Two denominators, two questions
Trigger frequency is typically bonus triggers divided by completed base rounds. A feature payout distribution uses only the relevant activated features as its sample. Mixing the denominators produces misleading rates.
Examples from live shows
Coin Flip, Cash Hunt, Pachinko and Sweet Bonanza Candyland bonus features can involve different internal interactions. A large feature result does not imply the feature itself has become more likely to trigger.
Build separate tables
Publish trigger hits, base-round count and last trigger timestamp in one table. Place feature payouts and their separate sample count in another to keep comparisons meaningful.
Working with the historical data
For a useful comparison, specify the recording date, timezone, game table and observation window. A historical count without those details can easily be interpreted outside the circumstances in which it was measured. Distinguish theoretical probabilities described in game rules from estimated frequencies measured in a particular data source.
Record-based tools are most useful when they make observations reproducible. If a chart cannot be reconciled with the underlying event history, it needs a data-quality review before anyone treats its values as authoritative.
Frequently asked question
Can you infer the next feature payout from trigger counts?
No. Trigger occurrence and feature payout behaviour require separate models and data.