Analyzing Fantasy League Data
Project Type: Passion/ Hobbies
Tools and Programming Language: Python, Anaconda
Background/ Context
In 2018, a friend of mine reached out to me to join a private group/league he had created on Fantasy Premier League. Being a member of the site, I happily obliged. He had also created a WhatsApp group to make participating in the league more exciting. However, we were less than 15 on the group which made for a mostly boring chat.
At the end of the football season, we had a brief chat on getting more members and adding spice to the group. We concluded on designing a flier, organic advertising and adding stats to boost participation. The group members soon ballooned to 40+ and the onus was on me to come up with interesting stats.
Process
Searching around online, I found some great articles on how to access the official (but not publicly available) API for Fantasy Premier League. Adapting the process, I was able to pull the data from the API. Analyzing the data yielded interesting insights which I shared with the "admin" members of the group. Few of the insights included
Most Wasteful Coach
Most Loyal Coach - Captain
Most Loyal Coach - Team
Best Defensive Coach
Best Attacking Coach
Best Midfield Coach
Financial fair Play Coach
Best Coach for the Game Weeks
Best Coach for the Game Months
Super Star Coach
In order to share these insights and generate good natured banter, we had a bit of fan fare/ red carpet event on the WhatsApp group while encouraging the best performances with gifts.