Prospect Cohort Success – Evaluation of Results

2008 NHL Entry Draft Stage.JPG
2008 NHL Entry Draft Stage” by Alexander Laney. Licensed under CC BY-SA 3.0 via Commons.

Identifying future NHLers is critical to building a successful NHL team. However, with a global talent pool that spans dozens of leagues worldwide,  drafting is also one of the most challenging aspects of managing an NHL team. In the past, teams have relied heavily on their scouts, hoping to eek out a competitive advantaging by employing those who can see what other scouts miss. Quite a challenge for many scouts that may only be able to watch a prospect a handful of times in a season. While there has been some progress in the past few years with teams incorporating data into their overall decision making, from the outside, the incorporation of data driven decision making in prospect evaluation has been minimal.

To address this, Josh Weissbock and myself have developed a tool for evaluating prospect potential which we call Prospect Cohort Success (PCS), with the help of others in the analytics community including Hockey Graphs Supreme Leader, Garret Hohl.

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Sunday Notes: September 13, 2015

Math lecture at TKK.JPG
Math lecture at TKK” by Tungsten – photo taken by Tungsten. Licensed under Public Domain via Commons.

Welcome to Sunday Notes, where we try to rehash important developments occurring on Hockey Graphs and elsewhere in the CORSI twitter league in less than 500 words. I’m sorry if we forgot about your post, or misconstrued what you said. We don’t care. Don’t @ us. Just do better next time. – Asmaen

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NHL Player Physical Peak Estimation

File:Hockey. Canadiens. Mosdell . Getliffe . Filion BAnQ P48S1P12151.jpg

Probably want to keep that middle guy out for the next shift. Photo by Conrad Poirier, via Wikimedia Commons

Determining NHL player peaks has frequently focused on production and, occasionally, wrinkles are added to account for the steeper fall-off for goal-scoring as opposed to playmaking. Generally, the peak appears to be around the ages 23-25, with some skills like shooting exhibiting fairly early peaks and others a bit later.

Poking around some spreadsheets, I came across data that I’ve always meant to get to: time per shift. The NHL has been keeping a measure of average time per shift for players going back to 1997-98, so I licked my chops over the robust data set. The “Why?” for looking at it, I think, takes us to an interesting place. To some degree, time per shift can allude to a player’s stamina and overall physical fitness; it can also allude to the coaching staff’s assessment of their performance — though there are plenty of shifts ended on the fly in a hockey game. What’s more, we simply haven’t had a lot of player peak estimations using time on-ice, and when done carefully, I think we can capture something like a total physical peak for players.

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Schedule Adjustment for Counting Stats

Edit:There is another version of this article available in pdf which includes more explicit mathematical formulas and an example worked in gruesome detail.

Rationale

We all know that some games are easier to play than others, and we all make adjustments in our head and in our arguments that make reference to these ideas. Three points out of a possible six on that Californian road-trip are good, considering how good those teams are; putting up 51% possession numbers against Buffalo or Toronto or Ottawa or Colorado just isn’t that impressive considering how those teams normally drive play, or, err, don’t.

These conversations only intensify as the playoffs roll around — really, how good are the Penguins, who put up big numbers in the “obviously” weaker East, compared to Chicago, who are routinely near the top of the “much harder” western conference? How can we compare Pacific teams, of which all save Calgary have respectable possession numbers, with Atlantic teams, who play lots of games against the two weak Ontario teams and the extremely weak Sabres? Continue reading

A Nice Tool to Have: BehindtheNet.ca Player Name Converter, plus Age and Position, 07-08 through 13-14

Those of you who have worked with Behind the Net data would be the first to say it’s a great, important site. I feel the same way, but I also know that anybody that’s worked with it close enough knows that there is a bit of a pain-in-the-ass there, with the different name spellings. Also, there are some position discrepancies and, for those that like to look into that sort of thing, player ages aren’t on there. Well, because I just brought the data together for something else I’m working on, I decided to share what I had for those problems. This link is to a Google doc that has the Season, regular Player Name, their age and position that season, and their BTN name for that season.

The players include all players that played a season from 2007-08 up to last week Thursday, 2013-14. Let me know if the link below doesn’t work:

BehindtheNet.ca Player Name Converter, plus Age and Position, 2007-08 through 2013-14

Hope this helps, happy researching!