Lundqvist’s year-to-date stats compared to previous years.

I looked at how Henrik Lundqvist’s play (a) has compared on a year-to-year basis and (b) how it tends to progress on a year-to-date basis. I used 5v5 Sv% and expected goals saved above average per 60 mins (xGSAA/60) as my two metrics. Both sets of data came from Corsica.

I chose 5v5 Sv% because it tends to eliminate a lot of team effects. All situations Sv% can be drastically affected by special teams based on the systems play that a team employs (as well as the efficacy at pulling it off). Overall, 5v5 Sv% tends to map decently year-to-year so it’s a good metric to look at as a measure of skill.

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I chose xGSAA/60 because it helps address the issue of shot quality. xGSAA/60 basically looks at the types and distances of the shots taken and determines how many goals a hypothetical league average goalie would give up that night (ie, expected goals). By comparing this expected goals number against Lundqvist’s actual goals allowed number, we get an idea of how he performed against a league average baseline. The “per 60 mins” part helps to normalize for time in net. This stat is also only measuring 5v5 play.

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I wanted to emphasize Lundqvist’s play this year against previous years as a composite so I chose colors that aren’t well differentiated for previous years (ie, 2007-16). However, if there is interest in having all of the years differentiated I can fulfill that request.

My observations:

  1. Lundqvist isn’t doing well this year. His 5v5 Sv% is among the lowest of his career for this point in the season and his xGSAA/60 through yesterday is the worst of his career. There is some teeth to the idea that he is not playing at the same level as before.
  2. His xGSAA/60 this year is very close to 0, suggesting that he is overall playing at a league average level during 5v5 play.
  3. The idea that he is a slow starter is only true for the last few years. Earlier in his career he had solid starts to the season. There’s also a noticeable dip in almost every year for December, which is likely him cooling off from what looks like to be a lot of hot Novembers (just not this year).
  4. His two best years by both stats appear to be 2015-16 and 2012-13. One year he received no votes for the Vezina while the other year he finished 2nd in voting. The Vezina voters really aren’t good at their job.

Honestly, I think it’s very fair to say we are seeing a below average performance from Hank relative to what we expect from him. This year he is facing a pretty normal mix of low-, medium-, and high-danger shots. It’s not like last year where the very high average shot quality he faced gave him average traditional stats and amazing underlying stats. Given the fact he’s coming off such a stellar season, I have a lot of hope that he can bounce back and play at a really high level this year.

However, going by the eye test I am at least a bit worried. There definitely have been games where he has let in total stinkers. And not just one stinker in one game, but a few stinkers in a few games each. But he still has long stretches where he plays solidly and makes great saves. Nonetheless, I would say there is not anything in particular that I see as an explanation for his poor play. No apparent injury or major changes in style of play. As far as I can tell, he’s more or less just playing more sloppy than usual.

Something I might investigate in the future is his progressive win threshold % (WT%) and loss threshold % (LT%). WT% is basically a measure of how often a goalie “steals games” and LT% measures how often they let in so many goals that the team in front is very unlikely to overcome it. Both are based on per game expected goals and goals against numbers. And at least for last year, he was absolutely elite by those numbers. One of the best in the NHL for both stats.

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A Detailed Analysis of the Brassard for Zibanejad Trade

by @Chris_Beardy

The Rangers and Senators made a very noteworthy trade on June 18, 2016:

In this post I will evaluate this trade from the perspective of a New York Rangers fan. I plan to evaluate the following aspects of this trade:

  1. Statistics-based performance review
  2. Statistics-based usage review
  3. Brief “eye test” statements
  4. Contract, cap, and asset considerations
  5. Concluding remarks

1. Statistics-Based Performance Review

Unless noted otherwise, all of the charts to follow were constructed using 2014-16 data for 5v5 situations with zone-, score-, and venue adjustments where applicable. The data has been sourced from Corsica. I will frequently use stats that say “/60”, which means the stat is adjusted to “per 60 minutes of time on ice.” This addresses any difference in time on ice by these players.

Here are some basic player data just to set the stage:

Brassard Zibanejad
Age 28.8 23.3
Height 6’1″ 6’2″
Weight 205 lbs 220 lbs
Shoots L R
GP 241 230 ( + 6 AHL)

Continue reading “A Detailed Analysis of the Brassard for Zibanejad Trade”

Estimating Arbitration Salaries Using JT Miller’s Contract Extension

By @Chris_Beardy

J.T. Miller became the first of four arbitration-bound New York Rangers to ink a deal. On July 13th, he signed a 2-yr, $2.75m AAV contract, leaving him just one year shy of UFA status. Many have proclaimed this contract is a steal for the Rangers, but I think it is right on point. I also believe it will provide a benchmark for many of the other players lined up for arbitration right now. Thus, I have used Miller’s contract to develop a simple cap hit prediction model for those other players.

Miller is being paid at market value.

@HockeyStatMiner wrote a great article for Blueshirt Banter in May that predicted:

Continue reading “Estimating Arbitration Salaries Using JT Miller’s Contract Extension”

Do the Rangers play worse offensively in front of Lundqvist than his backups?

For at least the last two years, the idea has been floated around  that the New York Rangers play two different games: one when Lundqvist is in the net and one when he isn’t. That idea goes on to posit that the New York Rangers who show up when Lundqvist is off the ice is a much better team. Those Rangers create more offense knowing that they can’t just grind out low scoring, one goal games, and they play a tighter defensive game knowing that the King can’t just bail them out when they misstep. Overall, they play a better game of hockey without their safety net.

In this article I intend to begin looking into the notion that a team plays differently depending on who the goalie in net is. I will do so by examining something less directly tied to the goalie position. I will be looking at the GF60 and CF60 rates of three separate teams split based on whether their franchise goalie is in the net or not.

The Narrative Behind it All

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Lundqvist being attended to by the Rangers’ medical trainer after being struck in the throat by a shot.

This idea isn’t without some cursory evidence. The Rangers were without Lundqvist for a sizable stretch in the 2014-15 season. A puck to the neck caused Hank to miss all of the Rangers’ games between Feb. 4th and March 26th, which was a 26-game stretch. Cam Talbot went on to shoulder much of the weight as the interim starter and ultimately he put up a great 0.926 performance. However, an early story line was the unexpected goal support from the Rangers for Talbot. From Feb. 10th to Feb. 22nd, the Rangers logged 33 goals in 7 games. And while they came back to earth shortly thereafter, the goal support still seemed to be there more for their goaltenders who aren’t named Hank. Talbot and MacKenzie stood in net for 38 games where the Rangers put up 3.04 GF60. Lundqvist received the slightly lower goal support of 2.89 GF60 over the course of 46 appearances. That came out to an extra 5.2% goals for Talbot and MacKenzie, which is an extra goal every 7 or so games.

Less Narrative, More Data and Graphs

I began by pulling 5v5 TOI, GF, and CF data for the New York Rangers’ goalies’ individual seasons from 2010-16, excluding 2012-13 (because the sample size is small for the lockout year). The data for guys besides Lundqvist were merged into a composite for each year.

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The differences between the totals over these five seasons ended up being much larger than I expected. The Rangers scored an extra 0.23 GF60 in 5v5 play and generated 2.4 CF60 extra in 5v5 play when Lundqvist was on the bench or otherwise not playing. And in fact, the GF60 and CF60 data on a year-by-year basis was higher for the backups in 4 of the 5 seasons. The 5-year TOI totals were 16,202 min with Lundqvist and 5,758 min without. On a year-to-year basis, the TOI ranged from 2,225 to 3,088 min with Lundqvist and 743 to 1,785 min without. So the samples are decent in size.

Now, I do think this is compelling evidence that for some reason the Rangers are generally performing slightly better without Lundqvist in net. However, it is not possible to discern a reason from this data. Perhaps the players actually are more motivated to generate offense because they do not have their safety net. Perhaps the team’s coaches have made measurable changes to their personnel choices, such as decisions that are meant to try and keep the puck in the attacking zone. Perhaps the goalies themselves have significant contributions to the production, such as through stick handling.

To try and learn more about this apparent phenomenon, I investigated whether this situation has arisen on other teams. I chose to look at Nashville with Pekka Rinne and Chicago with Corey Crawford. Nashville, I thought, would provide a close parallel to the Rangers as both teams has generally been centered on an elite workhouse goaltender with a below average offensive team to support him. Chicago was to provide a stark contrast where I felt that Crawford was only depended upon to consistently deliver acceptable results and was infrequently leaned on to steal games. The Blackhawks tend to depend more on their offensive capabilities than the Rangers or Predators do, meaning that goaltending need not be as prized in Chicago.

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The results were quite contrary to my expectations. The Predators, who seemingly would need to emphasize offense more without their star goaltender in net instead have mostly faltered in Rinne’s absence. Chicago, on the other hand, ramped up their production when they didn’t have their starter in net. It seems that the Blackhawks seek to capitalize more on their scoring opportunities to help support their backups.

Below is another look at the data. These graphs give direct year-by-year comparisons of the data for all three franchises with and without their starting goalie:

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The GF60 data seems really noteworthy. For all three teams, 4 of the 5 dots fall on one side of the line. In the case of the Rangers and Blackhawks, they fall above the line, where offense is up when the starter is out. The Predators see a dip in production on all but one year when Rinne is off the ice.

The CF60 data mirrors the trend for the Rangers and Predators. However, the Blackhawks see their CF60 drop in 3 of the 5 years without Crawford in the net. This is starkly different than what was seen in the GF60 data.

Closing Statements

I intend to investigate this idea further with other teams that have had a consistent starter across the past six-year period (e.g., Pittsburgh, Los Angeles, Dallas, etc). I will avoid teams that have significantly switched their starters in that period to try and avoid adding yet another set of variables into this analysis. Even someone like Lehtonen will be pushing it as he has effectively gone from being the #1 goalie in Dallas to a 1A/1B goalie this past year.

I know the Rangers better than I do any other team in the league so they would be the best team for me to dig deeper on this matter. I have a suspicion that personnel choices by the coaches may be a significant driver of what was observed for the Rangers. However, failing that I can also look into “puck luck” in those games. It is possible that what we’re seeing is just an aberration by pure chance.

Ultimately I think something like this could be an important part of understanding how a major roster change could affect a team in indirect ways. There could be an argument made that moving Lundqvist for a slightly above goalie could be a better change than would be expected than just by the GA60 and salary cap impacts. There is evidence that that a slight GF60 bump could occur, which would in part mask a rise in GA60.

Quick Look: Do Diving and Embellishment Calls Correlate with Rates of Penalties Taken and Drawn?

Thanks to the 2015-16 Diving/Embellishment List on Scouting the Refs and the easily accessible data on Corsica, I made a quick graph of how 5v5 player penalty rates break down depending on whether the player had either 0, 1, 2, or 3 diving/embellishment calls in 2015-16 or had been fined by the league for the same offenses.

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po8arov Continue reading “Quick Look: Do Diving and Embellishment Calls Correlate with Rates of Penalties Taken and Drawn?”

Challenge Accepted: Can you find three players who, between the three of them, have played for all 30 teams?

Yesterday someone posed the following challenge on the /r/hockey community:

Can you find three players who, between the three of them, have played for all 30 teams?

The challenge was pretty simple. The 30 teams were the 30 active NHL franchises. (So someone who played for the Atlanta Thrashers would count as a Winnipeg Jet, etc.) I made it a personal rule to exclude WHA seasons so that means WHA-era Edmonton Oilers, Quebec Nordiques, etc., were out of the picture. Only NHL seasons with those franchises would count.

And when it became apparent that the challenge could not be answered favorably, I decided to figure out the maximum number of teams I could cover with different sized combinations of players. The results are as follows:

One Player – 12 Teams

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This was an easy one. Most hockey fans know that Mike Sillinger is the king of the suitcase. Over his 17 season NHL career he played 1,049 games spread across 12 different teams. He was involved in 10 separate trades. He never spent more than 4 seasons with a single team (Detroit – although he only played 3 games in his inaugural season) and maxed out at 155 games with a single team (Columbus).

The next closest players have all played for 10 teams: JJ Daigneault, Jim Dowd, Olli Jokinen, Michel Petit, and Mathieu Schneider.

Two Players – 19 Teams

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There are four such pairs of players in NHL history who have played for 19 different franchises between them. And all of these pairs share Mike Sillinger in common. His companions here are Jim Dowd, Bryan Marchment, Dominic Moore, and Lee Stempniak.

So the futures for Moore and Stempniak could see them moving their respective pair up to top spot in this category if they move on to the right team next year. Stempniak’s resurgence with the Devils and continued success with the Bruins should help him land a contract next year (at the tender age of 34). Moore, despite being 36 next year, could possibly find a new home. He still has a good reputation as a dependable fourth line center in this league.

Three Players – 25 Teams

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Full size – The data table for players who played for 9+ NHL teams. (Carl Voss played for four teams that are now defunct.)

So the original challenge: 30 teams among 3 players. Can’t even get close. We top out at 25 teams in three separate trios. Even more shocking: The first one listed does not contain Mike Sillinger! Instead, they all share Grant Ledyard. Ledyard played 18 seasons in the league spread among 10 different teams. He did play five years each with Dallas and Buffalo in the middle of his career, but the bookends of his time in the league involved a lot of roaming. The trios were:

  • JJ Daigneult, Grant Ledyard, Bryan Marchment
  • Jim Dowd, Grant Ledyard, Mike Sillinger
  • Grant Ledyard, Bryan Marchment, Mike Sillinger

Ledyard probably ended up in all three lists because of the teams he played for. Among the 24 players who played for 9+ teams, it was most difficult to find players for Columbus (only 1 player), Buffalo (2 players), and Washington (3 players). Sillinger was great because he had played for Columbus, but a lot of his other destinations were places that had had 8+ other players play there. As a result, he was prone to “overlapping.” Ledyard played with Buffalo and Washington and played mostly for teams with 6 or less other “overlappers.”

Four Players – 29 Teams

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This outcome totally sucks. With Carter Anson, Dominic Moore, Mike Sillinger, and Jarrod Skalde, I maxed out at only 29 teams! Why are you doing this to me Colorado? Fortunately Dominic Moore is still active so there is that teeny, tiny chance that this gets fixed next year, but I doubt it.

Calculating this with four players required me to move towards a programming solution. I did find a 25-team trio running through combinations in Excel, but I was not able to exhaust my search that way. And then I found a source that let me expand my pool to players with 8+ teams on their resumes, which put this all out of the reach of handwork.

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Full size

I had a 54 player pool, which meant I would have to run through 316,251 different combinations. So I wrote a script in python to do it for me and keep track of the results. The frequency of the results can be found in the chart to the right. The data seems to be normally distributed with a mean of approximately 21.5. (Or maybe it’s more like a binomial distribution? I’m bad at stats so please correct me.) The range of the data is from 14 to 29.

Five Players – 30 Teams

As can be deduced from the previous section, you can easily find five players who, between them, have played on all 30 active franchises. There are literally hundreds of combinations stemming off the Anson + Moore + Sillinger + Skalde quartet above. And I would not be surprised to know there are hundreds or even thousands more that can be formed with any of the 54 quartets that cover 28 teams, the 509 quartets that cover 27 teams, etc.

Disclaimer

So it’s worth noting that the player pool I worked with only included those who had played for 8 or more NHL teams in their career. In the 54 player pool I used in my programmatic approach, I removed players with less than 8 teams due to defunct teams (e.g., Carl Voss) or from “doubled up” teams due to relocations (e.g., Hartford moving to Carolina).

I can confidently stand by my one player result for obvious reasons. My two player result cannot be beaten by a player with 7 or less teams to their credit, but it could possibly be tied. So both of those results as a maximum number will stand.

But I cannot rule out the possibility of there being higher results for three or four person combinations. A 7-team player and any of the four pairs that cover 19-teams could possibly form a 26-team trio. Similarly, Sillinger and two 7-team players could also reach 26. Similarly for quartets, there are a number of scenarios in which including 7-, 6-, or even 5-team players could lead to covering all 30 teams. And considering how much the player pool grows when going down as far as 5-teams, it becomes slightly plausible that a 30-team quartet does indeed exist.

So overall, I have an interest in adding 7-team player data to my set to determine what effect it might have on the results. I don’t see it being a challenge programmatically; the challenge seems to be finding an easy enough data source to work with. However, if I need to dive down into 6-team and 5-team data sets I might start encountering some challenges with my limited programming knowledge.Nonetheless, if any of you know where I might be able to come across helpful data sets, please let me know.

Clarifying the Long-Term Injured Reserve (LTIR)

The collective bargaining agreement (CBA) agreed to by the NHL and NHL Players’ Association (NHLPA) in 2013 describes how a player can be placed on the LTIR, how this status effects his team’s cap situation, and what can be done with the player after receiving this status. In this post, I will describe how the LTIR status is granted, how it interacts with the salary cap, and provide an example of it being used in the league.

How does a player get placed on the LTIR?

The LTIR is specifically defined in Article 50.10(d) in the 2013 CBA. A player is eligible to be placed on the LTIR if the player has been determined to be unfit to play by the team’s physician for a minimum of 24 days and 10 regular season games. In the league believes that a player is being placed on the LTIR in bad faith, the league can issue a challenge. In this situation (which to my knowledge has not yet happened), the NHL and the NHLPA would select a neutral physician to evaluate the player and make a ruling.

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The form needed to put a player on the IR.

What is described above is effectively a special version of the injured reserve (IR), which only requires an expectation that the player will be out for 7 days. Another key difference between the two is that the IR can be triggered retroactively and only creates a maximum roster size exception. The LTIR cannot be deemed retroactively and it can create exceptions to both the maximum roster size and the salary cap ceiling. Both statuses for a player can be designated using the form found as Exhibit 28 in the CBA, which is shown off to the side. A team would simply need to fill out this form and submit it to the NHL Central Registry and NHLPA.

Once the NHL Central Registry has approved the LTIR status, the team is allowed to add a replacement player or players to its roster.

How does the LTIR effect the salary cap?

The trickiest thing about the LTIR is determining how it effects the salary cap for a team.  Article 50.10(d) in the CBA actually provides eight separate examples to demonstrate the “Bona-Fide Long-Term Injury/Illness Exception to the Upper Limit [of the Salary Cap].” Interestingly enough, the LTIR exception and the performance bonus cushion (another post for another day) create the only two exceptions to the salary cap ceiling during the regular season.

Perhaps the biggest misconception about the LTIR is that the player’s cap hit does not get removed from the team’s payroll. In fact, what happens is that the team is allowed to exceed the designated salary cap ceiling by as much as the cap hit of the contract for the player entering the LTIR. The value of the allowed overage is determined on the day that the player is moved to the LTIR. That player on the LTIR both continues to count towards the cap and continues to receive his salary. Article 50.10(d)(ii) specifically states

“The Player Salary and Bonuses of the Player that has been deemed unfit-to-play shall continue to be counted toward the Club’s Averaged Club Salary [….]”

The next Article, 50.10(d)(iii), states that

“The total replacement Player Salary and Bonuses for a Player or Players that have replaced an unfit-to-play Player may not in the aggregate exceed the amount of the Player Salary and Bonuses of the unfit-to-play Player who the Club is replacing[.]”

Finally, Article 50.10(d)(iv) states that

“[….] A Club may then exceed the [salary cap ceiling] due to the addition of replacement Player Salary and Bonuses of Players who have replaced an unfit-to-play Player, provided, however, that when the unfit-to-play Player is once again fit to play [including any time spent on a conditioning loan], the Club shall be required to once again reduce its Averaged Club Salary to a level at or below the [salary cap ceiling] prior to the Player being able to rejoin the Club [….]”

[emphasis in the original]

How has the LTIR been used in the league?

Thanks to Cap Friendly, I have found a resource that makes it a lot easier to show how the LTIR has been used this year by the Toronto Maple Leafs. They have engaged in some really interesting work with regard to cap and asset management through the use of a significant number of tools, including the LTIR. Below you can see a timeline of Nathan Horton’s status with the team over the course of the season. Most notably, he was moved from the Injured Reserve to the Long-Term Injured Reserve on October 27, 2015.

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Now there had been little question from before the season that Nathan Horton was not planning to make any return to the ice. Unfortunately, he likely has career-ending medical issues, but he likely will not be retiring. It is neither in his interest (since he will continue to be paid on the LTIR) or in the interest of the Maple Leafs (for the reasons to come below) for Horton to retire at this time.

Thus the question should be asked why the Maple Leafs did not place Nathan Horton on the LTIR at the start of the season. That can illustrated with the graph below:

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The black line represents the salary cap ceiling for the Maple Leafs. The blue line is their daily cap hit and the green line is a projected cap hit for the team at the end of the year. A team’s final salary cap number at the end of the year is actually the average of all their daily cap hits. The projected cap hit is that running average assuming that the current day’s cap hit were to be maintained through the end of the season.

Under normal cases, neither the blue nor the green lines are permitted to go over the black line at any single point during the season. So a team cannot operate at 150% of the salary cap ceiling for half the year and 50% of it the rest of the year to even out at 100%. No, teams must remain below the salary cap ceiling at all times. Unless they have one of two exceptions: an LTIR exception or a performance bonus cushion exception.

As mentioned above, the Maple Leafs can receive a salary cap ceiling exception equal to the overage created by the Horton contract after putting replacement players on their roster (whose total contract values cannot be greater than that of Horton’s contract), which occurs immediately after Horton is placed on the LTIR. At the start of the season, the Maple Leafs were operating at a projected $70.48m cap hit. If they had placed Horton on the LTIR at that point and fully replaced him, they could have created a maximum allowed overage of $4.38m. Instead, the Maple Leafs waited. Then on October 27, 2015, they called up Casey Bailey from the AHL (at a time when they had three players on the regular IR and needed a call-up), which put an additional $0.91m against the cap. This put the Leafs’ projected cap hit only $93,306 beneath the salary cap season. So they placed Horton on the LTIR at that point, granting them an allowed overage of up to $5,206,694 for as long as Horton is on the LTIR. (Note: Horton’s contract lasts through the 2019-20 season.) Thus, the Maple Leafs effectively have a salary cap ceiling of $76.6m while almost every other team can only spend up to $71.4m this year. (Note: Casey Bailey was back in the AHL after only two days up with the Maple Leafs. He arguably was only called up for this LTIR move.)

And so it can be seen that the Maple Leafs have used this allowed overage four separate times this year:

  1. On October 29, 2015, they returned Casey Bailey to the minors, called Byron Froese up to the NHL, and signed Richard Clune. Overall, those moves put the Leafs at $71.5m, just slightly over the normal salary cap ceiling. This only lasted for a single day.
  2. From December 30, 2015, to January 10, 2016, the Maple Leafs were at $71.7m in salary after an emergency call up of Mark Arcobello and Antoine Bibeau. (Both were sent back to the minors on January 3, 2015, for some reason but promptly returned to the NHL on the next day.)
  3. From February 8, 2016, to February 22, 2016, the Maple Leafs were well above the normal $71.4m salary cap ceiling. One of the main factors was the trade that sent Phaneuf out of town (along with four other players in the AHL) in exchange for four Senators players. The exchange ultimately added $1.8m to the Maple Leafs’s salary cap. Around that same time frame, Tyler Bozak, Joffrey Lupul, and Jared Cowen (from the aforementioned trade) were all placed on the IR and required roster replacements. The Leafs ultimately carried a maximum salary cap of $75.9m on February 13, 2016. This used up $4.5m of the LTIR exception. Had Horton been placed on the LTIR at the start of the season, these roster moves could not have been done this way.
  4. Not pictured above (because I made the image before all roster transactions were completed) is the Maple Leafs ending February 29, 2016, with about $440k above the normal cap ceiling. This is mainly related to them calling up a large number of their minor league prospects including Kasperi Kapenen and William Nylander.

Finally, it should be mentioned that sometimes the contract of a player on the LTIR can itself become a good asset. We saw that happen this past summer when Marc Savard was involved in a trade that sent him from the Boston Bruins to the Florida Panthers. The thing is: Savard has not played a single game since 2010-11, when he received a career ending concussion. So he had spent his entire time in Boston after his injury on the LTIR, which makes cap management a bit more complicated for the reasons described in detail above. However, Florida found his contract attractive as a cash-strapped team because it added $4.0m to their cap while only costing them $575k in real money each year. This helped Florida reach the salary cap floor. It was a move beneficial for both sides because now Boston has that $4.0m unlocked without having to do tricky movement of their assets.

So what next?

Well if you made it this far, you’re clearly interested in how the intricacies of the CBA. Consider taking a look at my three-part series covering the cap advantage recapture penalty. It’s just as good of a read and it covers an equally important but obscure mechanism described in the 2013 CBA.

Penalty Effects: Brad Marchand and How He Effects the Bruins’ Special Teams Stats

Brad Marchand, despite being one of the league’s biggest pests, is a highly skilled two-way winger who can play in all situations. He is both one of the best power play weapons for the Bruins and a key part of their penalty kill. So it is interesting to consider his prowess for getting calls and putting his team on the man advantage as well as for being called and sitting in the box while the Bruins play shorthanded. I plan to estimate the net  “penalty effects” on the Bruins from Marchand’s penalty taking and drawing abilities.

Marchand and Penalties

Marchand has often been both the among the leading penalty takers and penalty drawers for the Bruins. His penalties taken (PF) numbers have been top three on the team in 2010-11 (3rd), 2013-14 (1st), 2014-15 (1st), and 2015-16 (t-1st). In the penalties drawn (PA) category, he has had top three finishes on the team in 2010-11 (1st), 2011-12 (1st), 2013-14 (2nd), 2014-15 (1st), and 2015-16 (1st). This has led to a large spread in how Marchand effects the amount of time that the Bruins special teams receive over the season: Continue reading “Penalty Effects: Brad Marchand and How He Effects the Bruins’ Special Teams Stats”

Pitfalls to Avoid When Evaluating Players with Turnover Stats

Takeaways and giveaways are two turnover stats officially recorded by the NHL at every game. However much like other “real-time stats” like hits and blocked shots, there are issues to discuss before we can consider lending any credibility to the numbers.

Defining Takeaways and Giveaways

Perhaps the most concerning thing about takeaways and giveaways is that there is no definition for them from the NHL. The stats page does nothing to describe what either of these stats actually are. Worse yet there is no glossary or list of definitions on the entire NHL.com site that describes exactly what a takeaway or giveaway is. I asked Eric Hornick, the statistician for Islanders home broadcasts since 1982, if league had ever defined what giveaways and takeaways are:

After extensively using my google-fu, I was able to turn up a lone 2013 article from Dave Mishkin, radio broadcaster for the Lightning, that offered a definition. However, this text does not appear elsewhere on NHL.com and seems to originate from a Columbus Dispatch article in 2011. Nonetheless, Mishkin states:

Giveaways/Takeaways: Here are the league definitions: A giveaway occurs when a player’s own actions and decision making results in the loss of team possession of the puck.

A takeaway occurs when a defensive player causes a turnover and takes possession of the puck or when a defensive player makes a definitive effort to intercept a pass attempt and takes possession of the puck.

I would argue that this definition does not help us at all. It’s probably what most of us intuited before reading it. And that leads us to core problem of these two stats: They are highly subjective. While there are certainly going to be giveaways that are clearly giveaways (such as passing the puck right onto an opponent’s stick) and takeaways that are obviously takeaways (like lifting someone’s stick to take the puck), there are some times where it is less clear if a turnover was a bad offensive play or a good defensive play.

While I may sound like I am splitting hairs here, the subjective nature of these stats have led to unreliable record keeping. There have been many articles over the years describing “home rink bias” for turnovers, hits, blocked shots, and even more well-defined stats like shots on goal and assists. Any use of home stats for any player is going to introduce home rink bias into the statistics and make them rather suspicious. Below is a chart of home vs. away turnover stats for Colorado during the 2013-14 and 2014-15 seasons. The stats are for all situations and only for players with 80+ total games.

col_base stats

While the giveaway stats look fairly reasonable, it is exceedingly clear that Colorado’s home rink statistician is tracking takeaways in a way that deviates from the rest of the league’s statisticians. For this reason, I would advise the use of away stats only or the use of adjustment coefficients for home data.

(Note: While it is possible that the Avalanche’s statistician is recording takeaways in a way that is biased for the home team, we cannot say that for sure from this data alone. If his record of takeaways by Colorado’s opponents is similar, then the issue is instead a deviation of how this statistician defines takeaways relative to the rest of the league. That investigation would be another post for another day.)

Situational Bias

The turnover stats reported by the NHL are for all situations – even strength, power play, and shorthanded. In addition, these stats are reported as base counts, completely ignoring the influence of varying time on ice across players. I was actually inspired to look into this topic yesterday while reading the NHL Arbitrators blog yesterday on finding comparable players to Kucherov for estimating his next contract value. It was a great read and I really like the blog concept and approach. However, the author made use of turnover base counts from the NHL when making one of his comparisons:

For instance, over the last two [full seasons], O’Reilly had a takeaway-giveaway ratio of 181-59 while Stepan’s was 96-69.

In the course of my research, I found that this difference largely disappeared when using away stats and switching to rate-based statistics:

Player Home GV / 60 Home TK / 60 Away GV / 60 Away TK / 60
Ryan O’Reilly 1.22 4.70 1.00 2.07
Derek Stepan 1.99 2.17 1.06 2.08

The two players are almost exactly identical when removing the home rink bias from each sample. The switch to rate-based statistics adjusts for the fact that O’Reilly had a higher average time on ice per game as well as the fact that Stepan was injured at the start of the 2014-15 season, causing him to play less games than O’Reilly. Below is a further breakdown of the away stats by situation for the two players:

Player 5v5 GV / 60 5v5 TK / 60 PK GV / 60 PK TK / 60 PP GV / 60 PP TK / 60
Derek Stepan 1.12 2.25 0.00 2.75 0.94 0.31
Ryan O’Reilly 1.03 2.19 0.55 3.84 1.51 1.21

Do note that the PK and PP samples are rather small (100 -200 mins) so they may not necessarily be close to what they would be over a longer stretch of time.

Ultimately, I think it was still fair for NHL Arbitrators to say that O’Reilly had more favorable turnover stats than Stepan, but I would argue that they were much closer than the base counts implied.

An Incomplete Picture

Giveaways and takeaways make implicit descriptions about possession during a game. A giveaway signals that the player had the puck before the event occurred while a takeaway signals that the player did not. From there we can ask if a team’s ability to possess the puck is having an impact on the player’s stats.

Returning to the O’Reilly vs. Stepan comparison from before we can see that there might be something to this. Despite being one of Colorado’s best possession players, O’Reilly’s away 5v5 score-adjusted Corsi for was 45.42% over the last two full seasons. Stepan on the other hand had a 50.52% Corsi for in the same situations. This 5.10 percentage point difference suggests that the Rangers had possession of the puck more often than the Avalanche did. This makes it more likely that Stepan was in situations where he could commit a giveaway than O’Reilly, while O’Reilly would have had more opportunities to commit a takeaway than Stepan.

Now, it should be noted that Corsi is a proxy for possession because all it actually measures are certain types of shot attempts. It does not quantify time of possession, zone entries and exits, pass attempts, etc. And this also does not quantify O’Reilly’s or Stepan’s actually roles and successes on the ice. It tells nothing of how much each skates with the puck on their stick, how their positioning is, etc. All of that could be another topic for another day (depending on how much data is out there for these sorts of things).

This bridges over into a criticism that Mishkin had back in his 2013 article:

You would figure a player with a high giveaway total is prone to making bad decisions while one with a lofty takeaway total is adept at reading plays. But the player’s “giveaway” total only highlights potential bad decisions. There’s no corresponding number for how many good decisions he makes with the puck. (At least in football, the quarterback offsets his interceptions with other figures, such as completion percentage, yardage and touchdowns).

Mishkin is right that we need to know a lot more to make use of these turnover statistics when comparing players. It just further enforces the need for more data to properly make use of these statistics. Perhaps the biggest indication that we need to re-evaluate the use of these statistics is the fact that 2015 Norris trophy winner Erik Karlsson led the league in giveaways in the last two full seasons combined. Behind him were PK Subban and Joe Thornton. While these three players “did the most bad things with the puck” over those two seasons, I highly suspect that some record of the “good things they did with the puck” would vindicate them. Unfortunately, no such data is tracked by the NHL.

In Conclusion

To wrap it up, it’s important to do the following when using turnover stats in player evaluation:

  1. Either stick to away stats or find/calculate an adjustment coefficient for home statistics.
  2. Use rate-based statistics, preferably ones based on time-on-ice.
  3. Consider the situational usage of the players you’re using.Either use 5v5 stats or make a note of how PP and PK stats influence the players’ composite stats.
  4. Think about how a player’s stats can be influenced by their style of play as well as that of the team around them.
  5. Add a disclaimer about the shortfalls of turnover stats and use other metrics in your comparisons.
  6. Reconsider if using turnover stats is a good idea.