Applying data science to understand startups
The Inference and Machine Learning Group is Radicle's data science intelligence unit. We apply modern statistical methods to better understand startups and venture capital.
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The Inference and Machine Learning Group
Applied research on startups and building the state of the art data science tools that power Radicle’s future-focused research and discovery products.
Disruption Discovery Platform™
NLP engines to identify novel areas of disruption and the companies competing for a market opportunity
Investor Cluster Score™
A mathematical measure of the signal produced by the investors in a startup’s capitalization table
Startup Anomaly Detection™
A statistical algorithm to estimate the plausibility that a startup will exit via an IPO or acquisition
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Working papers
An Empirical Perspective on Startup Valuations
Sebastian Quintero
In this paper, we explore post-money valuations by venture capital stage classifications. We find that valuations can be described as being log-normally distributed, allowing us to think of them in terms of mean, median, and standard deviation, as well as build classical statistical models.

We present an OLS log-log regression model as a tool to help practitioners approximate an undisclosed post-money valuation with ease. Our results are statistically significant at the p < 0.001 level for all terms with an adjusted R-squared of 89 percent.

We also describe the results of an equivalent Bayesian model to better understand the uncertainty of our OLS estimates.
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An Empirical Perspective on Startup Valuations

Try it yourself

Select the stage classification of the startup’s most recent financing round
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Enter the amount of capital raised in the stage above
Introducing the Investor Cluster Score™ — a measure of the signal produced by a startup’s capitalization table. - Oct 13, 2017
Introducing the Capital Concentration Index™ - Dec 20, 2017
How much runway should you target between financing rounds? - Oct 26, 2017
Dissecting startup failure rates by stage - Nov 7, 2017