Select top N * 3 validators for nomination, where N - ledgers amount ĩ. Determine best validators according to the aggregated metric Ĩ. SumSlashing = cumulative slashing that validator received for all timeħ. Where apr = metric mentioned in (1) per week, month, yearĪctivity = metric mentioned in (2) per week, month, year (2 ∗ activityWeek + activityMonth + activityYear) / 100 - 10¹⁸ ∗ sumSlashing, Metric = (aprWeek + 3 ∗ aprMonth + aprYear) ∗ Calculate aggregated metric for each validator: Calculate average era points for validator for eraRange.Ħ. if a validator was in active set in 8 eras for last 16 eras, then its activity ratio is 50%).ĥ. for week eraRange = 28) Įra.commission - validator commission for i-th era Įra.rewards - validator rewards for i-th era Įra.otherStake - validator nominated stake for i-th era Ĥ. Where eraRange - specific era range (e.g. Mid- and long-term data will hedge us from choosing validators who show good performance in the short-term but might be unstable in the long run.Short-term data will show us if some relatively good validator in the long-term goes down or starts to lack in performance.Operational data lets us detect oversubscribing, fees increase and slashing.Then the scoring model considers dynamical changes of validators' performance, e.g.: Gathered data is aggregated and used to build several metrics for different time periods: Here is the data we gather and calculate for each validator and era: Era is a period of time during which there is a specific set of active validators: /support/solutions/articles/65000168050-what-is-an-era. Validator choice algorithm should be not just only transparent itself, but also based on public and provable data for each era.
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