Parameter Description

• n_days: Loockback period. If set to 0, start_date and end_date are used.

• start_date: Data collection start date.

• end_date: Data collection end date.

• binning: It is used to bin the empirical CDFs. Higher binning means higher smoothness of the plots.

• max_fee: Is used as a cut-off to make CDF plots visible.

Dynamical Evolution

The average fee per slot for validators is influenced by market conditions and the size of the validator’s stake. Market conditions impact transaction volume and fee levels, while stake size affects the likelihood of being a leader and earning fees during certain periods. The distribution of fees is also asymmetrical, meaning that sporadic high-value events can bias the metric. Analyzing the trend of the percentiles over time is crucial to account for these dependencies and identify patterns, outliers, or anomalies, enabling more accurate assessments of validator performance and the ecosystem’s economic dynamics.

Number of Non-Vote Transactions

The PDF of transactions per block is a key metric for evaluating a validator’s efficiency in building larger, more transaction-dense blocks. Validators that maximize the number of transactions per slot significantly enhance Solana’s throughput and scalability, directly contributing to a higher TPS (Transactions Per Second). For users, this is critical: efficient validators ensure faster, more reliable transaction processing, improving the overall network experience.

Since TPS depends on user activity and network congestion, we analyze the evolution of the p25, p50, and p95 as summary statistics of the underlying PDF, providing deeper insights into transaction distribution and validator performance over time.

Cumulative Distribution Function

The Cumulative Distribution Function (CDF) represents the probability that a variable takes a value less than or equal to a given threshold. Unlike the PDF, which shows density, the CDF provides cumulative probabilities, making it ideal for intuitive distribution comparisons. By mapping values to probabilities, the CDF highlights differences in data behavior over a range rather than isolated peaks. This cumulative perspective is particularly useful for detecting trends, assessing shifts in distributions, and comparing datasets directly, as it encapsulates the entire dataset's behavior in a probabilistic framework.

Once the dynamic evolution dependency has been clarified by earlier graphs, analyzing the CDF of each entity provides deeper insights into comparative performance and trends.

In the following three graphs we report the overall trend of the value extracted in each block, that is the sum of the fees and tips accumulated through Jito. In fact, to determine which solution extracts more value, it is necessary to take into account all the components in play.