About that Flyers challenge last night…

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Last night Dave Hakstol and the Flyers were the first team to get burned by the NHL’s new offside challenge rule. With a one-goal lead over Nashville and just 2:41 left in the 3rd period, Philadelphia was dinged for not one but two minor penalties at the same time. And on the ensuing 5-on-3 power play, Scott Hartnell banged in a loose puck to tie the game up.

https://www.nhl.com/video/embed/hartnells-late-game-tying-goal/t-290860626/c-53362803?autostart=false

Philly, however, decided there was something not quite right about Hartnell’s goal. They thought that Filip Forsberg may have snuck into the offensive zone just slightly ahead of the puck on the zone entry that preceded the tying marker. The Flyers decided to challenge, hoping that video review would negate the Preds’ goal and put them back on top with just under two minutes to play.

When news first came out of the league’s proposal to change the rules, there was a lot of skepticism that it would act as much of a deterrent to frivolous challenges. While no coach wants to see their team go on the penalty kill after conceding a goal, the odds were still stacked pretty heavily in favour of challenging even in low probability scenarios. In a normal even-strength situation, your probability of success doesn’t need to be all that high in order to make a challenge worthwhile, in fact you’re safe challenging a lot of the time with less than a 25% certainty of success.
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How certain do you need to be on an offside challenge?

Offside challenges are, to say the least, a controversial topic. While many have advocated for the benefit of getting the call right even at the cost of a delay in the game, it’s almost indisputable that the introduction of the offside challenge has slowed down the flow of the game. Over the past two years, coaches have challenged any play that was remotely close with the hopes of getting lucky on the video review, to the dismay of basically anyone other than replay technicians.

Those spurious challenges are one reason why the NHL modified the rules around coach’s challenges yesterday. Starting next season, instead of a failed challenge simply resulting in the loss of a team’s timeout, clubs will now face a 2 minute penalty for losing an offside challenge. Upon hearing of this change many fans were apoplectic, complaining that this rule change could bury teams who were already reeling from giving up a goal against, and would severely limit the willingness of coaches to challenge even legitimate missed offside calls.

Fan reaction notwithstanding, however, the question coaches should be asking is whether they should be changing their approach in response to the new rules. The threat of killing off a penalty for a failed challenge may seem like a big deal, but it’s important to note that teams only score on roughly 20% of their power play opportunities. Fans will surely remember when a failed challenge leads to a power play goal against, but there will certainly be occasions when the potential gain from overturning your opponent’s goal outweighs the risk.

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Introducing Weighted Points Above Replacement – Part 2

In part 1, I laid out the basis for Weighted Points Above Average (wPAA). Now it’s time to change the baseline from average to replacement level. A lot has been written about replacement level, but I’ll try to summarize: replacement level is the performance we would expect to see from a player a team could easily sign or call up to “replace” or fill a vacancy. In theory it is the lowest tier NHL player.

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Introducing Weighted Points Above Replacement – Part 1

Aggregate statistics in sports have always fascinated me. I might go so far as to say my need to better understand how these metrics work is one of the reasons I became interested in sports statistics in the first place. I also feel the process of developing them raises an incredible number of important questions, especially with a sport like hockey. Rarely are these questions raised in a more succinct and blunt manner than when a new aggregate stat first emerges and people see how good Oscar Klefbom is.

These questions mainly focus on how to value, weight, and interpret the various metrics that are available. For instance, should we value primary points per 60 more than relative Corsi for/against? How much more? Is there a difference? What’s the difference? Should we use some sort of feeling or intuition to determine which stats we like best? How do we address the issue of different metrics being used in conjunction to evaluate players? There have been multiple attempts to “answer” these questions (and many others) in hockey – Tom Awad’s Goal Versus Threshold (GVT), Michael Schuckers and Jim Curro’s Total Hockey Rating (THoR), Hockey Reference’s Point Shares, War-On-Ice’s (A.C. Thomas and Sam Ventura) WAR/GAR model, Dom Galamini’s HERO Charts, Dom Luszczyszyn’s Game Score, and most recently Dawson Sprigings’ WAR/GAR model… (Emmanuel Perry is also in the process of constructing a WAR model that I’m very excited about).

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Should teams play with 4 forwards when they’re down late?

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Tl;dr

  • There is some evidence to suggest that teams should play with 4 forwards when trailing late in a game.
  • The timing of when to switch to 4 forwards is dependent on how large an impact the switch has on goal scoring rates, however even with a low impact on goal scoring, using 4 forwards still makes sense.

One of the weird things about sports that I find fascinating is how often coaches and players seem to go out of their way to avoid having a negative impact on the game, even at the expense of potential positive impacts. People often seem to prefer to “not lose” rather than to win, which can result in sub-optimal decision making, even in the presence of evidence to show that the correct decision is not being made.

There are many examples of this across sports, but the biggest two in hockey are pulling the goalie and playing with 3 forwards on the power play. Analysts have been arguing for many years now about why teams should pull their goalies earlier, but it’s only been in recent seasons that teams have become more aggressive in getting their netminders out earlier.

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Improving Opposition Analysis by Examining Tactical Matchups

On Monday, I introduced some work on quantifying and identifying team playing styles, which built upon my earlier work on identifying individual playing styles. Today we’re going to discuss how to make this data actionable.

What are the quantifiable traits of successful teams? What plays are they executing that makes them successful? How can we use data to then build a style of play that is more successful than what we’re currently doing? The way we bridge the gap between front office and behind the bench is by providing data to improve their matchup preparation, lineup optimization, and enhance tactical decisions.

This is what I mean by actionable: applying data-driven analysis and decision-making inside the coach’s room and on the ice. All data is from 5v5 situations and is either from the Passing Project or from Corsica.

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Redefining Defensemen based on Transitional Play

Last time, I showed how passing data is a better predictor of future player scoring than existing public metrics. In this piece, I’m going to spend some time talking about how we can more reliably evaluate offensive and defensive contributions from defensemen, which has been difficult due to a lack of data. Not only due to a lack of data, but from a lack of flexibility regarding the identity of the position. Traditionally thought of as existing to defend and “make a good first pass,” I feel this limits the scope of both how we evaluate the position and its responsibilities.

In order to better evaluate defensemen, we need to identify specific metrics that we can tie to future goals. In looking at entry assists (a pass occurring in the neutral or defensive zones that precedes a shot), both for and against, we can quantify how effective that defensemen is at generating offense in transition, as well as suppressing those chances. The importance of those things at the team level is something I’ve previously discussed (transition here and defensive work here with Matt Cane). Once we identify these metrics as having a strong impact on future scoring and goal-suppression, we naturally then reevaluate what the proper roles are for a defensemen, which in turn forces us to reevaluate how we evaluate them.

Personally, I’d like to see us think of them more as fullbacks or midfielders in soccer (this is part of a larger concept of redefining positions and responsibilities, which will be posted in the next month or so, I hope). There are still going to be various types of players based on their individual skill set and team tactics, but supporting play, overlapping on the attack, and distribution are all pillars of what teams should look for. Let’s get to it.

All data is from 5v5 situations and special thanks to Dr. McCurdy for pulling the on-ice player data for me. All non-passing project data is from Corsica.

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#1MinuteTactics – Studying The World Cup Of Hockey

When you bring the best players and coaches together, entertaining things happen. Not only that, but many of the tactical habits employed by elite hockey team are actually not so hard to grasp.

Here are five teaching points brought to us by The World Cup Of Hockey 2016, broken down and served up in just over one minute apiece:

1) Transition Play: Team North America’s Neutral Zone Mastery

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Tactalytics: Using Data to Inform Tactical Neutral Zone Decisions

Breakout - Against 2-1-2...2

Last time, I showed how using data and video evidence can be combined to inform tactical offensive zone decisions. Today, I’m going to do the same thing in the neutral zone. Neutral zone play is something that has been a hot topic among analysts for many years, going back to this paper written by Eric Tulsky, Geoffrey Detweiler, Robert Spencer, and Corey Sznajder. Our own garik16 wrote a great piece covering neutral zone tracking. Jen Lute Costella’s work shows that scoring occurs sooner with a controlled entry than an uncontrolled entry.

However, for all the work that goes into zone entries, there have been few efforts to account for how predictable these metrics are. At the end of the day, what matters is how we can better predict future goal-scoring. Also, in looking at our passing data, what can we also learn about how actions are linked when entering the zone? Does simply getting into the offensive zone matter? Does it matter whether it’s controlled or not? Or, does what happen after you enter the zone matter exponentially more? Lastly, what decisions can we make to improve the team’s process using this data?

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