followon pro rata

followon-pro-rata

arc beats above · slides in the middle · loops below · scroll → 2 LOOPS

Deck intelligence map

coverage by narrative range · generated from this deck JSON

Narrative range 9 total

Metadata

Components

Metrics

Tools

Frameworks

Beats

Loops

Problem 2 slides 100% 2/2 slides 100% 2/2 slides · 11 hits

— 0/2 slides

100% 2/2 slides 50% 1/2 slides 100% 2/2 slides

— 0/2 slides

Agitate 3 slides 100% 3/3 slides 100% 3/3 slides · 17 hits

— 0/3 slides

100% 3/3 slides 66.7% 2/3 slides 100% 3/3 slides 100% 3/3 slides Solution 4 slides 100% 4/4 slides 100% 4/4 slides · 20 hits

— 0/4 slides

100% 4/4 slides 75% 3/4 slides

every slide · same image gating as the playbook

This page functions as a report cover and introduction, featuring an abstract and the beginning of the introduction section.front_matter

The slide sets up a comparative framework for investment decision-making.frame_problem

The slide defines the simulation parameters, including filtering criteria for 1,218 seed-stage investments between 2014-2017.analyze_data

Open slide detailBeat · AgitateLoop · Cost Of Inaction

The slide details the simulation parameters, assumptions regarding follow-on check sizes, and the resulting performance outcomes.analyze_data

Open slide detailBeat · AgitateLoop · Cost Of Inaction

The slide contains two tables summarizing simulation data for investment strategies.analyze_data

Open slide detailBeat · AgitateLoop · Cost Of Inaction

The slide uses a scatter plot to visualize simulation results based on AngelList platform data.analyze_data

Open slide detailBeat · SolutionLoop · Mece Breakdown

The slide discusses simulation clusters (large, red circle, green circle) and the power law of early-stage investments.analyze_data

Open slide detailBeat · SolutionLoop · Mece Breakdown

Table 3 compares performance of follow-on investments; concludes that performance is largely random with a slight momentum signal.analyze_data