Data Visualizations

• who is my audience 
• what value does the visual add 
• what type of visuals am I creating 
• options for effective visuals 
• scope 
• business role 
• time horizon 
• customization 
• level of detail 
• point of view 
• scope 
• broad 
• displaying information about the entire organization 
• specific 
• focusing on a specific function, process and product 
• business role 
• strategic 
• provides a high-level, view of performance
• level of detail 
• high 
• low 

• types of measurements 
• categorical ( groups or categories) 
• ordinal variables 
• rank order 
• not equal intervals between scores 
• continuous variables 
• information 
• inferential statistics 
• bivariate analysis 
• multivariate analysis 
• measurement levels guide the choice of bivariate tests 
• predictive analytics cycle 
• data access 
• find data of people who have similar skills and get them interested in the sport based on that and teach them those skills
• exploration
• data cleaning 
• statistical analysis 
• modeling 
• validation 
• implementation 
• correlation 
• if there is a relationship between the two variables and the direction of that a relationship is positive or negative 
• a positive correlation is when the correlation coefficient is 
• assumptions and cautions 
• the correlation coefficient is only an index of the linear relation between two variables 
• correlation does not imply causality 
• correlation does not queal causation 
• excel correlations 
• correlations are an efficient way to asses the relationship among many numerical variables at once 

Integrated Application Paper

Aaron Rodriguez

Dr. Benjamin Corbett

Quantitative Analysis in Sports

February 25, 2020

            In my work and internships, the fundamental concepts from the course has been applied to increase fan experience. One way it was used is to increase the usage of guest relations kiosks by using old reports of problems and running an analysis in order to understand where most complaints were and placing a kiosk in that area in order to service the most people. Other uses of statistics were to properly stock suites and understand the spending habits of those guests. This helps our team provide the right services to our guests to provide a consistently enjoyable experience. During conversations at work course topics have come up when talking about the new SoFi stadium. During conversations, we would talk about how the seats are being sold and how they are strategizing to sell more tickets. Statistical analyses are used to understand who has already bought tickets and who would be the best people to sell to based on the current information. During informational interviews, it has come up when I spoke with Steven Smith (Manager of Corporate Sponsorships at the Los Angeles Rams) we spoke about his use of data in his work. In his work, he provides data to brands such as impressions and reaches. They have a partnership strategy team that also provides data such as disposable income, how many fans bought a certain product, and will use that to decide what brands to partner with. Overall, data and analytics play a large role in sports because it provides information to tell managers more about the consumes that they are targeting to increase sales and experiences. This information tells interested parties whether it be a team or a brand to make decisions to be successful.