Modeling Bitcoin Mining Revenue and Risk from Hash-Level Probabilities

Summary

The document proposes an ex ante statistical framework for estimating Bitcoin mining economics. It models each hash as a Bernoulli trial, with the chance of success determined by Bitcoin block difficulty, and derives expected revenue per unit of hash rate from that setup. The framework also estimates downside risk under different scenarios and the probability of upside profit for fleets of different sizes.

The authors report that empirical calibration closely matches previously reported observations and say the model supports comparisons across hardware, mining pools, and operating conditions. This probability-based approach aims to represent uncertainty more directly than assessments based on historical proxies. The document does not provide specific parameter values, scenario results, or a detailed treatment of electricity prices and other operating costs, so it is not enough on its own to assess the profitability of a particular mining operation.

Key ideas

• The model treats each hash as a Bernoulli trial with success probability linked to block difficulty.
• It derives expected mining revenue per unit of hash rate.
• Scenario analysis covers downside risk and upside-profit probabilities across fleet sizes.
• Empirical calibration is reported to align closely with previously reported observations.
• The provided description lacks specific assumptions and operating-cost details needed for a project-level assessment.

Tags

# Expected Revenue, Risk, and Grid Impact of Bitcoin Mining: A Decision-Theoretic Perspective


# Expected Revenue, Risk, and Grid Impact of Bitcoin Mining: A Decision-Theoretic Perspective









Most current assessments use ex post proxies that miss uncertainty and fail to consistently capture the rapid change in bitcoin mining. We introduce a unified, ex ante statistical model that derives expected return, downside risk, and upside potential profit from the first principles of mining: Each hash is a Bernoulli trial with a Bitcoin block difficulty-based success probability. The model yields closed-form expected revenue per hash-rate unit, risk metrics in different scenarios, and upside-profit probabilities for different fleet sizes. Empirical calibration closely matches previously reported observations, yielding a unified, faithful quantification across hardware, pools, and operating conditions. This foundation enables more reliable analysis of mining impacts and behavior.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.