Bitcoin wrapped up this past Thanksgiving night with a bloody trading session. The first crypto by market cap was rejected as it made its way to $60,000 and has dropped over 6% in the 24-hour chart. As of press time, BTC trades at $54,084 but seems at risk of further downside.

Besides Bitcoin, the traditional finance market took a hefty dive on news of a recently discovered COVID-19 variant in Africa. This wrack havoc across many sectors with the S&P 500 and DOW Jones recording an almost 3% loss in the past 24 hours.

As 2020 and 2021 have shown, Bitcoin shows a high correlation with traditional markets during periods of macroeconomic development. Thus, one of the reasons the benchmark crypto has been trending to the downside as investors could fear a new phase of lockdown across the world to prevent the alleged new variant from spreading.

The U.S. Dollar as measured by the DXY Index has also taken a dive with a 0.71% loss in the 24-hour chart. The currency was showing significant strength since November 10th, when the U.S. Federal Reserve hinted at the beginning of tapering but was rejected at the 97-price mark.

The U.S. dollar rally has been attributed as one of the reasons Bitcoin display weaknesses in the past week. A rejection at these levels could provide BTC’s price with some relief allowing it to make a more convincing rally into $60,000 and uncharted territory if it’s able to prevent more downside in the short term.

Does anyone have experience with medications affecting WHOOP data? I've been prescribed two anti-depressants (SSRIs) for many years. I haven't taken them for some time but starting taking them again this week. I immediately noticed a severe change in nearly every WHOOP metric. I've cycled on and off a few times so I expected the loss of appetite and difficulty sleeping but did not have a WHOOP to track how it internally affected me.

I understand it takes time for these medications to take effect and there is a period of adaptation. I am consulting my doctor about whether it could be a short-term or long-term situation, but was curious if anyone had any insights or thoughts regarding my main question –

Should I take WHOOP reports with a grain of salt throughout this "regulation" time of 4-6ish weeks?

I rely on these reports to understand how to structure my weightlifting training, especially with the Arnold coming up in 7 weeks.

Screenshots

Average lifetime WHOOP data for comparison to screenshots of this week:

• HRV average 81
• RHR average 46
• Recovery average 64% (Annual P.A.)

From many news media outlets and youtuber finance experts and stock gurus, I keep seeing the notion that the stock market is heavily overvalued currently relative to its former valuation metrics that have held for decades.

To be honest, this is true, and they're not wrong, but what they fail to take into account is that until interest rates go up and/or median home prices come down, for the VAST majority of Americans there are only the following ways of avoiding poverty: education (becoming less and less worth it for most degrees), fraud (risky AND makes you a piece of shit), literal gambling, poker (which I don't classify as gambling if you're highly skilled but is NOT easy to be consistently profitable), starting your own business / youtube / social media (risky if you go all in and not definitely for everyone), and investing in stocks (admirable, and dramatically easier than all of the above to be profitable). Investing in housing is a very viable way to make money, but when the median home price is $400,000 this is no longer accessible to the everyday American for younger generations as a means of building wealth (fuck boomers, they have literally written and enacted laws that benefit them and only them throughout their lives).

Until investing is no longer the "easy" and accessible way to succeed in life for the everyday American, the stock market is going to perpetually be "overvalued" by former metrics and dips will always have rapid recoveries.

So when people and institutions say that the market is "overvalued" take this with a grain of salt and when the market reaches "insane valuations" rest assured you can ignore this until there is another more reliable means of ACTUALLY BEING ABLE TO FUCKING RETIRE SOME DAY.

So, as the 2022 season progresses, the MVP race has devolved into these common 4 narratives, around Jokic, Giannis, KD and Steph. Specifically that Steph isn't having THAT good of a season and he's being carried by his team in many of the games he's shot poorly. So, I'd like to take a look at the on/off stats and see if this narrative holds up.

Essentially, using PBP stats, and using medium, high and very high leverage moments to filter out blowouts, and paying attention to sample sizes of over 100 minutes, what are the results? What is Steph's impact on the game? Has it been reduced because of his shooting slump? How much of the wins should be attributed to Dray?

These stats won't answer any of these questions, but they will point you in the right direction. The more effective solution is to simply use EPM and RAPM, but I know a looooooooooot of people just absolutely refuse to use these stats, so here's a broken down version of a series of net ratings to show the overall impact of Steph.

Also note, the 100 minutes threshold is less of a threshold and more of a gradient. There is never an exact spot.

Starting off with Steph's on off stats.

Steph On/Off Net Rating

15.53 Net Rating On, 1.07 Net Rating Off. That's a +14.46 On/Off, which is absolutely elite.

Both the offense AND the defense are statistically better with him on the court. The defense part is especially insane, because his replacement is GP2, arguably a top 3 guard defender in the league in terms of pure talent. AND, the defense is already elite without him, so it's ridiculously difficult to improve it. A 7 DRTG improvement is kinda nuts.

But, we know that the Warriors defense is elite, and that's thanks to Draymond, right?

Steph and Dray On/Off Net Ratings

Steph and Dray On: +12.15

Dray On Steph Off: +2.07

Steph On Dray Off: +23.07

....? Well, this is possible because without Dray on the court, the Warriors have a +121.60 ORTG, while actually having a BETTER DRTG, by 3.25 points or so. This will be important later. Now, note, the replacement for Dray on the bench isn't some DPOY player. It could be JTA, or OPJ, or Bjelica, or a fe

TLDR; The breadcrumbs dropped by the disclosure team might have a strong tie to the time component of the time evolution operator of quantum mechanical systems.

Disclosure: I am not a physicist.

Lue has stated twice that he leaves breadcrumbs in every interview.

21OCT2021 - Theories of Everything with Lue https://youtu.be/qfa5rWYHCCg?t=1216&#x200B

>Lue Elizondo - "I've always left breadcrumbs in every interview I ever do for the last four years"

19NOV202 - New Frontier Network https://youtu.be/P4yjMl6mUyc?t=4837

>Lue Elizondo - "And again, I kind of left some breadcrumbs. If people know how I work they'll realize every time I have one of these conversation I always leave breadcrumbs; ALWAYS!"

Operating under the assumption that he and his cohorts have been leaving both intentional and unintentional breadcrumbs for the community to find, you can discover some absolute gold nuggets to many aspects of the phenomenon. This post may reveal one of those nuggets.

You are highly encouraged to dig deep into and re-watch all of the interviews and presentations of Lue Elizondo, Tom Delonge, Jim Semivan, Eric Davis, and Hal Puthoff looking for those breadcrumbs. This post will contain both transcript excerpts, timestamped videos, and web resource links for your reference. Please pardon any misspellings, slight alterations for clarification, or minor errors. Any glaring errors will be corrected if they are found and pointed out.

Please try leaving your preconceived notions of what the true story may be and your personal biases aside. Leaving biases aside means trying to take every word they say literally and applying an analysis as to their intent for saying what they did. The results have lead down a very interesting path of discovery to some very deep and interesting physics. There are several topics of the phenomenon that can be pieced together in this manner that may be posted at a later date.

All persons involved in the disclosure effort have been forthright mentioning that there is something very peculiar about the crafts' ability to manipulate time in various fashions. The evidence for this statement is so overwhelming that there is no need to put every reference to it in this post. This post will contain excerpts of the most interesting comments made on the subject of time manipulation.

A copious amount of questions will be raised to help the reader follow the

I recently hit the big $1M, and then dropped down again. Boo hoo. But it got me thinking about monthly % gains and losses vs $$ gains and losses. So far, the largest single month gain I've seen was 12%, but that was only $50k. The largest loss I've seen was -9%, but that was only $40k. (hilariously, those two months were back to back, March and April 2020).

But now I'm getting into a portfolio range where I will start to see six digit swings in portfolio value from month to month. This is what I signed up for. This is the way. But the thought of making or losing the equivalent of my annual base salary in a single month is seriously messing with my head. What do you do to handle this?

Edit: Yes, I am dramatically privileged to be in a position where this is a problem I face.

Edit 2: Thank you all for sharing your insights and perspectives. For me, the most powerful takeaway was to 'zoom out', and take a higher level view. I reworked my investment tracker to include a 'dashboard' view at the top that focusses on the big picture. I added several new metrics and widgets, including:

• Major Milestones, and the date I achieved them (or will likely achieve them). Includes items like "portfolio at $XMillion", "nw at $XMillion" and also interesting tipping points like, "estimated new growth > expected new investments" and "total growth > total invested"
• A view that shows Portfolio and NW by year snapshot, rather than month. It's anchored on the current month, and then above that shows where I was one year ago, three years ago, six years ago. Below today, it shows one year from now, three years from now, etc.
• Finally added a XX% to FI calculator lol
• A countdown showing "X years, Y months to go", and a breakdown of how much of my future portfolio will likely come from growth vs new investments

I will still keep tracking everything at the monthly grain, but now when I open the sheet up, I will focus primarily on the big picture, as opposed to what happened just this month.

It's funny, but even just building this new dashboard I can feel my perspective changing. Thanks again for all your input

Data is taken from the Covid-19 portal and today's media availability with Premier Jason Kenney and Dr Deena Hinshaw. Dr Hinshaw's next availability will likely be next week.

RESTRICTIONS

• Multiple restrictions are currently in effect. A summary is given on this page

Restriction Exemptions Program Eligibility

VACCINE ELIGIBILITY

Proof of Vaccination

• You can get proof of your vaccination here. Alberta's general vaccination proof has shifted to only QR code documentation (Military and First Nations records still qualify as acceptable)

TESTING AVAILABILITY

• January 13: Pregnancy is now considered a high priority group

• Positive rapid test results can be documented for personal use via the "Documenting and sharing your rapid test result" tab here

1. TOP LINE NUMBERS

• For values where "Current" and "Total" are the same, I have left results under Total

Albertans with 1+ vaccinat

Hey everyone, I'm Tom from Pine Studio. I'm the team lead on Escape Simulator, our escape room game that is playable in co-op and features an editor for building and sharing custom rooms. As the title says, the game just passed $4M in gross sales in less than two months of being released on Steam. And that's just wild! As we're self-published and under no NDA, I wanted to share more about the success of the game :) How DID we do it?

Basics

You can do a lot of things on a shoestring budget. But some things are worth the money, like hiring a good PR firm and getting a pro trailer. I think you should spend cash on this. If you don't have the money, start with smaller projects and save up some. Marketing accounted for 6% of our overall development budget.

You also need to have a good game. The only way to do that is if you have a great team. At this point in our nine-year existence, this is the moment when I feel I'm working in a team that gets stuff done without much fluff and is completely focused on the same goal.

Zeitgeist

The world is still in a strange period. The pandemic caused a lot of success for select games in 2020. And I think we managed to catch on to some of that player behavior change. Our goal wasn't to design a game for people who can't hang out in real life, as we started working on it a year before all the craziness. But having co-op as one of the core features was a big push for the game.

Things that don't scale

We tried a lot of different "guerilla" marketing stuff. And we've seen good results from some of it. For example, we reached out to developers of similar games and tried to have them do a promo on our game. This ended up working well. It involved talking to many developers and having some super interesting discussions. I mean, they are making similar games to yours, and if that's not somebody you can talk to, who is :P.

Other than that, we tried to use unique features to our advantage. With the help of our PR, we pitched that we would create tailor-made rooms in our level editor for select channels. Some responded, we made the rooms, and they ended up covering our game! The bonus was that we tested the heck out of the editor.

When it came to pricing, we had endless discussions. It was comical how often players asked us what the game's price was, and we just said we couldn't share. And it was like that till the last week before release. So why was it s

Hello,

Grab a beer, grab some popcorn, grab what ever you want. This is gonna be a long one. I know many of you are just going to scroll past this entire post, go to the comments and type some shit like, "Didn't read. All In", but please I highly recommend that you read this DD since it might possibly be the biggest squeeze to end the year... but it also may be the riskiest one, so you need to tread with caution. For me, I like these risky stocks because they can generate a large percentage return, and with proper risk management, can minimize substantial losses.

If you've been following me so far, during the entire month of December, I've made a lot of great trades, with many of my picks going up over 40% on the day, all caught before the big move, and verifiable through my entries and exists posted on Twitter.

• Dec 8: $PPSI went 40%+
• Dec 9: $CNTX went 40%+ then 50% AH
• Dec 10: $PTPI went 40%+
• Dec 13: $PTPI went 40%+
• Dec 22: $SOPA went 80%+
• Dec 22: $ENSC went 80%+
• Dec 23: $SOPA went 40%
• Dec 23: $ENSC went 40%+
• Dec 23: $BFRI went 30%+

I don't take credit for finding most of these tickers, most of them I found from the DD being posted within the community, waited for the best possible time to hop in, and traded it. I usually go for stocks that can net me a minimum of 40% return in one day with some extra change going into the next trading day. For example,

• $PTPI - had two back-to-back 40% green days
• $ENSC - had an 80% day, then a 40% day the day after
• $SOPA - had an 80% day, then a 40% day the day after

Now enough bragging about my trading history, I only bring it up because I genuinely believe that the next stock to go minimum 40% is $BFRI. However, I think it might go 100%+ since it just might be the biggest squeeze to end the year due to the stars being aligned. It's currently #1 on the fintel squeeze list, has a crazy short interest, has strong social media sentiment, and many more. So without further ado, I present to you, $BFRI.

Table of Contents

• Part 1: Squeeze Data
• Part 2: Technical Analysis
• Part 3: About the Company
• Part 4: Catalysts
• Part 5: Bear Case and the FUD
• Part 6: Price Targets
• Part 7: How I am Playing it

Disclaimer

*Our reports are not "buy" or "sell" signals, and are not intended to be a form of "market manipulation" or "pump and dumps". We are simply providing information that is alrea

I've been seeing a lot of discussion of data to derive how well The Wheel of Time is doing, but a lot of the discussion has been flawed or derived only from certain sources, and difficult to translate into real numbers. Here, I intend to use several sources and derive an estimation for the number of people watching the show.

My sources for this are the following:

1. The initial statements released by Amazon after launch
2. Whip Media's publicly available reports
3. Parrot Analytics' "demand" data
4. MUSO's weekly torrent popularity data
5. Netflix's public information about the number of streaming hours
6. Samba TV data for Hawkeye

Skip to the conclusion section if its too long and the tl;dr for an even shorter summary.

Part 0: Background

Amazon Prime currently has about 200 million worldwide subscribers, with only about 60 million of them being outside the US, including those that only subscribe to Amazon Video. Amazon earlier this year boasted of 75% subscribers have used the video service in the past year, but this still is going to be below the competition's nearly 100%, and its likely that much of the 75% only used it a once or twice over the year, which again is likely to be behind its competition.

The two biggest competitors are Netflix with 214 million active subscribers, with 140 of them million being outside of the US, and Disney Plus with 118 million subscribers, and 78 million of them being outside the US.

Part 1: Amazon's statements a few days after launch

Statement 1: We can firmly say that Wheel of Time was the most watched series premiere of the year and one of the Top 5 series launches of all time for Prime Video

Statement 2: There were tens and tens of millions of streams in the first 3 days of release

Statement 3: The top countries were US, India, Brazil, Canada, France, and Germany

Statement 4: The series’ first three episode made available at launch also logged some of the highest completion rates on the service ever

Statement 5: It’s definitely trending to exceed our expectations which were high.

This doesn't get us much for hard numbers, which we can derive from later sections. However, what we can firmly say is that the total delivered streams were in the 10s of millions, which then needs to be divided between 3 episodes. With the highest completion rate list on the service, we expect 90%+ to have watched all 3 episodes (for comparison, about 87% went from Hawkeye episode 1 to 2 according to Samba TV). The st

Firewood is somewhat regularly sold in a Klafter, nowadays usually meaning 3 m³. Historically it was a volume measurement of one cubic (length)Klafter. A length Klafter was 6 Austrian feet (316mm).

The liquid volume measurements of Pfiff (177ml) and Seidel (354ml) are exclusively used for beer nowadays, historically they used them for different luquids and for some reason grain. In Germany and western Austria I think, a Maß (1,4l) is common for beer.

I heard that tailors still sometimes use the Elle, but it has barely anything to do with the historical one (originally the length of an adult forearm, so it was very different in each city, the Viennese Elle was 77cm). I think nowadays it's just a short way to say "50cm", because the german standard Elle was about 50cm.

I think in some regions they sometimes use a Joch for areas, it was originally the size of a field that an ox could plough in a day.

Of course all of these are only used informally these days and are nowadays fixed to metric measurements.

Normally I wouldn't be that concerned, but because this is a specialty malt I have to ask... is there any reason I should be concerned other than my beer is going to be much stronger and maltier?

0. Preface

Hello apes - I am not a financial advisor and I do not provide financial advice!

There's some misconceptions that the price should be blasting off into the stratosphere due to apes direct registering. Of course, I have seen posts and comments pop up wondering why the price is going down, with some negative sentiment carried with it. I also see concerns that they could "keep infinitely shorting" with just one share not registered, which is not necessarily true.

I'm going to provide you my reasoning why you should relax and be Zen. Which in turn gives a possible explanation of what may have driven the January sneeze: a choke on the clearing house which the shorters could not keep up with.

Keep in mind that this is not fact, and everything I say should be taken with a grain of salt. Hence "possible DD". But in the end, we're all just throwing shit at the walls and discussing to try to figure this out.

TL;DR: >!I love you!<

1. Direct Registered Vs DTC Owned (Brokerage/Beneficial/Street)

Computershare released a great FAQ page with a flow chart showing how stock ownership is partitioned. We're only really concerned with the "Outstanding Shares" portion, so I've highlighted it from the chart they provided on what to focus on:

https://www.computershare.com/us/becoming-a-registered-shareholder-in-us-listed-companies

You can see that "Outstanding Shares" is broken down into two subsections: "Registered-ownership shares", and "Beneficially-owned shares".

• Registered-ownership shares is essentially all executives, retail investors, insiders, and others who direct registered their shares with the company via the transfer agent so that GameStop knows their ownership.
• Beneficially-owned shares is essentially the float. These are shares all under the DTC which they've produced a chain of "beneficial ownership" to lead to the shareholders. Say you buy under Fidelity. You are a beneficial owner of Fidelity's shares, and Fidelity is a beneficial owner of the DTC's shares. It's a long chain but the key point is that the DTC is the outstanding owner of the shares.

The "Outstanding Shares" for GameStop happens to be 76.49M while the float is approximately 61.83M. This is the total number of shares currently issu

I did a lot of video encoding to get some numbers that may be useful to some Plex server admins here. Enjoy y'all.

^(Yes, I did format it as a research paper. No, I'm not sure why. No, I have no idea if that makes it better or worse.)

Abstract

Video compression is a science of art. It's math that's viewed subjectively, ephemerally, and smeared 20 to 60 times per second. So it's no wonder that we argue all the time about settings without being able to quantify the way video makes us feel. I'm not going to present anything to change your mind.

TL;DR at the bottom. Read the whole thing anyways, it's a fantastic mad ramble.

Introduction

So I got bored one day and wanted to know, "how much does transcoding a file in Plex hurt the quality?" Pretty simple question, right? How bad can it possibly be? So I grabbed a video in my library, encoded it, and watched it again. Didn't look too bad. But then I realized it was already compressed from a higher quality source, so maybe it was so low quality that I didn't notice how bad it was? So I encoded it again, same settings. And it still looked file.

That's when I remembered, if my server transcodes it uses an Nvidia 1060 to encode. Maybe the GPU makes it look worse? I watched a few minutes of it, making sure the GPU was transcoding, and again, didn't notice a problem. So I did what any sane person would do - I grabbed a bunch of different files, set up a bunch of machines in my homelab, and started encoding like my life depended on it.

Thanks to some previous research, I know that there's some math out there to actually quantify the difference in quality between reference and compressed video. Peak Signal-to-Noise Ratio is the classic, and Structural Similarity Index Measure was made for exactly this. And on top of that, noted Internet Content Delivery Company Netflix developed VMAF for their entire library of content. So I used those three metrics to compare the 450 final encodes I created.

Methods

Encoder Settings

You can find my encoding/calculation scripts, encoder presets, and ramblings at this github repo. In short, I selected 9 videos to serve as "sources" for comparison:

^(Note: these are numbered 0 to 8, but reddit's markdown start

I’m sure asking in this community will have some biases, but I’m sure some of you are versed in other liquors as well.

Im a scotch guy myself, and the reason behind it is how many different olfactory experiences you can get out of fermented grain juice.

I think of all the categories of booze, scotch takes the Cake by that metric. what do you guys think? what kind of alcohol has the most amount of flavor potential without added flavorings?

Is it because of the spin-off of VMWare or is it really just undervalued?

It's currently at $94bln in revenue with an approx. average historical revenue growth rate of 13.56% YoY with rising gross margins of 16.5% in FY 2016 to 31.2% in FY 2021.

This is a really obvious buy, some might point out to operating margins being low, but I'll object to the reason why they're currently low is because of growth (If you factor in operating margin's growth, we could expect a reasonable 10%~ operating margin for the company)

Basically, a growing company, that should trade at multiples of growth stock, trading at stupidly cheap levels.

Not only that, but they're oozing Free Cash Flow for the past 4 years.

Average FCF margins for the past 5 years (+TTM) is 7% and this is on MARGINS.

If we look at FCF yield (FCF/MARKET CAP)

Average FCF yield for the past 5 years + TTM also is at 17%!

If you'd like to look at the FCF yield's growth aspect, from FY 2017, FCF yield as 3% while right now at FY 2021, we're seeing a 21% FCF yield which is beyond crazy as to why DELL is trading at less than 1 P/S when it's FCF margins are at 10%!

What gives? Could it be because of the VMWare spin off?

[Note: I don't track Dell stock press release or news so give my take with a grain of salt as I only looked at the financial metrics]

Edit 12/3 23:12 EST - Glad everyone seems to be enjoying this!! I made a few changes to some typos people pointed out. Gonna try to go in this weekend and add in stuff from Warbonnet an El Coyote as well as the Nunatak stuff I missed. Lmk if you find any other typos or anything else!!

Edit 12/3 23:23 EST - Baffle construction style is almost certainly inaccurate for most things here as manufacturers (at least from what I've seen) usually don't explicitly state what kind of construction they use. As some have pointed out, box baffles are far more common so I'd assume something is that construction unless it's otherwise noted on the actual manufacturer website (as of 12/3/21, NOT accurate here, since I assumed (wrongly) that most quilts used sewn-thru baffles and I haven't yet gone in and corrected it). But I'll try to go in over the weekend and correct that.

Edit 12/5 16:52 EST - went in and made a few changes (added Warbonnet's diamondback quilts, added metric values, added column denoting "class" of insulation (sewn-foot quilt, zip-foot quilt, false bottom bag, etc), truncated decimals for improved readability, corrected EE and HG baffle style, etc). Gonna try to get some more updates done in the coming days/weeks but I also have finals for grad school to study for in a few weeks so this is gonna be a bit of a back-burner priority for a bit. But anyways thanks for the support...I'm really glad this seems to be at least somewhat helpful for people :)

Inspired by this very excellent project comparing the performance/characteristics of several dozen down jackets by u/ormagon_89, I decided to go through and do a similar project for quilts. Though it ended up taking longer than I anticipated (I was hoping to have this done for my own purposes by the time Black Friday sales went live), I've now cobbled together a big ole spreadsheet that tries its best to give a quantifiable "score" to every quilt on the market and help see how different offerings stack up against each other in various metrics. Here's how I went about setting it up, and how the actual document works.

The "All" sheet is the database of all the products included in this project. The 5 sheets with "TW" in their titles are in reference to "total warmth" (=fill weight*fill power) and include all products with a TW value in the specified range. The 5 sheets organized by temperature are a bit different in

tl;dr -- Palantir has a one of a kind product but it doesn't solve my small pp issue. If you want a video that does a bad job of me describing this, here you go https://www.youtube.com/watch?v=l2k4ZBHydz4

Background on myself, currently I work as a backend engineer, doing data intensive projects. I exclusively work in AWS and use redshift, snowflake, AWS Glue, lambdas, etc. I'm also retarded, have no clue what I'm doing 100% of the time at work (bless the worker shortage) and have shit myself twice this year (not joking sadly). You have been warned.

So often, I see people saying "I have no clue what Palantir does, the company is too much of black box, pp poopie." And I get it, the company sucks at explaining what foundry and gotham do, so despite me being too dumb to work there (applied and got rejected instantly), I'll try and explain it.

Let's say we have a big industrial dairy company called "Double D Milkshakes". Double D Milkshakes has lots of large scale industrial farms. In addition, they have facilities that pasteurize the milk. After pasteurizing the milk and bottling it, they then have to transport it to various distributors across the country.

This means, that Double D Milkshakes, is sitting on a fucking trove of data. They have data on each individual cow (what feed the cow is being given, how much milk it is producing, the breed, who its mommy and sperm donor daddy is). They also have an immense amount of data on the living conditions the cows are in (such as what temperature the pastures are kept in). Double D Milkshakes also has data on the facilities that pasteurize the milk (lot of sensor data that goes into this). They also have all kinds of supply chain information and customer information trapped away in horrific ERM and salesforce systems (kill me please).

Now, every once in a while, some happy, unscarred monkey in management, goes "Hey, if we can tap into all this data, we could make all kinds of optimizations, increase milk production, reduce cost and oo ee ee".

So this monkey is granted funding, hires idiots like me who can't get jobs at real tech companies and tasks us with centralizing all this data into a data warehouse where the data scientist who are also lower tier can analyze the data and unlock all this value hidden in the data. Here is where it all falls apart. Moving all this data from the corporate farms into a data warehouse proves to be a difficult task. Setting up

Do your worst!

I know, it's a game, and it's somewhat abstracted. That said, Victoria 2 is far more grounded in reality than most strategy games; spot-checking their population figures against historical censuses, it's clear that they really mean it when they say that "1 pop is one adult male plus an average of three dependent women and children." Also, units are explicitly 3,000 men, or individual ships^(1). So we can perhaps get close to tying the abstract figures to real-world quantities. At least, I had fun trying! If this interests you too, I'd love to hear your suggestions or corrections.

Here's a summary:

Here's how I got to some of those numbers:

Where to begin? If a brigade is 3,000 soldiers, and most require 10 Small Arms to build, that implies that 1 Small Arms resource represents 300 muskets or rifles. Obviously, there are caveats: some officers will have just a sidearm; a cavalryman might have a carbine and a revolver, or two revolvers, or only a cold steel saber. Later units will have expensive machineguns. But one firearm per soldier is decent starting place. As a bonus, 300 19th-century rifles weigh roughly one metric tonne - a nice round number. It doesn't make sense that one would need 30 tonnes of steel to make two tonnes of rifles, based on 3 Steel -> 2 Small Arms... but then again small arms factories also require Ammunition, and don't require wood, so the game rules don't make much sense in this case.

If 1 Ammunition is ten tonnes, that's 300,000 rounds at 33 grams e

Prelude

This is going to be a very mathy post, so for those who don't care about all of that and want just a number: there are conservatively around 34.8M shares registered of GME in CS. You can also scroll to the bottom after all the math where I labelled it "Conclusions" for some further insights on this or skip straight to "TL;DR / TA;DR" section if that is more your speed.

As always, I am not a financial advisor and none of this is financial advise. Please do your own research and make your own conclusions before making any investment decisions.

The Math

^(Note: To keep this clean I'm going to link all references at the bottom of this section instead of at the bottom of the post to keep it open for the summary.)

Let's first lay out the foundation of what I'll be doing here. I started trying to speculate on how many shares were registered in CS back on 09/20 using u/Criand's post^(0) about DRS as a way to take the pulse on how much attention the idea was capturing. At the time, the post^(0) had a karma score of 17.3K with 93% of the people upvoting it. Doing simple math (17.3K / 93%) we can extract that around 18.6K people upvoted the post. I tried running some numbers with this, but on it's own wasn't too helpful because I didn't have a good grasp on what percentage of users that did a DRS transfer would also take the time to upvote the post. It did end up being useful a few days later, though, so just keep that 18.6K number in mind.

Two days later on 09/22 we get a post the outstanding ape u/jonpro03 who went through the trouble of sorting through all the CS posts and provided us with this post^(1) that presents the purchase value and source post for each of the users who posted screenshots of buying new shares of GME through CS. With a total of $508,342 spent and an average cost basis of $200 that gives us 2,541 shares from 37 individual apes, which we can use to compute a mean of around 69 shares (nice). However, looking at the actual data we compute the medium value as 625 shares and the standard deviation as 240 shares. This is because around 60% of those purchases were made with at least $1,000 or more (i.e. purchasing at least 5 or more shares) while the other roughly 40% were purchased with less than $1,000 (i.e. less than 5 shares worth), giving us a minimum and maximum of 0.5 shares and 1,250 shares. At this point I tried to extract some more data from this by using the insight that on social networks like Re

Lap times taken from: https://f1teammatestats.herokuapp.com/f1analysis.php.

My methodology:

1. Percentiles are used for laptime deltas, this is to adjust for track length.
2. Medians are used instead of averages so that all sessions are worth roughly the same, otherwise a big gap in one session can completly skew the final result.
3. If one driver crashes in a session this means a win by default to the opponent. This is to make sure a small mistake (a lock up costing 4 tenths) is not more costly then crashing which is a bigger mistake.
4. Wet sessions are excluded to make cross-comparisons possible, the amount of them vary from season to season and as such they are not a reliable metric, they are better left as a seperate skill that can also be measured.
5. In the case that there was considerable dry running in a wet session those times might be used if I deem them to be representative.

Comparison:

I avoided using “End Game” for the longest of times now, because I find it cringey (oof, felt cringey writing cringey), but mainly because I have not perceived us to be in an “End Game” scenario till now.

So, what is the “End Game”? We are all Direct Registering Shares and i’m sure some know what the end may look like, but there may also be those who do not. So let’s go through some scenarios that I perceive as being real possibilities based on the effects of the Direct Registration System (DRS).

Scenario 1 - Retail Forced Recall

A standard way borrowing works is that there are three actions that can happen after shares are borrowed.

Rerate = Borrow wants to renegotiate a borrow rate, so they submit a request - generally, if the request is denied, shares get returned to the lender

Return = The borrower returns the shares lended to them

Recall = The lender recalling shares

Rerate and return are an unlikely use case by short hedge funds (SHF), recall is something we talk about, but generally from GameStop’s perspective, speculating on if/when they do a share recall. We implicitly talked about this back months ago when everyone spread information about making sure your account was cash and not margin to avoid lending and essentially “recalling” your shares (supposedly).

With the DRS, it might just trigger recalls from brokers via retail, not GameStop. Let’s say you’re with one of the many brokers who do not allow you to turn off lending and/or you think they lend them anyway if you have a cash account with one of the larger brokers.

As many are aware, brokers can lend the shares to short-sellers when it’s held in street name i.e. their name. DRS is the literal process of taking shares away from the broker (don’t take my word, but Fidelity’s - won’t link as it’s against rules - but you can find a post on their sub from the mods that go into it).

The act of directly registering shares can essentially be compared to the equivalent of a share recall - so you, that’s right, you - are recalling your shares from existing shorts or borrowed shares being used to roll FTDs.

For the existing shorts, this will result in them needing to cover them, which means buying = increased buy pressure and possible increase price.

Failure to Delivers (FTDs)

As for FTDs - it’s either buy shares to cover them or borrow shares to cover them (or option conversions…….). As we’re removing the borrow option and they certainly don’t want to buy to cover them,

EDIT: we've since learned that account numbers are not sequential. Metrics like total registered and percent full are incorrect. Everything else is still valid

Salutations APEs! My cheerios were purple this morning and I took it as a sign. It's time for another DD.

In my post last weekend, I talked about my methodology for aggregating screenshots to determine the number of shares in the average Ape ComputerShare account.

I'll quickly review my methodology, and highlight changes since last week as we've learned more about Computershare account numbers. Good stuff down below :D

The Methodology

Please take anything you find on the internet with a grain of salt, including this sentence.

I'm pretty confident in my results, because I did this for me, not for you. I wrote a lot of code to automate as much of this process as I could. I'll share links for the code/databases at the end of the post if you'd like to check it out.

It is not infallible. Shortcomings include missing posts where the Ape attached multiple images, posts with super-high-resolution images, posts with pictures of monitors with visible moire patterns (this completely jacks up computervision), and I was completely unprepared for videos of refreshing the portfolio page. These are added manually, though.

Every hour, the code:

• Downloads every post from GME-related subs on reddit and throws it into a local database.
• Downloads images associated with those posts.
• Uses a computer vision library to extract the text from the images and stores it alongside the post's record in the local database.
• Runs an algorithm to do a high-level classification of the screenshot to determine if it's a one-time purchase from ComputerShare, or a screenshot of a portfolio.

I wrote a handful of scripts that:

• Pull new purchase and portfolio posts out of the main database and put it in scoped databases.
• Prompt me to review posts where computervision failed to find a value on the screenshot.
• Prompt me to review all other posts to make sure computervision got the right value.
• Reconcile duplicate posts (mostly when a user posts the same image to multiple subs).
• Give me the ability to audit any record to change the value or remove the record. (Shenanigans)
• If a portfolio screenshot just shows a dollar amount and not number of shares (this happens a lot), the code will guess the number of shares using the average price of GME for the day.

Then I wrote a script to aggregate the posts and apply the

Mitigating Anxiety is Mitigating the Unknown, and Mitigating the Unknown is Mitigating Downside Risk

Note: Its a long post. Please don't forget to upvote if you like the content. I worked hard on it and I want folks who experience trader anxiety to benefit from my work. Over 5000 words, 15 minute read.

Overview: For those of you who consider yourself seasoned traders, ignore this... although I promise you that you will learn something. For those of you who have anxiety issues I think this will prove to be an invaluable resource. I'm going to put a lot of time into writing this and it is directed at those who have high amounts of anxiety while trading in the markets.

Reddit is an Enabler of Trading Anxiety:

Back in the day I used to post a metric ton of highly detailed DD on Reddit. I posted my research on Reddit for a simple reason ... I wanted feedback ... I wanted to crowdsource my DD. One of the primary issues I continued to run into was few, if any, added any substantive value in the comment section. I would post DD and 2-4 weeks later I would get a flood of comments of folks who never commented on the thread before, but nevertheless tanked me for all the money they made as a result of reading and taking action on a ticker I wrote about. It was then I noticed that people on Reddit are frequently looking for justification to throw money at anything that may bring them a return. As it turned out the information I posted provided that justification. But this was not the reason I posted the research. Once again, I wanted to crowd source DD.

I stopped posting DD on Reddit altogether. It was a lot of work for too little feedback. I can do a complete deep dive & workup on a company in my head with a pad and pencil within an hour. Putting it all together and posting the information takes considerably more time. I realized that there is a large segment of people who wanted to be told what to invest in with a detailed summary as to why. They generally want to know the risks, they want to know the possible rewards, and they wanted to know when to buy in, average down, and sell. I therefore opted instead to publish a daily newsletter instead of positing on Reddit.

People on Reddit are buying what they don't understand with information posted by people they ought not to trust. A great deal of DD on Reddit only comes

It really does, I swear!

For context I'm a Refuse Driver (Garbage man) & today I was on food waste. After I'd tipped I was checking the wagon for any defects when I spotted a lone pea balanced on the lifts.

I said "hey look, an escaPEA"

No one near me but it didn't half make me laugh for a good hour or so!

Edit: I can't believe how much this has blown up. Thank you everyone I've had a blast reading through the replies 😂

I'm going to show some data charts to Illustrate why ADA is doing well even though the price action (market chart) says other wise. On-chain metrics are the most data of any token; more important than the price of said token.

To clarify for the following pie chart: whales (wallets with more than 1% of total circulating supply), investors (wallets that have between 0,1% and 1% of total circulating supply) and retail (wallets that have less than 0,1% of the total circulating supply)

Here I'm comparing holders of ETH and ADA, as ETH is the closest token to ADA.

So here is the chart from ADA

Whales: 6&#37;. Investors: 22&#37;. Retail: 72&#37;

And for a comparison, I'm going to show ETH stats.

Whales: 21&#37;. Investors 20&#37;. Retail: 59&#37;

Comparing the holders of ADA and ETH it's interesting that the number of Investors are the same, while ADA has 70% less whales holding the token. In addition, the market cap of ADA is 12% the market cap of ETH (ETH : 523bn. ADA : 62bn).

Now if we compare the ratio of market cap to the ratio of whales we get:

• ADA market cap / ETH market cap = 62/523 = 0.12
• ADA whales / ETH whales = 6/21 = 0.29

This I believe is bullish because whale ratio is higher than market cap ratio by 240%, which means there are more whales for a relatively smaller market cap project. This is my interpretation of this data so take it with a grain of salt ,and there are various ways of interpreting this.

Now lets look at long term holders (holding for more than 1 year+) and the number of these addresses

May 2.78b ADA. Oct 6.34b ADA. Nov (month not over) 6.63b ADA

As we can see whales have been accumulating for the past 7 months, with an increase in 245% owned by whales. In addition, the number of addresses have gone from 167k to 258k, meaning more whales are coming into ADA. Both of these signals are bullish.

Source (Sorry, but you have to create an account to view the data. You can start a 7 day trial with no credit card requi

Hello everyone! It's ya boy, BirdSemen, dropping off some early rankings of the 2022 RB class in order to hopefully start some discussion and receive some critiques of my current opinions.

Method

I like to use a blend of analytics and light film study in order to form my opinions on prospects. I lean heavily on the analytic numbers. Statistics I use include rushing yards over expected per attempt (RYOE/Att), yards per carry (YPC), team receiving market share (Rec MS), dominator, and yards per team play (YPTP). Resources used to accumulate these data points include Campus2Canton, Playerprofiler, and sports-reference. I use film as a mean to justify the numbers, either supporting what the numbers represent, or contradicting the numbers and hopefully either eliminating prospects that look bad on tape but have a good analytical profile, or those that look great on tape but have a bad profile.

These rankings are for .5 PPR.

I am learning more and more everyday about how to scout these players more effectively so any advice is more than welcome. I know there is a lot of information out that that I haven't even discovered yet.

2022 Running Back Rankings

• Tier 1
  • Breece Hall - Breece Hall has been my personal RB1 for this class for some time now. This year I haven't seen anything to change my mind. Hall profiles as a 3 down back, provides a good balance of strength and agility, and shows a fair amount of patience behind the line of scrimmage. My knock on Breece at the moment is that he's prone to dancing behind the LOS in order to find a big play, pretty common trait in college RBs and I don't expect it to be an issue when he reaches the league. I've seen talk of concern over Hall's top end speed, however I do not see that as an issue in the slightest. During a run against West Virginia this year, Hall reached a top speed of approximately 21.5 mph. He also offers the highest YPTP amongst notable RBs in this class, which is encouraging that he can offer that level of production. Breece is the only RB in this class that I could see entering the tail end of the 1st round of the NFL draft this year.
  • Chris Rodriguez - I love Chris Rodriguez, however I do not expect you will have to draft him highly in order to acquire him. Rodriguez profiles as a large powerback who consistently pushes for more yards after first contact. The biggest downside is he doesn't offer much in the passing game. This ranking of his will drop if he runs a very slow (>4

They’re on standbi

Do you also use grains as a base measurement for powder & projectiles or grams with an extended decimal? This is to settle a disagreement over 'proper' measuring systems with a particular SI/metric oriented person. *"ThEy OnLy UsE gRaInS wItH tHe ReTaRdEd US PoUnD sYsTeM! The ReSt Of ThE wOrLd UsEs GrAmS!"

Yeah, I know... 0_o

TLDR; The breadcrumbs dropped by the disclosure team might have a strong tie to the time component of the time evolution operator of quantum mechanical systems.

Disclosure: I am not a physicist.

Lue has stated twice that he leaves breadcrumbs in every interview.

21OCT2021 - Theories of Everything with Lue https://youtu.be/qfa5rWYHCCg?t=1216

>Lue Elizondo - "I've always left breadcrumbs in every interview I ever do for the last four years"

19NOV202 - New Frontier Network https://youtu.be/P4yjMl6mUyc?t=4837

>Lue Elizondo - "And again, I kind of left some breadcrumbs. If people know how I work they'll realize every time I have one of these conversation I always leave breadcrumbs; ALWAYS!"

Operating under the assumption that he and his cohorts have been leaving both intentional and unintentional breadcrumbs for the community to find, you can discover some absolute gold nuggets to many aspects of the phenomenon. This post may reveal one of those nuggets.

You are highly encouraged to dig deep into and re-watch all of the interviews and presentations of Lue Elizondo, Tom Delonge, Jim Semivan, Eric Davis, and Hal Puthoff looking for those breadcrumbs. This post will contain both transcript excerpts, timestamped videos, and web resource links for your reference. Please pardon any misspellings, slight alterations for clarification, or minor errors. Any glaring errors will be corrected if they are found and pointed out.

Please try leaving your preconceived notions of what the true story may be and your personal biases aside. Leaving biases aside means trying to take every word they say literally and applying an analysis as to their intent for saying what they did. The results have lead down a very interesting path of discovery to some very deep and interesting physics. There are several topics of the phenomenon that can be pieced together in this manner that may be posted at a later date.

All persons involved in the disclosure effort have been forthright mentioning that there is something very peculiar about the crafts' ability to manipulate time in various fashions. The evidence for this statement is so overwhelming that there is no need to put every reference to it in this post. This post will contain excerpts of the most interesting comments made on the subject of time manipulation.

A copious amount of questions will be raised to help the reader follow the thought pr

Hello,

You want a stonk that's gonna fckin squeeze and has all the momentum behind it? Look at $BFRI

• Recently made it back to #1 fintel list
• High social media sentiment
• Short interest estimate anywhere between 80-40%
• If you want an actual squeeze its gonna be on a stonk that's about to make new all time highs
• All experienced short-squeeze traders on here don't really care if a stock already ran 2-3x, if the data is there, if the momentum is there, stonk RIPS. Squeezes happen best when the stock creates new all time highs on high short interest. Take $BBIG and $SPRT for example. Everyone bought high and sold higher.

Grab a beer, grab some popcorn, grab what ever you want. This is gonna be a long one. I know many of you are just going to scroll past this entire post, go to the comments and type some shit like, "Didn't read. All In", but please I highly recommend that you read this DD since it might possibly be the biggest squeeze to end the year... but it also may be the riskiest one, so you need to tread with caution. For me, I like these risky stocks because they can generate a large percentage return, and with proper risk management, can minimize substantial losses.

If you've been following me so far, during the entire month of December, I've made a lot of great trades, with many of my picks going up over 40% on the day, all caught before the big move, and verifiable through my entries and exists posted on Twitter.

• Dec 8: $PPSI went 40%+
• Dec 9: $CNTX went 40%+ then 50% AH
• Dec 10: $PTPI went 40%+
• Dec 13: $PTPI went 40%+
• Dec 22: $SOPA went 80%+
• Dec 22: $ENSC went 80%+
• Dec 23: $SOPA went 40%
• Dec 23: $ENSC went 40%+
• Dec 23: $BFRI went 30%+

I don't take credit for finding most of these tickers, most of them I found from the DD being posted within the community, waited for the best possible time to hop in, and traded it. I usually go for stocks that can net me a minimum of 40% return in one day with some extra change going into the next trading day. For example,

• $PTPI - had two back-to-back 40% green days
• $ENSC - had an 80% day, then a 40% day the day after
• $SOPA - had an 80% day, then a 40% day the day after

Now enough bragging about my trading history, I only bring it up because I genuinely believe that the next stock to go minimum 40% is $BFRI. However, I think it might go 100%+ since it just might be the biggest squeeze to end the year d

TLDR; The breadcrumbs dropped by the disclosure team might have a strong tie to the time component of the time evolution operator of quantum mechanical systems.

Disclosure: I am not a physicist.

Lue has stated twice that he leaves breadcrumbs in every interview.

21OCT2021 - Theories of Everything with Lue https://youtu.be/qfa5rWYHCCg?t=1216

>Lue Elizondo - "I've always left breadcrumbs in every interview I ever do for the last four years"

19NOV202 - New Frontier Network https://youtu.be/P4yjMl6mUyc?t=4837

>Lue Elizondo - "And again, I kind of left some breadcrumbs. If people know how I work they'll realize every time I have one of these conversation I always leave breadcrumbs; ALWAYS!"

Operating under the assumption that he and his cohorts have been leaving both intentional and unintentional breadcrumbs for the community to find, you can discover some absolute gold nuggets to many aspects of the phenomenon. This post may reveal one of those nuggets.

You are highly encouraged to dig deep into and re-watch all of the interviews and presentations of Lue Elizondo, Tom Delonge, Jim Semivan, Eric Davis, and Hal Puthoff looking for those breadcrumbs. This post will contain both transcript excerpts, timestamped videos, and web resource links for your reference. Please pardon any misspellings, slight alterations for clarification, or minor errors. Any glaring errors will be corrected if they are found and pointed out.

Please try leaving your preconceived notions of what the true story may be and your personal biases aside. Leaving biases aside means trying to take every word they say literally and applying an analysis as to their intent for saying what they did. The results have lead down a very interesting path of discovery to some very deep and interesting physics. There are several topics of the phenomenon that can be pieced together in this manner that may be posted at a later date.

All persons involved in the disclosure effort have been forthright mentioning that there is something very peculiar about the crafts' ability to manipulate time in various fashions. The evidence for this statement is so overwhelming that there is no need to put every reference to it in this post. This post will contain excerpts of the most interesting comments made on the subject of time manipulation.

A copious amount of questions will be raised to help the reader follow the thought pr

So, as the 2022 season progresses, the MVP race has devolved into these common 4 narratives, around Jokic, Giannis, KD and Steph. Specifically that Steph isn't having THAT good of a season and he's being carried by his team in many of the games he's shot poorly. So, I'd like to take a look at the on/off stats and see if this narrative holds up.

Essentially, using PBP stats, and using medium, high and very high leverage moments to filter out blowouts, and paying attention to sample sizes of over 100 minutes, what are the results? What is Steph's impact on the game? Has it been reduced because of his shooting slump? How much of the wins should be attributed to Dray?

These stats won't answer any of these questions, but they will point you in the right direction. The more effective solution is to simply use EPM and RAPM, but I know a looooooooooot of people just absolutely refuse to use these stats, so here's a broken down version of a series of net ratings to show the overall impact of Steph.

Also note, the 100 minutes threshold is less of a threshold and more of a gradient. There is never an exact spot.

Starting off with Steph's on off stats.

Steph On/Off Net Rating

15.53 Net Rating On, 1.07 Net Rating Off. That's a +14.46 On/Off, which is absolutely elite.

Both the offense AND the defense are statistically better with him on the court. The defense part is especially insane, because his replacement is GP2, arguably a top 3 guard defender in the league in terms of pure talent. AND, the defense is already elite without him, so it's ridiculously difficult to improve it. A 7 DRTG improvement is kinda nuts.

But, we know that the Warriors defense is elite, and that's thanks to Draymond, right?

Steph and Dray On/Off Net Ratings

Steph and Dray On: +12.15

Dray On Steph Off: +2.07

Steph On Dray Off: +23.07

....? Well, this is possible because without Dray on the court, the Warriors have a +121.60 ORTG, while actually having a BETTER DRTG, by 3.25 points or so. This will be important later. Now, note, the replacement for Dray on the bench isn't some DPOY player. It could be JTA, or OPJ, or Bjelica, or a f