In business, it's necessary to rank products and services in order to identify and prioritize the company's strengths and create a strategic advantage.
But how do organizations decide on these priorities? Rankings are sometimes arbitrary, but if subsequent data is analyzed and compared to product information, then interesting correlations can be discovered and managers can make better-informed decisions.
How might that scenario work? Let's look at another ranking system, the 2017 Associated Press NCAA Preseason College Football Top 25 rankings. Here's a link to the Top 25. Is your school on the list? Will it be by the time the season ends?
As an Oracle employee and college football fan, I have the responsibility and pleasure of crunching these numbers. In this case, I analyzed the 2017 AP rankings based on some 2016 stats from ESPN. I know the 2017 rankings aren't based directly on 2016 stats. Numerous things change for a team each year. Players leave. Coaches leave. New people arrive. All those changes are taken into consideration when AP members vote on their Top 25. However, those considerations can be subjective. AP members must guess how the changes each organization went through during the off-season will affect the teams they are ranking. All we can truly rely on are numbers.
The two statistics I will use for this analysis are the 2016 College Football Power Index (FPI) and Team Efficiencies. The FPI is a measure of team strength that is meant to be the best predictor of a team's performance. FPI represents how many points above or below average a team is. Projected results are based on 10,000 simulations of the rest of the season using FPI, results to date, and the remaining schedule. Team Efficiencies are based on the point contributions of each unit to the team's scoring margin, on a per-play basis. The values are adjusted for strength of schedule and down-weighted for garbage time. The scale goes from 0 to 100; higher numbers are better and the average is roughly 50 for all categories.
I pulled these definitions directly from the ESPN college football web page, which can be found here.
For this exercise, I will use Oracle's Data Visualization Desktop and Microsoft Excel. I will document step-by-step how easy it is to take something as subjective as rankings and, through analytics, create objective arguments for and against those rankings.
Why does this matter?
In business, we often make decisions based on gut feelings.. It's time-consuming to perform an analysis, and we don't have the tools we need to make it easy.
This is where a dynamic, sophisticated application like Oracle Data Visualization comes in. All you need to do is upload a spreadsheet and/or connect to a data source and the tool offers hundreds of ways to slice, dice, and present your data in ways you probably never imagined, dynamically and quickly.
Let's get started.
I already mentioned above that I'm using ESPN's website for my data. I went to the web page, copied the data I wanted, and pasted it into an Excel spreadsheet. After some sorting, I was ready to go. Here is the file.
Next, I opened Oracle Data Visualization, uploaded the spreadsheet, and added all my data to a new visualization. As you can see below, this looks like a spreadsheet. In fact, at this point, that's about as useful as my visualization is. Just numbers. No "aha" moment here.
By clicking on the chart drop-down menu, I see all the various kinds of charts at my fingertips.
I decided to go with a radar bar chart. However, as you can see, there are a lot of teams out there and taking this complete view isn’t particularly useful.
After adding a filter, I am finally able to make some sense of my visualization. The image below only shows teams with an FPI of 14 or better.
By removing all of the efficiency data points and sorting my visualization by FPI, I can gain even more understanding. Now, my visualization allows me to see the teams in order by FPI only (for teams with an FPI of 14 or greater).
Perhaps I feel efficiency is a better metric to rank teams. Also, maybe I want to change chart types. The chart below is a tag cloud. It makes analysis very easy by simply using bigger letters for the highest value metrics. This chart shows that Alabama, Clemson, Washington, and Michigan had the highest 2016 overall efficiencies.
Using a DV tool enables very quick, dynamic, user-friendly manipulation of data. No more using archaic spreadsheets to try and glimpse nuggets of learning from your data. DV tools make the information at your fingertips more useful than ever before.
But don’t 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.
Also, if you already have Oracle Data Visualization, here is the .dva file for this analysis.
Password is "NCAA" (without quotes), all uppercase. Enjoy!