I was looking for another project to do this weekend – something time and attention consuming but not actually too hard because I was fairly distracted. Someone suggested looking at what makes a player “clutch” and I thought “hmm, that’s interesting.” So here we are.
What does make a player clutch? If I was going to use fancy stats to rank players, I needed to figure out how to define the term first, as it would set the guidelines for the investigation. From dictionary.com, they define it as:
done or accomplished in a critical situation
example: a clutch shot that won the basketball game.
In hockey, we have already proven that teams play very differently depending on the various “game states” (eg, tied, winning by 1, losing by 1, etc). Micah McCurdy has an excellent presentation illustrating this exact phenomenon, and the term “score effects” is used frequently amongst analytics types. In fact, most data you’ll see presented (here or elsewhere) is usually what we call “score adjusted”, meaning it has modifiers to counteract these score effects.
But if we’re trying to determine who is the most “clutch”, that is, who is the most effective in critical situations when the game needs to be tied or won, then these score effects are exactly what we want to examine.
First, some parameters. Because the burden of scoring most often falls to forwards, that is who is in this data set. I also only looked at 5v5 data because it’s easier to score on the power play (which is kind of the point), and then I set the minimum 5v5 minutes played at 750 for the season, to cut out call ups and the guys the coach doesn’t trust even in winning scenarios (sorry not sorry Tanner Glass). All data is from war-on-ice, and is regular season only (actually am sorry, Justin Williams).
Next, I tried to determine which factors to use in my ranking. While Time on Ice was important for my special teams rankings, per McCurdy’s work, there seems to be little change in “roster strength” across game states (especially when Leading by 1, Tied, or Losing by 1), which leads me to believe it’s not important in this scenario. Instead, I chose to look at individual effort in the form of individual High Danger Scoring Chances per 60*, effect on team in the form of Scoring Chances per 60, and efficacy in the form of Points per 60.
Now to determine who was actually being “clutch”, I had to look at the difference between their play in a state of relative comfort (Leading by 1) vs states that could be considered crucial scenarios (Tied, Trailing by 1, Trailing by 2). I didn’t look at Trailing by more than 2, because it’s less likely a team will come back and win.
Once I had these differences, I ranked them. Large differences were good, as it meant a player was outperforming their play in the “Leading by 1” state. A player received a rank for iHSC/60, SCF/60, and P60 in each of the three game states. Then, I gave each of those rankings equal weighting, to create one overall rank per game state. Last, I weighted each game state – 40% for Tied, 40% for Trailing by 1, 20% for Trailing by 2**, to achieve a final “clutch” ranking.
Top 20 Clutch Forwards, 2014-15 Season
Top 20 Clutch Forwards 2013-14 Season
While it may not look it, there were actually several forwards who proved clutch each year – Bonino was #1 and #18 in 14/15 & 13/14 respectively. Bergeron (#2 & #25), Horcoff (#5 & #28), Getzlaf (#8 & #36), Seguin (#23 & #17), B. Boyle (#29 & #13), Girgensons (#33 & #1), and Jokinen (#34 & #14) all also put in good showings. In 13/14 the list contained 258 forwards; in 14/15 it had 267.
There were 61 names who didn’t make the 14/15 list who were on the 13/14 list, mostly because of injury (all of CBJ’s roster), or for other obvious reasons like retirement (Saku Koivu) or just not being very good at hockey in general (FHBF Brandon Bollig). One interesting bit of trivia – players who were traded during these two seasons typically dropped an average of 22 spots in the ranking. Players who remained on the same team? Average drop of 1 spot.
Comparing the two years gave rise to some excellent questions about the mental part of the game. For instance – in 13/14, the entire Chicago 4th line (Bollig-Kruger-B.Smith) made the top 20. In 14/15, Bollig & Smith had been traded away, and Kruger, formerly #4, dropped to #111 – 107 spots difference. Now, we know that Bollig isn’t a particularly great hockey player, but it certainly looks like his absence on Kruger’s wing was felt. On the other hand, Brandon Saad was a 3rd/2nd line guy for most of 13/14, but last season spent much of his time with Hossa & Toews. He went from #212 to #68. Hossa also climbed 94 spots, and Toews jumped 35. Coincidence or chemistry?
In 13/14 Zemgus Girgensons held down the #1 spot. In 14/15, he was out of the top 30 – and doing better than most of his Buffalonian peers. On average, the untraded Sabres forwards dropped 38 spots year over year, higher than the rate of traded players. Buffalo was considered the model for “tank nation” in 14/15 – is that just a coincidence? Vanek, who was traded from Buffalo to about a million teams in 13/14, ended up at #69 in 14/15, climbing 131 spots. Coincidence?
Anyway, I think the best data both answers and raises questions, and this certainly did that for me. I’ve stuck the rankings in a google doc for everyone to look at themselves. I think the next logical step for this kind of ranking is to look at playoff performance – see if guys like Justin Williams and Jonathan Toews have really earned their “clutch” reputation.
Please check out part 2, where I address some of the weaknesses with this methodology and include defenders.
* I chose High Danger Scoring Chances over Scoring Chances for the individual portion because they are both more difficult to create (separating good players from bad) and more difficult to defend (greater effectiveness)
** Trailing by 2 received a lesser weight because it’s more difficult to win a game when down by 2, and therefore “less clutch” to score in that state.