Still mostly bad news, but there is a light at the end of the tunnel.

By any measure, the United States is on track to experience more infections and deaths from coronavirus than any other developed country on the planet. Using the latest data from John Hopkins, we see that the number of people dying in the US is growing at an unprecedented rate. As of April 9th, 2020, the mortality curve in the United States is both steeper and earlier than anywhere else from the 100th confirmed death.

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This is likely driven by the sheer scale of infections in the United States. …

Update 4/10/2020: Recent data indicates that the US is on a tragic path toward experiencing more infections and mortalities from coronavirus than any other developed country on the planet.

According to the COVID-19 data aggregated by John Hopkins, there are now over 200 thousand cases of coronavirus globally. Unfortunately, the most recent data suggests that things are going to get much worse before they get any better.

In this article, we describe an approach to estimate the number of future coronavirus cases after 1000 confirmed infections using simple statistical distributions. We can do this by assessing how quickly the disease has spread in other countries that have already passed the 1000 case threshold. As specified in the title, the model(s) outlined below may work for most countries. I also provide updated estimates specific to the United States from an autoregressive exogenous AR-X(1) statistical model. …

Update 3/14/2020: The model predictions at the bottom of this article have a mean absolute error (MAE) of 86 between 3/11 and 3/14. That implies that the AR-X(1) model outlined in this paper has been accurate to within an average of 86 coronavirus confirmations thus far.
Update 3/18/2020: Updated statistics are available in a follow-up article.
Update 4/10/2020: Recent data indicates that the US is on a tragic path toward experiencing more infections and mortalities from coronavirus than any other developed country on the planet.

With the CDC and US authorities scrambling to get enough coronavirus tests ready for the public, it would be helpful if we could estimate the number of future COVID-19 cases in the United States on a daily cadence. We can do this by assessing how quickly the disease has spread in other countries. And while the title of this article specifies the United States as the target audience, the models outlined below may work for most countries with more than 500 confirmed coronavirus infections. …

Update 3/11/2020: I’ve posted a follow up that uses the background laid out in this article to create a statistical model that estimates the future number of coronavirus cases in the United States.
Update 3/18/2020: I’ve added a second follow up using more recent data to project growth globally.
Update 4/10/2020: Recent data indicates that the US is on a tragic path toward experiencing more infections and mortalities from coronavirus than any other developed country on the planet.

The Center for Systems Science and Engineering at John Hopkins University recently released a really useful dashboard to track COVID-19’s spread throughout the planet. The service is built using data from multiple sources, including the WHO, CDC, ECDC, NHC, and DXY. Thankfully, they’ve also made that data publicly available on Github. …

What percentage of ownership do investors acquire at each financing stage?

If you’re a startup founder, you may struggle to find a reasonable percentage of equity to give your investors in return for their capital. While this is still something you need to figure out on a case by case basis, here’s some data to help guide your judgment.

Background

Earlier this year my research lab at Radicle released a working paper and online model that makes it easy for anyone to approximate an undisclosed startup valuation. The log-log model we published uses the amount of capital raised by the startup and the financing round’s stage classification to predict the valuation. …

The following is a condensed and slightly modified version of a Radicle working paper on the startup economy in which we explore post-money valuations by venture capital stage classifications. We find that valuations have interesting distributional properties and then go on to describe a classical statistical model for estimating an undisclosed valuation with considerable ease. With that said, we would suggest reading the entirety of this article before using the model. This is not magic and the details matter. With that said, grab some coffee and get comfortable––we’re going deep.

Introduction

It’s often difficult to comprehend the significance of numbers thrown around in the startup economy. If a company raises a $550M Series F at a valuation of $4 billion [3] — how big is that really? How does that compare to other Series F rounds? Is that round approximately average when compared to historical financing events, or is it an anomaly? …

To better understand coin correlations we deployed an Affinity Propagation algorithm and found three distinct clusters of crypto assets, at the top end of the market capitalization table, that move in tandem.

Introduction

A few months ago Radicle’s research team began working on a blockchain composite, not as an investment vehicle, but rather for the purpose of having a clear and unbiased benchmark while evaluating new decentralized projects in the crypto economy. This paper discusses some preliminary statistical work that helped us better understand coin movements. …

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A measure of startup competition.

How can we measure competitiveness in private startup markets? And what does it really mean when a company raises over $100m in institutional venture capital? Lemonade ($120m), Vacasa ($103m), Compass ($450m), Slack ($250m), Reddit ($200m), and Peloton ($325m) — these companies have all recently raised massive rounds of capital, but in the absence of competitive context, isn’t the absolute value of a single round of capital for a random company a bit of a hollow statistic? Where do these capital injections position the aforementioned startups vis-à-vis their unique competitors? These questions have occupied my mind for quite some time.

Economics, more specifically the field of Industrial Organization, has a long tradition of measuring market concentration and competitiveness. However, current standard measures are usually based on realized market shares. In private markets, and for early-stage startups in particular, it’s impossible to assess market share. Not only do startups rarely disclose the information that would allow us to calculate those statistics—often times they’re focused on developing a product idea, and the product isn’t even available for purchase at the early stage. So how should we think about the competitiveness of a market that has millions of dollars invested by multiple VC firms but no market share data? …

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Disclosure: The following is not investment advice, and should not be considered as such. Invest in tokens at your own risk. Members of Radicle’s analysis team hold positions in one or more cryptocurrencies. Those positions do not influence this work.

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Since January of this year, approximately $4 billion (USD) has been raised in Initial Coin Offerings (ICOs). Indeed, ICOs are increasingly not a novelty.

But with China and South Korea’s ban on ICOs, as well as President Putin’s recent mandate for new regulations on cryptocurrencies and ICOs in Russia, we started to wonder how ICOs are distributed geographically and relatedly, therefore, where we might see future regulatory responses. …

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A few weeks ago in an article titled “How much runway should you target between financing rounds?, we discovered that the conventional wisdom of targeting 12–18 months of runway between financing events could be a factor that leads to startup failure. We’re delighted with the positive response to that piece (thanks for the shout-out Crunchbase!), and we sincerely hope it helps entrepreneurs make more informed decisions when assessing their cash runway.

The results of that analysis further sparked our curiosity around startup failure, which is often generalized to be nine out of ten. If we consider that statistic for a moment — it implies that a startup always has a 90 percent likelihood of failure. Without even diving into probability theory and statistical philosophy, I just don’t think that’s actually true. …

About

Sebastian Quintero

Founder, CEO and Chief Scientist @ Invariant Studios

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