Outside of Niklas Lidstrom’s playing status next year, no story will dominate the hockey world quite like the fast-growing goalie controversy in Vancouver. The Canucks certainly have an awkward situation on their hands – on one hand they have an elite goalie who has fallen out of favor with the majority of the fanbase, (unfairly) becoming the scapegoat for a team that has come up short on their expectations. On the other hand, they have a younger, cheaper goalie that has shown great promise, winning over the Vancouver fanbase in the process. I make two assumptions here, one is Vancouver will not keep both, the other is that other teams will make offers for Schneider. I do believe Luongo will be the odd man out, but I certainly do not believe that as strongly as others, and I will explain this below.
Ostensibly, Vancouver will begin the process by comparing the value of Luongo’s contract to a range of contracts that they theoretically see themselves giving to Schneider. But there is more – and at this point is where I believe many stop the analysis. The very factors that make Schneider more valuable than Luongo to the Vancouver Canucks also make him more valuable to every other team.
As outsiders, there is no way we can accurately speculate on just how much more valuable they see Schneider. It’s easier (but still more difficult, and still beyond the scope of this article) to identify each player’s relative trade value. What we can do, though, is talk about how the trade value of one impacts the necessary value of the other, and from there, how that relationship impacts Vancouver’s ultimate decision.
We begin with obvious – there is a whole lot of uncertainty in the market for Roberto Luongo. On production alone he is a hot commodity, but his contract precludes many teams (and possibly teams that have been speculated as trade partners) from acquiring him. There are other teams that both need goaltending and can handle the financial burden (Chicago and Edmonton), but it is not clear that Vancouver is willing to trade an elite player to one of their biggest rivals. I believe the decisions of these smaller market teams (Columbus, Florida, Tampa Bay) on whether or not they are willing to take on the burden to be one of the biggest factors in Vancouver’s ultimate decision, because if the market for Luongo becomes liquid, then we can almost guarantee that he’s gone. But if Vancouver finds the offers to be lacking, then the possibility of an offer for Schneider that closes the gap between the value I mentioned earlier becomes more and more likely.
In other words, there is an inflection point in this scenario – some point where Schneider’s advantage over Luongo is mitigated by the value Vancouver could acquire by trading Schneider. What that exact point is can only be known by Vancouver, but assuming that Luongo has played his last game as a Canuck neglects a very important part of this calculus.
Showing posts with label Vancouver Canucks. Show all posts
Showing posts with label Vancouver Canucks. Show all posts
Saturday, May 5, 2012
Friday, April 27, 2012
WC Second Round Preview Podcast
Chase, Triumph and I were joined by podcast regular Corey (@ShutdownLine) from Shut Down Line for a breakdown of the Western Conference first-round series and previews of Nashville - Phoenix and Los Angeles - St. Louis.
Download (Right Click, Save Link As)
Click Here to subscribe on iTunes.
Tuesday, April 10, 2012
Playoff Preview Podcasts: Canucks - Kings
This edition follows a little different format due to timing issues. Cam Charron (@CamCharron) of The Province and Canucks Army, to whom I apologize for my Cherryesque pronunciation of his name, was kind enough to take time away from visiting his family on the holiday weekend to talk Canucks. We wrapped things up by bringing on our own Chase to give his perspective on the Kings.
Download (right-click, save as)
Download (right-click, save as)
Tuesday, March 6, 2012
The Driving Play Podcast - Trade Deadline Recap
Good evening, friends. We're finally back with the final installment of our NHL trade deadline series of podcasts. This time, we're evaluating the winners, losers, and the catastrophe that was Scott Howson and the Columbus Blue Jackets.
We were also privileged to again be joined by Dirk Hoag of On the Forecheck to get his reaction to our take on Nashville's deadline dealings.
All four of us were able to get together to record this edition, so go ahead and give it a listen.
Download (Right Click and choose "Save Link As...")
We were also privileged to again be joined by Dirk Hoag of On the Forecheck to get his reaction to our take on Nashville's deadline dealings.
All four of us were able to get together to record this edition, so go ahead and give it a listen.
Download (Right Click and choose "Save Link As...")
Wednesday, October 5, 2011
Driving Play Season Preview: Teams 3-1, The Stanley Cup Favorites
3. Washington
Key Statistics:
Fenwick- 50.4%
Even Strength Shooting%- 7%
Even Strength Save%- .928
2010-2011 Review:
Washington's year last year was filled with up and downs. Most notable was their extended losing streak in December that provided some sweet Bruce Boudreau rants on '24/7'. After this lull however, the team played extremely well, finishing first in the Eastern Conference and second overall in total points. Unfortunately for Caps fans, the playoffs brought another early exit, as the Caps were swept in the Eastern Conference Semis, a series which was much closer than a sweep would indicate.
Offseason Changes:
Where do we start? George McPhee was a busy man this offseason, with resigning key players (Brooks Laich, Karl Alzner), fleecing teams in trades (Semyon Varlamov for a 1st round pick), signing elite goalies for a back-up's cap hit (Tomas Vokoun), and filling out the rest of his team with veteran players capable of playing tough minutes. The team added precious forward depth with the additions of Joel Ward, Troy Brouwer, and Jeff Halpern. They also bolstered their blueline with the addition of Roman Hamrlik. George McPhee took a team that was already very good and turned them into Stanley Cup favorites. The Capitals now have it all.
Key Questions for 2011-2012:
Will the Capitals be better territorially this year?
2. Vancouver
Key Statistics:
Fenwick- 50.4%
Even Strength Shooting%- 7%
Even Strength Save%- .928
2010-2011 Review:
Washington's year last year was filled with up and downs. Most notable was their extended losing streak in December that provided some sweet Bruce Boudreau rants on '24/7'. After this lull however, the team played extremely well, finishing first in the Eastern Conference and second overall in total points. Unfortunately for Caps fans, the playoffs brought another early exit, as the Caps were swept in the Eastern Conference Semis, a series which was much closer than a sweep would indicate.
Offseason Changes:
Where do we start? George McPhee was a busy man this offseason, with resigning key players (Brooks Laich, Karl Alzner), fleecing teams in trades (Semyon Varlamov for a 1st round pick), signing elite goalies for a back-up's cap hit (Tomas Vokoun), and filling out the rest of his team with veteran players capable of playing tough minutes. The team added precious forward depth with the additions of Joel Ward, Troy Brouwer, and Jeff Halpern. They also bolstered their blueline with the addition of Roman Hamrlik. George McPhee took a team that was already very good and turned them into Stanley Cup favorites. The Capitals now have it all.
Key Questions for 2011-2012:
Will the Capitals be better territorially this year?
- Last year the Capitals were a middle of the pack team territorially, though some of their signings are players capable of driving the play forward (Joel Ward, Troy Brouwer). It'll be interesting to see if this has any impact, as Ovechkin, Backstrom, and Semin are the only remaining forwards who can be counted on to control play.
Was last year's mediocre Power Play just variance or should it be a cause for concern?
- After spending the last handful of years with one of the league's best power plays, last year's Capitals saw their success with the man advantage dwindle, posting the NHL's 16th best Power Play. Was this bad coaching or bad luck? Either way, if these problems creep back up it could be a problem, as Washington probably has less margin for error here given their relative weakness at even strength.
2. Vancouver
Key Statistics:
Fenwick- 53.9%
Even Strength Shooting%- 8.2%
Even Strength Save%- .939
2010-2011 Review:
Vancouver was the class of the NHL last year, earning 117 points on their way to winning the President's Trophy. Their postseason nearly ended in disaster before Alex Burrows scored an OT winner in Game 7 of the first round, and from there they handled Nashville and San Jose en route to their first Stanley Cup Finals appearance since 1994. The rest, as they say, is history, as Tim Thomas and the Bruins won the final two games of the series as Vancouver began to burn.
Offseason Changes:
Mike Gillis (correctly) resisted the temptation to overreact, as nearly all of their regulars return, with Christian Ehrhoff as the only key piece to leave. Marco Sturm was their most notable endeavor in UFA. Most of the offseason work came with re-signing their own players, as Kevin Bieksa, Max Lapierre, Jannik Hansen, Sami Salo, and Andrew Alberts all re-upped this summer.
Key Questions for 2011-2012:
Is Vancouver deep enough along the blueline?
Fenwick- 53.9%
Even Strength Shooting%- 8.2%
Even Strength Save%- .939
2010-2011 Review:
Vancouver was the class of the NHL last year, earning 117 points on their way to winning the President's Trophy. Their postseason nearly ended in disaster before Alex Burrows scored an OT winner in Game 7 of the first round, and from there they handled Nashville and San Jose en route to their first Stanley Cup Finals appearance since 1994. The rest, as they say, is history, as Tim Thomas and the Bruins won the final two games of the series as Vancouver began to burn.
Offseason Changes:
Mike Gillis (correctly) resisted the temptation to overreact, as nearly all of their regulars return, with Christian Ehrhoff as the only key piece to leave. Marco Sturm was their most notable endeavor in UFA. Most of the offseason work came with re-signing their own players, as Kevin Bieksa, Max Lapierre, Jannik Hansen, Sami Salo, and Andrew Alberts all re-upped this summer.
Key Questions for 2011-2012:
Is Vancouver deep enough along the blueline?
- This is slightly nitty, as Vancouver is clearly an elite team, but one chink in the armor is their depth along the blueline, especially after nothing was done to replace the departure of Christian Ehrhoff. If Vancouver runs into injuries which is a possibility given the history of Sami Salo and (to a lesser extent) Kevin Bieksa, there could be issues, as giving big minutes to players like Andrew Alberts and Aaron Rome is a recipe for disaster. Keith Ballard returning to form is essential.
1. Chicago
Key Statistics:
Fenwick- 54%
Even Strength Shooting%- 6.5%
Even Strength Save%- .919
2010-2011 Review:
Last season was a disappointment for the Blackhawks. Coming off a Stanley Cup and the ensuing cap hell, Chicago was really hamstrung with last year's lineup, and it showed, as players such as Fernando Pisani, Jack Skille, Jake Dowell, Nick Boynton, and Jassen Cullimore all played substantial minutes at various points throughout the season. The team got better later on after adding Chris Campoli and Michael Frolik, but still needed a lot of luck to even make the playoffs. Chicago ended up losing in 7 games to the eventual Western Conference champions.
Offseason Changes:
The Hawks FO was not shy this summer, as they moved two key cogs from the 09-10 cup run on draft night, sending Troy Brouwer to the Washington Capitals for a 1st round pick. Later that night they moved Brian Campbell to the Florida Panthers for Rostislav Olesz. The money freed from the Campbell deal was quickly put into use, as the Blackhawks then acquired and signed Steve Montador, and on July 1st, signed Andrew Brunette, Jamal Mayers, Daniel Carcillo, and Sean O'Donnell. Sami Lepisto was also signed later in the Summer.
Key Questions for 2011-2012:
Did the Blackhawks do enough to replace Brian Campbell?
- While the Campbell move was a huge win from a cap management perspective, it left the Blackhawks with a huge hole on defense. The Hawks brass continues to insist that Nick Leddy is indeed ready to fill the void, but that obviously remains to be seen. Campbell played a huge role for the Blackhawks, one that was often under appreciated by certain types of Hawks fans. Chicago's possession game is predicated on quick transitions from the defensive zone to the offensive zone. Losing his skating and his offensive skills will be hard to replace. The depth should be better than last year, but the Blackhawks could find themselves in trouble if Leddy doesn't take a step forward.
Driving Play Power Rankings from 30 to 1:
30. Edmonton
29. Colorado
28. Dallas
27. New York Islanders
26. Minnesota
25. Ottawa
24. Toronto
23. Florida
22. Phoenix
21. Winnipeg
20. Anaheim
19. Carolina
18. Calgary
17. St. Louis
16. Nashville
15. New York Rangers
14. Columbus
13. Buffalo
12. Philadelphia
11. Boston
10. Montreal
9. Tampa Bay
8. New Jersey
7. Los Angeles
6. Detroit
5. Pittsburgh
4. San Jose
3. Washington
2. Vancouver
1. Chicago
Thursday, September 8, 2011
On Zone Starts
How valuable are offensive-zone faceoffs? How much should we adjust the Sedins' stats to take into account that they take three faceoffs in the offensive zone for every one at the other end? Are coaches using their guys effectively, or should they almost turn them into specialists like Vigneault does in Vancouver? Is it better for a coach to focus on zone starts, matchups or just roll lines to keep his guys fresh and the best playing the most?
These are just a few of the many, many questions that relate to zone starts that come up in hockey analysis. We'll put off dealing with most of those until later, this article will be more of a broad discussion and introduction to what I feel is a novel approach that we'll be using a lot.
The Data
I'm going to start with team stats. This is pretty strange because zone starts aren't a team issue. A team that has more offensive-zone starts than defensive has earned them; good zone starts aren't just handed down to the team by some suit. In contrast, a player only reaps what he sows, as it were, if he or a teammate ices the puck or possibly after a very short shift. Most faceoffs are taken with fresh players that were on the bench when the stoppage occurred. Despite that, using team stats is a good place to start because we can get a pretty good idea how the location of the most recent faceoff affects results.
The data come from a common source: NHL play-by-play and roster reports. In this case, I am using every game available for the 2010-2011 regular season. As usual, it's even strength with both goalies on the ice. Using the roster reports, which say when each player was on the ice during the game, I isolated these 5 situations:
Ozone, first shift - the most recent faceoff was in the team's offensive zone. All players that were on the ice for the faceoff remain on the ice.
Ozone, on-the-fly - the most recent faceoff was in the team's offensive zone. At least one player who was on the ice for that faceoff has left.
Neutral Zone - the most recent faceoff took place in the neutral zone.
Dzone, on-the-fly - the most recent faceoff was in the team's defensive zone. At least one player who was on the ice for that faceoff has left.
Dzone, first shift - the most recent faceoff was in the team's defensive zone. All players that were on the ice for the faceoff remain there.
To clarify, once any of the original faceoff guys have left the ice the rest of the time before the next stoppage is in the on-the-fly category. So the faceoff shift is only that first shift, even if later it so happens that the 10 skaters that were on the ice are out there together.
Note: I have separated out the team results to keep this post about the general concepts. If you want to see how good your team is in each situation, you can find that here.
Ice Time and Goals
Let's take a look at how much time was spent and how many goals were scored on average in each situation. This will give us a very rough idea how important faceoffs are in the offensive or defensive zone.
The first thing I noticed is that the first shift only accounts for about 37% of the ice time following a faceoff at either end. Part of that is that I'm being very strict defining the first shift; it would increase if I allowed for one guy to leave the ice, for example. In any case, you might wonder why we focus so much on who is on the ice for a faceoff when so much of a player's ice time after an ozone/dzone faceoff started with him jumping onto the ice.
The goals columns give you a pretty strong hint. Look at the second and fourth rows. Following a faceoff outside the neutral zone, once a change was made more goals were scored by the team that took the faceoff in their defensive zone! We'll later see that this is likely just due to random chance, but it seems clear that if you come on in an on-the-fly change it's more like a neutral-zone start than being on for a faceoff at either end.
Corsi
Let's look at the average team Corsi rate for each situation. I use the average team rate for each situation so we don't have the endogeneity effect I wrote poorly about a couple months ago. In other words, if we simply averaged out all the ice time we'd overestimate how important it is to be in the offensive zone because good teams tend to get more faceoffs in the offensive zone than bad teams so we'd be lumping in team quality with ozone-faceoff value.
Here you can see how much having a faceoff in the good zone helps your team out territorially. I'm sure that there is an effect didn't surprise you, but suspect how large it is might have. It's also interesting to note that this almost completely goes away once the first change happens. This shouldn't be a surprise either, teams don't change without the puck leaving the zone, but one might have expected more of a ripple effect. The most common way to change lines is to dump the puck and give the other team possession, albeit starting behind their own net. It does not appear that being able to breakout the Flying V gives you much of an edge.
This is a nice segue back to why we should care so much more about who is actually on for the faceoff and not just ice time afterward. Let's exaggerate and suppose that Henrik Sedin and Manny Malhotra spend the same amount of post-offensive-zone-faceoff time on the ice but Hank's is all first-shift time while Manny doesn't take a single offensive-zone faceoff. Sedin's ozone time will be extremely favorable, and Malhotra's only slightly so. In this extreme scenario we would need to adjust Sedin's stats a lot to take his type of ice time accurately into account. Being out there on the faceoff in front of their goalie is over 15 times as favorable as jumping on the ice after.
Conclusion: More To Come
When I thought of this method of separating out the ice time, I did a little fist pump. While I don't think this, or really any metric, will blow everything else away it seems like a good way to analyze zone starts and give us better insight into player value and coaching decisions. We will be using this and related methods a lot in the future, especially the coming weeks leading up to the season. Coming down the pipe is a new player metric to adjust for zone starts. We'll also do in-depth analysis on zone starts for the two teams that focus the most on them: the recently rivalrous Vancouver Canucks and Chicago Blackhawks. Are Vigneault and Quenneville outsmarting the rest of the league or is it a case of fancy coaching syndrome? If you ask nicely, we could probably do something similar for your favorite team, or even your favourite team.
Here is a link to the team data.
These are just a few of the many, many questions that relate to zone starts that come up in hockey analysis. We'll put off dealing with most of those until later, this article will be more of a broad discussion and introduction to what I feel is a novel approach that we'll be using a lot.
The Data
I'm going to start with team stats. This is pretty strange because zone starts aren't a team issue. A team that has more offensive-zone starts than defensive has earned them; good zone starts aren't just handed down to the team by some suit. In contrast, a player only reaps what he sows, as it were, if he or a teammate ices the puck or possibly after a very short shift. Most faceoffs are taken with fresh players that were on the bench when the stoppage occurred. Despite that, using team stats is a good place to start because we can get a pretty good idea how the location of the most recent faceoff affects results.
The data come from a common source: NHL play-by-play and roster reports. In this case, I am using every game available for the 2010-2011 regular season. As usual, it's even strength with both goalies on the ice. Using the roster reports, which say when each player was on the ice during the game, I isolated these 5 situations:
Ozone, first shift - the most recent faceoff was in the team's offensive zone. All players that were on the ice for the faceoff remain on the ice.
Ozone, on-the-fly - the most recent faceoff was in the team's offensive zone. At least one player who was on the ice for that faceoff has left.
Neutral Zone - the most recent faceoff took place in the neutral zone.
Dzone, on-the-fly - the most recent faceoff was in the team's defensive zone. At least one player who was on the ice for that faceoff has left.
Dzone, first shift - the most recent faceoff was in the team's defensive zone. All players that were on the ice for the faceoff remain there.
To clarify, once any of the original faceoff guys have left the ice the rest of the time before the next stoppage is in the on-the-fly category. So the faceoff shift is only that first shift, even if later it so happens that the 10 skaters that were on the ice are out there together.
Note: I have separated out the team results to keep this post about the general concepts. If you want to see how good your team is in each situation, you can find that here.
Ice Time and Goals
Let's take a look at how much time was spent and how many goals were scored on average in each situation. This will give us a very rough idea how important faceoffs are in the offensive or defensive zone.
| Situation | Ice Time | % | Goals | % |
| Ozone, first shift | 452.5 | 11.4% | 21.2 | 13.8% |
| Ozone, on-the-fly | 765.1 | 19.3% | 32.3 | 21.0% |
| Neutral | 1538.9 | 38.7% | 54 | 35.1% |
| Dzone, on-the-fly | 765.1 | 19.3% | 34.8 | 22.7% |
| Dzone, first shift | 452.5 | 11.4% | 11.4 | 7.4% |
| Total | 3974.1 | 100% | 153.6 | 100% |
The first thing I noticed is that the first shift only accounts for about 37% of the ice time following a faceoff at either end. Part of that is that I'm being very strict defining the first shift; it would increase if I allowed for one guy to leave the ice, for example. In any case, you might wonder why we focus so much on who is on the ice for a faceoff when so much of a player's ice time after an ozone/dzone faceoff started with him jumping onto the ice.
The goals columns give you a pretty strong hint. Look at the second and fourth rows. Following a faceoff outside the neutral zone, once a change was made more goals were scored by the team that took the faceoff in their defensive zone! We'll later see that this is likely just due to random chance, but it seems clear that if you come on in an on-the-fly change it's more like a neutral-zone start than being on for a faceoff at either end.
Corsi
Let's look at the average team Corsi rate for each situation. I use the average team rate for each situation so we don't have the endogeneity effect I wrote poorly about a couple months ago. In other words, if we simply averaged out all the ice time we'd overestimate how important it is to be in the offensive zone because good teams tend to get more faceoffs in the offensive zone than bad teams so we'd be lumping in team quality with ozone-faceoff value.
| Situation | Corsi/60 |
| Ozone, first shift | 39.957 |
| Ozone, on-the-fly | 2.603 |
| Neutral | 0.019 |
| Dzone, on-the-fly | -2.784 |
| Dzone, first shift | -40.103 |
Here you can see how much having a faceoff in the good zone helps your team out territorially. I'm sure that there is an effect didn't surprise you, but suspect how large it is might have. It's also interesting to note that this almost completely goes away once the first change happens. This shouldn't be a surprise either, teams don't change without the puck leaving the zone, but one might have expected more of a ripple effect. The most common way to change lines is to dump the puck and give the other team possession, albeit starting behind their own net. It does not appear that being able to breakout the Flying V gives you much of an edge.
This is a nice segue back to why we should care so much more about who is actually on for the faceoff and not just ice time afterward. Let's exaggerate and suppose that Henrik Sedin and Manny Malhotra spend the same amount of post-offensive-zone-faceoff time on the ice but Hank's is all first-shift time while Manny doesn't take a single offensive-zone faceoff. Sedin's ozone time will be extremely favorable, and Malhotra's only slightly so. In this extreme scenario we would need to adjust Sedin's stats a lot to take his type of ice time accurately into account. Being out there on the faceoff in front of their goalie is over 15 times as favorable as jumping on the ice after.
Conclusion: More To Come
When I thought of this method of separating out the ice time, I did a little fist pump. While I don't think this, or really any metric, will blow everything else away it seems like a good way to analyze zone starts and give us better insight into player value and coaching decisions. We will be using this and related methods a lot in the future, especially the coming weeks leading up to the season. Coming down the pipe is a new player metric to adjust for zone starts. We'll also do in-depth analysis on zone starts for the two teams that focus the most on them: the recently rivalrous Vancouver Canucks and Chicago Blackhawks. Are Vigneault and Quenneville outsmarting the rest of the league or is it a case of fancy coaching syndrome? If you ask nicely, we could probably do something similar for your favorite team, or even your favourite team.
Here is a link to the team data.
Friday, August 19, 2011
On Luck, Skill and Sample Size in Shooting Percentage
The roles of skill and luck in shooting are an important and often misunderstood part of hockey analysis. This is particularly true when analytical and traditional fans get together. A typical discussion might go something like:
A: Steven Stamkos got 91 points with 45 goals at the age of 21. He's definitely getting 100 points next year, and could easily score 50 goals a season as he improves.
B: Yeah, but he was lucky to make 16.5% of his shots. That's not sustainable and his numbers will probably go down next year, even if he does actually improve.
A: So shooting is all luck? You clearly know nothing about hockey and should actually watch some games instead of just sitting at your computer all day coming up with fancy stats that don't mean anything.
Turns out both guys have a point. Stamkos should probably expect his stats to drop next season because, in addition to goal numbers trending downward for the league as a whole, he's probably not burying 16.5% of his shots. On the other hand,I person B probably should watch more hockey, it is a great game.
A lot of the confusion comes from incorrect either/or thinking. Scoring on a high percentage of your shots is a result of both luck and skill. I'm not just putting down the traditional fans here. Those of us in the analytical community, myself included, are prone to bad thinking as well. While most people ignore or underrate the importance of luck in hockey in general and shooting in particular, we tend to go too far the other way, chalking everything up to luck and ignoring the skill aspect.
Shooting for a high percentage is skill based. Two of our favorite writers, JLikens of objectivenhl fame and Gabe Desjardins from BTN and arcticicehockey have written several articles on the subject.
In case their articles are not convincing enough, let's consider two of the best players in the game: Henrik Sedin and Sidney Crosby. While not necessarily known as snipers, these players have all the skills that one might think lead to their team putting a high percentage of shots in the net. Both have elite vision, passing ability, hands, positioning and, in one case, telepathy. We would all expect their teams to have better shooting percentages when they are on the ice than when they are sitting on the bench or worse. The numbers bear this out. Here is a chart with their teams' performances at even strength with both goalies in net from the last four seasons combined. The stats are courtesy of noted Driving Play reader Vic Ferrari's timeonice scripts, which you can find information on how to use here.
As you can see, the Pens with Crosby shot 3.2 percentage points higher than they did without him. While some of it may be variance, with the number of shots they took with him on, that's a difference of 69 goals or more than 17 goals per season due to better shooting. The Canucks shot 2.8 points higher with Henrik on the ice, a difference of over 73 goals, more than 18 per season, when you consider how many shots they took with him on. For the statistically minded, these shooting-percentage differences are very very very significant. To give you an idea, it varies field to field but the most common benchmark is for there to be less than a 5% chance of results this extreme, or more so, due to variance alone. That's a 1-in-20 chance. For Crosby, there is a 0.00045% chance, or less than 1 in 222,000. For Hank there is a 0.00235% chance of results that extreme due to randomness alone - less likely than 1 in 42,000. Again, 1 in 20 is the usual mark. The data confirm what anyone would guess from watching a few games - Henrik Sedin and Sidney Crosby help their teams shoot better. (Note: if you are a hater and/or think that it's the likes of Alex Burrows and Pascal Dupuis who are driving these results, feel free to be wrong. The point of this is to provide evidence of shooting skill and clearly someone has it when these two are on the ice.)
Let's now look at the role of luck on shooting percentage. To do this, I will run simulations comparing the results of a typical team that shoots well and one that does poorly. In this article on objectivenhl, which is worthy of being linked again, JLikens finds that the average team shoots at an 8.1% clip 5-on-5, with a standard deviation of 0.48%. Going by this, a team that is good at shooting, let's say 7th or 8th best in the league, would have a true 5-on-5 shooting percentage of something like 8.42%. On the other hand, a team that is bad at shooting, say 7th or 8th worst in the league, would be expected to score on about 7.78% of their shots.
Let's see how things shake out. Below is a chart giving the results of 10,000 simulations for various numbers of shots where team A has a true shooting percentage of 8.42% and team B shoots at 7.78%. The first two columns tell you the given time period and number of shots for each team. The next three columns tell you how often the team good at shooting outshot the bad (column 3), the bad team outshot the good (4) and how often they had an equal shooting percentage (5). The last two columns give what percent of the time someone looking at the data, and not knowing the underlying percentages, would get statistical signficance at the 5% level. Notice that in the last column, the statistical test would reveal that B is significantly better at shooting than A despite their shooting skill actually being over half a percentage point worse.
You probably didn't find the results surprising for that first row, representing a period of play. The most common outcome, happening about 40% of the time, is that the two teams remain tied, most often at 0. The team that shoots better due to getting higher-quality shots, hitting the corners better and so on is only slightly more likely to be the one that is ahead if you know that one of them is. Less than 32% of the time will the better team find themselves ahead after a period in which both get the league average 8 shots, whereas they'll be behind almost 29% of the time.
Lower on the chart it gets more troubling, especially for us bloggers. The most common sample point for analysis is half a season. Generally the best way to study the persistence of something is to split the season in half, typically first half vs second half or even-and-odd numbered games, and compare the two samples. This works well because teams should be the same or very similar. If you study something over multiple seasons you aren't getting the same teams every year due to player and coaching changes. In half a season, the team near the top in shooting skill has only about a 2 in 3 chance of outscoring the team near the bottom with the same number of shots. There is also little chance, roughly 13%, of finding that the better team is significantly better at shooting if you were looking at the data. Even over a whole season of shooting data, there is a 1 in 4 chance that the worse team will get better results. It isn't until we get several years worth of shooting results that it tilts heavily in favor of the better shooting team and that's not realistic because teams change so much each offseason and the simulations assumed the same percentage each season.
As you can see, luck plays a huge role for all reasonable sample sizes. This is the fundamental reason why shooting stats are better than goals. Luck is less of a factor for number of shots taken than number of shots made, so they are more reliable indicators of skill over samples of a season or less. If over a season there is a 1 in 4 chance that a good-shooting team is outshot by a bad-shooting team then it's tough to say that a team's results are due to skill and not just random luck.
In a future installment I will look at how persistence is affected by sample size.
A: Steven Stamkos got 91 points with 45 goals at the age of 21. He's definitely getting 100 points next year, and could easily score 50 goals a season as he improves.
B: Yeah, but he was lucky to make 16.5% of his shots. That's not sustainable and his numbers will probably go down next year, even if he does actually improve.
A: So shooting is all luck? You clearly know nothing about hockey and should actually watch some games instead of just sitting at your computer all day coming up with fancy stats that don't mean anything.
Turns out both guys have a point. Stamkos should probably expect his stats to drop next season because, in addition to goal numbers trending downward for the league as a whole, he's probably not burying 16.5% of his shots. On the other hand,
A lot of the confusion comes from incorrect either/or thinking. Scoring on a high percentage of your shots is a result of both luck and skill. I'm not just putting down the traditional fans here. Those of us in the analytical community, myself included, are prone to bad thinking as well. While most people ignore or underrate the importance of luck in hockey in general and shooting in particular, we tend to go too far the other way, chalking everything up to luck and ignoring the skill aspect.
Shooting for a high percentage is skill based. Two of our favorite writers, JLikens of objectivenhl fame and Gabe Desjardins from BTN and arcticicehockey have written several articles on the subject.
In case their articles are not convincing enough, let's consider two of the best players in the game: Henrik Sedin and Sidney Crosby. While not necessarily known as snipers, these players have all the skills that one might think lead to their team putting a high percentage of shots in the net. Both have elite vision, passing ability, hands, positioning and, in one case, telepathy. We would all expect their teams to have better shooting percentages when they are on the ice than when they are sitting on the bench or worse. The numbers bear this out. Here is a chart with their teams' performances at even strength with both goalies in net from the last four seasons combined. The stats are courtesy of noted Driving Play reader Vic Ferrari's timeonice scripts, which you can find information on how to use here.
| Team | Goals | Shots On Goal | Shooting % |
| Penguins, Crosby on Ice | 236 | 2160 | 10.9% |
| Penguins, Crosby off Ice | 414 | 5347 | 7.7% |
| Canucks, Henrik on Ice | 273 | 2624 | 10.4% |
| Canucks, Henrik off Ice | 359 | 4709 | 7.6% |
As you can see, the Pens with Crosby shot 3.2 percentage points higher than they did without him. While some of it may be variance, with the number of shots they took with him on, that's a difference of 69 goals or more than 17 goals per season due to better shooting. The Canucks shot 2.8 points higher with Henrik on the ice, a difference of over 73 goals, more than 18 per season, when you consider how many shots they took with him on. For the statistically minded, these shooting-percentage differences are very very very significant. To give you an idea, it varies field to field but the most common benchmark is for there to be less than a 5% chance of results this extreme, or more so, due to variance alone. That's a 1-in-20 chance. For Crosby, there is a 0.00045% chance, or less than 1 in 222,000. For Hank there is a 0.00235% chance of results that extreme due to randomness alone - less likely than 1 in 42,000. Again, 1 in 20 is the usual mark. The data confirm what anyone would guess from watching a few games - Henrik Sedin and Sidney Crosby help their teams shoot better. (Note: if you are a hater and/or think that it's the likes of Alex Burrows and Pascal Dupuis who are driving these results, feel free to be wrong. The point of this is to provide evidence of shooting skill and clearly someone has it when these two are on the ice.)
Let's now look at the role of luck on shooting percentage. To do this, I will run simulations comparing the results of a typical team that shoots well and one that does poorly. In this article on objectivenhl, which is worthy of being linked again, JLikens finds that the average team shoots at an 8.1% clip 5-on-5, with a standard deviation of 0.48%. Going by this, a team that is good at shooting, let's say 7th or 8th best in the league, would have a true 5-on-5 shooting percentage of something like 8.42%. On the other hand, a team that is bad at shooting, say 7th or 8th worst in the league, would be expected to score on about 7.78% of their shots.
Let's see how things shake out. Below is a chart giving the results of 10,000 simulations for various numbers of shots where team A has a true shooting percentage of 8.42% and team B shoots at 7.78%. The first two columns tell you the given time period and number of shots for each team. The next three columns tell you how often the team good at shooting outshot the bad (column 3), the bad team outshot the good (4) and how often they had an equal shooting percentage (5). The last two columns give what percent of the time someone looking at the data, and not knowing the underlying percentages, would get statistical signficance at the 5% level. Notice that in the last column, the statistical test would reveal that B is significantly better at shooting than A despite their shooting skill actually being over half a percentage point worse.
| Time period | Number of Shots | A scores more | B scores more | Goals scored equal | A > B Significant | B > A significant |
| One Period | 8 | 31.9% | 28.7% | 39.4% | 1.3% | 1.1% |
| One Game | 24 | 42.6% | 36% | 21.4% | 6% | 4.5% |
| 1/4 Season | 500 | 62.3% | 33.2% | 4.5% | 10.5% | 2.2% |
| 1/2 Season | 1,000 | 68.6% | 28.5% | 2.8% | 13.4% | 1.4% |
| 1 Season | 2,000 | 76.4% | 21.7% | 1.9% | 17.9% | 0.8% |
| 2 Seasons | 4,000 | 85% | 14% | 1% | 27.8% | 0.2% |
| 3 Seasons | 6,000 | 90.1% | 9.3% | 0.6% | 36.1% | 0.1% |
| 4 Seasons | 8,000 | 93.6% | 6% | 0.4% | 43.7% | 0.1% |
| 5 Seasons | 10,000 | 95.6% | 4.1% | 0.3% | 50.3% | 0% |
You probably didn't find the results surprising for that first row, representing a period of play. The most common outcome, happening about 40% of the time, is that the two teams remain tied, most often at 0. The team that shoots better due to getting higher-quality shots, hitting the corners better and so on is only slightly more likely to be the one that is ahead if you know that one of them is. Less than 32% of the time will the better team find themselves ahead after a period in which both get the league average 8 shots, whereas they'll be behind almost 29% of the time.
Lower on the chart it gets more troubling, especially for us bloggers. The most common sample point for analysis is half a season. Generally the best way to study the persistence of something is to split the season in half, typically first half vs second half or even-and-odd numbered games, and compare the two samples. This works well because teams should be the same or very similar. If you study something over multiple seasons you aren't getting the same teams every year due to player and coaching changes. In half a season, the team near the top in shooting skill has only about a 2 in 3 chance of outscoring the team near the bottom with the same number of shots. There is also little chance, roughly 13%, of finding that the better team is significantly better at shooting if you were looking at the data. Even over a whole season of shooting data, there is a 1 in 4 chance that the worse team will get better results. It isn't until we get several years worth of shooting results that it tilts heavily in favor of the better shooting team and that's not realistic because teams change so much each offseason and the simulations assumed the same percentage each season.
As you can see, luck plays a huge role for all reasonable sample sizes. This is the fundamental reason why shooting stats are better than goals. Luck is less of a factor for number of shots taken than number of shots made, so they are more reliable indicators of skill over samples of a season or less. If over a season there is a 1 in 4 chance that a good-shooting team is outshot by a bad-shooting team then it's tough to say that a team's results are due to skill and not just random luck.
In a future installment I will look at how persistence is affected by sample size.
Subscribe to:
Posts (Atom)