One of my favorite videogames is pokémon as you can probably guess from the title. As a player I always wanted to optimize my chances of obtaining the rarest and best pokémon in the game. I have been working on an application that aims to use graph theory to analyze the game Pokémon Blue. The application explores the following questions:
Which is the rarest pokémon in the game?
Where can I find an specific pokémon and with what probabilities?
What is the place with most different species of wild pokémon?
I also included algorithms for the following: Given a certain desired team
- Find the minimum amount of places to visit to catch them and return the list of the places the player will need to visit.
- What are the routes with best probabilities to catch each pokémon from my desired team?
Check out my application at: https://www.maplesoft.com/applications/view.aspx?SID=154565.
The following are some of the results obtained in the app:
What is the most common pokémon?
I did not only considered the amount of places a pokémon can appear in but also the probabilities of it appearing in each place.
What are the connections between pokémon and places?
In my graph, I connected a pokémon and a place if such pokémon could be caught in that place. The following is an example for the pokémon Pidgey. The weights of the edges are the probabilities of finding Pidgey in each route.
Viceversa, I did the same for how a route is connected to the pokémons in it:
Map of the Game
I also generated a colour coded version for the map of the game: where blue means that the place is a water route, brown means it's a cave and green means it's a tall-grass route.
It's amazing what Maple's graph theory toolbox can do.