  Maximal Extractable Value (MEV) searching has gained prominence on the
Ethereum blockchain since the surge in Decentralized Finance activities. In
Ethereum, MEV extraction primarily hinges on fee payments to block proposers.
However, in First-Come-First-Served (FCFS) blockchain networks, the focus
shifts to latency optimizations, akin to High-Frequency Trading in Traditional
Finance. This paper illustrates the dynamics of the MEV extraction game in an
FCFS network, specifically Algorand. We introduce an arbitrage detection
algorithm tailored to the unique time constraints of FCFS networks and assess
its effectiveness. Additionally, our experiments investigate potential
optimizations in Algorand's network layer to secure optimal execution
positions.
  Our analysis reveals that while the states of relevant trading pools are
updated approximately every six blocks on median, pursuing MEV at the block
state level is not viable on Algorand, as arbitrage opportunities are typically
executed within the blocks they appear. Our algorithm's performance under
varying time constraints underscores the importance of timing in arbitrage
discovery. Furthermore, our network-level experiments identify critical
transaction prioritization strategies for Algorand's FCFS network. Key among
these is reducing latency in connections with relays that are well-connected to
high-staked proposers.
