This novel approach could potentially outperform existing methods by forming block templates from mempool transactions to predict fees more accurately. However, considering the dynamic nature of block formation, which completes approximately every 10 minutes, there's an acknowledgment that the composition of these block templates might undergo significant changes during this interval. A question arises about how many transactions, initially considered for inclusion, would eventually fail to be part of the final block.

Further analysis into fee rate estimates reveals inconsistencies, particularly with rates appearing low at the 50th percentile. This prompts a query about whether selecting transactions from the bottom 25th percentile would lead to any significant alteration in outcomes. An important aspect highlighted is the discrepancy between the transactions in users’ mempools and those accepted by miners, potentially due to miners sourcing the majority of their transactions through channels other than the peer-to-peer network.

The conversation suggests a solution to improve fee estimation accuracy by integrating data directly from the blockchain. This implies that leveraging comprehensive blockchain analytics, beyond just mempool state information, could refine the process of estimating transaction fees more effectively.