Individual Point Percentage on the Power Play (2008-2012)

Scott Reynolds
October 16 2012 07:59AM

Photo: Beanhugger/Wikimedia/CC BY 3.0

Over the few days I've been talking about individual point percentage (i.e. the number of times an individual player gets either a goal or an assist compared to the number of total goals-for scored while he's on the ice) during five on five play. Of course, there are also a significant number of goals scored on the power play, and so today I'll be looking at the individual point percentages for forwards at five-on-four.

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Withdrawing from Eastern Europe: A Shift in Which Leagues are Producing NHL Draft Picks

Derek Zona
October 15 2012 03:51PM

NHL Draft Floor
Photo by Bri Weldon, via Wikimedia Commons

Lockout talk dominates every level of the hockey world, so we're doing our best to avoid it at all costs.  My focus this week is on the NHL draft, specifically the last 10 years.  I'll poke and prod the data in search of interesting conclusions and trends.  After the break, I'll lay out the raw data.

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Individual Point Percentage for 2008-2012

Scott Reynolds
October 15 2012 09:06AM

Photo: Michael Miller/Wikimedia/CC BY-SA 3.0

Late last week, I wrote about individual point percentage, and specifically about the individual point percentage of forwards during five-on-five play in the 2011-12 season. As a brief refresher of the concept (for those who don't like clicking through), individual point percentage is a calculation of the number of times an individual player gets a point (either a goal or an assist) relative to the number of total goals scored while he's on the ice. So, for example, if a player is on the ice for fifty goals-for during five-on-five play over the course of the season and he gets a point on forty of them, his individual point percentage would be 80%.

The idea is that this statistic will tell us which players were driving play in the offensive zone. One of the problems is that, because of the small sample size at the level of the individual season (no player was on the ice for more than 87 goals-for), the results are swamped by luck, which is how you end up with Kyle Brodziak and Matt Halischuk finishing second and third respectively. In order to move the conversation forward, I think we need to have a better sense of how players do over several seasons, which should help to deal with the sample size problem, and give us a sense of what a reasonable range of looks like.

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The Best of the Nation – 10.14.12

Jonathan Willis
October 14 2012 10:03AM

Across the Nations this week: the fantasy world some bloggers are living in, what the lockout is doing to careers, the value of a starting goalie, full team previews for every club in the NHL, how a trade 25 years ago is still powering the Pittsburgh Penguins, and much more.

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Context Clues: Finding Gems

Josh L.
October 13 2012 08:32AM

 Image via Wikipedia Commons

NHLNumbers is a site dedicated to pushing hockey thinking further up Bloom's Taxonomy from the lower understanding stages to higher levels of cognitive thought. The best research is always going to be supported by data, or numbers, which is why we do what we do. The work being done lately doesn't have a direct one to one application to fantasy hockey so one of the challenges we face as the site continues to grow is finding unique ways to provide interesting but helpful content to empower fantasy players to make better decisions when building their rosters. 

One approach to this problem is focusing on the context in which an individual player operates. No one is going to be able to pin down how well a player will perform perfectly, but with a holistic approach we can establish reasonable expectations for player performance based on the situations (context) in which they will operate.

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