DataSense8 Research
Staking Fund Portfolio Model: ROI and Makeup Forecast
A portfolio model for staking funds and backing teams: how much the fund is likely to earn over the next months, how much makeup it will carry, and how wide the range really is. Runs in your browser; nothing is sent anywhere.
Who it is for
Funds that plan cash flow and makeup across a portfolio of players. The model answers three planning questions: what range of profit to expect over the horizon, how likely the fund is to end it in the red, and how much makeup will still be outstanding.
We do not rate players and do not advise whom to stake. The model works with the ROI the fund enters: your own estimate, from your own data.
Results
Fund profit over the horizon
Makeup at the end of the horizon
Methodology
- Result of a month
- For each player and month the model draws the net result R from a normal distribution with mean ROIᵢ · ABI · N and standard deviation sd · ABI · √N, where N is tournaments per month and sd is the standard deviation of one tournament result in buy-ins. This is the normal approximation of a sum of many independent tournaments.
- Player ROI
- Each player’s ROIᵢ is drawn once per run from a normal distribution around the portfolio average with the spread you enter. The spread stands for two things at once: real differences between players and the error in the fund’s ROI estimate.
- Makeup
- Settlement is monthly. A losing month adds |R| to the player’s makeup and the fund carries the loss. A winning month first pays down makeup, all of it to the fund; the rest is split, the fund keeping its share. This is the standard “50/50 after makeup” deal when the share is 50%.
- Markup
- If the fund resells part of the action, it carries only the unsold part of results and earns resold share × (markup − 1) on every buy-in played.
- What counts as profit
- Cash only. Outstanding makeup is money the fund has already lost; it is shown separately and not added to profit, because it is recovered only if the player wins it back.
- Speed and repeatability
- Up to 10,000 runs, computed in your browser in well under a second for typical portfolios. The random generator is seeded: the same inputs and seed reproduce the same numbers, so a result can be shared by link.
Simplifications to keep in mind
- Normal approximation. Tournament results are heavily skewed: a few deep runs carry most of the profit. Over hundreds of tournaments a month the monthly sum is close to normal; with small volumes the model understates the chance of a single large score and of long dry spells.
- Players are independent. Shared schedules and the same fields make results somewhat correlated, which widens the real range.
- ROI and volume stay constant over the horizon. No fund costs (coaching, salaries, data), no fees or currency costs beyond what is already inside ROI.
- ROI is an estimate with error. The standard error of ROI is roughly sd / √n: with sd = 7 and 1,000 tournaments it is about ±22 percentage points. How many tournaments it takes to estimate ROI is worked out in a separate piece, linked below.
How to estimate sd from your own data
Take each tournament result in buy-ins, (prize − buy-in) / buy-in, for a player or a group with a similar schedule, and compute the standard deviation. Use several thousand tournaments: a handful of deep runs moves the estimate a lot. Smaller fields give lower values, very large fields and knockout formats higher ones.
Not advice
This model is an illustration for planning. It is not investment, financial or legal advice, and it does not predict any individual result. Past results do not guarantee future results. The outputs depend entirely on the inputs you enter. The fund is responsible for its own decisions and for following the rules of the platforms where its players compete.
Want the model on your real data?
The tournament selection audit builds the forecast from your fund’s own results: ROI by segment with confidence ranges, losing segments, makeup forecast, and a dashboard you keep.