I created a Tableau Story to visualize Divvy Ridership Data in Chicago. The story should be visible and embedded below, but it can also be found here.
Divvy Bike Ridership Analysis
Final Project – Tableau Story
The following is a post that I have preserved in its original form. It was the final post that I created for a computer science class at Northwestern.
I created a Tableau Story to display the data that I gathered. The story should be visibly embedded below, but can also be found here. I chose to develop a “Factors” story so that we can see exactly what categories can affect a player’s market value, and which categories have a more significant or nuanced effect than others.
Assignment 1.1 – Preview
The following is a post that I have preserved in its original form. It was the 2nd of 6 posts that I created for a computer science class at Northwestern.
Casual passerbys trying to watch a soccer game struggle to find themselves engaged. More often than not this happens because of how rarely it seems that any action actually occurs in a match. People want to see goals. Score one or more goals than your opponent, and your team wins the game.
However, oftentimes teams will play for a full 90 minutes without scoring a goal at all. This is why teams spend so much money to bring in the right players to help score more goals. In order to figure out if they received a strong return on investment, teams need to be able to measure a player’s performance. Squawka’s 2016/17 Goals Scored table is a great example of how soccer data can be kept and organized.
The site organizes allows the user to sort statistics in a few different ways. We can dissect the LATCH method in order to understand the data. Squawka allows the user to sort by Category – Games Played, Minutes Played, Right Footed Goals, Left Footed Goals, Headed Goals, Other Goals, Goals Inside the Area, Goals Outside the Area and Total Goals. It also allows us to sort Alphabetically – by player name. While the examples are not in this data set, some benefit could be found in sorting soccer data by Location (stadium or national region), Time (chronology of goals in a game) and Hierarchy (professional league performance vs minor league performance.
I hope to dive into those more specific methods in the coming weeks.
Assignment 1.0 – Teaser
The following is a post that I have preserved in its original form. It was the 1st of 6 posts that I created for a computer science class at Northwestern.
I was originally born England. My family lived there for six years before we moved to Chicago in the summer of 2000. While I didn’t live in England long enough to hold onto an accent, much less an understanding of how to play cricket, I did hold onto a love for the game of soccer.
The English Premier League is probably the sport’s most famous league. It produced superstars like David Beckham and Cristiano Ronaldo, and is home to teams like Manchester United and Liverpool. Even for people who are relatively unfamiliar with the sport, those names are generally recognizable. The Premier League is the epicenter of soccer.
For this class I will be studying and dissecting information that is publicly accessible on Squawka, a “web-app that delivers you real-time data on the football match you are watching on TV”. While there is no evident “master” dataset, the service provides multiple smaller ones. I plan to use each of these in class.
To get the ball rolling (pun not intended), in my next post I will discuss an easy-to-understand dataset that makes sense to those with only basic soccer knowledge.