# Finding Signal in the Noise: A Statistical View of Deal Flow

## (BVC Partner Content via Seth DeGroot)

Feb 26 · 5 min read

Intuitively, one would expect that a 90% selection accuracy rate at distinguishing good from bad investments leads to the same % of winners in the portfolio. However, because this selection accuracy rate is applied on a dealflow that intrinsically contains a very small number of winners, a VC’s accuracy rate doesn’t directly translate to portfolio wins. Furthermore, quality of a VC’s dealflow is directly proportionate to % of winners in said VC’s portfolio.

To better understand the math, let’s make a very simple model based on 3 assumptions:

• Assumption #1: The future success of a startup is predetermined. The VC acts solely as a startup picker.
• Assumption #2: There are only two types of investments: Winners (green rocket) and Losers (red rocket)
• Assumption #3: The unbiased inbound dealflow of a VC has a 5% concentration of winners and 95% of losers.

In this model, the unbiased inbound dealflow of a VC would look like this, with the green (red) rockets representing the winner (loser) investments:

In this model, the quality of dealflow of a VC can be described using one single parameter, the Concentration ©, which represents the % of winners in the total dealflow. The formula of Concentration © would then be:

C = Winners / (Winners + Losers) = Winners / Total Dealflow = 5/100 = 5%

When analyzing and selecting which startups to back, the VC applies a selection process aimed at distinguishing the good from the bad investments. The quality of this process can be described with one single parameter, the Selection Accuracy Rate (A), which represents how many times in %, the VC is able to successfully distinguish a winner from a loser. The formula of Selection Accuracy Rate (A) would then be:

A = Correct Decisions / Total Decisions

Thus, the result of applying a selection process with Selection Rate Accuracy A on the basis of a dealflow with Concentration C is what determines the quality of the portfolio of a VC.

P = Portfolio Quality = Winners / Total Portfolio

In table form:

This process can be schematically described as follows:

# How to improve (P) part 1. Increase the quality of Dealflow ©

Besides improving the Accuracy, the other lever to pull is, obviously, get better dealflow. However, improving the dealflow cannot just mean to “see more opportunities” because as long as the Concentration stays the same, the outcome will not improve. So, to improve the odds, the VC must increase the concentration of dealflow quality ©. Quality dealflow cultivation takes work and largely results from replacing sources of low quality dealflow (investment bankers, low quality angel investors and accelerators, low quality VCs, deal promoters, etc.) with sources of high quality dealflow (high quality VCs, portfolio founders/CEOs, high quality angel investors and accelerators). Schematically, the results of increasing dealflow quality concentration © from 5% to 10% speak for themselves:

# How to improve (P) part 2. Bias the Dealflow ©

Biasing the dealflow is something that many investors consciously or unconsciously already do by specializing on specific sectors, geographies, themes, stage or investment theses. VCs should be wary of implementing the wrong biases because this can dramatically reduce their odds of finding successful investments. The best way to go about it is to be data driven, i.e. to carefully analyze what are the biases imposed by the investment strategy and to simulate how these will affect the universe of portfolios that can be built. At Brightstone, we bias our dealflow by the sectors we look at (Tech and Life Science), by themes (we look for disruptive/defensible technologies attacking large addressable markets with favorable TAM growth trends), by geographies (we focus outside of Silicon Valley), by stage (we focus on early growth stage companies), and by investment thesis (first and foremost we look for teams with prior successful exits and/or significant traction with their current business). Schematically, the results of applying bias to dealflow and the resulting improvement of concentration © from 10% to 18% look like this:

At Brightstone, we estimate that we look at 250 deals per year, per partner for a total of 750 deals annually. Our investment period is five years, thus over the course of a fund we look at 3,750 deals. Over our five year investment period, we placed 14 investments into our vintage 2013 fund, and we’re tracking to a portfolio (P) score of 86%. We were recently ranked by Pitchbook as the third best performing US-based micro-VC fund for our 2013 vintage year. There is more to that success than dealflow strategy (i.e. investment structures, active portfolio management, exit timing to maximize IRR, etc.), but dealflow strategy, in terms of maximizing both Quality Concentration © and Selection Accuracy Rate (A), on the front end of our process has played an enormous part in our success to date.

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