I specifically used a quote for “random bot”, because I am not talking or discussing about bots (I was replying @FritzS about random play, which is poorly defined), but pure theoretical and mathematically defining “strategy” in a general framework. A “random bot” that you can interact with here is simply a set of instructions/conditions that are simple enough for statistical analysis.
I’ve made a post years ago about treating Go games as a high-dimensional lattice walk (route), this is just the extension and work I’ve been working on since. Which I’ve discovered that we need to separate “game rules” (defining legal routes), “conditions/instructions” (probabilities of taking which routes) and “prior knowledge about opponents”(reactions about knowing opponents’ probabilities of routes) in order for any pure theoretical framework to function and have a base for discussion. And by doing so we can find some very fundamental concepts like “playing a random move” is not really that simple at all (because, as I explained in my previous post, just-playing-a-move, and playing-a-legal-move are different, since the simpler just-play-a-move allows random walk on the lattice has a uniform distribution of probabilities on all intersections, but the latter shrink and limit the probabilities to only legal move options, and disallow self-filling and self-atari, making the path to terminal conditions even faster)
The takeaway if you will, is that your idea of a “game-plan” or conditions/instructions you want to follow (like you want to avoid capture play in x amount of moves) reduce and limit the path you can take in the total possible games, and compare to a “random bot” of various known conditions, you should have a very high chance of winning statistically with different random strategies (regardless of the validity and usefulness of these strategies, they can still win game against something).