The terms are everywhere these playoffs, part of hockey's new analytics lexicon.

Corsi. Fenwick. Zone starts. Score effects. Percentage Driven Outcome. Scoring chance share. Expected goals.

This is Big Data, NHL style, like baseball's Moneyball but with the complicating factor that hockey is more heavily influenced by randomness and variance, with so much depending on how the puck caroms around the ice. One bounce off a shin pad or a post can change a game. Or a series.

Can any of it be predicted by the numbers?

Many NHL teams are trying. Even laggards such as Edmonton, Florida, Toronto and Washington made high-profile additions in their stats departments last year, attempting to catch up to what other franchises had been doing for years. (Hockey remains years behind baseball and basketball.)

The most useful and popular statistics in the NHL all focus on measures of puck possession, since the team that possesses the puck more often than its opponent – even by small margins – generally has more success.

It's more complicated than that because the measures vary with the impact of great goaltending, penalties, special teams and whatever the hockey gods have to say.

"[Puck possession] is one of the things that's proven to be more repeatable," explains Matt Fenwick, an engineer from Edmonton who was in on the ground floor when these stats started being accumulated and assessed, around 2008. "What this is about is the search for real skill."

The first two rounds of this year's playoffs have offered some great examples of the strengths and limitations of hockey's new numbers. What follows are breakdowns of four key teams, their stories and how their playoff paths were – or weren't – foreseeable from the start.