Two key elements in assessing the offensive component of any team’s overall game can be simply categorized as “creating” scoring chances and then “converting” on those scoring chances that were created. Simply put, you need to create before you can convert.
We can also assess each player, individually, by how well they are doing at creating scoring chances and then how well they are doing at converting on those chances they created. To further refine resolution, we can add time on ice to normalize comparisons between all skaters. This posts takes a look at the Capitals skaters and their scoring chances created and the chances converted per minute of ice time.
[Data utilized in this post is provided by Natural Stat Trick, Money Puck, NHL.com and NoVa Caps Advances Analytics model. If you have any questions or would like to learn more about the terms in the post you can check out our NHL Analytics Glossary.]
You have to create scoring chances before you can convert them, and thus, we should look first at how well each Capitals skater is doing at generating scoring chances. One stat to consider is expected goals for per minute of ice time.
Expected Goals For Per Minute Of Ice Time
The term “expected goals” can be best described as a way of quantifying scoring chances based on shot location and type of shot indexed with historical success rates (goals) over time. If a shot type and location has scored a goal on 5% of all the shots ever taken from that spot and by that shot type, an expected goals for (xGF) value of .05 is assigned to that particular chance.
If we compare the expected goals for values for all players and divide by minutes of ice time for each player, we can see how effective each player is at creating scoring chances on a normalized basis (per minute of ice time). The following graphic plots just that. [ Click to enlarge]
There are really no surprises at the top of the list. Connor McMichael, Alex Ovechkin, John Carlson and now Anthony Mantha, continue to generate the most scoring chances and at the best rates (chance/minute). Matt Irwin has done really well, but his sample size is fairly small. We’ve written at length about how well McMichael generates offense.
Possibly the first surprises are Martin Fehervary and Conor Sheary, although we’ve also provided some depth on Fehervary’s “fantastic” play in an earlier article. Sheary, who was signed by the Capitals just three weeks before the start of the 2021-22 season, has been nothing but tremendous fiscal value for the Capitals.
Justin Schultz and Aliaksei Protas could also be considered a surprise in this specific instance.
High-Danger Shot Attempts Per Minute Of Ice Time
Drilling down in the scoring chances generated data we can also look at who is working the puck deep and generating shots from the high-danger area. We can also apply that data to time on ice to get a normalized value for comparing all skaters. [Click to Enlarge].
The players generating high-danger shots at the best rates are similar to the expected goals for presented in the first graphic, although there are a few interesting tweaks. Michal Kempny and Trevor van Riemsdyk show up in the top half of the plot.
It’s also interesting that Aliaksei Protas generates high-danger chances at an impressive rate (chances per minute).
There several handy stats to assess ability to convert on chances. We can start with the basic points per minute of ice time at even strength (5v5).
Points Per Minute Of Ice Time
One of the most basic assessment stats is points per minute of ice time. It can also be used to determine a player’s true “value” by dividing by contract dollars (Points/(Min/$$). The graph below plots the basic points per minute for each capitals skater at even strength. [Click to enlarge].
Alex Ovechkin, Anthong Mantha, Evgeny Kuznetsov, Conor Sheary and T.J. Oshie are generating the most points per minute of ice time. Joe Snively is also making the best use of his time, but the sample size is relatively small.
Connor McMichael falls away, somewhat, and Justin Schultz and Martin Fehervary fall all the way to the bottom of the list. They’ve been generating Chances but not converting. It’s not too surprising defensemen Schultz and Fehervary fall off, but the amount of falloff is somewhat surprising.
Goals For Minus Expected Goals For
Another metric for gauging overall offensive effectiveness is the difference between actual goals scored and expected goals for (GF – xGF), also known as goals differential or goals above expected. The following graph plots the the difference of goals scored and expected goals for each Capitals skater.
Tom Wilson leads all Capitals skaters in overall goal scoring efficiency, outscoring his expected goals by 13.89. Nick Jensen ( 11.36) has the third best differential and Dmitry Orlov (5.56) has the sixth best differential. The other names at the top of the list align with those skaters at or near the top of the list in the first metric (xGF/TOI).
The bottom of the list, those generally in the negative, have been scoring (converting) less than expected.
Offensive Zone Starts
Again we can reference the percent of offensive zone shift starts for each player to get a sense of who may have the advantage of generating chances (starting shifts in the offensive zone a majority of the time) and who is less likely to create scoring chances because they begin a majority of their shifts in the defensive zone.
It’s no secret that the Capitals desperately missed T.J. Oshie and Anthony Mantha when they were out during the first half or so of the season. The aforementioned metrics continue to amplify their importance.
Of the names outlined in the previous two graphs, T.J. Oshie and Anthony Mantha have the least amount of offensive zone starts, further highlighting what they do on the offensive end. In other words, they are doing a good job of generating chances, converting on those chances, and doing so with relatively limited offensive zone starts.
Nic Dowd and Garnet Hathaway have been highly praised for the last two season and rightfully so, but they deserve additional honorable mentions in this post. They are assigned to stop opposing teams top lines, play defense, but have also generated offense all while getting relatively little in the way of offensive zone starts.
By Jon Sorensen