Using Oracle Analytics for fun: analyzing FIFA 19 players!
I assume I'm not the only one who enjoys playing Fifa now and then. If you are playing it, probably you are one of those guys who wants to get the best talents out there, increase their overall and sell them for good money. Or keep them and win everything. It’s up to you.
You can do that by playing several seasons, and check how each player evolved. But also you can do it in a fun way, using the best Analytics platform out there: Oracle Analytics Cloud + Autonomous Data Warehouse! Start with FIFA, you will get the knowledge to apply the same principles anytime, anywhere.
Getting the data
Analytics without data is not possible, so we should start by checking this dataset on Kaggle. You can find there almost everything we need: players, clubs, wages, contract ending date and all the other attributes we are searching for. Data is coming directly from FIFA 19 database, thanks to Karan Gadiya
You will get the dataset in a ~2MB .csv file. Go ahead and download it.
Making the data useful
Of course, we could use directly the .csv file, but I’m planning to be professional about this and I will use only the best in class tools out there. You can anytime use the same architecture for your super important, mission-critical enterprise analytics workloads. Don’t know about you, but I’m really serious when it comes to FIFA!
Using your Oracle Cloud account, you need to create an instance of Oracle Autonomous Data Warehouse. We will use it to import our data and run our analysis. I’m not going to talk about why we are using ADW and not other database-as-a-service, it’s a no brainer: wouldn’t you want Cristiano Ronaldo/Leo Messi in your team? Like those two, depending who you like more, Oracle ADW is out of this planet. It will take years for others, if they will, come close to what ADW can do.
With your database service up and running, we need to import our .csv file. For this step, we will use SQL Developer, the swiss knife of database developers. Start counting. In under 4 minutes, you will have your dataset in your ADW.
First things first, you need to download your wallet. We are going to use it to connect remotely to our instance.
Importing data to ADW from our .csv file is a wizard-driven process. Should not take more than 30 seconds, while you will correct some column names (you are not allowed to have spaces).
You have your data up there (in the cloud), SQL developer is connected, all you have to do is to query it. Of course, you can run your scripts directly through SQL Developer, but remember that we will use the best-in-class tools for beautiful analytics. What beautiful means? Oracle Analytics Cloud, of course.
Making the data pretty
Oracle Analytics Cloud is a scalable and secure public cloud service that provides a full set of capabilities to explore and perform collaborative analytics for you, your workgroup, and your enterprise. Based on what you are planning to use it for, you can deploy it with different functionalities. Check the official documentation in order to create your service.
Once you can access your instance, we will use Data Visualization to import, modify and visualize data. Check the animations to see exactly what we are going to do, you can follow the steps with no kind of previous expertise.
Connecting OAC to DWH is very simple, just follow the graphical wizard. You need to provide some info about your connection, afterward, you are ready to explore your dataset.
We can visualize our dataset, but what if we want to check a column which doesn’t exist? We can create a Data Flow, which basically means you can operate complex data manipulation without having to write any SQL statements. We will create a simple GAIN column ( which is equal with POTENTIAL-OVERALL) to highlight how much a player will evolve in time, but in the same way, you can apply advanced techniques like regression or other machine learning algorithms.
We will use GAIN column to sort our players. With Data Visualization you can do any sort of charts, pivots, forecasts etc. I’m mapping the players based on their nationality. You can then start searching for whatever kind of attribute you would like to find.
What’s next?
You can grab my project from GitHub, and by following the steps in this article you can start planning your FIFA career. Getting back in the real world, you can use the same techniques and services to gain valuable insights for your business, your customers or your hobbies. With Autonomous Services you are minutes away from gaining valuable information.
Meanwhile, you should check Kangin Lee (Valencia CF), A. Meret (Napoli) and E. Barco (Atlanta United). For all the others, go ahead an find them on your own by following the steps described above.
It’s really easy to get started: just use our Oracle Cloud trial and follow this article. Who said data science cannot be fun?
