CRM and Data visualization
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.
The following is a post that I have preserved in its original form. It was the 3rd of 6 posts that I created for a computer science class at Northwestern.
In our 3rd and 4th classes we briefly discussed what it would be like to create a site structure for a professional sports team. While at first this was difficult, we soon learned that we could write out sentences that could help us divide up the website based on what we would want to know. For example, imagine an advertisement posted by the team:
“Manchester United’s next game will be against Brighton and Hove Albion on Friday, May 4th at 2:00PM CT at Falmer Stadium. United will be playing for 2nd place in the Premier League, so make sure to order your favorite player’s shirt now so that it arrives to you in time for kickoff!”
Given that this advertisement is directed a user that the club considers to be their own, we can unpack these two sentences to see what relevant information it contains, and whether it could be broken into separate categories. For emphasis I have highlighted unique pieces of information below:
“Manchester United’s next game will be against Brighton and Hove Albion on Friday, May 4th at 2:00PM CT at Falmer Stadium. United will be playing for 2nd place in the Premier League, so make sure to order your favorite player‘s shirt now so that it arrives to you in time for kickoff!”
Out of these two sentences, I can begin to list information that I should provide on the team’s website. Visitors to the website will want to see who the next opponent is. They will want to see what time they are playing, and whether the game is home or away. They will want to see up-to-date Premier League standings, and where the team falls in the standings. They will want to order a new jersey online, and they will probably want to see player profiles where they can learn more about individual members of the team.
Given the above, we could build out entire categories for Next Match, Standings, Shop and Player Profiles.
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.
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.