Economies of scale can arise from various factors, including optimization of network infrastructure, spreading fixed costs, and implementing batching strategies, among others. A more comprehensive dataset could enhance our understanding of the underlying forces driving these correlations. For instance, different fee structures might be another contributing factor. Take, for example, Optimism, which calculates the transaction size (distinguishing between zero and non-zero bytes, leading to different costs akin to Ethereum) along with associated gas costs. In contrast, Arbitrum dynamically computes the L1 fee based on an estimated transaction size (without distinguishing between zero and non-zero bytes, but charging a uniform 16 gas per byte) and gas cost, incorporating dynamic and flexible additional components. Furthermore, in Arbitrum, a transaction incurs L1 gas fees only if it is part of a sequencer batch and not sent directly to the L2 contract on the L1, whereas this distinction does not seem to apply to Optimism.

In addition to expanding the dataset and considering additional variables, a more rigorous research methodology, such as regression analysis, could help determine whether a causal relationship exists between the variables rather than just a correlation.

Finally, it could be interesting to extend the analysis to other rollups. Base, which gained considerable steam despite representing only about 3% of the market share, is not considered in this analysis as it’s built on the OP Stack and only officially launched on August 9th, 2023. Nonetheless, including Base in future examinations may yield valuable insights, as initial observations suggest that it may exhibit even stronger economies of scale than Optimism.

BASE

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