Analysis Of Fantasy Sports Using Spark

Kevin Feasel



Jordan Voiz knows how to get to my heart:

Although the data involved is not large in volume, the types of data processing, data analytics, and machine-learning techniques used in this area are common to many Apache Hadoop use cases. So, fantasy sports analytics provides a good (and fun) use case for exploring the Hadoop ecosystem.

Apache Spark is a natural fit in this environment. As a data processing platform with embedded SQL and machine-learning capabilities, Spark gives programmatic access to data while still providing an easy SQL access point and simple APIs to churn through the data. Users can write code in Python, Java, or Scala, and then use Apache Hive, Apache Impala (incubating), or even Cloudera Search (Apache Solr) for exploratory analysis.

Baseball was my introduction to statistics, and I think that fantasy sports is a great way of driving interest in stats and machine learning.  I’m looking forward to the other two parts of this series.

Related Posts

Installing Apache Mesos On EC2

Anubhav Tarar has a guide for setting up Apache Mesos along with Spark and Hadoop on EC2: Apache Mesos is open source project for managing computer clusters originally developed at the University Of California. It sits between the application layer and operating system to manage the application works efficiently on the large-scale distributed environment. In […]

Read More

PySpark DataFrame Transformations

Vincent-Philippe Lauzon shows how to perform data frame transformations using PySpark: We wanted to look at some more Data Frames, with a bigger data set, more precisely some transformation techniques.  We often say that most of the leg work in Machine learning in data cleansing.  Similarly we can affirm that the clever & insightful aggregation query […]

Read More


June 2016
« May Jul »