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On-Ice Passing Metrics: Offense and Defense in the Passing Game

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This is a preview of what will be available on War on Ice in the, hopefully, not-too-distant future. Previously, all passing data has provided insight into how teams generate offense, but nothing in the way of how they prevent it. That’s about to change.

Ed Mulholland-USA TODAY Sports

A week or so ago, I wanted to look at the NHL Play by Play sheets and pull out which players were on the ice for passing events, both for their team and against. Literally the day I was going to start this, my Islanders tracker, Ryan Stoll (aside: good name), sent me an email saying he’d been thinking about it as well and had actually done it for the last ten Islanders games! Great minds think alike. So, we chatted for a bit on how he pulled the data and the easiest way to go about parsing valuable data from our passing game sheets.

Why should you care? If we already have Corsi/SAT numbers, why would we need to know which players are on the ice for passing events? Well, we’ve gathered around 340 games of data at the time I’m writing this, and teams consistently shoot at a higher percentage on shots from passes as opposed to shots preceded by no passes. The average team shooting percentage in the NHL is 7.9%. The average shooting percentage across our sample size on shots preceded by two or more passes is 10.8%. That’s a significant gap. I’d like to know who is on the ice when teams are passing the puck. A shot from a pass is more dangerous than a shot by itself.

As I mentioned above, all of this will be available on War on Ice, but below is an example from a single game on the amount of detail we’re gathering. This data includes all shot attempts from passes and is at even strength from the Devils clash against the Toronto Maple Leafs on February 6th. John’s recap from that game is here.

Terms

Here’s how to read the charts and acronyms. It’s been a while since I’ve posted definitions, but a lot of this is new, so here goes.

SAG For /SAG Against(Shot Attempts Generated For/Against): This is all attempts generated from passes. Or, think of it as Passing Corsi events. So, the number here represents how many shot attempts were generated from passes while that player was on the ice. SAG Against is the opposite of SAG For. This tells us how many shot attempts were generated from passes by the opposition while that player was on the ice.

SC SAG For/Against (Scoring Chance Shot Attempts Generated For/Against): Similar concept, but this highlights only those passes made into the scoring chance area in front of goal that led to shot attempts.

A1 Transition F/A (Primary Pass Preceding Shot Attempt was made in Defensive or Neutral Zones): This illustrates which players are on the ice in terms of generating offense in transition and entering with possession, or are repeatedly getting caught in transition.

A2 F/A (Sustained Passing Offense): This number tell us how many shooting events preceded by multiple passes that the player was on the ice for. Think of this as sustained offense: which players are involved in completed passes prior to a shot on offense, and which players can’t get the puck away from the opposition.

RRF/A (Royal Road Passes For and Against): Simply, the number of Royal Road passes leading to attempts.

SG F/A (Shots Generated For/Against): Similar to the SAG totals, this looks strictly at which attempts from passes forced a save or resulted in a goal.

Now, after each metric, you’ll see a percentage, i.e. SAG%, A2%, RR%, etc.. Just like Corsi/SAT For%, we can get a percentage of each metric for while that player is on the ice. Over 50% will mean their team had the majority of those metrics.

The last two columns show how efficient (Shots Generated / Shot Attempts Generated) the team was while each player was on the ice.  Okay, that was a lot. Let’s get to it.

The narrative from that game was that the Devils had the better "Grade A" scoring chances and Toronto had the quantity of overall chances. Both teams had plenty of scoring chances, but how were they creating these chances? You’ll see a chart for each category. Toronto’s players are highlighted in blue to reflect the despair of a long-suffering fan base. Let’s see how both the defensemen stack up first.

Defensemen

Def_SAG

In terms of the raw attempts generated from passes, Toronto had a significant edge over the entire game (40 – 19), so it’s no surprise that their players were on the ice for more attempts for their team than against. When Morgan Rielly was on the ice, the Leafs generated sixteen attempts from passes and had eight generated against them, both totals were the highest for any Leafs defenseman. You can plainly see that when Korbinian Holzer was on the ice, the Leafs passing volume dipped dramatically.

The Devils defense wasn’t involved that much in the passing game during this matchup (only three attempts generated from them in total), so we see Jon Merrill with the "best" on ice display at 40% SAG. Adam Larsson and Andy Greene had a rough outing by the total numbers, but, again, the narrative coming out of this game was that Devils had the better chances, so let’s see how things look in the next category.

Def_SC

Here we see the chart flip. Every Devils defenseman came out ahead in SC SAG possession. Merrill led the group with seven for and zero against, followed by Marek Zidlicky on the ice for five for and zero against.

For the Leafs, it wasn’t pretty as the Devils continually were able to set up players with passes for shot attempts inside the home plate area. No one looked good.

Def_A1

Now the chart flips again! This chart represents attempts generated on plays in transition. Toronto was able to complete passes and break out of their zone, enter the Devils zone, and attempt shots throughout the game. The Devils numbers mirror the Leafs SC SAG numbers. It’s a complete reversal of how the teams played and addresses style of play and offensive and defensive set up with and without the puck.

Def_A2

Now we start to see some mixing, but that’s based on percentages. Toronto still held the edge in volume of sustained passing plays. What does that mean again? It means that Toronto completed more passes before attempting a shot on average during this game. They led in total A2 events (17 – 9) as well. So, when Jake Gardiner was on the ice for the Leafs, six times they were able to complete multiple passes and attempt a shot without losing possession; the Devils were only able to do this twice with Gardiner on the ice.

The Devils only had Peter Harrold and Mark Fraser finish above 50%, but they were only on the ice for three total events. After those two, Larsson was the best Devils defensemen and Zidlicky the worst from a sustained possession standpoint.

Def_RR

Here we see more of the quality over quantity that took place in this game. Merrill (4) and Zidlicky (2) were on the ice for multiple Royal Road events for the Devils and none against. Once again, these are the highest of quality chances as passes across the face of goal force the goalie to move and process new shooting situations in rapid fashion. There are not many of these events in a typical game, so for the Devils to generate five to Toronto’s two is a big advantage.

Def_SAGE

Lastly, we come to how efficient each team was with each defenseman on the ice. You’ll see the SAGE For and SAGE Against, sorted by SAGE For here. Defensively, Zidlicky and Merrill were most effective and limiting opposition efficiency, while Larsson and Greene were most efficient going forward. They had an easier time generating shots against Rielly and Stephane Robidas than any other Leafs defensemen.

Forwards

Now, we’ll go through these same charts for the forwards.

Fwd_SAG

Similar to the defensemen charts, these will follow similar patterns. While Jaromir Jagr led the Devils, he failed to even break even in terms of SAG possession. The Leafs had plenty of forwards who enjoyed the lion’s share of passing possession. David Booth was on the ice for seventeen attempts from passes for the Leafs, while only six were attempted by the Devils while Booth was in play. Phil Kessel, Tyler Bozak, Nazem Kadri, James van Riemsdyk, all of them had good days at the office.

Fwd_SC

Once again, when we refine what we’re looking for and focus on only passes that set up scoring chances, the chart flips. The Devils generated more scoring chances with Scott Gomez (6) on the ice than any other forward. Adam Henrique and Steve Bernier were just behind him (5). All Leafs were victimized, with Richard Panik and Kadri leading the way in terms of most given up. Half the Leafs forwards weren’t even on the ice to generate a scoring chance.

Fwd_A1

Similar to above, we switch again for transition events. Here is where Booth impacted the game as eight of his seventeen passing events were in transition. Kadri was next with six. The Devils were terrible at this phase of the game and have been all season, even going back to last season they were unimpressive. Whether that is speed, style of play, or combination of those factors, here we can quantify it.

Fwd_A2

With Kessel and Booth on the ice, the Leafs enjoyed their most sustained possession leading to shot attempts. Bozak, JVR, and Kadri were just behind them. Only Tuomo Ruutu broke even, with Gomez and Jagr the next "best" on the Devils.

Fwd_RR

Here’s the Royal Road even list for the forwards. Again, there’s not many of these events, but to even generate one is a big advantage. For soccer fans, you can think of these as "clear-cut" chances that you often hear players being involved in or creating. Gomez and Jagr managed to be on the ice for two for the team and none against. Only Jacob Josefson and Jordin Tootoo were victimized without having a RR event of their own. Daniel Winnik had the worst of it for the Leafs.

Fwd_SAGE

Here we see the Devils managed to convert their attempts into shots more efficiently than their Leafs counterparts. Mike Cammalleri and Dainius Zubrus were especially stingy while on the ice, as the Leafs operated with a SAGE of 40%.

Conclusions

So, this is just one game, but it is a preview of what’s to come as Andrew and Sam continue to work to incorporate our passing data onto War on Ice. It peels back several layers of how teams generate offense with various players on the ice, both offensively and defensively. What are some ideas you have on how to use this data going forward? What does this data tell us about possession? Style of play? Additional strengths and weaknesses of specific players? What am I leaving out? Sound off below!