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Revolutionizing Soccer with Metrics.<h5>ODSC Exclusive interview with Michael Caley, Famous ESPN columnist</h5>

Revolutionizing Socc...

A common aspect of soccer is that the final score isn’t always representative of the game and of who actually “won” it. If Team A and Team B draw 1-1 in a match where Team A had three times as many shots on target as Team B did and Team B scored because a lucky penalty call, […]

Clustering The Beautiful Game: Part Two

Clustering The Beaut...

Introduction This is the second part of our “Clustering The Beautiful Game” series in which we use unsupervised learning to derive the soccer player positions and roles from FIFA stats. In the first part we dived into the data and the attributes, and underwent an initial round of clustering to determine the general positions. In this second part, I extracted […]

Kaggle FIFA Stats

Kaggle FIFA Stats...

The phenomenon of the “yearly sports game release” is a well established tradition in the videogame industry. The biggest is, perhaps, the FIFA franchise, reigning supreme leader in its niche, simulated soccer, for most of its over twenty year history. EA Sports released the latest iteration, FIFA 17, a few weeks ago to the usual […]

Win, Lose, or Draw: Where NBA Fans Spend $

Win, Lose, or Draw: ...

With the NBA Conference Finals underway, I thought it might be interesting to take a look back at the 2015-16 regular season attendance numbers. Which road teams were the best draw? Which teams need to re-engage their fan base? In what possible way was Kobe better than Steph? We will answer these questions and more […]

Uncovering Corruption on the Tennis Court

Uncovering Corruptio...

You have probably heard by now about Buzzfeed’s investigation into match-fixing in tennis. Based on the betting numbers of 26,000 matches, John Templon and Heidi Blake unearthed some of the dark practices in international tennis tournaments. The original data and methodology was recently made public on BuzzFeed’s github repo. And the actual code the journalists […]

How to Track NBA Player Movements in Python

How to Track NBA Pla...

In a series of three posts, Savvas Tjortjoglou analyzes the NBA data: from Visualizing the NBA Draft, Creating NBA Shot Charts to tracking NBA player movements. Each post is in the form of a Jupyter Notebook with very detailed code walkthrough in Python. You’ll be able to plot Avery Bradley or Isaiah Thomas movements through […]