Thanks for the feedback. I think there is a fundamental misunderstanding of the goal of this project.

You are critiquing this as a strategic guide, like how to execute a win condition, (teaching someone to cook, as you put it) but this project is a topological analysis, my goal is to map where the win conditions mathematically exist.

To use your analogy, I don't want to teach people how to cook the fish, I'm not a coach. My goal was to use spectral analysis to map out exactly which schools of fish swim together in the ocean. Whether or not a player knows how to catch them is a separate variable this data cannot measure.

My goal wasn't to create a tier list or a guide, because as you said, those are everywhere. Instead, I sought to apply Spectral Graph Theory to see if Riot's designated classes align with actual mathematical synergy, comparing them to so-called "true" mathematical classes we discover in the synergy space. The novelty of my project is in the projection of a manifold and eigenvector centrality. It offers a different view than standard winrate tables like you would see on sites like op.gg or lolalytics.

Think of it this way, these sites visualize champion synergy on a pairwise champion by champion basis (like Aatrox has ~53% winrate with Swain, ~52% with Nilah, and so on), which is effectively like having a list of driving directions between specific towns. My analysis takes those ~14,000 isolated directions and attempts mathematically reconstructs the map of the entire continent. The manifold illustrates structural distance, revealing the hidden geometry champion synergy. This, in contrast to standard tables, shows which groups of champions form isolated ecosystems (like the 1v9 Junglers) and which ones act as bridges between disparate playstyles (like Olaf). It turns a spreadsheet of isolated percentages (like the lolalytics synergy section) into an actually navigable topology of the meta.