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Reading: NCAA Data Visualizer for March Madness Face-Offs
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SmartData Collective > Big Data > Data Mining > NCAA Data Visualizer for March Madness Face-Offs
AnalyticsData MiningData VisualizationModelingPredictive AnalyticsR Programming LanguageStatistics

NCAA Data Visualizer for March Madness Face-Offs

DavidMSmith
Last updated: 2013/03/24 at 8:04 AM
DavidMSmith
2 Min Read
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If you’re laying down a friendly bet on the March Madness games or just tweaking your fantasy roster, this NCAA Data Visualizer by Rodrigo Zamith will be a boon. Just choose two teams to compare head-to-head, choose an attribute to compare them on. You can look at more than a dozen invividual player attributes (e.g. points scored, assists, 3-point shots made) or over 20 team attributes (e.g.

If you’re laying down a friendly bet on the March Madness games or just tweaking your fantasy roster, this NCAA Data Visualizer by Rodrigo Zamith will be a boon. Just choose two teams to compare head-to-head, choose an attribute to compare them on. You can look at more than a dozen invividual player attributes (e.g. points scored, assists, 3-point shots made) or over 20 team attributes (e.g. fouls, wins, and turnovers), and compare against other top players or NCAA team averages.

For example, here’s how the top players match up for individual points scored for tomorrow’s Oregon/Saint Louis game:

NCAA data visualizer
All of the data used was scraped from the NCAA website for the 2012-2103 season. Rodrigo created the visualizations using the R programming language, which means he can used some advanced techniques like the boxplots above. Rather than just showing the median or highest/lowest scores for each player, he can show all of each player’s games, and highlight the bulk (the middle 50%) of their games using the box. But you can still see, for example, that Saint Louis’s Kwamain Mitchell had a couple of hot streaks last season and was in fact their highest scorer last season — a fact that you might miss just looking at averages.

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You can play with the NCAA data visualizer and choose your own teams and stats to compare at the link below.

Rodrigo Zamith: Visualizing Season Performance by NCAA Tournament Teams (via Myles Harrison)

TAGGED: r language
DavidMSmith March 24, 2013
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