As a reminder, the Blueshirts Breakaway Hockey Lexicon is a huge resource that attempts to explain all major advanced stats and concepts in an easy-to-understand manner, and I encourage you to check it out if you are interested in learning more about fancy stats. The two previous editions of this article series focused on Game Score and Teammate Relative Statistics. As always, I also encourage you to reach out to me on Twitter if you have any questions about anything.[/text_output][custom_headline type=”left” level=”h5″ looks_like=”h5″ accent=”true” id=”” class=”” style=””]What is WAR?[/custom_headline][image type=”thumbnail” float=”none” src=”2462″ alt=”” href=”” title=”” info_content=”” lightbox_caption=”” id=”” class=”aligncenter” style=””][text_output]WAR stands for Wins Above Replacement (just like baseball) and is one of a handful of catch-all statistics that can be used to evaluate hockey player performance. Specifically, WAR aims to measure a player’s value by calculating the number of wins a player contributes to their team above that of a “replacement level player.” The now-defunct WAR On Ice hockey stats site (both site creators were hired by NHL teams, so the site has since been shut down) made the first prominent foray into hockey catch-all stats with their WAR statistic, which they published a series of posts about. More recent catch-all models for individual player evaluation include Dawson Sprigings’ (better known as DTM About Heart) Goals Above Replacement (GAR), The Solberg twins’ (Evolving Wild) Weighted Points Above Replacement (wPAR, and they are currently working on a new GAR model) and Manny Perry’s (Corsica creator) version of WAR. This post will specifically focus on Manny Perry’s WAR, which anyone can access from Corsica.
Manny’s WAR model aims to quantify the total value a player brings to the team by accounting for a myriad of individual components. Specifically, WAR consists of the following eight components for skaters: offensive shot rates, defensive shot rates, offensive shot quality, defensive shot quality, shooting, penalties taken, penalties drawn and zonal transitions. Manny also has a WAR statistic for goalies that measures their ability to prevent goals above that of a replacement level goalie. Since WAR measures how much better a player is than a “replacement player,” it should be noted that Manny defines a replacement player as “one who can be signed at the league minimum salary.”
Unlike Game Score, which has an easy-to-understand formula that anyone with a calculator and access to the game data can calculate themselves, WAR is a complex model that the vast majority of individuals would never be able to compute themselves (myself included), so I’m not going to bother getting into the weeds of how it is calculated. It is worth noting however that Manny states in his methodology that he has incorporated multiple control variables into the WAR equation to avoid any overlap between WAR components and to adjust for factors beyond a player’s control (e.g. game states, home ice advantage and zone starts).[/text_output][image type=”thumbnail” float=”none” src=”2463″ alt=”” href=”” title=”” info_content=”” lightbox_caption=”” id=”” class=”aligncenter” style=””][custom_headline type=”left” level=”h5″ looks_like=”h5″ accent=”true” id=”” class=”” style=””]How Can I Use WAR?[/custom_headline][text_output]Catch-all statistics like this and baseball’s now-popularized WAR are valuable metrics that help us quantify the overall value a player brings to their team. Nobody will argue that these catch-all stats are perfect and that they should be the end all be all in player analysis (well, nobody worth listening to will argue that at least), but they most certainly are valuable analytical tools. In fact, not even the analysts who created the statistics would argue that their models are perfect. In a Hockey Graphs podcast in early October of 2017, Dawson Sprigings flat out stated that he hopes people examine his GAR model and offer him suggestions with how they feel it can be improved. It is the hope of Dawson and many other leading hockey statisticians that these productive conversations, as well as improved access to data (such as player tracking or improved passing data for example), will lead to even better and more accurate models. So please, whether you are a complete stats nerd or you believe traditional scouting methods are the only worthwhile analysis techniques, don’t be that guy that just looks at a stat and shouts “this is stupid, watch the game!” Let’s have a conversation about it. Why do you think it is stupid? What about it do you think can be improved? What would you do differently? These sorts of conversations can lead us to better data and analysis that we all can enjoy.
Manny Perry states in his Corsica blog post, The Art of War, that the underlying theory of war is to “measure a player’s value according to the fraction of wins they contribute above what a replacement-level player could provide.” Unlike Game Score, WAR is not meant to be used at the single game-level, and instead is similar to the baseball version, where it is best applied over the course of an entire season. However, WAR is similar to Game Score and the baseball version of WAR in that it is a cumulative statistic. Because of this, we can view WAR as a rate stat (per-60) in order to better compare players that received different levels of playing time throughout the season. Corsica also provides a WAR per-82 games played statistic, that measures the per-game WAR of a player, which is very different than the per-60 minutes of ice time version. You can also view WAR data in terms of all of its individual components to dissect which players are the best at very specific elements of the game that impact a player’s value, such as shot rates, shot quality, shooting ability, penalties and zonal transitions.
For the purposes of this discussion, let’s start with the practical applications of viewing the data of the individual components first, and then we will build up to the overall WAR metrics. In the table beneath, I list the leaders in each of the WAR components throughout the 2017-2018 season (minimum 700 minutes played to qualify) at both the NHL-level and specifically for the New York Rangers.
As a reminder, all of the WAR data is meant to depict how the player compares to a replacement level player, and it is not a raw measurement of that particular statistic. For example, if a player has a shot rates against WAR of 0, it does not mean the team allowed 0 shots against while the player was on the ice. Instead, it means that the player performed at the exact level of a replacement level player in terms of shot rates against. Because of this, to get the differentials, you add the individual stats together to obtain the overall value the player gets instead of subtract. For example, if you just want to measure pure shot rate differential of a player, you subtract the shot rate against from the shot rate for; however, because in WAR you are calculating value compared to a replacement level player, you would add the shot rate for WAR to the shot rate against WAR to get shot rate differential WAR.[/text_output][image type=”thumbnail” float=”none” src=”2464″ alt=”” href=”” title=”” info_content=”” lightbox_caption=”” id=”” class=”aligncenter” style=””][text_output]There are a few things we can take away from analyzing the components of WAR for the 2017-2018 season. First and foremost, the Rangers were absolutely pathetic defensively this year, a notion that is likely not breaking news to any of you reading this. The fact that the Rangers didn’t have a single player above replacement level in terms of shot rate and shot quality against is remarkably alarming, and is probably just about the biggest indictment of Alain Vigneault’s defensive system there is. In fact, the Rangers were the only team in the entire NHL that did not have a single player above replacement level in either shot rate against WAR or shot quality against WAR. Even the Islanders, who had a near-historically bad season defensively, at least had Andrew Ladd and Cal Clutterbuck that posted above replacement level projection in one of these stats.
Another Rangers-related takeaway is that Kevin Hayes had a very strong season. Many in the Rangers fanbase have come to realize that Hayes has turned into an effective two-way player. However, I believe many don’t grasp how talented of a shooter Hayes is, which likely is due to the fact that Hayes maddeningly does not shoot nearly enough. Hayes led the Rangers in overall offensive WAR with a 2.48, which was largely bolstered by his shooting WAR. The shooting WAR component measures, “the ability to convert shots at a rate better or worse than expected.” In other words, shooting WAR measures a player’s ability to put the puck in the net, relative to the quality of chances he obtains. While nobody will mistake Kevin Hayes for Patrick Laine or Brock Boeser (who finished 1st and 3rd in the NHL in shooting WAR, respectively), Hayes led the team with a shooting WAR of 1.4, good for 25th in the entire league.
At the NHL level, I think there weren’t many surprises as to who led each component, particularly the offensive-oriented ones. Nobody should be shocked that Sidney Crosby led the league in shot rate differential WAR, that Connor McDavid cleaned house in shot quality differential WAR, that Johnny Gaudreau had the best penalty differential, or that Patrick Laine reigned supreme in shooting WAR. It’s also not the least bit surprising to me that Ryan Suter finished the season with the best shot quality against WAR. However, I’d be lying if I said that I wasn’t surprised by Mikko Koivu accumulated the best defensive WAR. Don’t get me wrong, I absolutely believe that Koivu is one of the best defensive forwards in the league, and the fact that he isn’t even a finalist for the Selke is a bit of an indictment as to the flawed methodology of measuring the best “forward who demonstrates the most skill in the defensive component of the game.” That said, I never would’ve guessed that even a forward as defensively gifted as Koivu would have the highest defensive WAR in the NHL. However, when you dig into his numbers and how much better the Wild were this season at suppressing shot attempts and scoring chances while he is on the ice, it makes a lot more sense.
Now that we’ve discussed the individual components of WAR, let’s examine the overall WAR numbers. Below is a table of the Rangers and league leaders in the three skater versions of the aggregated WAR statistics—WAR, WAR per-60 minutes and WAR per-82 games—as well as the goaltending WAR metrics (minimum 700 minutes played to qualify).[/text_output][image type=”thumbnail” float=”none” src=”2466″ alt=”” href=”” title=”” info_content=”” lightbox_caption=”” id=”” class=”aligncenter” style=””][text_output]The primary takeaway for Ranger fans here is that Kevin Hayes had a damn good season, and the Rangers absolutely should prioritize resigning him above the other RFA forwards in Namestnikov, Spooner and Vesey. Obviously, this is all cost-dependent, but if the Rangers decide they need to ship out one (or more) of their pending RFAs, I would value Hayes significantly higher than the rest. Don’t get me wrong, I’m not saying Hayes is untradeable; to be frank I don’t believe there is such a thing as an untradeable player, and every player can be had for the right price. But, I’d really need to be impressed by an offer to be willing to part ways with Kevin Hayes at this point if I’m GM Jeff Gorton.
At the NHL level, I would say that this WAR data, coupled with the Game Score data I shared in my previous piece, clearly demonstrate that Connor McDavid was the most outstanding player this season. I’m not about to get into arguments over what the definition of the MVP is or should be, and whether or not you should include McDavid on your ballot. But, in my opinion, McDavid clearly had the best statistical season, and he absolutely has a spot on my non-existent five-man Hart Trophy ballot.
Sean Tierney, writer for Hockey-Graphs and The Athletic, recently made excellent WAR data visualizations that are available on his Tableau Public profile. His visualization interface conveniently allows you to filter data by team, position or WAR value and presents the applicable data in the form of stacked bar charts. It breaks down the overall WAR value by component, and lists the positive contributions to the right and negative to the left. You can hover-over any specific listed segment to view the player’s overall WAR, offensive WAR, defensive WAR, WAR/82 and the value of the specific component (e.g. shot quality for, shot rate for, shooting etc.). These visualizations help to make the WAR data more digestible and shareable for the average fan, as it clearly illustrates how players perform in each component of WAR and how they compare to other players. Below is a visualization for the overall NHL leaders in WAR for the 2017-2018 season for Sean Tierney’s tool.[/text_output][image type=”thumbnail” float=”none” src=”2467″ alt=”” href=”” title=”” info_content=”” lightbox_caption=”” id=”” class=”aligncenter” style=””][text_output]Sean’s WAR visualization tool also has a number of additional noteworthy views worth exploring, including Goalie WAR, WAR/60 and Team WAR. The Team WAR view, pictured below, provides aggregated team-level WAR component data for all 31 NHL teams. As you can see, the Winnipeg Jets, Tampa Bay Lightning, Nashville Predators, Vegas Golden Knights and Boston Bruins occupied the top-5 team WAR spots, while the bottom-5 consisted of the Vancouver Canucks, New York Rangers, Detroit Red Wings, Buffalo Sabres and Ottawa Senators.[/text_output][image type=”thumbnail” float=”none” src=”2468″ alt=”” href=”” title=”” info_content=”” lightbox_caption=”” id=”” class=”aligncenter” style=””][custom_headline type=”left” level=”h5″ looks_like=”h5″ accent=”true” id=”” class=”” style=””]How Can I Access WAR Data On My Own?[/custom_headline][text_output]The fantastic hockey stats site Corsica is the home of Manny Perry’s WAR model data. To access it, you must click the Skaters tab in the navigation menu at the top, and then select WAR from the subsequent dropdown menu.[/text_output][image type=”thumbnail” float=”none” src=”2472″ alt=”” href=”” title=”” info_content=”” lightbox_caption=”” id=”” class=”aligncenter” style=””][text_output]The WAR page by default will display WAR data for the current season and for all players that have accumulated at least 200 minutes of ice time throughout the season across all teams and all positions. Users can adjust any of these parameters, and search for specific players, from the filter options at the top of the screen. The table beneath is sortable by every column. One note is that there are more columns than what will fit on most computer monitors, so make sure to use the horizontal scroll bar at the bottom to be able to view all of the columns. The three columns furthest to the right are perhaps the most important: WAR, WAR/82 and WAR/60. The screenshot below depicts the top-13 players in the NHL in overall WAR for the 2017-2018 season.[/text_output][image type=”thumbnail” float=”none” src=”2471″ alt=”” href=”” title=”” info_content=”” lightbox_caption=”” id=”” class=”aligncenter” style=””][text_output]If you want to access the data visualizations I shared earlier in this article, you can do so from Sean Tierney’s Tableau Public profile. I’d also highly recommend following Sean on Twitter, as he frequently shares his content on Twitter, is an engaging follow, frequently answers questions posed to him by his followers and also has links to his articles and Tableau profile in his bio. When you arrive at his Tableau Public profile, the WAR visualization tool if bookmarked as the featured. Clicking on the WAR option will bring users to the Overview tab, which houses the player-level WAR charts I shared earlier, which can be filtered by team, position or WAR value. You can easily navigate between all of the available views from the tabs at the top of the screen, and you can click the Favorite button to facilitate future access.[/text_output][image type=”thumbnail” float=”none” src=”2473″ alt=”” href=”” title=”” info_content=”” lightbox_caption=”” id=”” class=”aligncenter” style=””]
Author: Drew Way
Diehard New York Rangers fan since 1988! Always has been fascinated by sports statistics, and is a big proponent of supplementing analytics with the eye test. Also a big Yankees, Giants and Knicks fan.