Wild West: Head-to-Head Matchups, Erik Karlsson

by chriskane on November 12, 2018

 

So this week I wanted to start with a slight deviation. I had something happen last week in one of my leagues that I am sure everyone who plays head-to-head category leagues is familiar with: I had a great week and got killed. This is a 12-team Yahoo League that uses 11 categories, and according to our stat tracker I was top three in seven of them (first in three categories) and fourth in one more. I also lost the week eight to three. Obviously there was some luck at play here as if I had been playing pretty much anybody else I would have had a very different score. I have always assumed that the randomness of the matchups more or less evens out over the course of the season, but this week got me wondering, does it?

 

I decided to do a little digging (if you don’t use categories leagues or head-to-head matchups feel free to scroll down to the Western Conference Quick Hits for your weekly dose of tips, but this gets interesting I promise.) Luckily I had some data from our league’s previous season that I could pull from. Essentially I took each of our categories and had data on the amount of each of  stat that was ‘scored against’ each team throughout the year. The result? Well no, it does not even out. Interestingly though, but probably not surprisingly, all categories are not made equal.

 

I am including just a few of the categories below.

 

 

 

G

A

PPP

SOG

W

GAA

SV

SV%

Min

163

304

147

1733

40

2.48

2300

0.906

Max

202

343

173

1929

62

3.03

2965

0.920

Average

182

320

157

1824

50

2.73

2729

0.913

Range

39

39

26

196

22

0.55

665

0.013

Average Per Wk

1.95

1.95

1.3

9.8

1.1

 

33.3

 

 

Ok great, so what does this table mean? Well if we look at the first column for goals, the Min is total for the team that had the fewest scored against them (163), and Max is the number for the team had the most scored goals against them (202). Average and Range are pretty self explanatory in this context. Average Per Week is the difference in the number of goals per week between the top and the bottom teams. So in this case if I was the team with only 163 goals against it meant I saw almost two fewer goals per week against than an opponent of mine did with 182 total goals against. That is a pretty impressive difference when the average number of goals per week in our league was nine. It means that 20ish% of our goals against numbers are basically random (or I suppose not-yet-explained would be the more accurate term). That means one person would have had to generate 10 goals a week to get wins in that category, while the other would only need to generate 8. The remaining offensive categories aren’t quite so dramatic with that percent number ranging from about 11% to 17%.

 

In net we see a similar story. First let’s look at wins. As you can see from the table our teams averaged 50 wins over the course of the season, that amounts to 2.5 per week. We can also see that the total range in wins against averages out to just about one win per week. That means one (lucky) team had about two wins against every week (on average) and another (unlucky team) had over three wins against (on average) every week. It actually adds up to 44% of the total wins against in the league seems to be random (or not yet explained). Again it means that one team would have to generate about three wins a week to get points in that category, while the other would need to generate four.

 

So what does this all mean? Well it seems like a big swing in totals not to have an impact on the outcomes of the league. And indications are it does (or at least did in our league). If we do a quick ranking of each team for each total stat against (so the person with the least goals against is a one, the second a two and so on) we can average those to get a single overall ranking for each team (not the most scientific process, but it will do for now). When we run a quick comparison between that ranking and each team’s final place in the standings we get an r^2 value* of .497. That those two data sets are that closely related seems to suggest that the difference between team’s ‘stats scored against’ has a pretty significant impact on our placement (or at least did in this one instance).

 

Conclusion? Not sure. On the one hand it seems like an obvious statement to make that the amount of stats scored against you will impact your overall placement. On the other I am surprised, not just by how wide those ranges for individual stats seem to be over the course of the year, but also by how large of an impact those deviations (which are presumably outside of your control) seem to have on your final standings.

 

Questions for the future: are the deviations actually random? Are they actually outside of a managers control?

 

* Quick refresher: r^2 values tell you how closely two data sets are related. A score of 1.0 would mean they are identical, a score of 0.0 would mean not at all related. Generally .30 is thought of as a weak correlation, .50 as moderate, and .7 as strong.

 

 

 

Western Conference Quick Hits

 

Potential Streaming Pickups:

Pontus Aberg – He is off the top line unfortunately but still has a goal, an assist, eight shots, and a power play point over this last two games. He is only three percent owned and has a great schedule this week.

 

Jake Virtanen – So the theme of this segment should just be “pick up the guy with Elias Pettersson” and Virtanen is. Vancouver has four games this week and Virtanen has managed two goals and eleven shots over this past three games. He is also seeing about two minutes of power play time a game.

 

Last article’s recommendation:

Brian Little: Two weeks ago I recommended Little because of shot production and ice time for a back-to-back series midweek. If you listened he gave you an assist and three shots (plus 15 faceoff wins and one hit if you count those). Not terrible, but I was hoping for more shots.

 

Nikolay Goldobin: Goldobin was back on a line with Pettersson and in his three game week he gave you three assists, one power play point, and two shots. Hopefully the assists made up for the lack of shots in your league.

 

 

Drop or Not:

 

Erik Karlsson: I am hoping you (owners) don’t really need me to tell you this one, but don’t drop or sell him right now. His value has probably never been lower. I am sure he was a high pick or expensive bid and thus far has been killing you little by little everyday. To make matters worse, Brent Burns has been great recently and Karlsson is definitely taking the back seat. His time on ice and time on the power play is down (a low of 19 minutes total TOI last game), and he doesn’t have a point in seven games.

 

He is in a different role on a much different team and it definitely seems to be a tough adjustment for him. It is hard to know exactly what to project him for though I for one am expecting an increase from his current 32 point pace. He is still shooting, plus a number of his and his team’s numbers with him on the ice indicate some luck should swing back in his favor at some point.

 

If you don’t own him I would suggest trying to buying low, but it also hard to know what price to pay. We don’t have many examples of a team boasting two point per game defensemen so I don’t think I would expect a guarantee of that in return. A frustrated Karlsson owner might be willing to part with him for a 45 point guy in a good spot in the lineup and on hot streak if you are lucky, and then you can probably hope for a higher point pace once everything rounds back out.

 

Thanks for reading.