"Slow is smooth, smooth is fast" - Conor Mcgregor
Tracking mixed martial arts competitions and betting activity to identify optimal trading strategies. Take a statistical and automated approach to placing wagers on weekly events in an effort to maximize returns and minimize human engagement with detailed analysis.
Create a betting strategy that outperforms generic approaches (random chance, bookmaker odds, etc) and delivers supierior, and uncorrelated, returns to the broader markests.
Bill Benter, a successful horse gambler active mostly during the 1990s in Hong Kong that one of the first to popularize quantitative betting models in a sports context.
Many notable figures are big fans of the UFC and broader MMA, such as Facebook founder Mark Zuckerburg. He would surely be captivated by a data-driven perspective on the sport.
| Name | Link | Description |
|---|---|---|
| UFC Stats | ufcstats.com | Historical UFC fight data and roster |
| Tapology | tapology.com | Comprehensive event and figter data across numerous MMA venues |
| Best Fight Odds | bestfightodds.com | Historical odds for MMA events from a variety of sportsbook platforms |
UFC data currently scraped from UFC Stats site periodically.
Data cleaning process in progress.
Current model is simple logistical regression with variations on outcome (win, KO, submission, etc.).
Future model implementation should also account for public odds.
A few main focus questions:
- What are the main fighter characterics that influence win/lose probability?
- How random are fight outcomes?
- Can public odds markets accurately predict fight outcomes?
- What types of fights are the most predictable/unpredictable?
- Is there enough publically available data to make informed decisions about fight outcomes?
- What is the best model choice for predicting fight outcomes?
- Can I create a specifc ELO system that seems to reflect other MMA ranking systems well?
- FanDuel (NY), BetMGM (NY), Caesars (NY), WynnBET (NY), BetRivers (NY), DraftKings (NY), PointsBet (NY)