Kreider may have a slight impact on his overall production, but it is likely minimal at best, so we can trust all of the data stating that Kreider was easily among the best Rangers in 2016-2017.

Final Conclusion

So there you have it, the overwhelming statistical case that Chris Kreider was the best forward on the Rangers during the 2016-2017 season. Kreider is understandably a polarizing player, and the data shows that Kreider can be a dominating force but also takes far too many penalties. He is 26 years old, so he is currently in the heart of his prime. Despite the undisciplined nature of his game at times, Chris Kreider unquestionably has been, and currently is, a true first line winger in today’s NHL, and easily ranks among the best skaters on the New York Rangers roster. He was the strongest driver of possession and scoring chances on the Rangers last year, and also among the team leaders in many standard counting stats, including leading the team in goals. He also is a strong asset in helping the team enter and establish play in the offensive zone, and better than most realize in defending his own zone. Finally, Kreider puts up all of this great production in a role typical of a top-6 forward, and often against the opponents’ top players. If Kreider can continue to evolve his game a bit, and become a more disciplined player, there is ample evidence to suggest that he can raise his game to even another level, and not only cement his status as one of best wingers on the Rangers, but one of the best in the entire NHL.