Curriculum·S202 Token Analysis and Tokenomics·about 31 min
Distribution and concentration
By the end of this lesson you can
- →Read a holder list correctly by classifying addresses before counting them
- →Explain why address count is a poor concentration measure and what to use instead
- →Compute the outcome distribution of a launch from wallet-level data, and state what an average participant should expect
- →Identify concentration that is deliberately disguised, and the on-chain traces it leaves
Sophomore · enrolled learners
This lesson opens with LIBRA, February 2025, read as a distribution problem.
- What happened
- A token launched on Solana on 14 February 2025 and was promoted the same day from a head of state's verified account. Market capitalization reportedly reached around $4.5B before falling roughly 95 percent. Analysis of the wallet-level data by Nansen found that about 86 percent of participating wallets lost money, with roughly 114,410 wallets carrying combined losses estimated at $251M or more, while 36 wallets each cleared over $1M and insiders withdrew more than $107M of liquidity during the collapse. F111-03 uses this incident to teach that verifying an identity is not diligence; this lesson uses the same data to teach what a concentrated distribution does to everybody else's outcome.
- The decision point
- The endorsement was the story and the distribution was the mechanism. Thirty-six addresses out of 114,410 captured the upside, which is about three in ten thousand, and the liquidity removed during the fall exceeded the total losses of most participants combined. None of that required the promotion to be insincere; it required the supply to be held by a small number of parties who could sell into whatever demand the promotion produced.
- Recorded loss
- $251,000,000
What you will be able to answer
- →What is the first step in reading a holder list?
- →Why is address count a poor concentration measure?
- →What did LIBRA's wallet data show?
- →What traces does disguised concentration leave?
Orientation and Year One are open: anyone can read them without an account. From Year Two onward the lessons are for enrolled learners, because progress through the later years only means anything if it is tracked against a record.
It is free. We do not sell the list and there is nothing to buy at the end of it.
Sources and review
- https://www.coindesk.com/markets/2025/02/20/libra-memecoin-fiasco-destroyed-usd251m-in-investor-wealth-research-shows
- https://en.wikipedia.org/wiki/$Libra_cryptocurrency_scandal
- https://www.coindesk.com/tech/2025/04/30/inside-movement-s-token-dump-scandal-secret-contracts-shadow-advisors-and-hidden-middlemen
- https://public.bnbstatic.com/static/files/research/low-float-and-high-fdv-how-did-we-get-here.pdf
Confidence medium·Volatility medium·Reviewed 2026-08-05·Owner unassigned
Contested
F111-03 owns the endorsement and identity-verification reading of LIBRA. This lesson owns the distribution reading, using the same verified wallet data. Keep the split; a revision that starts arguing about the promotion here belongs in F111-03.
LIBRA loss estimates range from about $251M to $400M depending on methodology and window, and the widely quoted $4B figure is destroyed market capitalization rather than money lost. Legal proceedings were open at the time of writing and no culpability is asserted.
Address clustering is inference. Common funding and coordinated timing are strong signals and are not proof of common control, and legitimate patterns produce false positives. Report clusters as hypotheses with the evidence attached.