Baseball Team Salary and World Series

Software

Software used is Microsoft Excel.

Source
Finding Major League Baseball salary through http://www.usatoday.com/sports/mlb/salaries/2014/team/all/ .

Data Extraction
Extracting the data with a simple copy and paste to a notepad.

Process
The process of this was a simple copy and paste to a notepad. After that I upload the file to an Excel and cleaned the data and sorting the data to my preference. The next step was sorting the data by “Salary” largest to smallest for each year. After that filling the team with proper visual analytics colors.

Analyzing the Data
The visualization shows MLB team salaries from each year from 2004 to 2014. Each column represents the lowest (bottom) to the highest (top) paid salaries. All the World Series Championships winners (highlighted orange) are aligned horizontally to easily compare relative team salaries.

I’ve also wanted to show a simple bar chart from 2004-2006 graph made with excel to compare the different visualization techniques.

Both charts have its’ pros and cons. If I wanted something to pop out with a quick glance the first graph would be better, but if I wanted detail data the second graph would be a better choice.

The question we have to ask “Does a higher salary equate to a better chance or more World Series Championships? “

Twitter Tweets Social Media NODEXL

Software

Software used are NODEXL an open source for social media analytics and Microsoft Excel.

Source
Twitter has become a big source of big data text analytics and a big social media platform.

Data Extraction
The process of extracting tweets from http://www.ifttt.com using my own formula of if new tweet then create a new document.

Process
The process of this you must have an twitter account to link up with http://www.ifttt.com account that lets you create your own recipes of “if -> then” statements. I created a statement of gathering twitter tweets with hashtags of #kershaw (LA Dodgers pitcher). He currently won the CY award and I knew I would be able to gather a good amount of social media data. My “ifttt” account collects #kershaw tweets from users and updates the tweet to my google drive account. After gathering all the data and converting the file to an excel file to upload to NODEXL.

Analyzing the Data
Each node is a completely different color and shape indicating a twitter user. The tweets with circle are just normal tweets not mentioning anyone or replying towards anyone. For this case people are just tweeting #kershaw on their twitter account. The tweets with a line and arrow are tweeting towards someone else, which in this case is tweeting towards “ClaytonKersh22” with twitter hashtag of #kershaw. As you can see the area of the map with a lot of concentrated arrows pointing towards ClaytonKersh22’s account.

It just shows how easy it is to gather data from a social media source and use the data gathered and create some sort of explanation of social media data.