ClashRoyale

Decks used by the top players in the world are recorded using a custom wrapper for the official Clash Royale API. Each card is assigned to a characterizing node that track its usage to other cards. By recieving these decks, a graph network is created, which attempts to model how the cards are used together based on their specific attributes. The node2vec algorithm is implemented to obtain a feature vector for each node (card) in the graph, which loosely descibes how the cards interact with one another. Since the graph network is non-Euclidean by nature, node2vec's feature vectors represent the usage data in a Euclidean space, which can then be passed to a traditional machine learning pipeline. With this, we can not only understand which cards are commonly used in a deck, but more importantly why certain card attributes make a strong deck. This project is currently under development, and is really just an outlet to explore and gain experience in the graph ML space, while investigating a superb game.

Using this data, I'd like to develop a more refined Bayesian Network that makes decisions to minic player behavior.

Usage

The RoyaleAPI acts as an intermediary in converting .JSON responses into networkx graph structures. A JSON Web Token is used for request authorization, so you must pass in your own dev_key, which can be obtained by creating a Clash Royale API account. A proxy solution is also used.

This project aims to provide many functionalities in time for analyzing player trends with graph structures. Here is some demo code to show off the API:

from RoyaleAPI import Client

# save your own developer key to a key.txt file into the RoyaleAPI directory
with open('RoyaleAPI/key.txt', 'r') as file:
    dev_key = file.read().replace('\n', '')
proxy_url = 'https://proxy.royaleapi.dev/v1'

# build a graph representing cards used by the top 10 players in the world
client = Client(token=dev_key, url=proxy_url)
graph = client.create_empty_graph()
top10_graph = client.build_graph(graph, depth=10)

All the regular networkx methods can then be utilized for analysis. Here we can see which cards are most popular amoung the top 10 players:

import matplotlib.pyplot as plt
nx.draw_circular(top_graph, with_labels=True)
plt.show()

Disclaimer

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