<｜begin▁of▁sentence｜>### Instruction:
As a sports agent, you're tasked with maximizing your clients' opportunities both on and off the field. Imagine you represent a team of athletes competing in various sports tournaments. To ensure they have the best chance of success, you need to strategize their match-ups effectively.

You have access to data regarding the upcoming tournaments, including the pairings of teams. Utilizing this data, you aim to create a comprehensive analysis to optimize your team's performance. One critical aspect is understanding the connections between teams and their matchups.

Given the pairings of teams, represented as edges in a graph, you want to employ a tool akin to the `.Biadjacency()` function in igraph, which can generate a bi-adjacency matrix. This matrix will help you visualize the relationships between teams and their opponents.

Once you have this matrix, converting it to JSON format will enable seamless integration with your analytical tools. This allows you to further dissect the data and derive actionable insights to guide your team's strategy for the upcoming tournaments.
### Response:
import igraph as ig
import json

# Create a new graph with 10 vertices and some edges
g = ig.Graph(edges=[(0, 1), (2, 3), (1, 4), (3, 5)])

# Although .Incidence() is deprecated, you can still use .Biadjacency()
b_matrix = g.Biadjacency()

# Typically, you'd convert this matrix to an appropriate JSON structure
b_matrix_json = json.dumps(b_matrix.tolist())
print(b_matrix_json)
<|EOT|>
