The Tampa Bay Lightning spend a lot of time in the offensive zone during 5v4 man advantages. The Chicago Blackhawks spend a lot of time in the offensive zone during 5v4 power plays. this isn’t new, in fact traces are evident from earlier in 2015-16, and even as far back as early 2014-15.
Tampa Bay ranks among the bottom five in power play efficiency; Chicago is battling Washington and Anaheim for the league’s best.
Randomness abides (the Lightning ranked in the middle for GF60 in 2014-15, and dead last in SF60, while GF60 is and thrashing around the bottom five in ’15-16) while SF60 ranks at the bottom of the NHL. Chicago ranked 20th in GF60 in ’14-15 and 14th in SF60 in ’15-16. In ’15-16 the Blackhawks ranked 26th in SF60 and sixth overall in GF60.
Offensive zone time clearly matters, but only with the constructive ability to create offensive chances. In other words, there has to be progression to scoring chances, not just endless cycling or keep away with the puck. I took a recent look at the effects of multiple passes in the offense of zone at 5v5 on shooting percentage. Intuitively, the greater the propensity of sustained zone time with multiple passes greatly increases the chance of one of the shots ending up as a goal.
Unless you’re the Tampa Bay Lightning.
In that study, the Lightning are shooting a meager 2% from multiple passes in the offensive zone prior to the shot event. At 5v4, they don’t fare much better as their 26th ranked power play suggests. Chicago on the other hand, have a fairly consistent approach to zone time across a variety of situations, and the 5v4 power play has benefitted this season.
I dipped back into the Passing Project data, where a bunch of people are tracking passing events for NHL teams. I’ve spoken about and written about this project ad nauseum and I’m bullish with their latest data released the day after the NHL trade deadline. The data has 270 odd games tracked with some teams significantly represented more than others, so there are limitations (and sample sizes even with these limitations are clearly small). Some actionable data exists for first glimpses, but we need to remain conscious of the fact that this is a very small data set. For this writing, workable data within the passing project is restricted to teams with above average games tracked, instead of the entire NHL where two-thirds of the league teams lack significant results.
The study for shooting percentage was based at even strength (5v5), my curiosity led to effects of offensive zone time at 5v4. I didn't want the focus to be strictly on shooting percentage here, however, instead I wanted to investigate teams offensive zone time at 5v4. A focused special teams blog headed up by Arik Parnass is examining various scenarios of the power-play – Arik is showing how the 20% of the game matters – however sustained zone time doesn't seem to be one area that's been touched yet.
For this purpose, nine teams from the passing project, almost a full one third of the NHL has a greater than average games tracked, the list including Boston, Chicago, Dallas, Edmonton, New Jersey, San Jose, Tampa Bay, Toronto and Washington.
Passing project data fortunately tracks up to three passes prior to any shooting event, be it shot on goal or missed/blocked shots. The project also tracks where the passes originated from, including the side of the ice in addition to zone. In the table below all the per/60 data is courtesy of War-On-Ice specifically isolated for only the gains that have been tracked – so keep in mind the sample.
Using the sequencing feature, we could start to isolate components of the power-play, such as the rate at which teams make one to three or more passes prior to shooting events. With more data, this could eventually be used as a proxy for zone time, or even cycling based on the sequence of passing.
The table looks like it does below.
| Raw | Per Game | |||||||||||
| Team | GP | CF60 | SF60 | HSCF60 | Sh% | TOI | 3 OZ Pass | 2 OZ pass | 1 OZ pass | 3 OZ Pass | 2 OZ pass | 1 OZ pass |
| BOS | 21 | 122.06 | 56.26 | 22.56 | 13.86 | 4.86 | 32 | 24 | 13 | 1.52 | 1.14 | 0.62 |
| CHI | 49 | 84.42 | 47.83 | 19.07 | 20.83 | 4.63 | 75 | 21 | 21 | 1.53 | 0.43 | 0.43 |
| DAL | 27 | 103.25 | 57.11 | 20.34 | 13.22 | 4.97 | 37 | 40 | 26 | 1.37 | 1.48 | 0.96 |
| EDM | 20 | 98.00 | 56.40 | 27.91 | 13.46 | 5.12 | 28 | 12 | 12 | 1.40 | 0.60 | 0.60 |
| N.J | 50 | 88.87 | 45.62 | 16.90 | 14.18 | 4.98 | 72 | 37 | 41 | 1.44 | 0.74 | 0.82 |
| S.J | 21 | 101.56 | 53.80 | 23.91 | 16.08 | 5.46 | 42 | 23 | 20 | 2.00 | 1.10 | 0.95 |
| T.B | 33 | 84.52 | 42.57 | 16.99 | 14.82 | 5.68 | 68 | 23 | 11 | 2.06 | 0.70 | 0.33 |
| TOR | 24 | 111.54 | 55.90 | 35.29 | 11.74 | 5.53 | 42 | 26 | 23 | 1.75 | 1.08 | 0.96 |
| WSH | 29 | 109.17 | 61.38 | 18.20 | 13.35 | 5.19 | 66 | 29 | 26 | 2.28 | 1.00 | 0.90 |
Right off the bat we see is separation from some teams in all the per 60 category such as CF60 and SF60, but it's also very impressive to see how the passing data potentially correlates to the shooting metrics expressed in the per/60 data.
For instance, Tampa Bay generates 84.52 Corsi events per 60 at 5v4. Incorporating passing data, we can see the Lightning get a lot of zone time that includes plenty of passes, but generate very few actual shooting events. They had a (data-set sample size) low of 42.57 shots for every 60 min, highlighted by a 16.99 high danger scoring chances per 60, meaning there aren’t a lot of shooting events from high danger scoring areas. From the sample that we've been using, 67% of Tampa Bay’s shot events originate from three or more offense of zone passes, while a meager 11% are derived by one solitary pass in the offense of zone. That’s not necessarily a bad thing after all. Teams shouldn’t be trying to gain the zone and fire aimlessly for the sake of getting shots on goal, but sustained zone time is about creating lanes for shooting opportunities.
We can further surmise teams ability to gain the zone and keep it. In the case of the Edmonton Oilers, they seem to want to gain the zone and retain possession, but they only seem to be able to generate about 9.33 events of three or more passes for each 60 minutes. Contrast that to a shot event happening once almost every minute for the Blackhawks and Lightning, Edmonton doesn’t seem capable of extended zone time. The table below breaks down the per60 for events by each primary event.
Toronto leads the sample with 35.29 HD scoring chances per 60, with a split among all three pass categories. The Leafs like to get the shot on net after setting up and crash hard to look for rebounds and second chances.
| Team | GP | 3 OZ/60 | 2 OZ/60 | 1 OZ/60 |
| BOS | 21 | 11.20 | 8.40 | 4.55 |
| CHI | 49 | 61.25 | 17.15 | 17.15 |
| DAL | 27 | 16.65 | 18.00 | 11.70 |
| EDM | 20 | 9.33 | 4.00 | 4.00 |
| N.J | 50 | 60.00 | 30.83 | 34.17 |
| S.J | 21 | 14.70 | 8.05 | 7.00 |
| T.B | 33 | 37.40 | 12.65 | 6.05 |
| TOR | 24 | 16.80 | 10.40 | 9.20 |
| WSH | 29 | 31.90 | 14.02 | 12.57 |
Similarly the Stanley Cup champions Chicago Blackhawks, generate 64% of their power plays with zone time based on three or more offensive zone passes, while lacking the shooting frequency of some other teams in the data set. With an 84.42 Corsi For per 60 rate, they rank lower than the Lightning among teams here, and only 19.07 highh danger shot events per 60 minutes. This itself can be further studied for effect, but its out of scope of the theme of this blog post.
It's clear, more shots will lead to eventual scoring chances and then goals, however Chicago leads the league in power-play efficiency, and they do it based on sustained zone time rather than just a barrage of shots in ’15-16.
Once again, with more data across the board for more teams, the 5v4 offensive zone time study coupled with zone entry data could prove vital to analyze difficulties on the power play. In the absence of RFID technology and resultant data, this is likely the best tool available to analysts.
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