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Business Forecasts and Fantasy Football Projections

Chris Garcia
Director of Marketing

In business, we use data to make predictions. Financial forecasts, quarterly pipeline reports, and market outlooks are a few examples of using data to project where our business (and industry) will be in the future.

But analyzing data to gain a glimpse into the future is challenging. Massive amounts of data that contain information around your customers, your industry, and your company can be hard to rein in and tame. This is especially true if you don't use tools that are designed to cut up data and make it understandable. Companies that make investments in tools that help them visualize the future are better prepared for it.

For example, the NFL season is upon us. There's a good chance your inbox has an email from one of your friends asking you to join a fantasy football league. You're thinking about it. But how do you make the best choices at your draft party? Easy! There's no shortage of websites offering their NFL fantasy projections for the upcoming season. So you simply go to Google, pick a website, copy, and paste its projection data into a spreadsheet, and voila! You have a spreadsheet with lots of numbers.

As an Oracle employee and football fan, I have the responsibility and pleasure of crunching numbers. I went out to the ESPN website and found this page. I will use Oracle Data Visualization to slice and dice my way through these numbers and make sense of them.

Let's get started.

Here is a spreadsheet I put together with the data from the ESPN website. I included only the top 200 players for simplicity's sake. I then uploaded the spreadsheet in Oracle Data Visualization and began my analysis. After selecting the data I wanted to see for yardage, and adding a background to make it look nice, I see this:

These are the top passing, rushing, and receiving projections for 2017. This can be very useful when selecting your players.

Let's say I want to focus on touchdowns. I simply remove "yardage" from the values field and select "touchdowns."

After a few clicks, I see the data I need. Here's a better view:

As you can see, Aaron Rodgers is expected to throw the most touchdowns, David Johnson is supposed to have the most rushing TDs, and Jordy Nelson should have the most TD receptions.

Each of these graphs has its own filters. You can use filters in this manner, or you can apply them to all the graphs in your project—it's up to you and depends on how you want to analyze your data.

Let's say I want to see the data a bit differently and change to a "Radar Bar" graph. Also, let's suppose I want to see passing yards again but only for quarterbacks who are expected to throw 25 or more touchdowns. After a few quick clicks, I see this:

This is the value of using a data visualization tool. The quick, dynamic, user-friendly manipulation of information. No more using archaic spreadsheets to try and glimpse nuggets of insight from your data. Oracle Data Visualization tools make the information at your fingertips more useful than ever.

But don't just take my word for it.

If you like what you see, visit www.oracle.com/goto/datavisualization to learn more about Oracle Data Visualization and get your free trial.

If you already have Oracle Data Visualization, here is the .dva file for this analysis.

The password is "NFL" (without quotes), all uppercase. Enjoy!

 

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