Frozen Pool Forensics: Players Due for a Turn in Luck (2016)
Cam Robinson
2016-11-25
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Three players down on their luck who could use a turn in fortune.
While the battleground between old-school hockey assessment and the new-school analytics community remains a twisted and grotesque arena – one that won’t be delved into today. At no point in history, has a fantasy hockey manager been privy to such a wealth of data and information.
The days of sniffing out a potential hot streak purely on your gut is a thing of the past. Today, we can rely on such statistics as a player’s shot attempts and conversions versus career norms; the percentage in which a team scores at even strength while certain individuals are on the ice, PDO, offensive zone start times, snake-bitten power plays… we can use this information to logically conclude that a player is destined to return to the norm, for better or worse.
With that in mind, this week on Frozen Pool Forensics, we’ll look at some players whose advanced stats suggest that they’ve fallen victim to the curse of ‘If it wasn’t for bad luck, I’d have no luck at all!”
After tying his career-high of 51 points a season ago with the Arizona Coyotes, the Danish product signed a four-year, 16-million-dollar contract with San Jose on July 1st. With it came expectations.
To date, Boedker has not lived up the paycheque he’s been receiving, but some poor luck hasn’t helped matters.
Through 20 games with his new squad, the speedy winger has scored just two goals and no assists. While he’s seeing a full two minutes a night with the man-advantage, that’s way down from the four minutes he saw a season ago. However, despite his offensive struggles, Boedker has been given a host of opportunities amongst the team’s top six forwards.
Frequency |
Strength |
Line Combination |
28.23% |
EV |
|
23.19% |
EV |
|
11.37% |
EV |
|
9.35% |
EV |
|
5.41% |
EV |
For a supposed scoring threat, the former Kitchener Rangers’ star is not directing many pucks on net – just one per game, while his team mates certainly aren’t doing him any favours with their sharpshooting. When Boedker is on the ice, his mates are converting just 1.82 percent of their shots at even-strength. That paltry figure sits way down at the bottom of the list of NHL-regulars.
Advanced Stats
Year |
PDO |
5 on 5 SH% |
Off. Zone Start % |
PTS/60 |
2016-17 |
933 |
1.82 |
53.55 |
0.4 |
2015-16 |
973 |
7.09 |
51.44 |
2.1 |
2014-15 |
986 |
8.24 |
58.54 |
2.1 |
2013-14 |
1001 |
7.39 |
49.78 |
2.1 |
2012-13 |
1006 |
7.74 |
54.95 |
1.8 |
2011-12 |
1009 |
7.47 |
54.27 |
1.3 |
2010-11 |
1073 |
10.32 |
52.94 |
2.3 |
With some renewed confidence, a few more shots and a little luck, the Sharks should start to see some dividends on their investment. For fantasy owners, keep an eye on him and once the pucks start to go in, be sure to jump on him for a hot stretch as he’s gone on quality streaks in the past.
It seems like Tomas Plekanec has been the de facto top line centre for the Montreal Canadiens forever. Well, Alex Galchenyuk has fully arrived and has pushed the 34-year-old Czech down the pecking order. With that demotion, has come some terrible, unsustainable luck.
Through 21 games this season, Plekanec has managed to score a single goal on 41 shots (2.4 percent) and a total of five points overall. His power play ice time is down a full 90 seconds from a season ago, yet he’s still managed to do most of his ‘damage’ with the man-advantage, recording three power play points.
Advanced Stats
Year |
PDO |
5 on 5 SH% |
Off. Zone Start % |
PTS/60 |
2016-17 |
980 |
2.34 |
38.24 |
0.9 |
2015-16 |
1009 |
8.22 |
47.57 |
2.1 |
2014-15 |
1008 |
7.53 |
44.76 |
2.3 |
2013-14 |
1018 |
7.11 |
38.2 |
1.6 |
2012-13 |
1011 |
7.42 |
47.57 |
2.2 |
2011-12 |
990 |
7.3 |
41.89 |
1.9 |
2010-11 |
1007 |
7.84 |
50.64 |
2.2 |
At even-strength, things could not be going any worse. His five-on-five shooting percentage sits 546th out of the 559 players to have played in at least 10 games at a miniscule 2.34 percent. Not helping matters is that he begins just 38 percentage of his shifts in the offensive zone, but he has seen nearly 70 percent of his even-strength ice next to either Alex Radulov or Max Pacioretty – not exactly shutdown grinders.
Even Strength Line Combinations
Freq |
Line Combination |
43.8% |
|
26.3% |
|
11.7% |
|
9.4% |
|
8.8% |
With statistics like these, it’s no wonder Plekanec ownership has dropped to 55.5 percent on ESPN on the heels of his recent six-game pointless streak. Throw him on a watch list and see if you can’t find him receiving more favourable start times, quality line mates and a little more puck luck. As we’ve seen in the past, the further away from the mean a player drifts, the more likely it is they’ll catch some good fortune to get back closer to square.
The five-time 60-plus point getter has shown he’s capable of pushing through.
Every team in the NHL wants Aaron Ekblad on their roster. He’s an ultra-complete defender that plays with a maturity that is rarely seen in a 20-year-old. He debuted two seasons ago as a freshman first overall pick and ended as the Calder trophy winner. He followed that up with a sturdy 36-point sophomore campaign and fantastic World Cup of Hockey experience. The expectations were high heading into season number three.
So far, however, the results have yet to come with great ease. Through 20 games, the former Barrie Colt has recorded five goals and a minus-seven rating. His personal 8.6 percent conversion rate is right in line with his early-career numbers, but his five-on-five shooting percentage sits at a paltry three percent – down from nine percent a season ago.
SEASON |
GP |
G |
A |
P |
PntPG |
+/- |
PIM |
Shots |
SH% |
HITS |
PPG |
PPP |
SHG |
BLKS |
FOW |
FO% |
PPTOI |
%PP |
%SH |
TOI/G |
%TOI |
2016-2017 |
20 |
5 |
0 |
5 |
0.25 |
-7 |
24 |
58 |
8.6 |
41 |
2 |
2 |
0 |
17 |
0 |
03:29 |
56.1 |
23.3 |
23:03 |
37.6 |
|
2015-2016 |
78 |
15 |
21 |
36 |
0.46 |
18 |
41 |
182 |
8.2 |
87 |
3 |
9 |
0 |
59 |
0 |
02:50 |
50.7 |
13.6 |
21:41 |
35.6 |
|
2014-2015 |
81 |
12 |
27 |
39 |
0.48 |
12 |
32 |
170 |
7.1 |
109 |
6 |
13 |
0 |
80 |
0 |
02:48 |
54.8 |
8.5 |
21:49 |
35.6 |
|
Average |
82 |
15 |
22 |
37 |
0.45 |
11 |
44 |
188 |
7.8 |
109 |
5 |
11 |
0 |
71 |
0 |
02:54 |
53.1 |
12.4 |
21:53 |
35.9 |
If you watch the games, you can see that despite his lack of assists, he continues to distribute the puck well. While those plays have yet to translate into production, his main weapon, the big shot from the point, is coming with regularity – just shy of three shots per night. With some patience, those too should begin to result in some further tip-in and rebound goals for his peers.
Seeing 3:29 of power play time per contest, on a team that, when healthy, boasts a formidable top unit, should further illustrate his potential to catch some further breaks and get producing in the right direction.
While it remains to be seen whether the young Canadian can score at clip that will garner the full attention from the fantasy community, he is certainly due for a rebound in his luck-based metrics. This could be a fine opportunity to buy-low on player with a lot of upside.
***
Thanks for reading! You can follow me on Twitter @CrazyJoeDavola3 where I often give unsolicited fantasy advice that I’m sure at least someone is listening to.
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“As we’ve seen in the past, the further away from the mean a player drifts, the more likely it is they’ll catch some good fortune to get back closer to square.”
To me, this sounds like it’s veering very close to gambler’s fallacy.
Not picking on you in particular Cam.
I read a lot of comments here from the writers that either show they may not properly understand the gambler’s fallacy, or it shows that I’m overly sensitive about people not understanding the gambler’s fallacy. Not sure which.
In the gambler’s case, there is no such thing as winning, since long term odds are always in favor of the house. Even if you return to the norm, you wind up losing. In hockey (and other sports), you can expect a player to return to their own norm, adjusted for situation (TOI, pp usage, linemates) and for individual levels of variation. I would have expected Patrice Bergeron to be on this list for sure. Going back to the 08-09 season, he has been one of the most consistent players around, winding up right around 60 points every year with similar shooting %, PPTOI, and the last few years he’s been paired with Marchand. This year he has scored 5 points in 17 games. No gambler’s fallacy there, just simple statistics. Could he fail to come back? Sure, but given his track record, that is less likely. Anze Kopitar is another one, and he is well known to be a slow starter.
In simpler terms, I think it’s neither; I think you are applying gambler’s fallacy where it is not strictly applicable. We are not talking about random chance, where each event has the same odds. In hockey, you have historical performance statistics that you can base expectations on. History is totally meaningless in gambling.
If you were to flip a coin fifty times and it came up heads 38 times and tails 12 times the ratio of heads to tails would be 76% heads and 24 % tails. We know mathematically that the more we flip the coin the higher the percentage odds of heads and tails coming up 50% each. There is no gamblers fallacy involved in saying a players scoring (tails) who is on the unlucky side of the equation due to bad puck luck, etc., (24%/76%) in a small sample size will not see an increase in the ratio of scoring (tails) to not scoring (heads) with repeated games (flips). This follows logically and mathematically. That said, players do have other person variables such as confidence, distraction, injury, etc., that might also account for poor performance, but all things being equal, if a player still looks good on the ice and is shooting and getting a high percentage of chances, they will indeed right the ship as the sample size increases.
Fair point. I’m quite aware of the gambler’s fallacy as I spent my first two years at university dabbling in Philosophy and Greek and Roman studies.. student loan money well spent!
We’re not speaking of a coin flip in which over time, the inevitability of a the results veering towards the mean is assured. Far more intangibles at play here. However, I do believe that a player in the midst of a mathematically unsustainable poor luck streak, is more likely to start trending towards the mean. Not assured, as bad luck can sustain for long periods, but it’s more likely.
Love the discussion though.
I’ve read your response three or four times now, and I still am not sure, but kinda think maybe that you’re making the mistake I thought you might be making.
A player on a streak of 53 games with bad luck is no more likely to break that streak in his next game than a player on a 3 game bad luck streak is. Luck doesn’t care how long you’ve been losing for.
EDIT EDIT EDIT
And, the fifth time reading it is the time I think I actually understood.
I believe you’re saying a player is more likely to trend towards the mean than away from it, which is something I will definitely agree with.
There it is! Simply stating that is more likely for him to begin moving the needle in the right direction than continue to drift further away.
Confidence is a difficult thing to quantify as well, and when a player is going through a tough stretch it can snowball.. a little confidence and success can help spring board that process of improvement towards the mean.
.. just to confuse matters further.
My point is just that a string of bad luck does nothing to ensure good luck in the future. I think that’s being agreed to in the responses to my comment, but again I’m not 100% sure.
Let me state this more simply, using Bergeron as the example. We’ll assume Bergeron is a 60 point player, under normal circumstances.
Bergeron has 5 points in 17 games so far.
The mean expectation for Bergeron in the last 65 games is:
65/82 * 60 = 48
So, at this point, Bergeron can be expected to finish somewhere around 53 points this year. (5 + 48) That represents a much better ppg number than he has now, but not the ppg you would expect as if he were starting this season from scratch.
Regression towards the mean, not regression to the mean.
If you’re expecting him to finish with 60, then you’re guilty of the gambler’s fallacy.
I agree with your point about luck, but not so sure about the last comment. While it is tough to tell exactly what is regressing (is it points or points rate or shooting %?), players consistently regress TO the mean rather than towards it. They have slumps and hot streaks, and you know very well that a scorer who is slumping will also have a hot streak where his scoring rate is higher than the mean. Without those, they would not have been able to establish that mean in the first place, so mathematically, it HAS to be regression TO the mean. Now I certainly agree that the longer it continues, the less likely it is they hit their historical points mean, but I feel that is a very conservative approach, but it could be an indicator of a change in some other factor. Tell me this: would you be looking to trade Bergeron at this point for someone who has a higher mean rate of scoring, so that you could catch up, or would you stick with him, hoping for a hot streak? I will say that I do tend to be more a risk-taker. I am not afraid of making trades, and many times I have traded slumping players, only to have them turn around and go on a killer hot streak. If you always traded your guys that were slumping, I definitely think you’d wind up on the down side.
Okay, so first you said that “the further away from the mean a player drifts, the more likely it is they’ll catch some good fortune…” but that’s just not true. Regression towards the mean doesn’t imply that bad luck gets balanced out by good luck — it simply means that a player is always most likely to perform in line with their expected long-run average. But then… then you threw your original statement completely out the window and instead said “the longer it continues, the less likely it is they hit their historical points mean”. Well, talk about having your cake and eating it, too!
Look, you’re right that hot-streaks and cold-streaks happen, but that’s a separate issue from regression towards the mean. It’s the very nature of a “streak” that it’s not sustainable. It’s a fleeting statistical aberration. And, if you’re trading your players while they’re slumping, you’re breaking the cardinal rule: buy low, sell high.
Allan Phillips, you’re making the exact mistakes that I’ve been warning about. If you disagree with my last statement, then you aren’t properly understanding this stuff. Bergeron could finish with 60 points but he’s just as likely to finish with 46 as he is with 60. And the mean expectation for him should be 53.
The fact that Cam has liked your comment, which is riddled with similar mistakes, is concerning and shows that I was definitely right to raise this issue. This stuff needs to be much better understood around here.
I usually like most of the posts on my articles. It doesn’t mean I agree with all that is said. You can use my personal comments of what I believe or suggest.