Hockey Prospectus – Player Level Weighted Shots

Over at Hockey Prospectus I’ve got an article up on calculating Weighted Shots (or, more specifically, Score Adjusted Weighted Shots) at the individual player level. Give it a read, here. The article expands on my presentation at the Ottawa Hockey Analytics Conference, which you can find here.

Lastly, if you’re interested in seeing the player level SAwSH data from 2008-2009 through to 2013-2014, it’s available here.

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Improving Corsi Rel: Adjusting for Team Talent

Corsi Rel is a stat that, in theory at least, is meant to address the fact that a good player on a poor team is still likely to post a bad CF%. We don’t want to punish superstars who are surrounded by replacement level players in the same way that we don’t want to reward hangers on playing on Cup winners (*cough* Dave Bolland *cough*). For defencemen in particular, Corsi Rel is often a better way to measure their impact, given that they have much less control over play in general and are driven heavily (at least in terms of raw results) by the talent up front that they’re paired with.

The problem with Corsi Rel, however, is that it’s too blunt of an instrument – it assumes that each player can only affect his team’s results by a set amount, regardless of the talent of that team. A good player on a bad team is assumed to be a good player on any team he plays on, which we know is unlikely to be true in practice. A player with a +1% Corsi Rel on a 42% team is unlikely to make a 56% team into a 57% squad, but pure Corsi Rel assumes that this would be the case. So while we know that there’s value in the information that Corsi Rel contains, the question is how to maximize that value.

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How much do zone starts matter part II: A lot on their own, not that much in aggregate

In Part I of our review of zone starts, we looked at the how the traditional definition of zone starts varied from what most people would consider a “true” zone start, and found that when we applied the true zone start definition to our data, the spread between players in zone start percentages decreased significantly. One key reason for the difference between methods is the inclusion of on-the-fly starts, which tend to make up around 60% of a players total shifts, and which drastically decrease the impact of each defensive/offensive/neutral faceoff. Another driver is the fact that often a player’s zone start percentage is impacted by their own performance: bad players end up with more defensive zone faceoffs due to their inability to drive possession, which incorrectly inflates their defensive zone start percentages. This also helps to create a false link between zone start percentages and possession numbers, leading people to incorrectly infer that tough zone starts are a key driver behind a player’s results.

While it’s useful to know that the true difference in zone starts between players is generally minimal, that doesn’t necessarily mean that we can just ignore them completely. To make a judgement about the overall impact that zone starts have we first need to figure out what the impact of a single zone start is on possession. To do that, we can simply look at all the 5v5 shifts taken since 2008 in aggregate, and calculate the overall Corsi For Percentage broken down by starting location.

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How much do zone starts matter part I: (Maybe) not as much as we thought

In 2013-2014 Boyd Gordon and Manny Malholtra were two of the worst players in raw CF% across the league at 42.3% and 41.6% respectively. Most people would argue that their results were not all that surprising given that they faced the toughest zone starts of any players in the league, with over 59% of their shifts starting in the defensive zone according to, almost 10% higher than anyone else in the NHL. The problem with this argument, however, is that neither player actually started 59% of their shifts in their own end. While both players did see 59% of the faceoffs they were on the ice for come at their own end of the rink, if we look at where each shift actually started and ignore faceoffs that started mid-shift, we see a much different story. While both players still faced some of the toughest zone starts of any player in the league, the actual percentage of Boyd Gordon’s shifts that started in front of his own goaltender was only about 32%, almost half of what’s traditionally reported. Malholtra, on the other hand, has a much larger gap: only 25% of his shifts actually started in the defensive zone, nearly 35% lower than his faceoff-based metric.

It’s not just Malholtra and Gordon and those at the extreme ends of the spectrum who are grossly misrepresented by traditional zone start percentage either. Every player across the NHL has their usage numbers skewed by the fact that most sites use faceoffs to measure zone starts rather than looking at the actual shift data (I should point out that most of the main stats sites do make it very clear that they use faceoffs, and that Hockey Analysis actually refers to the metrics as OZFO%/DZFO%/NZFO% now). Part of the reason for the differences is that the traditional measurements don’t take into account shifts that start on-the-fly as opposed to at a stoppage in play. And while this explains some of the difference we see, it’s not the bulk of the problem. The main issue with the current approach to measuring zone starts is that the measurement is often skewed (and sometimes heavily) by the performance and talent of the player in question. Bad players tend to end up with more defensive zone faceoffs because their opponents tend to get more shot attempts against them, which leads to more opportunities for their goalie to freeze the puck and more defensive zone faceoffs. The same idea is true in reverse for good players, and it all adds up to a false correlation between the traditional zone start measure and possession numbers.

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Hockey Prospectus: Whose special teams are really special this year?

Today on Hockey Prospectus I’ve got an article up looking at how each team’s special teams units have performed against expectations this year. Take a look at it here.

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Hockey Prospectus: Randy Carlyle’s Effect on Puck Possession and Scoring Chances

I’ve got an article up over at Hockey Prospectus on Randy Carlyle and his effect on puck possession and scoring chances. Hint: it wasn’t pretty.

If you’re interested, you can read it here.

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2015 World Junior Prediction Update – December 27th

Updated Predictions

Day 1 of the World Juniors is in the books and while there weren’t any upsets, two medal favourites nearly stumbled out of the gate with both Russia and the US needing the shoot-out to get past Denmark and Finland respectively.

Updated tournament predictions are given in the table below – as a reminder, these predictions take into account both our initial predictions generated using NHL equivalencies, as well as an Elo-based adjustment to take into account the results that we’ve seen to date.

Team P(1) P(2) P(3) P(4) P(5) P(6) P(7) P(8) P(9) P(10)
CAN 55.3% 26.1% 9.5% 1.4% 6.3% 0.8% 0.5% 0.0% 0.1% 0.0%
USA 35.6% 37.6% 12.7% 2.5% 8.8% 1.4% 1.1% 0.1% 0.2% 0.0%
RUS 4.5% 12.7% 26.6% 15.4% 19.2% 8.6% 8.4% 2.5% 1.7% 0.4%
SWE 2.7% 9.8% 20.3% 18.5% 18.5% 11.4% 11.3% 4.5% 2.3% 0.7%
CZE 0.1% 1.2% 1.8% 4.4% 5.9% 12.2% 11.3% 14.7% 26.8% 21.5%
SVK 0.0% 0.7% 1.8% 6.9% 2.4% 7.2% 6.9% 14.5% 21.9% 37.7%
DEN 0.2% 1.9% 3.1% 8.1% 8.4% 16.3% 15.3% 19.2% 15.6% 11.8%
SUI 0.4% 3.0% 5.4% 11.3% 11.1% 16.5% 17.1% 15.9% 11.9% 7.4%
GER 0.2% 1.7% 4.5% 13.2% 5.4% 12.6% 12.0% 19.1% 14.2% 17.3%
FIN 0.9% 5.4% 14.2% 18.2% 14.0% 13.0% 16.0% 9.5% 5.4% 3.3%

After defeating Slovakia handily to open the tournament, Canada remains the prohibitive favourite, with their odds of winning gold inching upwards to 55.3%. Most of the uptick in the Canucks’ odds comes at the expense of the Americans, whose shootout win decreased their standing in the model slightly. Sweden is the big winner of the day, however, with their medal odds jumping up to roughly 1 in 3, an increase of about 6% of their initial chances.

Today’s Games

Not many big games on the schedule today, with 3 teams odds of winning sitting above 70% according to our model. The lone “close” match looks like it could be the deciding factor in who avoids the regulation game in Group B, with the Swiss sitting as slight favourite over the Czechs. The other interesting match-up is the first of the day, where the Danes, who may be underrated by our model due to a lack of NHLe data for the Danish leagues, take on the Swedes, who had no trouble handling the Czechs in their opener. Canada takes on Germany, and will win.

Visitor Home Visitor Win % Home Win %
Sweden Denmark 73.0% 27.0%
Finland Slovakia 72.9% 27.1%
Switzerland Czech Republic 56.5% 43.5%
Canada Germany 95.8% 4.2%


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