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Is Passing or Running More Potent for FSU?

The Seminoles have been on one heck of a roll lately. They have been winning by wide margins all season. I’m sure a lot of people are wondering what is Jimbo Fisher’s secret? Is it the recruiting, the defense? What is he doing? Does he dance naked in Indian chief regalia every night chanting to the football gods?

So in this spirit of curiosity, we tried to look into Fisher’s impressive high output offense. We ran two simple correlations between passing yards and points scored and rushing yards and points scored. And we found something interesting.

We found a positive correlation of .58 between rushing yards and points scored, meaning the more rushing yards the Seminoles have in a game, the more points they score. However, we found a negative correlation of -.22 between passing yards and points scored. Technically this means the more passing yards, the fewer the points scored.

Rushing Correlation to Points = .58
Passing Correlation to Points = (-.22)

But common sense tells us this is not the case, though. There is no way a better passing offense hurts a team.

There are several explanations for this. Maybe the more the team passes the ball, the more turnovers they have? Maybe they have a harder time in the red zone when passing? Maybe rushing is Fisher’s secret? When looking at the data, the Seminoles’ passing yards numbers are still respectable even when they have a day of break out rushing. So, it would seem that the rushing offense is supplementary to the passing offense.

Either way, Jimbo Fisher needs to continue what he’s doing. He has turned a so-so team into a powerhouse and the reigning chieftains of the ACC. We will see if he can take his team all the way! Enjoy the chart below.

Post Contributed by @mrdanieldean

Jobs of Florida’s State Legislators

Many people outside of “the process” do not realize that Florida’s legislature is a part-time body. As most readers of this post will know, Florida’s lawmakers are “citizen legislators” who hold regular jobs outside of the Capitol.

What jobs are typical of legislators? Do professional differences exist between the parties? Does the House or Senate have more lawyers? We scraped data from the Clerk’s manual, categorized the jobs listed by industry, and found some interesting items.

- The top three overall fields represented in the legislature are legal, business and real estate.

- 1 in 4 legislators are lawyers and they account for 32% of Democrats and 23% of Republicans.

- Business people comprise 20% of the Republican Caucus but only 9% of the Democratic Caucus.

- Each party has six members who come from education related fields.

Explore the interactive chart below where you can hover over any area for more info and filter using the drop downs. To see full details (member name, occupation, etc) simply click “Show Data” icon in the mouse over box.

As always, if you like it, please share it.

SlideShare: Lessons From Ancient Greece on Big Data

8 Big Data Terms Every Policymaker Should Know

In the last few years, a long-brewing technology trend has begun to bubble up into the policy-making process at all levels of government. That trend is called “Big Data”, and industry experts expect policymakers will be dealing with the questions it raises over the next decade.

Big Data offers the potential to drastically increase our quality of life (self driving car) but is also raises questions about privacy and security (NSA snooping). The response of policymakers to the questions raised by Big Data technology will have an impact on every American business, from Google to Publix, and every American citizen, from high school students to cancer patients.

The six terms below are a primer on the lingo used in the Big Data discussion.

Data Science: An emerging field that combines statistics, computer science and business analysis to gain insight from data. Google’s chief economist has called data science the sexiest job of the next decade. Florida Poly will offer degrees in data science when it opens next year.

Big Data: A term that describes data sets of massive volume that change rapidly and come from a wide variety of sources. Big data sets are so big that they cannot be maintained on a traditional database and require new methods to process and search. Big data has applications ranging from the self-driving car to decoding the human genome.

Data Mining (Undirected Discovery)*: The methods used to explore big data sets for patterns, trends and relationships between data. A data mining project seeks to find the most compelling relationships in the data as a whole. Organizations use the insights extracted from these data mining activities to improve business functions, discover new trends, or explain the causes behind certain happenings in the business.

Analytics (Directed Discovery)*: Closely related to data mining, however the primary difference is that analytics tend to focus on improving a single business area or answering a specific question. Example: determining what key factors drive sales of a certain product.

Predictive Analytics: Using data to build a mathematical model that forecasts a future event. Example: an airline using data about certain parts to predict when they may be about to fail.

Business Intelligence (BI): A collection of key data sets of known significance to a business or organization. These key data sets are often formatted into charts, graphs and gauges on a “dashboard” for easy reference by decision makers.

Datatization: The increasing trend of everyday activities being digitized and recorded through sensors and WiFi internet connections. It is estimated that more data was created in the last two years than in all of preceding human history. The smart phone is the primary agent of datatization in everyday life.

*There is debate in the data science regarding the exact meanings of the terms “data mining” and “analytics.” Some even suggest ditching the term data mining completely because of its negative connotation. 

Click image to see a larger versionThe Real World of Big DataThe Real World of Big Data via Wikibon Infographics