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.

Finding a players true zone start percentage, that is the number of times they actually begin their shift in a given zone divided by their total number of shifts, isn’t all that difficult either. A simple approach, which only requires use of the NHL’s Play-by-Play files, is to check whether a player was on the ice for the stoppage directly before any faceoff. If they were on the ice, then we don’t need to count the faceoff towards our zone start score, while if they weren’t we know it’s a true zone start.

Alternatively, and more accurately, we can use the NHL’s shift files to check when a player’s shift start coincides with a faceoff time. The benefit of this approach is that not only do we see when a player starts in a given zone, but we can also find those shifts where the player starts his shift on-the-fly. And when we look at the data, it turns out that including these on-the-fly shifts is actually really important in terms of determining a player’s true zone start percentage.

Distributions of True and Traditional Zone Start Percentages

Distributions of True and Traditional Zone Start Percentages

The graph above shows the number of players from 2008/09-2013/14 who recorded a given zone start percentage using both the traditional faceoff count based approach and our true zone start measure. While it’s obvious that when we include on-the-fly metrics our measures should decrease, the actual number of shifts that start on-the-fly (and hence don’t really offer any zone-start advantage) is quite staggering. An average player should expect roughly 60% of his shifts to start on-the-fly, with the remaining 40% divided up between the offensive, defensive and (most commonly) neutral zones. While our traditional metrics have players starting approximately 30% of their shifts in the offensive or defensive zones, when we use True Zone Starts we see that the number drops down to about 10-12%.

True DZS% Traditional DZS% True NZS% Traditional NZS% True OZS% Traditional OZS% OTF%
Mean 10.32% 30.61% 17.85% 38.25% 11.77% 31.14% 60.05%
St. Dev 2.8% 5.0% 2.8% 3.6% 2.5% 4.9% 4.9%

The other thing that’s important to note is that the distribution curves are a lot narrower for our true metrics than our traditional zone start numbers. The standard deviation of Traditional OZS% and DZS% is about 5%, while for True OZS% and DZS% it’s only about half that. What this implies is that the actual difference in coaching usage between players is a lot smaller than we previously thought it was, and that a good deal of the variance we were seeing in the original numbers was related to the differences in talent we mentioned above.

Even if we ignore differences in talent though, it’s pretty easy to show why we see a smaller variance in true zone start percentages. If we count the number of faceoffs that occur in each zone for a true zone start in a given zone, we can estimate how many traditional zone starts each true zone start is worth.

Defensive Zone Start Neutral Zone Start Offensive Zone Start
Expected DZ Faceoffs 1.14 0.34 0.07
Expected NZ Faceoffs 0.06 1.28 0.06
Expected OZ Faceoffs 0.06 0.32 1.13

For every Defensive Zone start, we’d expect an average player to see 1.14 defensive zone faceoffs (including the original draw), and 0.06 neutral zone and offensive zone faceoffs. In other words, even if we exclude the original faceoff, a player who starts in the defensive zone is more likely to have his second faceoff of a shift in the defensive zone than the offensive zone. This trend also holds (albeit in the opposite direction) in the offensive zone, and it’s this difference that results in players with above average True DZS% having even more extreme Traditional DZS%.

While we’ve seen that traditional zone start numbers obviously differ a lot in magnitude from true zone start numbers, you may be wondering whether it’s still appropriate to use the traditional numbers as proxies for the true numbers. This is obviously a fair question to ask: after all, if the numbers are basically the same but just on different scales, you would still be able to get a decent idea of a player’s true usage from their traditional numbers. Unfortunately, however, the correlations between the true and traditional metrics aren’t all that high.

Comparison Correlation
True DZS% vs. Traditional DZS% 0.80
True NZS% vs. Traditional NZS% 0.53
True OZS% vs. Traditional OZS% 0.67

These would be great correlations to see if we were looking at year-over-year comparisons, but in this case it’s not encouraging at all since we’re looking at two numbers that purportedly measure the same thing over the same time period. To give a point of comparison, the correlation between same season Weighted Shots % and Corsi For % at the team level is above 0.95, and we still prefer Weighted Shots because it’s a more accurate measure of a team’s talent.

So while it’s clear that traditional zone starts are a flawed metric, we only really have half the story. While true zone starts are much less prevalent than the traditional numbers indicate, we still haven’t looked into what effect a true zone start has on a player’s possession numbers. We know that the difference in zone starts between players isn’t as big as we may have thought previously, but it’s still important to figure out the size of impact of a true zone start. After all, if a true defensive zone start makes a player 80% worse, a marginal difference in usage between players may result in an enormous differences in results. In Part II we’ll examine how we can measure these differences, and look at how we can properly adjust for true zone starts in our possession numbers.


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16 comments on “How much do zone starts matter part I: (Maybe) not as much as we thought
  1. […] None of the point totals are anything to write home about. Stafford leads the group in that category with 17 points, while seeing the least ice time of the bunch. He’s clearly the most productive player. But he also sports the lowest Corsi For %, an indicator that play is being driven against him when he’s on the ice. However, it is worth noting that Buffalo is by far the worst team in the league in that metric (37.1% as a team), so it’s not as ugly as it looks. Jagr is the only one who has a better CF% than his team (47.9%), but his heavy offensive zone deployment isn’t reassuring, despite new studies that show zone starts actually have minimal impact on Corsi numbers. […]

  2. […] CF% returns are not as stark, re-raises important points: zone starts and their impact are muted by the limits of the measure and characteristics of the game like the 60-40 rule for possession in hockey. Nevertheless, we […]

  3. […] If RNH and Gordon had had 50 per cent offensive zonestarts in 2014-15, RNH would have still have around 40 points and Gordon would have still had around 10. Maybe RNH would have had 38 with 50% zonestarts, and maybe Gordon 12 with the same, but that’s about the extent of the impact evening out their zonestart percentages would have had. It’s also worth noting that zonestarts percentage, at least the way they’ve currently ex… […]

  4. […] are various ways to judge how much an NHL coach trusts the players on his team. Zone starts, while they don’t impact possession as much as was once thought, are one way to judge how much a coach trusts a […]

  5. […] a general disclaimer, you can read these links about how zone starts and quality of competition are not enough to explain the disparity in performance between these two […]

  6. […] a good way of quantifying a player’s deployment. With many current metrics, such as both traditional and true zone starts, it’s difficult to express their effect except in a relative sense (i.e. by comparing zone starts […]

  7. […] debated not using zone starts, but I kept them in. Zone starts don’t impact a player’s possession numbers much at all, contrary to what we used to think. What they do tell us is how much a coach trusts a […]

  8. […] numbers are only raw, not relative. I reserve the right to change my mind on that at any point. Zone starts are gone because, while they can provide a caveat, they aren’t as important as you may […]

  9. […] a good way of quantifying a player’s deployment. With many current metrics, such as both traditional and true zone starts, it’s difficult to express their effect except in a relative sense (i.e. by comparing zone starts […]

  10. […] the effect of zone starts on a player performance over an entire season is largely overstated (see here, here, here, and here) and I’m primarily interested in highlighting the explanation as to […]

  11. […]  I usually don’t include zone starts because Matt Cane’s excellent post 1 and post 2 on zone starts made everyone think differently about them and to stop over valuing […]

  12. […]  I usually don’t include zone starts because Matt Cane’s excellent post 1 and post 2 on zone starts made everyone think differently about them and to stop […]

  13. […] to go along with the toughest zone starts of his career. It has been debated as to how much zone starts matter (not as much as you may think). The majority of shift starts begin  on the fly (approx. 60%) with […]

  14. […] also took the largest share of the team’s defensive zone draws at 38.8 percent. While there is pretty conclusive evidence that zone starts don’t significantly impact a player’s big picture results (largely because […]

  15. […] and Puck Plus Puck wrote two articles on how zone starts do not matter as much as we used to think. Part I defines true zone start percentage, which takes shifts started on the fly into account, and Part II looks […]

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