Playing Around With LLM For Idea Generation In MMA Analytics

I'm Lazy & Busy at times, so maybe this will include my productivity.

I’m been playing around with LLMs, like everyone else has, and feel it could help me get back to posting on here which I’d like to but have been busy. A quick example of something helpful is the output below of asking GPT-3.5 for article ideas:

1. Striking patterns analysis: Analyze the frequency, efficiency, and diversity of striking techniques used by fighters in different weight classes, skill levels, and fight styles.

2. Ground game analysis: Analyze the frequency and success rate of takedowns, submission attempts, and ground-and-pound techniques used by fighters in different grappling styles and positions.

3. Fighter performance analysis: Develop a statistical model to quantify fighter performance based on factors such as win-loss record, knockout rate, submission rate, significant strikes landed per minute, and takedown accuracy.

4. Scouting and game planning: Use machine learning algorithms to analyze fighter performance data and identify key strengths and weaknesses, enabling coaches and fighters to better prepare for upcoming opponents.

5. Fan engagement analytics: Analyze social media engagement, viewer demographics, and other metrics to identify trends and patterns in fan behavior, providing insights to promoters and advertisers for improving the fan experience and increasing revenue.

I’d love to get started on 1 of these 5 ideas, or other ideas I’ve come up with in the past. Time will tell!