Get Free Data for Powerful MMA Analytics Projects

How to Easily Access MMA Data for Analytics Projects

Accessing public MMA data is essential for anyone interested in MMA analytics. Whether you’re analyzing fighter performance, predicting fight outcomes, or evaluating match strategies, having access to reliable data is crucial. In this guide, we’ll show you how to retrieve MMA data using R and Python, with a focus on major leagues such as UFC, PFL, Bellator, and others.

Step 1: Utilize R Packages for MMA Data

R has several packages that make it easy to access and analyze MMA data from various leagues.

• fightdataR: This R package allows you to retrieve data from major MMA leagues, including UFC, PFL, Bellator, ONE Championship, and more. You can access fighter statistics, match outcomes, and performance metrics.

Example: Retrieving UFC Data

install.packages("fightdataR")
library(fightdataR)

ufc_data <- get_ufc_fight_data(year = 2023)
head(ufc_data)

Example: Retrieving PFL Data

pfl_data <- get_pfl_fight_data(year = 2023)
head(pfl_data)

Example: Retrieving Bellator Data

bellator_data <- get_bellator_fight_data(year = 2023)
head(bellator_data)

Example: Retrieving ONE Championship Data

one_data <- get_one_fight_data(year = 2023)
head(one_data)

Example: Retrieving Invicta FC Data

invicta_data <- get_invicta_fight_data(year = 2023)
head(invicta_data)

Example: Retrieving Rizin FF Data

rizin_data <- get_rizin_fight_data(year = 2023)
head(rizin_data)

Step 2: Use Python Packages for MMA Data

Python is another popular tool for MMA analytics, and several libraries make it easy to access data from UFC, PFL, Bellator, and other MMA leagues:

• mmadata-py: This Python package allows you to access fight statistics, fighter profiles, and event data from UFC, PFL, Bellator, ONE Championship, and more.

Example: Retrieving UFC Data

from mmadata_py import MMADATA
mma = MMADATA()

ufc_data <- mma.get_ufc_data(year=2023)
print(ufc_data.head())

Example: Retrieving PFL Data

pfl_data <- mma.get_pfl_data(year=2023)
print(pfl_data.head())

Example: Retrieving Bellator Data

bellator_data <- mma.get_bellator_data(year=2023)
print(bellator_data.head())

Example: Retrieving ONE Championship Data

one_data <- mma.get_one_data(year=2023)
print(one_data.head())

Example: Retrieving Rizin FF Data

rizin_data <- mma.get_rizin_data(year=2023)
print(rizin_data.head())

Step 3: Explore Public MMA Data Sources

In addition to using R and Python, several public sources offer valuable MMA data:

• UFC Stats: UFC Stats provides comprehensive data on fighter statistics, event results, and performance metrics.
• PFL MMA: The official PFL website offers detailed fight stats, including fighter rankings and performance data.
• Tapology: Tapology offers extensive fight statistics, rankings, and historical data across various MMA organizations, including UFC, PFL, Bellator, and ONE Championship.
• Sherdog: Sherdog provides fighter records, event results, and other MMA statistics for various organizations.

Step 4: Apply Your Data Skills to MMA Analytics Projects

Now that you know how to access MMA data, it’s time to apply your skills to real-world analytics projects. You can analyze fighter efficiency, predict fight outcomes, or evaluate strategies for upcoming matches.

To take your skills further, explore our MMA Analytics Courses, where you’ll learn to use tools like R, Python, SQL, and Tableau to build cutting-edge MMA analytics projects.

Final Thoughts

MMA analytics is an exciting and growing field, with access to data from UFC, PFL, Bellator, and other leagues. Whether you use R, Python, or public sources like UFC Stats and Tapology, gathering the right data is the first step toward making an impact in the industry.

Ready to master MMA analytics? Enroll in our MMA Analytics Certifications today and start building data-driven projects that will impress fight analysts, coaches, and fans alike.

Call to Action

Start mastering MMA analytics by enrolling in our MMA Analytics Courses.