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Chatter Charts – Visualizing Real-Time Fan Reactions

Jake (@ChatterCharts) Community Tags: , Leave a comment

Today, I’ll explain the methodology behind Chatter Charts and show you how I use statistics, R and Python to analyze hockey from a completely unexplored angle: your point of view.

I. Introducing Chatter Charts

Chatter Charts is a sports visualization that mixes statistics with social media data. And unlike most charts, it is specifically designed to thrive on social media; it is presented in video and filled with volatility, humour, and relatable moments.

It assumes a game is like a linear story—filled with peaks and troughs—except every story is written by fan comments on social media. It actually tries to recreate the emotional roller coaster fans tend to experience when watching sports.

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But most people don’t know about the math and code behind Chatter Charts. It isn’t just me picking words I think are funny or a simple word count—it uses a topic modeling technique called TF-IDF to statistically rank them.

I want to go through that with you today.

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A crowdfunding initiative to promote diversity at the Columbus Analytics Conference

pflynnhockey Community, Conferences, Uncategorized Leave a comment

I’ve been fortunate enough to be able to attend the last three years of the RIT Sports Analytics Conference. The first year I went, I was nervous to meet people whose work I admired. I was afraid that nobody would want to talk to this new person that few people knew and who was just starting to learn about the field. 

I could not have been more wrong. 

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How to Get Started in Hockey Analytics

Alex Novet Community, Practical Concerns, Resources 4 Comments

Intro

Analytics, so hot right now. But how do you get started? People from all sorts of background and levels of expertise have contributed valuable work to hockey analytics, but the journey can feel daunting.

In this post, I want to lay out my personal advice for what knowledge and skills are needed and how to get them. Your mileage will vary, but I think much of this will be useful to anyone who is interested in starting to do their own analytics research or writing.

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An Introduction To New Tracking Technology

prashiyer97 Community, Neutral Zone Analysis, Passing Data, Resources 4 Comments

The first significant breakthrough in hockey analytics occurred in the mid-2000’s when analysts discovered the importance of Corsi in describing and predicting future success. Since that time, we’ve seen the creation of expected goals, WAR models, and more. Many have cited that the next big breakthrough in hockey analytics will come once the NHL is able to provide tracking data. We’ve already seen some of the incredible applications of the MLB’s Statcast data and the NBA’s SportVu data. Unfortunately, the NHL has no immediate plans to publicly provide this data and as such, many analysts have decided to manually obtain the data.

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Hockey Analytics Data Sprint Wrap Up

Josh W Community, Conferences 2 Comments

On Saturday, November 4th, we hosted the first ever Hockey Graphs Analytics Data Sprint.  The idea was teams had 6 hours to take raw data and do something interesting with it as a trial for the Vancouver Hockey Analytics Conference. Local teams met up at La Casita here in Vancouver, but we also had online participants as well.

Thanks to all of the people who helped put it together, and thank you to all those who participated, especially those who travelled from as far away as New York.

In this post we link to the finished results and you can see the winners.  Their work is in a github repo which you can use for your own data analysis!

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