Featurette Friday: Winnipeg Jets Microstats Awards and other stuff
Discover the Best Reader Questions, Article Flashbacks, Hockey Analytics Basics, Powerlifting Principles, and Financial Wisdom
Greetings, everyone, and welcome to this week’s Featurette Friday post.
Every Friday, we'll bring you a collection of small write ups.
We'll start with reader questions, which you can submit via Twitter @TheFiveHohl, my personal Bluesky, and our Facebook page. Follow us both on Bluesky and Facebook.
Next, we'll review and analyze some of my past work and opinions.
There’s also a paywall at the end for premium subscribers for our final microstats overview of the regular season, so stick around for that.
READER QUESTIONS
Twitter Questions
https://twitter.com/mennoknight427/status/1780694005753213160
I examined this using a methodology similar to the PCS model developed by Josh Weissbock (CBJ), with some input from Cam Lawrence (CBJ), Rhys Jessop (CAR), and myself. Unfortunately, the cohorts were limited (3), and none of them became NHL regulars.
The cohorts include Jake Larsson, a late first-round pick with 172 NHL games but appears to be on his way out, Lucas Carlsson, a fourth-round pick with 60 games who also seems to be on his way out, and Arvid Lundberg, who was never drafted.
However, this isn't discouraging. Since we're discussing reader questions, I'll briefly cover some prospect analytical theory.
From a qualitative perspective, Salomonsson was a second-round pick, placing him ahead of 2/3 of his cohorts. The only player drafted ahead of him nearly reached 200 games. Additionally, Salomonsson is taller than Carlsson and Lundberg. He also maintains a higher era-adjusted points-per-game pace than any of the three cohorts, approximately 20-30% higher.
There aren't many close comparisons when considering all three variables: age, scoring, and size.
Moreover, Salomonsson outscored these three players without receiving power play ice time, which typically significantly impacts player point production. Lastly, Salomonsson's value lies in his defensive capabilities and his ability to tilt the ice favorably, factors that the PCS model cannot fully consider.
While the PCS model outputs are valuable for comparing Salomonsson's scoring relative to expectations, we can adjust those expectations based on other variables, such as qualitative data (eye test), even-strength production, and his significantly positive ice-tilting performance.
I believe he has potential, perhaps even enough to step into a limited role next year.
https://twitter.com/etchiboi/status/1779271496008872435
Unfortunately, I don’t track whether shots are from the rush or cycle. Corey Sznajder does have that data at allthreezones.com, but his dataset is considerably smaller for the Jets, covering only about a quarter of the season.
This does suggest that Scheifele is more dependent on the rush for offense, but no Jet seems to be dependent on the cycle for offense. Most of the Jets’ offensive players are capable at both.
https://twitter.com/AavcoCup/status/1781171674999865611
Okay, this is going to be fun. I already did some non-microstats fancystat team awards on Twitter. You can check there if you wish.
Ehlers Best Transitional Forward Award (highest xShots/60): Nikolaj Ehlers
I Don’t Want The Puck Award (lowest xShots/60): Alex Iafallo
Only Puck Moving D On Team Award (highest xShots/60): Josh Morrissey
Hot Potato Award for Defender (highest uncontrolled exit%): Logan Stanley
Puck Hog Award (Scoring chance minus chance passes/60): Tyler Toffoli
Saint Award (SC pass minus SC/60): Vlad Namestnikov
I’ll think of some better ones next year but I actually did this section last and now I’m really tired haha.
https://twitter.com/HLLivingLoco/status/1781177738704900274
Saitama misses tryouts and gets cut from the team because Walmart had a double coupon sale on the same day.
HOCKEY ANALYTICS EXPLAINED: The issues with binning, QoC, and scoring chances
In 2017, the talk about binning became a hot topic in hockey analytics, but also in other domains of analytics like biology, economics, etc. It became almost a catchall critique of models or stats, which caused people to dismiss the binning criticism altogether.
Binning is whenever you take something that’s a continuous variable (time, height, weight, probability, etc.) and turn it into a discrete variable by chopping out information.
In theory, every shot a player takes has a probability of being scored as a goal. Expected goals estimate that probability. There’s an infinite number of possible xG outputs, provided you’re willing to go to a small enough decimal point.
Scoring chances, for example, are one way to create a shot-quality-adjusted metric via binning. You could say all shots in the home plate are worth a 1 while all shots outside are worth a 0.
In the above graphic, the five ‘x’-marked shots on the left would be five scoring chances out of five shots, but the right graphic displays only three out of five being chances despite likely having a higher expected goal value.
It’s not that scoring chances are not informative. I use scoring chances for intrigue with my manually tracked scoring chances. However, I keep in mind how binning influences data.
Whenever you bin a continuous variable, you exaggerate the difference in value between each bin.
Back to the scoring chance model above, you could further improve it by adding another layer, like splitting up the high and low slot. This would be an improvement but would still have the issue of binning the data. However, if you were to take an infinite number of slices and an infinite number of bins, you would end up with an expected goal model.
FINANCE STUFF: Financial Literacy Part Two of Four
As I’ve mentioned previously, financial literacy is an extremely strong predictor of financial well-being and success. However, many people are unaware of the basic details and facts.
There was one 2019 study (Klapper, L., & Lusardi, A.) that used a very, very simple survey, and about two-thirds of Canadians passed. That was one of the best-performing populations too, with a survey designed to be very general and basic.
So, we’re going to go over each question in the survey over the next four weeks.
Question Two: Suppose over the next 10 years the prices of the things you buy double. If your income also doubles, will you be able to buy less than you can buy today, the same as you can buy today, or more than you can buy today?
This is hopefully a simple idea, but it can escape many people.
Inflation is the nominal increase in the price of the average goods and services people use. If what people spend increases but what they earn increases by the same amount, the net difference is nil.
If I take $10 from you every hour, while someone else is feeding you $5 every half hour… you come out unchanged.
This also teaches you that if your income increases faster than inflation, everything becomes relatively cheaper for you in real terms, even if the nominal value is rising.
This is one of the strongest reasons for investing in things like the stock market. Over the long run, the stock market has predominantly outpaced inflation. If you do not get a return on investment that is larger than the rise in the cost of goods, your life will eventually become significantly more expensive.
If you save your money under your matress or in a bank getting an interest return lower than inflation, you in effect lose money. The whole point of saving is to make your money make more money and to fuel future consumption with today’s excess.
STRONGER STUFF: Minimum Effective Dose
The Minimum Effective Dose is the tiniest amount of something that gives you the desired effect.
In lifting, volume tends to be the most focused on variable within a mesocycle. Volume is the primary driver of strength and muscle adaptions, provided the stimulus is sufficiently difficult enough (in proximity to failure).
The study of MEV, minimum effective volume, is an important one. The MEV is the theoretical least amount of work you have to do to get stronger or grow more muscle.
MEV is highly individualistic. Both your recent and long term training experience will influence your MEV. “Newbie gains” is the observed phenomenon on how untrained individuals will often see significant results with small investment.
Other factors impact MEV, such as diet, sleep, and stress. Also, MEV is a moving target, that doesn’t sit at one place all the time. It tends to move up through a mesocycle.
Academic research suggest that individuals MEV tends to be quite low, provided that those individuals still work relatively close to failure. This is good news, because that means one can still get decent results even with low levels of dose: volume.
It’s not necessary to know one’s MEV but to understand the concept, along with other concepts like ones we’ll touch on next time: MRV.
FINAL THOUGHTS
Thank you for joining us in this week's featurettes. We look forward to next week’s Monday Review, where we look at ongoing Jets story lines, prospects, and such.
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PREMIUM CONTENT: END OF SEASON MICROSTATS REVIEW
It’s the end of the season. Let’s take a deep dive into the Jets performance after tracking about 300 events per game for 82 games! Did I mention I’m tired?
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