NHL Salary Cap FAQ — Mike Colligan

Our running list of Frequently Asked Questions on the NHL Salary Cap, provided by site partner Mike Colligan.

For more from Mike Colligan, visit Colligan Hockey.

GUEST POST: A Call To Action on Crowdsourced Data — How You Can Help Usher In a New Era of Hockey Stats

Editor’s Note: This post was written by Emmanuel Perry (@MannyElk) and Ryan Stimson (@RK_Stimp) to describe their crowdsourced projects. We are happy to partner with them to help join their data back to our database, not just to spare them to extra work of linking back to the standard set, but to make it easy on them so that it can all be shared publicly.

EP: While listening to a particularly riveting episode of TSN Hockey Analytics featuring one co-webmaster of the site you’re currently reading this on, I heard something that piqued my interest. Andrew Thomas stated his openness to hosting fan-sourced data on the site and went on to mention he had begun working with Ryan Stimson to try and make this happen. I was already aware of Stimson’s Passing Project and was excited at the prospect of having such unique and valuable information shared publicly on an established online platform. I myself had been involved with collecting and sharing manually-tracked data of a different type, but had not considered expanding this project until now.

I believe fan-sourced data will provide the tools we need to advance this field into a new era. Certainly, in the absence of chip-tracking technology, this new data can catalyze new ideas and take our analysis of the sport new places. Good things don’t always come easy and indeed, entire seasons worth of data require thousands of often tedious hours. Projects like Stimson’s and mine require a collective effort and I’m asking for your help. Before I go on, here’s a little bit on our respective projects:

RS: The Passing Project

As the hockey analytics community gathers more information on how goals are scored, there’s been an emphasis on pre-shot movement and passing. Steve Valiquette introduced the concept of the Royal Road. The link Manny provided in the opening paragraph discusses the fact that teams shoot at a higher percentage as the number of passes preceding the shot attempt increase. The focus of this project is on capturing what happens prior to the shot attempt in several forms: sequence (one pass, two passes?), location (offensive zone passes, transition passes, Royal Road passes?), and efficiency (which players generate shots more often than others?). As we’ve gathered data on 340 games from this season alone, I’m more confident than ever in saying that what happens before the shot is attempted matters significantly more than where the shot is taken.

If you’re curious how we do this, you can visit this page where myself and five of my trackers take you through a period and explain what we do. For more detail, you can read some of my earlier findings from the hockey analytics conference at Carnegie Mellon University here and watch my presentation here (I start around the 20:00 mark.)

EP: Between The Lines

Our goal is to collaboratively record all blue line events during the 2015-2016 NHL season. In addition to zone entries, all instances of the puck entering or exiting a zone will be recorded. Thus, the location of the puck is known at any given second, allowing us to extrapolate stats dealing with zone time or transitions. In particular, these stats bring us closer to identifying specific aspects of the game on which players can have a direct positive or negative impact. I outlined potential applications of this data in my presentation at the hockey analytics conference at Carleton University in Ottawa, which you can listen to here.

Since Ryan’s project was initially proposed, he’s received significant interest and has accrued a handful of volunteers. In the few weeks since I proposed mine, I’ve gotten similar interest for which I am very grateful. In order for these missions to come to completion, however, I regret to say we’ll need more help. If you’re interested in contributing towards what we both firmly believe is a hugely important movement in the field of hockey analytics, please contact* either of us and we’ll be happy to provide additional details.

EP&RS: In addition to recruiting volunteers, Ryan and I have opened a crowd-funding campaign that you can view here. The money we raise will be put towards GameCenter Live subscriptions for our trackers and compensating Andrew and Sam for the time and effort they will dedicate to processing and hosting this data. [Ed: We'll use it to pay for the server costs. -AT] Know that your donations will go a long way in helping this latent information travel from the ice surface to your computer screen.

Contact information: Emmanuel Perry (@MannyElk) and Ryan Stimson (@RK_Stimp)

UPDATES: Calculation Changes for Teammate/Competition Statistics

Teammate and competition statistics have value for two main reasons:

  • Adjusting observed outcomes to account for boosts or drags in performance by factors beyond the player’s control, and
  • Assessing the coaching staff’s deployment (“usage”) of a player.

The main two of these are based on all shot attempts (Corsi) and the amount of time these players spend on the ice (since, oddly enough, better players are used more.) Both of these are useful for their purpose, but both are better served by having a summary quantity of these that are predictive of future behaviour, since they reflect both a locally accurate version of player ability and the state of the coaching staff’s current information.

To benefit both these factors (and to make our data processing operation smoother), we first predicted the next game’s CF60, CA60 and TOI60 for each player based on a lag of 30 previous games, then found that an exponential decay model was a satisfactory single predictor of this. For example, we use the formula

TOI60(new predicted) = 0.14 * TOI60 (this game observed) + 0.86 * TOI60 (this game predicted)

to update the game prediction for TOI60.

UPDATE, 2-25-15: We needed a longer “memory” for CF60 and CA60 events, so these are each

Cx60(new predicted) = 0.04 * Cx60 (this game observed) + 0.96 * Cx60 (this game predicted)


To calculate the teammate and competition statistics for that game, we then take the average of each player’s teammates (and opponents) weighed by the amount of common ice time, as before.

In addition, we’ll be adding the exponentially weighted statistics to our player, team and goaltender history once we establish the best predictive measure for each.

[WAR Off Ice] Updates to Bergeron and M. Staal Contract Info

Over the next few weeks, we will be releasing salary cap charts and information for NHL teams under the unofficial name, “WAR Off Ice”.  Leading up to the full release, we’ll post important contract news to the WAR On Ice blog.

We’re pleased to release early information on two player contracts today.  First, the widely reported contract terms for Patrice Bergeron are incorrect, according to two verified, high level sources.  Additionally, we have what were (to our knowledge) previously unreleased details on the structure of Marc Staal’s contract.

In Bergeron’s case, the average annual value (AAV) of his contract ($6.875M) is higher than what has been publicly reported to-date ($6.5M).  Staal’s AAV is $5.7M, same as previously reported.


Before today, it was publicly believed that Bergeron and the Bruins agreed to an 8 year, $52M contract ($6.5M AAV).  However, our sources have confirmed that they actually agreed to an 8 year, $55M contract ($6.875M AAV), structured as follows:

  • Years 1-4:  $8.750M salary, $0.0M bonus
  • Year 5:  $0.875M salary, $6.0M bonus
  • Year 6:  $0.875M salary, $3.5M bonus
  • Years 7-8:  $3.375M salary, $1.0M bonus

Bergeron will be 36 years old when the contract expires after the 2021-22 season.  We do not know at this time what implications this has for the Bruins’ salary cap situation for the 2014-15 season.


Below are details on the structure of Marc Staal’s contract with the Rangers, which goes into effect next season and expires after the 2020-21 season: UPDATE: this was reversed originally. Below is now correct.

  • Year 1 (2015-16):  $4.0M salary, $3.0M bonus  — $7.0 M total
  • Years 2-4 (2016-17 — 2018-19):  $5.0M salary, $1.0M bonus — $6.0 M total/y
  • Year 5 (2019-20):  $4.0M salary, $1.0M bonus — $5.0 M total
  • Year 6 (2020-21):  $4.0M salary, $3.0M bonus — $4.2 M total

Again, this results in a $5.7M AAV for the Rangers.

All of this information will be available soon on our NHL team salary cap charts site.

Partnerships and Exchanges

We’ve been busy on the development end here at war-on-ice.com, and that’s led to a number of new initiatives, but more importantly, new partnerships. So we’re pleased to announce three primary partnerships, in alphabetical order:

  • Mike Colligan (@MikeColligan) is our primary source for information on questions about the CBA and the salary cap. We’ll be cross-posting his FAQ questions to the WARblog and a master list. The first three are here:
  • Alexandra Mandrycky (@alexgoogs) has been utterly indispensable in the assembly of the war-on-ice.com player contracts database, and has also been developing upgrades to our charts, which will not only make them more usable and more pleasant, but will also speed up loading time and allow for additional portability. We can’t say enough good things about her contributions to the site so far.
  • Ryan Stimson (@RK_Stimp) is fronting a group of manual trackers as the leader of the Passing Project, who are collecting information on “pass assists” and other pre-shot information. Included in this is the tracking of passes across the line Steve Valiquette has coined the Royal Road, which we will of course think of instead as the Highway to the Danger Zone.

Thanks to these folks for everything they’re doing and will continue to do with us. Give them a follow and help them out if they ask, because they’re making our lives considerably easier and the site all the better for it.


We announced yesterday on Twitter that we’ve been working to rebuild the capacities of CapGeek from the ground up. Our biggest issue is taking raw contract information from a primary source and turning that into the database for use. Our second-biggest issue is making sure that we don’t take this data from any other public site — especially CapGeek’s archived pages. (We have no problem transforming existing public sources of data, but this salary info is the direct result of someone else’s hard work, not a value-added derivative.)

We got an extremely healthy response to our original query for help in doing this cross-checking of data. Once we’ve made some progress on this, we’ll open up the database for a public examination; following this, we plan to have some more of the tools built and available for use well before the trade deadline.

The only other issue is what to call it: RecapGeek is a fine name for the rebuilding project but not the services, and war-on-ice-cap sounds like we’re either pro-global-flooding or anti-Tim-Horton’s.

Predictability Differences for Forwards and Defensemen

Note:  If you haven’t yet read our post on how SCF% better predicts future GF% than does CF%, we recommend reading that first.  The definitions of metrics, data used, and methodology used in this post is the same as what is written here, so we refer interested readers to there for more info.

Summary:  This is an update to our previous post on the best metrics for predicting player performance.  Here, we split the analysis out by position (forwards vs. defensemen).

In this analysis, using all data going back to the 2005-06 NHL season:

  1. For forwards, SCF% is the best predictor of future GF% of the metrics we tested
  2. For forwards, CF% is a better predictor of future GF% than is FF%, but for defensemen, the opposite is true.
  3. For defensemen, FF% is the best predictor of future GF% (with SCF% finishing a close second) of the metrics we tested.
  4. SCF% is a much better predictor of future GF% for forwards than it is for defensemen.
  5. In general, future GF% is more accurately predicted for forwards than it is for defensemen.

Continue reading

NEW: Annual salary/compensation data for skaters

When CapGeek founder Matthew Wuest announced on Saturday that he was shutting the site down for personal health reasons, we were doubly saddened, for the well-being of an important member of the community but also for the loss of a stellar resource used by many. I was particularly in awe at the reach of the data he found — it’s one thing to work with public sources, but the dedication to finding what he had, or working to build a position where it comes to you, is outstanding. We also deeply respect his privacy as he goes through this tough time and we hope that he’ll be able to resume doing the things he loves (whether or not CapGeek was one of them.)

Needless to say, we won’t be replicating his massive efforts any time soon.

What we do have is access to public data on annual compensation, from past USA Today records and current NHLPA postings, dating across our database from 2002 to the present. After cleaning and matching, we’ve added it to the Goaltender HistorySkater History and Skater Comparison apps when individual season data is present. (Goaltender Comparisons will be added soon.)

This has total compensation in salary and bonuses by year; it is not adjusted for inflation or cap share. We see it as a stopgap first and a starting point second to have deeper discussions about what users want that we can provide.