Using micro data to understand the correlation between production and zone entries

The amounts of data that are readily available in hockey pale in comparison to some of the other sports. A primary reason for that is the continuous nature of hockey. It’s non-stop. Baseball, by comparison, is “one play at a time” (e.g., the game ‘stops’ between each pitch).

Corey Sznajder (@ShutdownLine), who manually compiles micro data, has graciously made the raw output available. Today we look at an example of the impact it can have on player evaluation.

Zone entries and correlation with production

At a basic level, possession is important because possessing the puck leads to shot attempts which lead to goals. A micro-driver of possession is entering the opposing zone with the puck (vs just dumping it in).

Note: carry-in % is the percentage of entries achieved by carrying the puck into the zone.

We ran a regression between 5v5 production (y-variable) and carry-in % (x1) and position (x2). Both X variables were statistically significant – i.e., they correlated with 5v5 production. Below we map out the results for wingers and centers:

31jul2017 -- micro data production

This makes intuitive sense. Entering the zone with possession of the puck means your team is in control of the puck in the opposing zone.

We then went one step further and plotted the same correlation using primary production instead (primary production = goals + primary assists / 60).

31jul2017 -- micro data primary.JPG

The results look similar – which they are – but the correlation is slightly stronger when considering primary production. This also makes sense as players who carry the puck in will often exhibit higher end skill and play making ability.


Entering the opponent’s zone with possession appears to be a valuable skill as it correlates with team goals. While this data is not readily available in large quantities, it’s an important skill to look for in forwards. Centers and wingers who can enter in control drive possession for their team and help generate strong scoring chances.

We take a sneak peak at a couple forwards who have excelled at entering the zone in control of the puck.

31JUL2017 -- micro data performance.JPG

Not surprising to see McDavid, Crosby, and Kane in the top quartile in the entire league. All 3 are top-end goal-scorers and play-makers.

Anisimov, Johnson, and J. Staal all excelled as well. Their ability to generate offense might fly under the radar for several reasons (e.g., hidden in the shadow of a big name; Anisimov behind Toews, Johnson behind Stamkos, or being known as a defensive center; J. Staal).

What we labeled ’emerging stars’ might have the biggest implications. Could Drouin’s strong carry-in % be a sign that he’s on the verge of a break-out season? Could Trocheck’s strong ability have been used to predict his strong 2016/17? While Draisaitl did play with McDavid, his 71% carry-in % is further testament to the strong, young player he’s become.


Hockey is really at the tip of the iceberg in terms of data and analytics. As more data becomes available, the depth of analysis and number of insights should grow accordingly. This example highlights some of the potential that micro data can have on player evaluation. But until more data exists, that potential will remain largely untapped.

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