Welcome to the Cannabis Data and Science Meetup Group.
So we meet here to talk about cannabis data, do some exploratory analysis, crunch some
numbers, have fun in general.
So just to give a brief introduction, my name is Keegan.
I started working in the cannabis industry as a laboratory analyst where I did everything
from hands-on analysis, so just the sample preparation and gradually found some ways
to automate some of the lab work.
Next thing you know, I'm a software developer and I've now launched a company, Cannalytics,
primarily to help provide software solutions to help people in the cannabis industry's
lives, primarily labs, lives to be easier.
And then there's just such a high demand for analytics.
And so that's what my background's in, so just trying to lend a hand.
And how about yourself, Stephen?
I guess Paul and Heather can introduce themselves here in a second, so I'm curious what brings
you to the group today.
So surprisingly, I am not a cannabis user and I don't intend to be.
My interest actually is about terpenes.
And so terpenes are also interesting to many cannabis users.
And my interest is in plant terpenes.
So I'm a consultant to a natural products company that makes insect repellents based
on natural products.
And plant products are, most of what you smell are volatile molecules, which are terpenes,
10-carbon molecules that are terpenes.
And so I'm very, very interested in terpenes, modeling terpenes, and particularly understanding
by plant species like rosemary and so forth.
If you take the essential oils or press them, or use distillation to take the oils out,
what is the relative contribution of the different constituent molecules, constituent terpenes?
And so I've done a fair amount of work in that area.
And then I'm very interested in the tidy verse in modeling and so forth.
So it turns out that one of the inexpensive ways of trying to understand plant terpenes
more generally, or outside of marijuana, well, it turns out, if you want to understand that
stuff, the inexpensive way to go is to use the marijuana testing labs.
So I'm interested in understanding more about the marijuana testing labs.
Also reportedly, some of the labs are cheating by showing higher THC.
And I also see inconsistencies reported between the labs for the same product.
And so I'd like to understand a little bit more about that, because from a production
perspective, I'd like to incorporate marijuana testing labs in production of our plant-based
insect repellent.
So it's a mosquito repellent, shipping, and so forth.
But to go through and just easily go through and test, which primarily I'm interested in
the terpenes.
Fantastic.
Your interests align with the group perfectly well.
So we've looked at a lot of laboratory data.
Terpene data is particularly interesting and an area that we have neglected so far.
So we've been working with this data set from Washington State, where we have a rich set
of cannabinoid data.
So and even, in fact, Paul and I were talking about this briefly.
So essentially, we're interested in maybe trying to see, OK, is there any hybrid, sativa,
indica distinction in the numbers?
And so I know people have done work with that in looking at terpenes.
So it would be interesting to see.
So long story short, I think you're in the right place.
And in fact, we may have a lot to learn from you as well.
Great.
Awesome.
We'll maybe return to that here in a second, just to go ahead and go around the group.
So Adam, another new face.
So it's good to see you here.
Would you be interested in introducing yourself to the group?
If not, Heather and Paul, would you mind introducing yourselves to Stephen real quick?
So Paul?
Sure.
Hi, Stephen.
My name is Paul Kitko.
My day job, I'm a data scientist with one of the big three automotive manufacturers.
What brought me here is I was actually finished up a master's degree in data science.
And I was looking for a big juicy data set to get my hand on to do my capstone project.
And Keegan was helpful in getting me started in that area.
So I was using this Washington state data set to do some what's called market basket
analysis and retail analysis on sales and dispensaries.
So I just wrapped that up last week, officially.
So I'm done with my master's program now.
So thanks, Keegan.
Thank you, Paul.
Thank you.
And also, Stephen, you mentioned the Tidyverse.
I like using the Tidyverse because it seems to be, at least for me, I'm not a natural
born programmer.
And in fact, if I didn't have to be programming, that'd be great.
But it seems the Tidyverse is very approachable and easy to use.
So that's my tool of choice as well.
That's about it.
That's interesting.
Whenever there's multiple lines of agreement, I get interested.
So what's this Tidyverse?
So this is a data wrangling tool?
So in R, there's a huge collection of packages for data analysis and statistical analysis.
And it's just written in a way, at least from my perspective, that's easy to pick up.
It's actually, to me, it's easier than Python.
So that's kind of like gravitated towards it.
OK.
Well, I could maybe supplement Paul's comments.
So generally, you'll find people who are doing production work.
That is to say, people have come up from this computer science background.
Typically, they'll have learned Python in school, and they like Python.
And it also feeds into machine learning well.
People who are statisticians or PhDs coming from areas that are not computer science,
typically, they'll use R. R has been around for many years.
As Paul's indicated, there's now a new collection, a subcollection, if you will, of highly integrated
tools.
So they snap together like Legos.
And the goes out of one goes into the next.
OK.
So you can think of them as block modules.
And so you can kind of connect these modules together to do hugely creative things.
So it's abstracted for you, that is to say.
So instead of trying to get down at the bit and byte level, you're dealing at a higher
level of extraction, kind of like Visio or so forth.
So the advantage here is, as Paul's pointed out, people who are not programmers can move
quickly.
I say relatively quickly with complicated data sets.
So once you get a data set, for example, that's beyond two pages of Excel, it starts to be
really difficult to try to understand, to visualize it, to model it, to just clean it
up, all that sort of things.
And so there's a fellow named Hadley Wickham, who has instigated this thing called the tidyverse.
And it basically looks at the complete workflow.
So if you start off with, hey, I've got some raw data.
It's always messy.
Maybe 78% of your time is spent cleaning up that dirty data.
And then you want to start saying, well, what do it mean?
What can I do with it?
And how can it help inform my business?
So there are a lot of tools around visualization that's important first step.
And then trying to characterize what equations or what algorithms might model that and what
might that inform about what you do on a go forward basis.
So for example, you might say, hey, I have a current, you know, I grow marijuana right
now and I want to increase a certain terpene.
How can I do it?
Well, one way you might try doing that is get some insects in to start munching on it.
Because terpenes are made by plants to keep, you know, munching insects away.
Or just you might say, I'm going to give it less water.
See what happens.
I'm going to pass it different ways.
And then you'll get different data.
And if you have that different data, then you can start to model it and say, hey, if
I give it 10% less water and 20% more light, what does that do to the resulting product?
You're hitting on something real clever.
So this is something that several people are talking about.
And even somebody's talking about with kinetics is essentially, yes, so just measuring the
things, some of the inputs in your production and trying to estimate some of the yields
or at least some of the effects on the yields.
Yeah.
I mean, if you were doing a production process of manufacturing something, you'd monitor
it.
You'd say, you know, here's what I expect to be in spec when it comes out of the production
line.
But natural products, especially, well, I know more about essential oils than I do about
marijuana.
But in natural oils, the way it's recovered is there's different ways to do it.
You can physically compress it to squeeze out oil, or you can do something called steam
distillation.
That gives you two separate products.
So that's a processing example that you might want to change or adjust your process.
On the growing side, it's a lot like growing wine.
Okay.
You know, the terroir makes the difference.
So what's in the soil, the water, the sun.
All that stuff will greatly change the essential oil composition.
So just as a general point, I would say there's probably some lessons to be taken from looking
at just general plant science and then applying those to the specific case of marijuana.
Because plants, there's a lot of conservation across plant types.
So what happens in one species of plant very likely shows up in another species of plant
as well, but different effect sizes.
Okay.
And this is where we like the cross pollination of ideas.
And so just to introduce our two new guests real quick.
So essentially, Adam, Mike's not working, but in the chat, Adam's also getting a master's
degree in data science.
So Paul's proving this is quite useful.
And then you have experience working with CAMS companies in California.
So excellent.
We'd love to hear from you there.
California is one of the states we haven't really studied too much in depth.
So it'd be interesting to hear your take on things in the markets there.
And now, Sasha, it's awesome to have you as well.
So welcome to the group.
If you want to take just 30 seconds to introduce yourself, you're more than welcome to.
Sure.
I don't actually have a data science degree.
I just have done some data sciencey things.
So I'm a data engineer and BI engineer.
And so currently I'm working on building softwares for the cannabis industry, specifically banking
and a point of sale.
So the intent is to be for Oklahomans by Oklahomans and to try to ensure that if Oklahomans are
going to spend money on a point of sale system, that it should be an Oklahoma based one so
that that revenue stays in Oklahoma and that those taxes stay in Oklahoma and helps the
community.
So I am here to glean as much information from all of you brilliant individuals and
hopefully be able to contribute as well.
Well, it's awesome to have you Sasha.
It's an area that we would love to learn from you about what's going on with banking.
We do talk a bit about sales.
Paul is probably the sales expert at the moment.
And in fact, we have a little bit of Oklahoma data to look at today, just a little bit,
but bite size to get us to start talking about this next idea.
So I guess just to go ahead and to dive into what I was looking at.
Hi Keegan, this is Paul.
I think we forgot Heather.
Oh, Heather.
No, no, no, no, no, no, no, don't worry about it.
I really appreciate you saying that.
It's all good.
Thank you.
Hi Heather.
Hey Sasha.
Well, Heather's an awesome attendee.
It is relevant because so essentially started just looking at a map here of, okay, so like
I started thinking, okay, so let's start to really just start to get our ear to the ground
and really start to figure out how much in sales and cannabis is really going on.
So we started talking about sales last week.
So we've been primarily looking at Washington State.
And so there are polls out there with companies estimating the size of illegal markets or
the size of the legal markets.
But here at the Cannabis Data Science Group, we try to get our hands on the data ourselves
and crunch these numbers ourselves.
So I figured, so why don't in, this could be a little bit of an undertaking, but here
in the next couple of weeks, I think we can do this.
So essentially, I thought that, okay, why don't we try to make the Cannabis Data Science
Meetup Group estimation of the size of the cannabis market.
So that way, you know, Leafly and New Frontier Data, they have their estimates.
And so then we can add our estimate to the hat to see where it, to see how it compares.
And so, and what will be cool is we'll have all of our tools and sources is open source.
So that way, you know, someone can follow in our footsteps.
So long story short, we're going to have to do a little bit of homework here because,
so for example, you know, they have New York as fully legal, however, as we know, and even,
so for example, they've got Virginia as fully legal.
And so I started looking in the, you know, the regulations here for Virginia, and I believe
things really don't, you know, kick off until 2023.
So right.
Exactly.
So, oh yeah.
And so this is where Heather may be interested.
So Heather is essentially about this dot.
In that was the other thing is I started to think about the interesting location of Maryland.
And so, you know, Maryland's surrounding DC.
And then, like you said, you've got Virginia, which is not technically legal, but we've
got some time to wait.
So long story short, there's a couple factors to look at here.
And so without further ado, I'll just start showing you some of the data that I've started
collecting.
So basically, we've been looking at a lot of the supply side.
And the reason being is, you know, the supply side data is quite clean.
So they've got the seed to sale tracking systems.
So they relatively have a good measure of sales that are happening.
And so, you know, and so that is essentially where supply and demand meet.
So that's the markets cleared.
That's equilibrium at our total sales right there.
However, you know, we've only been looking at the supply side of the picture.
And so, you know, I'm sure everybody can go through your data sheet.
I put it on a bigger screen here because I got my problems.
I need a big screen for spreadsheets.
But you have to describe your data.
That is to say, you know, you've got it on the screen there, but you need to go through
the columns, at least for me, to explain what are we looking at.
Yes.
So let's look at it.
Hold on one second here.
Sure.
I'll show you some of the visualizations here.
So for example, started to just look at some of the data here in Maryland.
And so now we're starting to.
Okay, so for example, you know, we've got the Maryland, you know, we've got Maryland
sales.
So you see, you know, in Maryland, we've got a nice, you know, they've got a steady increase
in sales.
You know, at the same time, you know, there's more and more patients.
So if you were, you know, only looking at the sales, it would be just, you know, kind
of hard to explain this.
And so then, okay, well, that can be maybe explained by the number of patients.
And so, well, you know, maybe the number of patients that may essentially track along
with population.
So or maybe it may out the number of people signing up may be outpacing any change in
population.
So long story short, some of the variables that I thought would be worth looking at would
be essentially, we'll start making panel data.
So we'll keep track of the state, and we'll keep track of the date and what's nifty is
the, you know, the census has recently come out with the 2020 estimates.
So we can now get relatively recent, you know, population for all the various states.
So this is where you see things start, there start to be some estimates.
So for example, you know, we just have the fixed point 2020 population for Maryland.
And so now if we're starting to incorporate population into our analysis, do we, you know,
start assuming that population's growing?
Or do we just leave population as a constant?
And you know, it's hard to get constants in this case.
Like when I looked at this data first, I thought that I could use the number of caregivers.
So in this case, these are people that don't necessarily have to have their Maryland card,
but they would arrive at the dispensary to pick up the product for the intended patient.
So I thought that that was a measure too, but it does go up, but not nearly to explain
the monthly increase.
So anyway, yeah, it's like 2% of the population in Maryland is roughly hold to card.
So we don't know about the other types of sales, but for legal sales in this case, yeah,
2% of the population.
Exactly.
And Stephen, were you going to make a comment here?
Well, yeah, I mean, there's just a few things.
One would be, I would get rid of the cents on your column, column D and column.
Which one?
For your revenue and total patients, just for clarity and for easy scanning that you
might just get rid of the dot zero zero.
Yes.
It's just, I mean, I realize it's a small thing, but trying to understand it and on
a go forward basis, that'd be nice.
The same thing for total revenue.
And then also revenue per patient, like just round it to the nearest dollar.
That will make things easier when you go to start plotting it and printing it and have
labeled points and stuff.
Because it's not really significant to have, you're showing six significant digits and
obviously it's not significant beyond the dollar.
Oh, yes.
That'd be the first thing.
And then as far as making it more readily understood.
The next thing is that, of course, the population number is not going to change once the census
is set.
I don't know that there's a way to show incremental increases to the census data.
So that's going to be a static thing for the next 10 years or something.
If you're looking for additional things, you might want to break that population down,
but by age tranche.
Because I think that you'll find that marijuana use, legal and illegal, you know, sanctioned
or unsanctioned, whatever, is not consistent across ages and across gender.
So I would think that that'd be useful.
Further, if you do it for one state, like we do it for Maryland, you might find that
that model will carry over to other states.
So you mentioned that you might like to do some predictions versus other states.
I suggest diving down on one state, maybe Maryland, and understand that really well
and then say, hey, people in Maryland might be similar to other states.
So there's a really nice database of census information that's free from the US government.
It's a census.gov or something.
So you can pull down a whole bunch of very specific information.
For example, you might want to model this if you've got data, dispensary data by zip
code in Maryland.
You can then go back and say, hey, in this zip code, here's the distribution, the population
distribution by age and gender, as well as income distributions.
So you can do a lot of that stuff to get a better model.
So in other words, I guess what I'm really getting at is you could choose to dive down
onto Maryland, come up with a characterization of Maryland based on characteristics that
you get from the census data, and then pretty quickly just flip it around and say, hey,
if the rest of the states were like Maryland, here's what we would expect to see.
And that may be really different than I think your other people are more likely to say,
hey, what do we think's happening?
People try to derive their estimate of the US market a different way.
But if you did it this way, it would be a useful, I think, I mean, I'm imagining not
knowing anything about your industry really.
But my guess is that they're not coming at it from a deep dive on one.
This is like you could use the Maryland data as a sample of the US.
It's a biased sample, but it's not bad.
And you have it, that's some information you can get.
So again, we look back at the population.
I recommend splitting that by age tranche.
And you can do that easily as I said, but it's like census.org or something.
They have data sets you can download.
And I started first by just looking at Maryland and trying to understand Maryland well.
Exactly.
And then from there, and so the first thing I would do is I'd download the population
data from Maryland and I do that.
There's typically I'll break it by zip code or there's something else called a FIPS, F-I-P-S
code.
So just two different ways.
I think the census maybe has both.
But the thing that's nice about that is that you can get a lot of correlations.
Exactly.
And so you've hit on basically what's key here.
So we're basically, so if you can see my screen, basically I just have these quick facts from
Maryland.
And so with some states, you're right, we can get more granular.
So Washington state, Colorado, you may be able to get a number of licenses by county
in Oklahoma, perhaps.
I believe you can because you know the county and the licensees, so you can at least get
licensees by county.
So with certain states, it can be a bit more granular.
We're almost just trying to do like a national demand for cannabis.
And so long story short, I like your idea.
And it's in this similar vein where we're basically we're trying to aggregate these
explanatory variables and see if they can't help us predict demand in other states.
So basically the idea was, okay, so we can get the revenue in Maryland.
And so this is about 2% of the population or the patients are 2% of the population.
And they're spending on average almost $400 a month.
And so looking at actually a smaller state, Oklahoma, so Oklahoma's population is a little
more than half of Maryland.
And here you have so recently there was a bulletin that showed, okay, in 2020, there
were about 330,000 patients in Oklahoma and then this year there was 375,000.
So we're just chalk those in.
You know, those are just yet more data points for us.
And the striking thing here is that Oklahoma's got 8% of the population or 9% of the population
is using it, whereas Maryland is only 2%.
So the elephant in the room is why is one four times bigger than the other?
Exactly.
And so this is, I think, where some interesting analytics may be worth bringing in.
So one is, could we just assume that everyone in the United States basically has around
the same baseline consumption?
And then the only variation is just being just the black market essentially?
Or are there actually systemic differences between consumption in Oklahoma and in Maryland?
So I'll pause you there for a second.
And you see stuff right now with COVID and the misreporting that happened with COVID,
for example, in Mexico.
And it was underreported.
But what they did, what some people did is in Mexico City, they really get death reports
by district.
And by giving the death reports, they could make an estimate then of what was happening
in Mexico City for COVID, way ahead of any published data, which showed it was much higher
than expected.
In a similar vein, you might be able to say, hey, I get 10 friends or 20 friends or whatever
and buy a sample, but just see what the range of use is.
Like how many, I mean, whatever the unit of measure is, is it a, I mean, joint doesn't
sound like it's a standard measure, but maybe you've sold by weight or something.
Here's the weight that I'm using per month and see what the difference is.
You might find that the distribution is rather small.
The difference might be small.
You might find that people who use it use some minimum and the maximum, there's a limit
maybe to how much you could possibly smoke.
So that would give you an idea then of that spread.
Just knowing what that is, even with a group of 20 people will tell you something as opposed
to, you know, it's a simple thing for you, relatively simple thing for you to do to get
just some basic understanding of what the distribution is.
Cause you may find that, Hey, there's some people who, I mean, it could, it could happen,
for example, that 80% of the people are light users and 20% are just super heavy.
That would be good to know.
I can tell you also being an Oklahoma and myself that getting a medical marijuana license,
at least in Oklahoma is, is much easier than some other places.
So that could also account for the higher number of individuals that have a medical
marijuana license.
I think Sasha's kind of hit it very hard.
You might add another column, which says just subjectively, do we think it's easy, hard,
you know, impossible, whatever to get a license?
Cause that's, I think, I think Sasha said it directly.
I mean, I tangentially, what I think I hear, you know, in my environment is that, is that
in California, like, Hey, if you're already smoking, how could you make it legal?
Oh, let's go get a marijuana license.
That's what I kind of think happens as opposed to, Hey, I'm in pain.
I tried, you know, painkillers.
Now I want to try, you know, I tried CBD.
Now I want to go to this, right?
So again, I don't know much about the patient process.
So I don't know much about what you do to get, to be a licensed marijuana receiver,
but I think Sasha's, it's gotta be something like that.
There's a huge difference.
And so that would be another predictive variable.
Okay.
So if you just simply went through the 50 states and say, would ask 10 people to say
how, you know, there's a lot of value to getting just, uh, getting a consensus estimate and
stuff, how difficult it is to get a license, get a permit by state.
And so you might get, you might go saying 10 people, where is it easy?
Where is it hard?
And I would make it like high, medium and low.
It doesn't have to be very difficult, not difficult.
Hey, anybody who wants one gets one.
Just some, some breakdown like that would go a long way to explaining this difference.
So your population doesn't change very much.
It might change somewhat by state, but the biggest contributing factor here is likely
to be how easy or how hard it is.
And even though you may look at that and say, well, there's no numbers for that.
You can make something called factors, just, just being able to say it's high, medium or
low, something like that, that would be very useful in explaining the difference between
states.
Exactly.
And so, well, I think Sasha, what you mentioned, I think that's going to be helpful.
So basically we can, you know, start making some assumptions here.
So basically we can say, okay, it's easy to get a license in Oklahoma.
So maybe this is close to like the, like the upper bound actual number of cannabis consumers.
So we can say, okay, you know, maybe almost everyone who consumed cannabis in Oklahoma,
you know, falls into this, these numbers.
And so, you know, it could be the case that in Maryland, that, you know, that about 6%
of cannabis users, they may be using illegal cannabis.
You know, growing their own.
Or not 6%, but more like 80%, but, you know, making up about 6% of the population.
You know, the suggestion that, you know, Sasha's making about, you know, essentially trying
to, I think Steve was saying to quantify the difficulty of obtaining a license as a measure.
You may also look at the different data sources from the time that the application of the
license was made until the time it was issued.
If they keep track of that, just as a, another way of trying to figure out how difficult
it is to, you know, as a proxy of difficulty.
One thing, this is going to be tough to parse, but I believe they have data on, okay, how
much in like tax revenue was brought in from license.
So like how much licensing fees were in there.
I'm not certain that perhaps the NN license, I'm not certain though.
So you may be able to divide that by the actual license cost to try to get the number per
month.
So long story short, when you start to look at, you know, the patients and consumers,
the data gets a little messier.
So we're going to have to be real rigid about any assumptions we make.
But essentially this is just going to be, you know, the beginning of the path.
So it's basically just going to start collecting the data points that are there for patients
and just to try to start putting together, you know, the demand side of the picture.
And then along the way we can, you know, utilize the census and say, okay, you know, in Oklahoma
we can, you know, use the population and, you know, we can use the like an age factor.
So it would be interesting to then just see, okay, can just using a crude panel because
if we add, you know, 30 states or so, or even just 20 states, we can start to get a picture
of, okay, are any of these explanatory variables correlated even just like at a rough level?
And then, like I said, then we can even start to just start to make conjectures.
So we can say, okay, let's just conjecture that every state has cannabis consumers at
the same rate as in Oklahoma.
Then what would you expect, you know, what would you expect sales to be in those states?
And so we could start to just start make estimates.
And so then that there was just estimates, but they may start to reflect, okay, what's
the size of the illegal market?
And so we could say, okay, you know, this is the population in Maryland.
And we could say, so, you know, I'm not certain what the populations are off the top of my
head, but you know, we could start maybe Maryland has a comparable population to South Carolina,
like not certain the population of South Carolina.
But you could start to make comparisons and see, okay, you know, what is the potential
demand if, you know, the whole country legalized and everybody had the same consumption rates
as in Oklahoma, or everyone had the same consumption rates as in another state?
So a couple things you could look at here.
One is that you could say the Oklahoma versus Maryland, the differences between is like
one's like twice the other.
Okay, not 10 times, twice.
Okay.
So that suggests that on average, you know, there may be both are averages.
It does suggest that, hey, there's probably distribution around that average, you know,
for sure there is.
But, you know, that would give you an estimate then of how much per user.
But you can also see that, hey, because it was easy in Oklahoma, a lot of these presumably
already current users said, hey, I'm going to go get that medical thing.
So if you want to try to start to estimate, if you say, if you took the perverse point
that people on medical marijuana, let's say none of them needed it for marijuana for medical
purposes, they just want to smoke it.
Then you could suggest, hey, this is really an indication of how difficult it is to get
a permit, but you can see that a place where it's easy, you might expect, you might expect
as a hypothesis, as you might call it, you might call it a hypothesis that where it's
easy to get a permit, everybody who wants to smoke it for any reason will get a permit.
And so I would suggest going and looking at the states that have the easiest rules and
then see if you can find out what percentage of population there has it because people
might be using the same thing nationwide, might be the same percentage nationwide.
And just a quick search, you can get the state by state policies from someplace called the
cannabis industry or in CIA news state policies.
And so maybe that's a way to kind of go and say, let's pick out the states that are easy
and then go from there.
And putting on my economist hat, the way we can potentially quantify the easiness is just
the price.
It's maybe a crude measure, but just, okay, how many dollars do you have to pay to get
a patient license?
So then one could assume that, okay, so in Illinois, you don't need a patient license.
So that would just be zero.
In Oklahoma, you would use their licensing fee.
And then, and then I'm curious, Heather, would you know what the licensing fee, if there
is or, and if so, what it may be in Maryland?
It depends if you have to renew or not.
So the first time that you come in, it might be like $200 cash.
And the next time you renew as cheap as $100 to 150.
Okay.
So I don't know for certain, but I want to say that in Oklahoma, I actually, I have no
idea.
I'll have to look this up for next week, but I don't think it's 200.
So I want to say it's between 60 and, I want to say it's between 60 and 100, but I'll have
to look this up to be certain.
Yes, it's like, exactly.
And so those are just, those are just two data points.
However, essentially that would just be a step, you know, a fixed state by state variable.
So basically we'll just start, we'll just basically just start adding variables here.
And so it'll just be the, you know, the patient license cost.
And so we'll add this variable, you know, for Maryland.
And then they'll, we'll add whatever, we'll add Oklahoma's fee here.
And so essentially we'll maybe even be able to use those as explanatory factors in, okay,
how many patients are there.
Yeah, but it's really small.
I mean, just not knowing much about your understanding.
You might also want to look at why I had a question first.
Does the patient pay a different price than the illegal price for marijuana?
Oh, I don't know, but that's a great question.
I will ask around.
Yeah, because if patients, if basically if quote medical use of marijuana had a lower
price or a higher price, that would be both, that'd be important.
If it was a higher price, it would suggest that, Hey, that's a disincentive to go get
it.
If it was a lower price or the same price, then, Hey, I want to, I want to join the book
club.
You know, it's cheaper.
It tends to be cheaper in Maryland.
It's like joining in Costco or something.
Right.
Well, we normally have a guest from Oregon and I think he could attest to this.
So essentially I think in Oregon, they're going through measures to try to, you know,
make sure that everybody is operating in the recreational market.
And it's one of the funniest tales because it's basically, you know, they're not, I forget
if it's Oregon, I want to say it is, but I feel they're, they're giving subsidies to,
you know, the cannabis producers.
And so I just thought it was funny where it was just sort of coming, coming full circle
that now something that used to be illegal, you know, strongly illegal.
And now they're, you know, the government's kind of giving subsidies to get people to
do it legally.
So in Oklahoma, you can, oh, sorry, go ahead.
The other thing you probably get numbers on is CBD.
So as I understand it, CBD does not have THC, the primary motivation for recreational people,
I think is the THC.
So if it's a CBD user, then I think what we'll have is that might be a good indication of
the medical use by state.
So I would go get CBD numbers by state if you can, because that, because I think it
will reflect people doing it for true medical reasons as opposed to, hey, I just want to
smoke.
And then, and that's those numbers you probably could get.
And that would also probably help you understand better the distribution of medical users as
opposed to recreational users, because you can look at CBD and then compare that to the
sense of the state that you can easily download.
And you want to get out of Excel because you're going nuts trying to do it in Excel.
Just to be a devil's advocate here.
So from a chemistry standpoint, marijuana has something called cannabinoid.
So CBD is one THC.
There are other ones a little bit more obscure like CBN, et cetera.
And the plant itself, medicinal, medicinally naturally occurring, not plants that are bred
specifically to have high THC, but naturally occurring THC and a plant that has not been
modified.
The cannabinoids work best together when they are used together.
So CBD itself, if you consume CBD, it's guaranteed to have less than like 0.3% or 0.03% THC.
So that it doesn't register on drug screens.
But I think probably some individuals consume THC with their cannabis because it gives them
a more holistic medicinal effect.
Yeah.
I mean, what you say is, I mean, I don't disagree that there are some people, but I think for
most people, they don't get much of a buzz off CBD as I understand it.
And- I would disagree.
I'm so sorry.
I would disagree.
But- Okay.
So I really don't, I'm speculating.
But I don't know how you would- But your position is that there are some people
who say, I'm going to take CBD for the buzz.
I mean, I'm looking for your input or your advice.
You can get quote unquote high, I guess, from CBD, but it's not the same as a THC high.
So the quote unquote high is more medicinal, like calmness, lesser anxiety, things like
that when it comes to altered mental state.
Would you agree, Heather?
Thank you.
Well said.
So along those lines then, you might want to just go, again, ask your 20 people, hey,
do any of you guys just take CBD without taking any THC or supplemental marijuana?
And that would give you an idea then of the, if you will, the medicinal usage of it.
I think in general, and again, I'm speculating here, this is a hypothesis, that the people
who are using it medicinally might not be the same people who use it.
Well, there's a group of people who literally do use it.
They have migraine headaches or something, and they literally just want to get the relief
from the migraine.
They're not looking for the THC.
The THC is in it, well, it's hard to remove THC.
It's a small amount.
They didn't get it for that effect.
And even though it does contribute, it has a specific receptors that it's hitting.
It's a different receptor.
So you know.
Yeah.
You can never have too many data points.
Exactly.
And Sasha, you basically hit on the key here.
And basically, I think what it comes down to is, basically, I'm just not satisfied with
just kind of just seeing these large estimates like, oh, the illegal market's this big or
oh, the legal market's going to be worth this many in sales in 2020.
So it's or in 2021.
So that's why we're going to start essentially collecting these data points ourselves and
then essentially let the data speak for itself.
Absolutely.
Yeah, you never know, you might plot some obscure data point that everybody thinks is
useless and it ends up being the outlier that explains everything.
You never know.
Exactly.
So thanks for bearing with us.
So essentially, this is what we're going to be embarking on in the next week or two is
essentially so on a state by state basis, on a month by month basis.
So that way we can get estimates for 2021 and we can look at the past.
We can see, OK, what are the actual sales that are occurring in the states?
What was their population?
If they have a medical program, how many patients are there?
And then what proportion of the state is that?
And then we can start filling in factors like, OK, what's the licensing cost in that state?
What is the age breakdown in that state?
And so then these will be explanatory variables and depending on the size of our data set
and the amount of variation, you know, we may not really be able to utilize them to
their full extent, or they may not have significant relationships, but it's always worth looking
and we can begin to make plots.
And perhaps there may be things that are striking visually that even if they don't have statistical
significance.
So to the long story short, that's the road we're embarking on.
And I think it'll be fruitful.
And so long story short, we'll put together the cannabis data science estimate of the
size of the cannabis market.
And then we'll start making some, we'll basically make scenarios with various assumptions.
So we're saying, OK, we may even have a calculator like this.
And let's assume that every state consumed similar to Oklahoma, you know, with about
between 8 to 10 percent of the population consuming.
Well then we could estimate the potential size of the entire US market.
Or we may just end up taking the average number of patients once we get even more data points.
So once we have Oklahoma and Maryland and Colorado and Washington and Illinois and where
have you, we can take the average and maybe the average is maybe looking maybe 5, 6 percent
of the population.
So we can play it out and we can kind of we can make our own estimate of, OK, what's the
size of the current market based on the cannabis data science group's estimates.
And then we may even be able to estimate, OK, what is the potentially illegal market
that's occurring that you may see become a legal market gradually.
Because I'll leave you with this, because basically my joke is, OK, as we see this map
fill in, you know, once all 50 states are permitting cannabis, then at that point, then
maybe the federal government may just have to legalize things by default.
So I'm not sure how that works at that point.
So long story short, that's the direction I see things going.
Does anyone else have any comments or thoughts as we round near the end of the hour here?
Well, I just suggest this looks like a market study.
So if you wanted to find other things to leverage, you know, so there's a lot of literature and
so forth about how to do a market study.
That's essentially what we're doing here.
And then I think that there's a whole lot about data science that would start to come
in as to what variables would be predictive and useful.
So you want to see which variations, which things vary a lot.
The ones that vary a lot are often very helpful for explanatory.
And it was things that don't vary very much, whether or not to be very useful data points
because they help explaining things that are varying.
So just a couple things to realize that there's a whole body of knowledge that you can tap
into outside of the narrow marijuana industry to better understand how to approach it.
Exactly.
And so essentially we are doing a market study.
And so because, well, one could imagine if there was federal cannabis laws, then there
could be interstate commerce of cannabis.
And then you could see, then it would be relevant to companies, you know, the multi-state operators
at the moment, but other cannabis companies now, because even if, you know, there was
cross-state, then, you know, you could have your craft growers in Oregon or maybe you
have craft growers in Oklahoma just shipping their products to people all over the country.
They could be shipping their products to New York or Maryland or where have you.
So I'm just laughing a little bit because there's this other thing that's pretty similar.
Cigarettes.
Turns out that the Indian nations are sovereign nations within the United States and they
don't pay federal taxes on their, you know, on their marijuana or on their cigarettes.
It could be, and they, and by the way, these Indian nations ship cigarettes between their
nations already today.
And so you could have a situation where people say, hey, go to your nearest Indian reservation
and you get your marijuana there.
And get by, I haven't, I don't have any insight into that particularly, but you've got, for
example, in Washington state, I know there are big reservations up there and people go
there already to buy gasoline.
So if you want to get something nationwide, well, that might be a way that you can legally
go to the Indian reservation and buy something.
It's not legal to take it off the reservation perhaps.
I can tell you that in Oklahoma, there are several tribes.
So anytime you do anything on a reservation, you have to get approval from, I guess you
would call the tribal board.
So there's a company, a chocolate company called Bedre and they, I think it's the Choctaw
nation.
I can't remember exactly where, but anyways, anytime that company makes any business decision
they have to run it by the tribal board.
Well, this company was going to partner with the cannabis industry to make edibles, chocolate,
et cetera.
But the tribal board would not approve it because they did not want any kind of marijuana
or marijuana derivatives on tribal land.
Well, I can see why they already have an alcohol problem.
But when it does occur, there could definitely be a cash cow for them, no doubt.
Exactly.
So lots of opportunities.
And as you pointed out, there's, you know, things are still getting hashed out.
You know, everybody's deciding, okay.
And even at a local level.
So that's the story in Michigan is the local jurisdictions, you know, different in Illinois.
And so there'll be, so basically people are deciding, okay, who are going to be the participants
and you know, some people may opt out and that's perfectly okay.
And so it'll just be, we're just doing our part to just start collecting data just so
people can have a lay of the land as, you know, as the rules are hashed out.
So that way, you know, people aren't flying blind.
So that way we can know, okay, you know, this is the consumption rate in Oklahoma.
It's similar to state X, Y, and Z. And then you can look at the characteristics in those
states.
So we're just doing our part to help crunch the numbers since data analysis is in high
demand and low supply.
I really appreciate this group and everyone's insights and background and bringing forth
different things that I never even thought about.
So I just want to thank everyone for taking out time to be here today.
Super helpful for me.
Definitely, definitely.
Thank you for coming, Sasha and Stephen and Heather, as always.
So next week we'll look at some more numbers.
This week we were just kind of talking a lot more.
So yeah, so thank you.
And yes, keep your nose, we'll keep you to the ground and let me know if I was talking
about nose, we were talking about terpenes earlier.
So yeah, let me know if anything sparks your interest or comes across your mind and we'll
pick up again next week.
Awesome.
Thanks everyone.
Awesome.
Enjoy your week.
Bye now.
Bye.