Friday Quick Graph: The Evolution of an NHL Defenseman’s Time On-Ice

Age progression TPCs for a hypothetical defenseman who has played from age 18 through 40. The progression is built on year-to-year age trends across the entire NHL defenseman population from 2007-08 through 2011-12.

Friday Quick Graphs are (initially) intended to revisit some of the better, potentially more-significant work I’ve posted over the past year on my Tumblr page (if you want to beat me to some of them, take a look at benwendorf.tumblr.com).

What you see above is a “Total Player Chart,” or TPC, a chart I developed about a year ago to visualize a player’s time on-ice (TOI) deployment. Using that chart, I took the NHL player population from 2007-08 through 2011-12 and recorded the year-to-year change in player’s TOI relative to their age and age +1 seasons. I took those trends and placed them upon an average 18-year old defenseman’s ice time, and tracked how that hypothetical player’s TOI would evolve if they played to the age of 40. The result is the GIF above.

For frame of reference, the hypothetical player is the dark blue triangle, the light, dotted triangle is the league average across the player population, and the light blue triangle is the league high in each situation.

As you can see, the trend is that young player’s tend to receive 5v4 minutes, and as they age they become more trusted with 4v5; as they get older, the 4v5 minutes stick around, but the 5v4 minutes fade.

It’s worth pointing out that this hypothetical defenseman, overall, is likely to be a decent player, by virtue of the fact that they would be getting NHL minutes at age 18 in the first place (and playing until 40).

Replacing Steven Stamkos: How the Tampa Bay Lightning Weathered the Storm

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Photo by “Resolute”, via Wikimedia Commons

One of the more remarkable and underreported stories of this season has been Tampa Bay’s continued competitiveness despite the loss of the NHL’s most dangerous sniper. You could hear the wind whoosh out of Lightning fans’ sails when Stamkos went down in November, and for good reason. Martin St. Louis’s Art Ross Trophy aside, Stamkos was the driving force behind the Tampa Bay attack.

Yet, at the time of this post, the Lightning are 3rd in the Eastern Conference, and 7-2-1 in their last 10 games. What changed when Stamkos went down? How has Tampa Bay managed to continue competing at such a high level? The short answer: they transformed from a star-driven team to a top-to-bottom threat. It was extraordinary, it was a model of what good management can accomplish, and it can be a lesson to teams in the future.

After the jump, I’ll break down how it happened.

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Crystal Blue Regression: Leafs, Avalanche, Ducks, Among the Most Likely to Regress in 2014

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Picture taken by Sarah Connors, posted to Flickr – via Wikimedia Commons

With the Winter Classic coming up, or should I say the Winter Classics since the NHL handles marketing success like the kid who found the cookie jar, we also ring in the rough middle of the season. It’s a time for reflection, maybe a chance to re-assess your decisions, lifestyles; and if you’re analyzing the NHL, it’s the perfect time to recognize trends that may or may not continue. Also known as “regression,” here I’m dealing with a concept everyone understands to a degree; you invoke it when you see a friend sink a half-court shot in basketball and say, “Yeah, bet you can’t do that again.” The trend, supported by a history of not making half-court shots, suggests that it is unlikely for your friend to sink the half-court shot, even if they recently made one. In the NHL, possession stats like Corsi are considered better predictors of future success than stats that can be influenced more greatly by luck, like goals (and, consequently, wins), shooting percentage, or save percentage. Much like your friend and their half-court shot, there are teams that are defying their odds (established by possession measures) to succeed, which can easily happen with less than a half-year of performance.

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