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How to Analyze Major League Baseball Statistics Using Oracle’s Data Visualization (DV) Desktop with the Quadrant Plugin
Posted on April 5, 2017
Author: Doug Ross, Performance Architects

One of the lesser known but powerful capabilities of Oracle’s Data Visualization Desktop (DV) tool is the ability to extend the available visualizations with plugins from Oracle’s BI Public Store.  Within this website, Oracle provides a variety of data analysis and visualization plugins that can be easily installed on a user’s local client installation.  Those plugins are then visible immediately within DV Desktop.

Oracle BI Public Store

In this example, we will add a new visualization of a style called “Quadrant” into DV Desktop and demonstrate the features. The Quadrant visualization plots dimension values within a sections of a rectangular grid (quadrant) using two different metrics – one for the X axis and another for the Y axis.  It is similar to a scatter chart but with the dots of the scatter chart being replaced instead with the actual names of the dimension values within the grid.

The process of installing the plugin begins by clicking on the “Quadrant” icon in the Oracle BI Public Store.  This will display a brief description of the plugin and a link to download the zip file.

After the download completes, the zip file is copied to a plugin directory under the user’s DVDesktop local application directory.  For example, for the Administrator user, the directory would be:

C:\Users\Administrator\AppData\Local\DVDesktop\plugins

Once the plugin zip file has been copied to the above directory, start or restart DV Desktop and the Quadrant visualization will be immediately available to use in new or existing DV Projects.

In the example that follows, the existing “Sample Order Lines” data source will be used to demonstrate the Quadrant visualization. First, select “Profit” and “# of Customers” metrics, along with the “Product Category” dimension column. Right-clicking brings up a menu that allows for picking a visualization, and from there the new Quadrant plugin can be selected.

The resulting visualization shows the three product categories placed within a default 3×3 grid with the X and Y axes designating “High” and “Low” ranges for each of the selected metrics.  In this example, the “Furniture” and “Technology” product categories each have a low number of customers relative to “Office Supplies,” while “Technology” has the highest profit.

By replacing the “Product Category” attribute in the panel properties with “Product Sub Category,” we can see a more detailed breakout of the dimension values that represent the relationships between those subcategories that have higher profits and higher customer counts compared to those that don’t.  In the example below, we can see that “Binders” and “Telephones” are high profit, high customer count areas while items like “Bookcases” and “Envelopes” have low profits and few customers.

The only properties that can be changed on the Quadrant view are the number of rows and columns in the grid.

Here’s an example of a 5×5 quadrant using the metrics “Profit” and “Sales” along with the “City” attribute.

As a further example, we’ll look at a real world set of data to determine any interesting patterns using the Quadrant visualization plugin.  Using 2015 Major League Baseball batting statistics, we’ll break out players into quadrants using their “Home Run” and “Stolen Base” statistics to determine which players show the best combination of power and speed.

In the visualization, we can see that players like Paul Goldschmidt and Manny Machado are good power/speed combination players. Notice how with some additional filters applied to the minimum number of HRs and SBs, and increasing the grid to 10×10 we see a clearer picture of who the top players are. Note that when there are too many attribute values to display fully in a quadrant box, the number of unique values is displayed instead.

In conclusion, it is worthwhile to examine all of the visualizations that are in the Oracle BI Public Store to determine if there is a useful application to your own data.  Within minutes you can visualize that data in new and interesting ways.

 

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