# Friday Quick Graphs: Shooting and Playmaking Contributions, 1967-68 through 2012-13

Those of you who’ve been following me on Twitter know that I’ve put together a pretty substantial dataset, and I’ve been working through the data with a metric I’ve used for a while. %TSh is a player’s shots divided by his team’s estimated shot total in games they played (Team Shots / Team GP, multiplied by player GP). The measure gives us an idea of the player’s shooting contribution to the team’s offense. It moves outside the pesky variance of shooting percentage and gets closer to a stable indicator of offensive role. I’ve done the same with %TA, which is the same equation for assists. The reason for estimated team totals is we don’t yet have good macro-data on specific games that players played before 1987-88, but the metric runs essentially in lock-step with the real thing and I want to provide a useful, historical point of comparison. Doing this allows us to look 20 years further back.

The distribution above includes over 23,000 player seasons over 20 GP; the orange distribution is %TA, and black is %TSh. I used the marks to connect back to the previous week’s bizarre flame war over Ovechkin’s value and approach to the game; the top one shows Ovechkin’s peak year, 2008-09 (20%), which also happens to be the highest %TSh of all-time. The bottom mark is Ovechkin’s 2012-13 (16.3%), which I’m using because his current season is just slightly higher – it would be good for 16th best in NHL history.

I also did a second graph, wanting to look at the relationship of %TSh to %TA, to see just how much they ran together:

In the graph above, the x-axis is %TSh, and the y-axis %TA. Intuitively, these run together a fair amount, as shots create rebounds that can be counted as assists, and a player that shoots a lot is likely to be more heavily involved in the entire offense. That said, they don’t run nearly so close together as to render either measure moot. I think %TA can be a valuable counter-weight for assessing defensemen. Anyway, this is the tip of an enormous iceberg of data, so don’t be surprised to see me refer to and use %TSh and %TA again.

# Friday Quick Graph: Season Stories Using % of Team Shots, Gretzky, Lemieux, Sheppard, and Simpson in 1987-88

Part of the reason I like doing graph work is because a good graph (with a little bit of contextual knowledge) can tell a really interesting story. In the past, I’ve been a proponent of digging deeper into the historical data, and noted that even though we have less data of the pre-BTN era it doesn’t mean we can’t make some intriguing graphs. %TSh, or % of team shots (in the games a player participated), provides a great opportunity to do just that, not just in a player’s career (as I’ve done before) but also over the course of a season. In the graph above, I took two well-known players, Mario Lemieux and Wayne Gretzky, and matched them to two (to the younger readers) lesser-known players from 1987-88, Ray Sheppard and Craig Simpson; I expressed their %TSh cumulatively, game-by-game. Craig Simpson, at the tender age of 20, was having the best year of his career (56 goals on an incredible 31.6% shooting percentage), but a trade to the Oilers mid-season would alter his offensive role for that season and into the future. Ray Sheppard, like Simpson very young (21), over the course of the season earned Ted Sator’s trust and responded with a 38-goal rookie season. Sheppard would go on to be a very good offensive player for about a decade.

Yet their lines relative to Gretzky and Lemieux also remind us that, for as good as they were, neither were driving the boat to the level of those legends (and probably wouldn’t). So you do get some perspective on what some of the best-of-the-best were doing. Lemieux, who was entering his prime, was literally carrying a middling Penguins team on his shoulders, and his ability to do that would bring him, in 1988-89, to convince people that Dan Quinn and Rob Brown were really good.

For frame of reference, in the BTN Era (2007-08 to present) only Ovechkin has been able to come close to the kind of shot volume Lemieux was demonstrating in 1987-88.

# Friday Quick Graph: Possessing the Puck in 1969, 1981, and 2013

Photo by Jim Tyron, via Wikimedia Commons

Just finished tracking possession times in a November 15th, 1969 game between the Flyers and the Leafs. This game, when compared to the games from this post, fits virtually in-between them, which is interesting because, unlike with the other two games, the Flyers and Leafs were two teams on the lower end of the spectrum in the league (8th and 9th in 2pS% in a 12-team NHL). Maybe that also contributes to their average possession time of 6.08 sec (n=349) compared to the 1981 game’s 6.15 (n=364) and 2013 game’s 6.17 (n=360). Another observation among these games: the standard deviation for the 1969 and 1981 games is right around 4 seconds, where it’s right at 5 seconds for the 2013 game. I’ll save any deeper ruminations until I have a larger sample, but it’s food for thought.

Not too long ago, I decided I wanted to try out tracking time of possession in historical games, with the hope of eventually having enough data to look into things. I realized it’s going to be a little difficult to get large enough samples of singular teams, but I also realized that we could potentially compare the game as a whole in different eras. I’ve always been of the mind that the game has evolved somewhat, but at its core there are a number of best practices that have kept it pretty much the same game from around the time that the red line was introduced in 1943. I wanted to test that as far back as I could go, though, so with this possession tracking I actually tracked each individual possession rather than just a total time of possession. For this chart, I displayed all those individual possessions as a distribution, longest possessions to the shortest. These three games, the Philadelphia Flyers vs. Toronto Maple Leafs in 1969 (Toronto won 4-2), Edmonton Oilers vs. Philadelphia Flyers in 1981 (Edmonton won 7-5), and Los Angeles Kings vs. St. Louis Blues (St. Louis won 4-2), had some surprising results when compared. As you can see above, the distribution is actually quite close, with the 1981 game seeming to have shorter possessions but then moving above the others in the middle of the line. The 1969 game actually seems like a trendline of the 2013 and 1981 games. The average possession time? 1969: 6.08 seconds, 1981: 6.15 seconds, and 2013: 6.17 seconds. Obviously, I need (and want) more data, but it is a really intriguing start.

The “possession battle” results?

All Situations Possession

• PHI (47.1%) vs. TOR (52.9%), 1969
• EDM (53.4%) vs. PHI (46.6%), 1981
• LAK (51.7%) vs. STL (48.3%), 2013

Possession, Score Close

• PHI (41.3%) vs. TOR (58.7%)
• EDM (48.7%) vs. PHI (51.3%)
• LAK (51.2%) vs. STL (48.8%)

# Friday Quick Graphs: Toronto Maple Leafs, Chicago Blackhawks, Edmonton Oilers, and Boston Bruins Shot Distributions, 5 Years

What you see above are the even-strength shots-for locations for the near-indisputable top team of the last five seasons (Chicago Blackhawks) versus the near-indisputable worst team of the last five seasons. This is a sort of visual anti-shot quality argument, a demonstration of why, across these five seasons, the indisputable #1 team would shoot 9.9% while the indisputable #30 team would shoot 9.6%. Notice the horseshoe design, about where defensemen normally sit, then jump up into the play. Notice the dense cluster around the high slot. All teams make these plays, try to make them, the difference being some are better at possessing and moving the puck to make the shot. What’s the primary difference above? The amount of shots.

None of the above charting is possible without Greg Sinclair’s awesome site, Super Shot Search. Bookmark it, use it, love it.

Oh, hey, what if I was to look at the teams with the best and worst save percentage these last five years? Would they look different in even-strength shots-against? Well, let’s see, Toronto and Boston:

There is a difference here, I think. I mean, the initial difference are the numbers, Boston’s SV% (92.1%) versus Toronto’s (89.5%). Another difference is it seems the two charts maintain roughly the same shot distributions, but flip ends of the rink. Not much to dwell on there. One thing I will say, that could relate to the SV% discrepancy, is that it doesn’t appear that Toronto records many, if any, shots from right along the boards. Now, I don’t know if this is a recorder’s error or not; it seems to me it’s pretty hard to get a shot from right tight along the boards. Maybe one recorder does it based on where the body of the skater was located, I don’t know. Or…Toronto does allow shooters to come in a little tighter, and Boston owns the center ice a bit better. Could that explain a near-3% discrepancy? I don’t think so; we know Toronto’s had worse goaltending. But it might’ve “helped.”

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

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).

I did a similar GIF one week ago, using defensemen, in an effort to understand how a player’s playing time evolves over their career. Taking NHL player data from 2007-08 through 2011-12 and identifying year-t0-year change, I’m able to create a hypothetical forward that plays from age 18 to age 40, and how that player’s ice time would change.

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.

There are some similarities to the defensemen GIF, primarily that player’s are given powerplay minutes early, but grow into penalty kill minutes. Unlike defensemen, though, forward TOI decreases uniformly at all strengths, whereas defensemen tend to retain some of their penalty kill time.

As with the previous post, it’s worth pointing out that a player playing from age 18 to age 40 would be a pretty unique, talented player, so this model is really just to demonstrate change.

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

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).