Analyzing the Defense

After trading Adam Larsson to Edmonton for Taylor Hall and losing David Schlemko to San Jose in Free Agency, many fans knew the Devils' defense would struggle. Struggle is precisely what they did. 

This season, the Devils saw their GA60 rise from the 2015-16 value of 2.43 to 2.93, a difference of a half a goal. Cory Schneider posted number below his career averages a result of a less talented, less organized defense. 

I've grouped the top 180 defensemen by TOI (2 men per pair x 3 pairs x 30 teams) and sprinkled in any Devils who did not meet the minimum TOI threshold. I used this sample to create two cluster analyses.

The first analysis judges a player's overall quality based on their P60 (scoring) and GA60 (suppression) values. (Please note that using GA60 as a proxy for defensive skill is imperfect, however the analytics space struggles to provide a profoundly more appropriate method). We are essentially gauging how many goals the player is able to prevent and directly produce. The players are characterized as "Poor", "Defensive", or "Scoring."

The second analysis identifies a player's scoring tendencies based on their G60 and A60. Basically, we want to know if they primarily assist other goals, score the puck themselves, or neither. The players are placed into groupings of "Grit", "Playmaker", and "Scorer." Typically, you'll find that players who don't have high scoring numbers will fall into the "Grit" category, the players who assist more will be labeled "Playmakers", and the players with high G60 values will be categorized as "Scorers." However, you may find scenarios in which a player's label may surprise you based on their scoring numbers (See Steven Santini below).

All NJ players are indicated by a circle whereas all other players are indicated by a plus sign.  Use the filter at the top-right of each sheet to filter by team.

The Good

• Steven Santini: The rookie fell into the Scorer category with about .9 points per hour individually while conceding about 2.5 goals against per hour of ice time. His closest player type peers are Oscar Klefbom, Alex Goligoski, and Jeff Petry. Of his .9 points per hour, Santini registered .68 assists per hour and .23 goals per hour, despite his labeling as a "Scorer" rather than "Playmaker."
• Damon Severson: Severson was categorized as a Scorer like Santini with his 2.2 goals against per hour and .8 points per hour. Of his .8 points per hour, he registered about .7 assists per hour and .14 goals per hour. His closest player type peers are Calvin De Haan, Deryk Engelland, Matt Dumba, Torey Krug, and Brent Seabrook.

The Mediocre

• John Moore: Although his offensive contributions were impressive, Moore bled goals at a clip of 3 goals per hour and contributed 1 point per hour as an offensive defenseman. His closest player type peers were Nick Jensen, Travis Hamonic, and Tyson Barrie. Of his one point per hour, he contributed about .6 goals per hour and .4 assists per hour. He is labeled as a Scorer in the Scoring Cluster remarkably owns the second-highest points per hour in the league, second only to Brent Burns' Norris Trophy-worthy totals. This likely has much to do with his smaller TOI sample due to his calamitous concussion sustained mid-season.

• Jon Merrill: Merrill fell into the Poor category as well as the Defensive defensive category with a 2.5 goals against per hour and .4 points per hour. His closest player type peers are Connor Carrick, Niklas Kronwall, Jason Garrison, and Shayne Gostisbehere. Overall, his 2016-17 campaign featured Merrill playing a more relied-upon role on the back end.

• Kyle Quincey: Quincey was categorized as Poor but a Scorer while allowing 2.6 goals per hour and scoring .8 points per hour. His closest player type peers were Mark Streit and Justin Faulk.

The Bad

• Dalton Prout: I can't say I'm shocked. Prout has no peers as he sits at the bottom of the chart conceding 3.9 goals per hour while contributing .6 points per hour. Yikes.