Advancements in hockey analytics will come more quickly if we borrow metrics from sports that have more developed analytical cultures. Basketball is one such example. The use of SportVU to track possessions in basketball has produced similar data to my work tracking possession in hockey.
Among the metrics I’ve borrowed from basketball is the metric used to measure how successful a player is offensively. Offensive-rating or offensive-efficiency in basketball calculates the number of points a player produces per-100 possessions. I’ve translated this to hockey by tracking the number of scoring-chances a player helps produce per-100 possessions.
How am I able to do this?
I have been tracking passes, dekes, shots, blocked shots, blocked passes, stick-checks, dump-ins, and many, many other events for the better part of the last four years. I track the majority of these events by documenting them as either successful or unsuccessful, while events such as zone exits, line carries, and zone-entries are simply tracked as they happen.
Why use scoring-chances?
Scoring-chances are tracked independent of anything else that occurs on the ice. Whether they occur as the result of a turnover, a stretch pass, or a successful pass to the slot is not taken into account. As such, we can use scoring-chances as the determining factor when comparing games where a team enjoys even-strength success or failure.
Why not use wins and losses?
Comparing wins and losses would produce too much noise. Save-percentage and powerplay opportunities have a huge impact on a team’s ability to win. Whereas a team’s ability to out-chance the opposition at even-strength is controlled solely by the skaters on the ice (goalies’ pass and dump-out attempts are also included in the team totals).
Obviously, the top offensive-players on any team create the most scoring-chances. The most efficient offensive-players create the most scoring-chances per-possession play. A possession-play is defined as any play a player attempts while in possession of the puck. Plays that fall into this category include; passes, dekes, dump-ins, and shots.
Scoring-chances reflect only those chances (attempted shots from the slot) where the player indicated was directly involved in the creation of the chance, and not just on the ice. Again, this metric is similar to offensive-efficiency in Basketball.
Numbers taken from the nearly 1000 games I’ve tracked show that teams that out-chance their opponents at even-strength average 5.87 scoring-chances per-100 possession plays, while teams that are out-chanced at ES average 5.21 scoring-chances per-100 possession plays. In other words, the quantity of possession plays does not necessarily increase when teams out-chance the opposition, although the quality of the possession plays does increase.
Offensive-efficiency also varies by position; with forwards producing more scoring-chances per-100 possession plays than defensemen. In fact, the more possession plays a team’s defense engages-in, the fewer scoring-chances a team produces. This is directly reflective of where possession plays occur on the ice, as possession events by defensemen generally occur further away from the opposition’s net than those of forwards.
Forwards average 7.31 scoring-chances per-100 possession plays, while defensemen average 2.95. Broken down even further, wingers average 7.47 scoring-chances per-100 possession plays, while centres average 7.11. Again, this is a product of where possession plays are occurring, as centres attempt more defensive-zone puck-possession plays than wingers. It is also a reflection of the importance of having defensemen who can move the puck up ice efficiently, as a forward with possession is substantially more likely to produce a scoring-chance than any defenseman.
Some examples of offensive-efficiency numbers among players I’ve tracked with a minimum of 150 even-strength minutes played:
• Thomas Vanek (11.0)
• Max Pacioretty (8.3)
• Douglas Murray (2.0)
• P.K. Subban (3.7)
• Andrei Markov (3.6)
• Phil Kessel (10.2)
• Dion Phaneuf (2.3)
• Nazem Kadri (6.1)
• Morgan Rielly (4.3)
• Bobby Ryan (7.8)
• Kyle Turris (8.1)
• Taylor Hall (7.8)
• Ryan Nugent-Hopkins (7.3)
• Jordan Eberle (8.5)
The goal within the hockey analytics’ community should always be the advancement of new metrics. The only way to develop new metrics is to track new events. The simple act of obtaining the data will produce the answers to which metrics are useful. This advancement will come that much more quickly if we borrow metrics from sports that have more developed analytical cultures, and attempt to translate them to the data we’ve collected.
For more on these statistics, please check out the archives of BoucherScouting.com
