High-fidelity analysis revealing how Rocket Lab's constrained payload capacity affects mission efficiency and customer satisfaction

PythonData AnalysisAerospace

The Problem

Rocket Lab's Electron is a small-lift launch vehicle that is the second most launched platform for multiple years in a row, yet its advertised 300 kg payload limit at 500 km low-Earth orbit (LEO) raised investor questions about addressable demand and pricing power. In mid-2024 clients asked for proof: Is Electron's limited capacity a real bottleneck or merely an optics issue? The objective was to quantify historical capacity utilization (payload mass / 300 kg) across every Electron mission to date and determine whether utilization patterns signaled unmet demand or comfortable operating headroom.

300kg
Electron Capacity
22,800kg
Falcon 9 Capacity
76x
Payload Capacity than Electron

“I wanted to dig deeper and find out how often Rocket Lab ran into issues with this limitation. For investors, the results would unlock a conclusive take on whether Rocket Lab was struggling to meet customer requirements or if there was ample demand for small satellite launches.”

The Early Findings

I compiled a master log of >50 Electron flights from Rocket Lab's post-mission documentation which provided ample information including launch site, date, orbital altitude (km), orbital inclination, and the payload mass (kg). Payload mass, the most important variable, was occasionally disclosed, but more often than not required some internet investigating.

To close the gaps, I sourced payload mass from the following sources:

1. Customer websites and press releases (or contracting data for government customers)
2. Satellite-builder data sheets and space flight tracker logs
3. Calculated mass based on the maximum size NASA standards for variance CubeSat and related form factors (6U, 12U, etc.) if nothing else was available

Very early into collecting mission data, I recognized something deeply flawed with my approach; the advertised 300kg capacity isn't universal—it's only for 500km…

A flat 300 kg divisor ignores two key rocket-science realities:

1. Orbital Altitude: Higher target altitude demands more delta-V, reducing usable payload. Most of the fuel is burned getting into orbit, so fuel efficiency increases by orders of magnitude once in orbit.
2. Orbital Inclination: Launching into a steeper inclination sacrifices lateral velocity for plane-change, again cutting payload.

Mission Example: February 19, 2024

100kg
Actual Payload
750km
Orbital Altitude
33%
Capacity Utilization?

At 50% higher altitude than the baseline 500km, the actual capacity had to be significantly lower than 300kg.

What is Orbital Inclination?

Orbital inclination is basically the angle that describes which direction your rocket goes around Earth compared to the equator (the imaginary line around Earth's middle). Think of it like jumping off a spinning merry-go-round, jumping in the direction it's spinning gives you free extra speed, but jumping sideways or backwards means you miss out on that boost and have to work harder to go the same distance. That isn't how physics actually works but neither of us are rocket scientists.

[Orbital Inclination Visualization]
0° (Equatorial)Least Fuel
45° (Inclined)More Fuel
90° (Polar)Most Fuel

Mission details from Rocket Lab's website provided inclination and payload mass, but there was no clear way to calculate the actual efficiency of launches and how much Rocket Lab was “red-lining” their vehicle.

The Key Discovery

After digging through Electron's user documentation, I found the solution: performance curves showing how payload capacity varies with altitude and inclination.

Electron Performance Curves

40056072088010401200100144188232276320Circular Altitude (km)Payload Mass (kg)

Key Insight: These curves revealed significant performance drop-offs due to both altitude and inclination. More importantly, this graph was the solution to calculating significantly more accurate payload capacity for any mission parameters.

The Solution

Using the performance curves, I built a Python interpolation model to calculate precise payload capacity for any altitude and inclination combination.

Methodology

  1. 1Extracted data points from performance curves
  2. 2Created interpolation arrays in Python
  3. 3Applied SciPy 2D linear interpolation
  4. 4Validated against actual mission data

Code Preview

interpolation.py
# Multi-dimensional interpolation
from scipy.interpolate import interp2d
import numpy as np
# Performance curve data
altitudes = [400, 600, 800, 1000, 1200]
inclinations = [40, 60, 80, 98]
# Interpolation function
f = interp2d(altitudes, inclinations, capacity_matrix)
# Calculate capacity for any mission
capacity = f(target_alt, target_inc)[0]

Interactive Capacity Calculator

Calculate Electron's payload capacity for any mission parameters

400km500km1200km
40°60°98°
Typical range: 50-300kg

Key Results

The interpolation model revealed significant differences between simple capacity assumptions and actual mission requirements.

Capacity Utilization Analysis

[Capacity Utilization Chart]
Simple Method Average:45%
Interpolated Method Average:78%
Missions Over 90% Capacity:12 missions

Mission Efficiency Insights

High Efficiency
Rocket Lab consistently operates near capacity limits, indicating strong demand for small satellite launches
Operational Insight
Several missions exceeded 95% capacity, suggesting customers are willing to optimize payloads for Electron's constraints
Market Validation
Results validate the small satellite market thesis and Rocket Lab's positioning strategy
0
Missions Analyzed
0.0%
2017
Time Period
0.0%
0
Orbit Types
0.0%
±0.0%
Model Accuracy
0.0%

Technical Implementation

The analysis pipeline combines data processing, interpolation, and validation to provide comprehensive mission efficiency insights.

Tools & Technologies

Python - Core analysis framework
Pandas - Data manipulation and processing
SciPy - 2D linear interpolation
DuckDB - SQL analytics
Matplotlib/Seaborn - Visualization

Key Features

  • Multi-dimensional interpolation across altitude and inclination
  • Mission validation against actual payload data
  • Capacity utilization analysis and benchmarking
  • Customer analytics and success rate tracking
  • Launch site utilization patterns

Mission Database Explorer

Browse and analyze Rocket Lab's launch history

It's Business Time

1/21/2018

Payload
13.6 kg
Orbit
SSO
Success

NROL-199

2/19/2024

Payload
100 kg
Orbit
MEO
Success

Catch Me If You Can

1/31/2024

Payload
180 kg
Orbit
SSO
Success

Conclusion

This analysis provides a data-driven answer to the key investment question: Rocket Lab is efficiently serving strong demand in the small satellite market, not struggling with capacity constraints.

Key Takeaways

High Utilization
Missions consistently operate near capacity limits
Market Demand
Strong validation of small satellite launch market
Model Accuracy
Significantly improved capacity calculations