Part I: The Aftermath of the Mike Richards and Jeff Carter Deals

On June 23rd, 2011, Flyers General Manager Paul Holmgren sent shockwaves through the hockey world when he dealt arguably the two most notable faces of the franchise – Mike Richards and Jeff Carter – to Los Angeles and Columbus respectively. Since then, much has been (and will continue to be) written about possible motives behind what he and team owner Ed Snider were pondering to make such bold moves. So far, speculation has included both off-ice issues as well as the need to create salary cap space for newly signed goaltender Ilya Bryzgalov. Starting with the former, Richards’ tumultuous relationship with members of the Philadelphia media is no secret. For the last few seasons, there have been accusations that he (and Carter) enjoyed a lifestyle where partying was the main focus, leaving hockey on the back burner. Richards’ leadership inside the locker room hasn’t been looked upon any more favorably. It has been rumored that a longstanding rift between the team’s young stars and seasoned veterans – most notably Chris Pronger – could also have played a factor in the stars’ departure from the team. Whether or not these accusations have merit, it is certain that the moves will accomplish one of the team’s likely intended goals: a culture change inside the dressing room.


In the aftershock of what happened almost four weeks ago, another highly-debated question has naturally emerged: are the Flyers still one of the premier Stanley Cup contenders in the Eastern Conference? In order to answer such a question, I am going to break my study into three parts. Part one will look into the deals for Richards and Carter themselves, evaluating what it was that the Flyers added to their lineup. Part two will evaluate Philadelphia’s signings on July 1 and speak to where Jaromir Jagr, Maxime Talbot, and Andreas Lilja fit into the equation. Finally, part three will decipher what the Flyers lost when they made the decision to deal their captain and his swift sidekick.


Of course, trying to answer our question is a bit of a double-edged sword – only time will tell if Holmgren’s return of Wayne Simmonds, Brayden Schenn, a 2012 second round pick, Jakub Voracek, the 8th overall selection in this year’s draft (Sean Couturier), and a third round selection in this year’s draft (Nick Cousins) were an adequate return for both superstars. Fortunately, we can still attempt to decipher what the numbers tell us about these players (and even draft picks). In order to do this, I like to start by looking at players’ average ice time per game. This tells us 1) what situations the players are being used in, and 2) how often they are being used. Per nhl.com, here are the numbers for Simmonds and Voracek, the two players coming to Philadelphia who saw significant ice time at the NHL level last season:

From these numbers, we can conclude that Simmonds played a bottom-six checking role in Los Angeles, the same role that he will most likely see in Philadelphia. Voracek, on the other hand, was one of the Jackets’ top forwards, ranking in the top 5 on his team at even strength and on the PP. This is good news for the Flyers – they will need him to replace any and all minutes in both situations without Richards and Carter in the fold.
With our ice-time analysis complete, we can now attempt to give this raw data its proper context.

 

What do these numbers tell us? Starting with Simmonds, he was asked to play a moderately defensive role against the toughest competition of any forward on the team (min. 20 games played). However, his zone start percentage should probably see an expected Corsi score of zero or slightly worse (see chart), and instead Simmonds sits at -4.02 . DobberHockey tells us that he most often played with Michal Handzus and Alex Ponikarovsky (21.25%), and Handzus and Kyle Clifford (15.33%). Of these forwards, Handzus and Ponikarovsky both have Corsi scores around what we would expect for someone given their roles, but no player seems to be “carrying the water” as we like to say. Unfortunately for the Flyers, Simmonds is no exception and his low scores in just about every category show that he cannot send the play in the right direction on his own against the opponent’s best players.


Voracek, on the other hand, is an interesting case in and of himself. Once again using the expected Corsi graph linked, Voracek actually slightly over-performs what we might expect from somebody given his zone start percentage. However, his impressive Corsi scores and Fenwick percentage are perhaps correlated with a few points of interest. First, his aforementioned high zone start percentage gave him an immediate advantage in generating shots towards the opponent’s net as he quite often started his shifts in prime scoring position. Second, his competition was anything but impressive, actually averaging a negative relative Corsi score. Finally, DobberHockey shows us that among his three most common line combinations, Rick Nash was on the ice a healthy 57.81% of the time. I hardly think explaining why playing with Nash would be beneficial to Voracek, but it is worth noting that Nash was among the league’s leaders in shots last season – his total of 305 ranked 6th in the entire NHL. Though Nash’s Corsi score of 4.49 may be lower than expected considering his own 57.1% zone start, taking into consideration how often he shoots it is easy to see how Voracek’s own score was undoubtedly affected for the better. It will be most interesting to see if Voracek can repeat such gaudy scores without a line-mate sporting the credentials of Mr. Nash.


We have already noted Voracek’s 2:57 of average PP time per game in ’10-11 which will be immensely valuable to Philadelphia in the absence of Richards and Carter. Though he ranked amongst the team’s leaders in said category, however, he only registered 8 total points for his efforts on the man advantage. Perhaps it is unsurprising that Columbus’ Power Play ranked 8th in the league in shots for/60 minutes with Nash in the fold (remember: shots are a better indicator than goals), but for a team that saw success in generating pressure on the opponent, Voracek’s totals still seem low. However, some of this effect can be explained when we realize that Columbus’ opponents saved 91.1% of all shots while on the PK according to Behind The Net. Thanks to mc79hockey, we know that the historic average is around 86.6%, a full 4.5% disparity. Had Columbus’ opponents not been so lucky, both the team and Voracek most likely would have sported slightly higher power play production.


Moving on to the relative unknowns of what the Flyers got back in the deal, on the surface Brayden Schenn and three draft picks may seem like an appetizing return. However, Derek Zona’s study on draft picks and their value tells us something slightly different. Putting Schenn aside for a moment, what can we realistically expect from the first, second, and third round picks that the Flyers gained? Zona’s study is particularly excellent because it shows the historical chance of drafting a “top” player with a certain selection. He notes to “...consider the 'Top Players' to be top five forwards and top three defenseman [on their team].” Knowing what we know about Richards and Carter, I don’t think anybody would argue that the Flyers subtracted two established “top 5” forwards from their lineup. However, the article also notes that the odds of drafting such a player with the number 8-13 selections (they selected Couturier 8th overall) is a mere 41.2%. Looking at the other two picks, the 68th overall selection this season which turned into Nick Cousins has a 7.4% chance of turning into a Richards or Carter-esque player. Considering the Kings figure to be among the top western conference contenders next season in the wake of their offseason, I would most likely expect the 2012 second round pick to fall within this same range. The fact that the Flyers didn’t receive higher than a 50% chance to replenish their lineup with two established stars puts a bit of a hindrance on their returns.


Adding this to the fact that both Richards and Carter were on long-term, cap-friendly deals, and I’m not sure that there is a net positive to be found here. Perhaps Sean Couturier or one of the other selections will make a difference, saving the Flyers money in the short-term should they produce while on an entry level contract. So far, all indications are that Brayden Schenn will be given every possible opportunity to make the final roster, but much like Couturier, there are still question marks surrounding his development. Unfortunately, prospects are called prospects for a reason – there is no guarantee that they will meet development expectations. Considering what the Flyers gave up in these deals, while the return could most certainly prove lucrative, the odds simply do not stack up in their favor.

Yeah, But: QualComp

"Going by these stats, X is better than Y." "Yeah, but…"

In the Yeah, But series, I will be taking a closer look at the stats lurking in the background. We don't care about them per se, but they provide context and help us compare and rate players. My initial plan was to start the series with a discussion of shooting percentage, but after reading this article at arcticicehockey by Dirk Hoag this seems like a good time to take a deeper look at QualComp.

If you've ever argued with someone about whether one player is better than another, you've probably had a conversation that went something like:

Pens fan: Letang had a better year than Lidström. Look at how much better his +/- and Corsi stats are!
Wings fan: Yeah, but Lidström faced much tougher opponents!

Here are their Corsi/60 and Corsi QualComp stats, courtesy of Behindthenet:

Player	Corsi	Corsi QualComp
Lidström	2.65	1.205
Letang	12.01	0.281

Roughly speaking, Lidström put up decent numbers against the toughest competition in the league, while Letang did very well against opponents that were a little above average. How much should we boost Lidström's numbers to compensate?

The obvious way to figure this out would be to list the Corsi QoC and Corsi rate for each player (maybe above some time-played threshold) and run a regression. Unfortunately, that won't work very well and will drastically underestimate the importance of QualComp.

Say you are watching your favorite team play on home ice. It's tied in the second period and, with both teams at full strength, you see the opposition put their best players on the ice. Let's stop there. What should you expect in the next minute or so?

The first thing is that the opponents will be good, so you shouldn't expect much. That's what I'll call the competition effect - the better the opposition, the worse your expected results will be. This is what we would like to measure. On the other hand, your coach will see who is on the ice and will probably put out one of the top lines and a good defense pairing. This means you should expect good results. I'll call this the matchup effect - the better the opposition, the better your players on the ice, which will raise the expected results.

Which one will win out depends on how important the competition effect is and, importantly, how much the coaches focus on the matchups. In the regular season, they put some importance on them, but they have to take the long haul into account. In the playoffs, matchups get a lot of focus by everyone from casual fans to bloggers to on-air analysts to the coaches themselves. Barring serious injuries like Crosby's concussion, coaches do close to everything they can to win the game they're in. A top line facing a bottom line is rare, especially on face offs that don't follow icing. In the playoffs we would expect the matchup effect to dominate the competition effect. More on that in a bit.

To see how this all works, let's oversimplify things and say that Corsi rates are simply additive (subtractive?) - if your line is +5 and you face my line, which is +2, then your Corsi rate will be 5 - 2 = 3. Here's a graphical representation a possible matchup between two teams:


You can see the competition effect within each line - the slope of the line connecting the points is -1, which comes from the (over)simplifying assumption that Corsi rates are additive. You can see the matchup effect by noting that as you move from left to right from the opponent's fourth line to their first, you will tend to also move up toward where your first line is because of matchups.

Let's look at a real-world example - the first-round series between the Vancouver Canucks and the Chicago Blackhawks. There are several things about this series that make it a good one to demonstrate the matchup effect. It's in the playoffs, when coaches go out of their way to avoid randomizing matchups. The series went seven games, with a couple overtimes for good measure, which provides more data. The sample size is in a sweet spot where it's small enough for me to be able to break it down but big enough for there to be evidence of the effects I'm trying to demonstrate. The teams also fit the bill nicely - both feature two very strong lines with a significant dropoff. As usual, I will restrict attention to 5-on-5 play where both goalies were in net.

Overall, Vancouver dominated this series 5-on-5. Chicago had no answer, apart from Crawford in goal, for either of the Canucks top lines. Meanwhile, the Blackhawks' best struggled. Looking at the matchups, the only place where Chicago had an edge was when the bottom lines faced each other. The Blackhawks show the competition effect quite well - those players, including their best, that faced the best on the Canucks had awful stats while those playing mainly against the bottom lines did better. On the other hand, the Canucks were an example of the matchup effect swamping the competition effect. The players that faced the toughest opposition were their top players, and they did extremely well. The less skilled players struggled, even though they played far worse opposition.

Here are three plots giving the QualComp and Corsi rates for all players with at least 15 minutes of ice time against both the top and bottom two lines (combined) of the other team. For QualComp, I took the weighted average of the regular-season Corsi from BTN.

It doesn't look like much of a relationship and this is confirmed in the regression, with similar results to what Dirk Hoag discussed:

	Coeff.	t	R2
Corsi QualComp	-0.54	-0.23	0.0016

Instead of averaging everything out, let's look at within-player results. That is, after all, the information we want to know - e.g. if Lidstrom played against competition similar to Letang's, what would we expect his Corsi to be? To do this, we need to create some kind of split in the data. Vic Ferrari compared the first half of the season and the second. I will instead look at performance when facing the opponent's top two lines compared to the bottom two. Keep in mind that I'm only trying to demonstrate the matchup effect. Trying to determine the importance for the league based on a 7-game sample between two teams would obviously not work all that well.

Here is a similar scatterplot to the above, but this time with the data split:

Now that we're looking at differences for each player, we can see a much stronger relationship. It is stronger for the Chicago players but the most impressive change is on the Vancouver side. Aggregating all the minutes, there was a positive relationship between opponent strength and Corsi rate due to the matchup effect. Splitting it up we see that the Canucks players tended to be less productive against better opposition, although the effect was quite weak - this almost surely would be bigger over a more reasonable sample.

Running the same regression on the pooled data gives us:

	Coeff.	t	R2
Corsi QualComp	-1.92	-2.87	0.1054

Splitting the data up so we capture the change in Corsi for each player gives us a regression indicating that the effect is over three times as large, is statistically significant (before it wasn't even close) and an R^2 over 65 times larger.

When looking at correlations, you have to be careful to think about how coaching decisions affect everything. Good players tend to play with other good players and against other good players. This influences basically all the "yeah, but…" stats including quality of competition and teammates, zone starts and special teams. I'll explore those more in future articles. To measure effects like Qual Comp it is important to use a method that captures changes for a player or group like what I've done here, WOWY or the method Vic Ferrari used in his article on Qual Comp.

In a future article I will try using a similar methodology on a larger scale to figure out how large the effect actually is.

Putting Skaters in a Context: The World of Advanced Hockey Metrics

With the world of advanced hockey metrics continually improving, we are now beginning to see hockey players evaluated in more diverse ways than ever before. Since the beginning of many a hockey fandom, a quick glance at a skater’s goals, assists and total points has been the measure that grades offensive prowess across the league’s scorers. Now, however, the emergence of a few newer (and quite frankly, better) statistics allows us to take these age-old points totals and put them in a context, showing just how valuable a player may or may not be to his team’s success. Here at Driving Play, while attempting to evaluate different players across the league we will be commonly referring to many of these newer statistics within our analysis. Below is a quick list that will attempt to make clear just what we may be referring to if an unfamiliar term happens to appear within one or more of our posts.

A Corsi Number – Similar to a +/- statistic, Corsi gives a player a (+) upon the event of his team generating either a shot on goal, a missed shot, or a blocked shot directed at the opponent’s net while he is on the ice. Similarly, a player earns a (-) if the opponent generates a shot on goal, missed shot, or a blocked shot directed at his own net. Sometimes this can be expressed as a percentage, i.e. the percentage of the total shots that are directed at the opponent’s net while a player is on the ice. Corsi can also be expressed in a “Relative Corsi” number which is the difference between a player’s on-ice Corsi score and the shot differential while he is on the bench. Relative Corsi is generally used to look at which players are having the most positive effects on shot totals relative to their teammates.

A Fenwick Number – Since many consider shot-blocking a measurable skill in the hockey world, a Fenwick number is the same as a Corsi number, except blocked shots are taken out of the equation. So, a player will earn a (+) if his team generates a shot on goal or a missed shot whilst he is on the ice, and a (-) if either event occurs for the opponent.

Quality of Competition (QUALCOMP) – The fact is, all ice time in the NHL is not created equal. Having to line-up toe-to-toe with Sidney Crosby is a much different task than Jesse Winchester, the hockey player or the musician. QUALCOMP more or less weighs the on-ice +/- (the familiar statistic measured in goals) of a player’s opponents relative to the rest of his teammates, and averages this rating across every player faced during the season. The higher the resulting rating, the better the competition a player is facing and vice versa. There is also a CorsiRelQUALCOMP number which does the same thing, except uses Relative Corsi instead of +/-.

Quality of Teammates (QUALTEAM) – Similar to QUALCOMP, QUALTEAM weighs a player’s teammates using the exact same formula as QUALCOMP. Just like QUALCOMP, a player’s QUALTEAM rating will be higher if he is playing with first-line teammates and vice versa if he is playing with fourth-line enforcers. Also similarly, CorsiRelQUALTEAM will measure a player’s teammates using Relative Corsi.

Zone Start Percentage – A zone start percentage measures the percent of the time any player starts his shift in the offensive zone. As you might expect, players with a high defensive prowess are often called upon to start in the defensive zone frequently, and vice versa is true for those players who are more inept in their own end. This particular statistic is important in that it can directly affect a player’s aforementioned Corsi or Fenwick percentage since players who are starting in the offensive zone more frequently will have an easier time generating more shots towards the opponent’s net. What’s more, players who are more immediately deployed in defensive roles will have a harder time finding shot opportunities than their counterparts who are already starting in prime offensive positions. 

Score Effects – Within the ebbs and flows of a hockey game, it has been a long-believed ideal that teams will go into more of a “defensive mode” while ahead and try and get just about every shot possible on net while behind. Using Corsi and Fenwick percentages, it has been shown that teams who enjoy an advantage in the score are commonly outshot at improving rates as the game progresses and vice versa. With the score tied, the disparity in shot totals is most close to even which is why many advanced hockey statisticians choose to look at Corsi/Fenwick with the score tied at even strength to put players’ ice time on a level playing field.

Coming back to the original point regarding putting different skaters in a context, we are now able to more closely examine the situations that different players are playing in. For this reason, it is now much easier to come to a conclusion about their value to their respective teams. Before these statistics came into play, we could look at two players, Patrice Bergeron and Ville Leino for example, who had similar point totals during the regular season (57 and 53 respectively). In a vacuum, it may seem as if they are both comparable players toward Boston and Philadelphia’s total success. However, a little scratching beneath the surface reveals that Bergeron played against much tougher competition than Leino, and Leino enjoyed the luxury of skating with better teammates. Leino started in the offensive zone a walloping 62.3% of the time compared to Bergeron’s 42.7%, showing us that Leino was given far more prime scoring opportunities to begin his shifts which undoubtedly had a positive effect. Finally, Bergeron’s Corsi and Fenwick percentages with the score tied at even strength were 52.7 and 52.8% respectively, compared to Leino’s 54.9 and 53.1%. While a higher percentage of the on-ice shots were directed at the opponent’s net while Leino was on the ice, we have of course already noted that Bergeron faced tougher opponents and played with worse teammates than Leino which gave Leino an advantage in putting up better numbers in these categories. Had Leino, a notoriously subpar defensive forward (see: 2 seconds of average shorthanded time-on-ice/game in ’10-11) been given minutes similar to Bergeron’s, the point totals most certainly would not have looked anything similar. Considering the minutes they were given, Bergeron most certainly had an excellent season while Leino performed at a level around what we might expect from a forward given “softer” minutes during each game.