Before a single token trades, experienced investors already have a verdict on your project. They are not guessing — they are scoring. Every allocation table, every cliff schedule, every vesting curve tells a story, and investors know exactly which chapters to read first.

We built a tokenomics risk score engine that evaluates five distinct factors, each weighted by its impact on long-term token health. Then we ran all four default templates in the Build My Tokenomics designer through it. The modeled results reveal what investors actually look for — and where even well-designed tokenomics can trigger a flag.

The 5 Factors and Their Weights

The scoring engine evaluates tokenomics across five dimensions. Each factor produces a sub-score from 0 to 100, then gets weighted to reflect its relative importance to investor decision-making.

1. Insider TGE Unlock (25% weight)

This factor measures how much of the total supply is unlocked for insiders — team, advisors, and private investors — at the TGE unlock event. The higher the insider TGE percentage, the higher the risk that early holders dump on the open market immediately after launch.

Investors treat this as a trust signal. A project that hands insiders large liquid positions at TGE is asking the market to trust that nobody sells. Most investors have seen that trust broken too many times.

2. Short Cliff Periods (25% weight)

The cliff period is the lockup window before any tokens begin vesting. Short cliffs mean insiders gain access to their tokens sooner. This factor penalizes schedules where the average cliff across insider categories falls below the 12-month benchmark used by the scoring engine.

A cliff under 6 months raises eyebrows. A cliff under 3 months raises alarms. The scoring engine measures the average cliff duration across all insider allocations and scales the penalty accordingly.

3. Inflation Rate (20% weight)

Some tokenomics models include programmatic inflation — minting new tokens over time, often to fund staking rewards or ecosystem incentives. This factor scores the annualized inflation rate projected over the first five years.

Moderate inflation (1-3% annually) scores low on the risk engine. But when inflation climbs above 5%, the dilution effect on existing holders becomes meaningful, and the score reflects that.

4. TGE Circulating Supply (15% weight)

This factor measures the total percentage of supply circulating at TGE — not just insider tokens, but everything: public sale, liquidity, airdrops, and any other immediately available tokens. Very high TGE circulation can create overwhelming sell pressure on day one.

The threshold is not binary. The engine applies a gradient: designs below 20% TGE circulation score well; designs above 50% start triggering this factor more heavily.

5. Concentration Risk (15% weight)

Concentration risk measures whether any single allocation category holds a disproportionate share of the total supply. When one bucket — whether it is team, community, or treasury — dominates the allocation table, the token’s future depends too heavily on how that single group behaves.

The engine flags allocations where the largest single category exceeds a threshold relative to the total supply.

How the 4 Templates Score

We ran the Standard DeFi, Community DAO, Venture-Backed, and Fair Launch templates through all five factors. Here is what the designer shows:

Every template lands in the Conservative range (scores of 9-11 out of 100). That is intentional — these templates are designed as starting points, not stress tests. But the breakdown by factor reveals how different design philosophies create different risk profiles.

What the Numbers Reveal

Standard DeFi: Balanced but Slightly Front-Loaded

The Standard DeFi scenario carries a 1.5% insider TGE unlock with an average cliff of 8 months. Its TGE circulating supply sits at 17.5%, and the maximum single allocation is 20%. The modeled results show 41 unlock events over the schedule with 1 cliff-drop event.

The Insider TGE factor scores 10 — not zero, because insiders do receive a small unlock at launch. The Short Cliffs factor at 33 is the highest sub-score in the table. An 8-month average cliff, while reasonable, is shorter than the other templates. This is where an investor’s eye would land first.

Community DAO: Low Insider Risk, Inflation Trade-Off

The Community DAO template shows just 0.3% insider TGE unlock with a 10-month average cliff. TGE circulating supply is 14.0%, and the maximum allocation is 30%. The model produces 41 unlock events and 1 cliff-drop.

The standout here is the Inflation factor at 30 — the highest single factor score across all four templates. The Community DAO includes a 3% inflation rate over the first 5 years to fund governance incentives. Every other factor scores low, which is why the total still lands at 11. This is the key lesson: a single elevated factor does not necessarily mean high overall risk. Investors weigh the full picture.

Venture-Backed: Higher Insider Exposure, Offset by Long Cliffs

The Venture-Backed template has a 2.5% insider TGE unlock — the highest across all four templates — producing an Insider TGE factor of 17. But the average cliff is 10 months, which keeps the Short Cliffs factor at 17. TGE circulating supply is 16.7% with a maximum allocation of 25%. The schedule includes 40 unlock events and 1 cliff-drop.

This template demonstrates a common trade-off: higher insider participation at TGE, balanced by longer lock-up periods. Investors see both sides of this equation.

Fair Launch: Maximum Day-One Liquidity by Design

The Fair Launch template shows 0.0% insider TGE unlock — the only template that completely eliminates insider tokens at launch. The average cliff is 9 months, with TGE circulating supply at 58.5% and a maximum allocation of 40%. The schedule is compact: 12 unlock events and 2 cliff-drop events.

The TGE Circulating factor fires at 17, which makes sense given that 58.5% of supply is available from day one. But this is the entire point of a fair launch design — broad distribution from the start. An investor evaluating a fair launch project would expect this factor to be elevated. Context matters.

The Concentration factor scores 0 despite 40% sitting in the public sale allocation. This means the 40% figure falls just under the engine’s threshold, but it is worth noting how close it sits to the boundary.

How Investors Use This Information

Sophisticated investors do not simply reject any project above a score threshold. They look for mismatches between stated philosophy and actual structure:

• A project claiming “community-first” but scoring high on Insider TGE
• A project claiming “long-term aligned” but showing short cliffs
• A project claiming “deflationary” but carrying hidden inflation mechanics

The risk score is a conversation starter, not a verdict. When the modeled results show a factor is elevated, investors ask why. The answer matters more than the number.

Try It With Your Own Design

The templates above are starting points. Real tokenomics require customization — different allocation splits, different cliff durations, different vesting curves. The Build My Tokenomics designer lets you adjust every parameter and see how each change affects the 5-factor risk score in real time.

Knowing what investors check before they check it is the difference between answering their questions and being caught off guard by them.

Methodology

All data in this post was generated using the Build My Tokenomics risk scoring engine. Each of the four default templates (Standard DeFi, Community DAO, Venture-Backed, Fair Launch) was evaluated at a 1 billion token supply with moderate market assumptions. The five risk factors — Insider TGE Unlock, Short Cliff Periods, Inflation Rate, TGE Circulating Supply, and Concentration Risk — each produce a sub-score from 0 to 100, then are combined using the stated percentage weights (25%, 25%, 20%, 15%, 15%) to produce a total score. Risk levels are assigned based on the total: 0-33 is Conservative, 34-66 is Moderate, and 67-100 is Aggressive. No external market data or price assumptions were used. All figures represent modeled outputs from the tool’s default configurations.

For educational and illustrative purposes only. Not financial, investment, or legal advice.

All numbers in this article were generated by running Build My Tokenomics' tokenomics engine with the specified parameters. No data was fabricated or estimated. This content is for educational purposes only and does not constitute financial advice.

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For educational purposes only. Not financial, investment, or legal advice. See Terms of Service.