This is an update to my previous post on finding over/undervalued receivers based on the realtionship between signal-callers and their receivers. A higher drafted quarterback, presumably, will throw for more yards and touchdowns than one drafted lower. Receivers are the ones catching the ball and accumulating those yards, touchdowns and fantasy points. By analyzing the relationship between our assessments of a team’s quarterback and receivers, we can see which part of the equation is undervalued versus the other.
Last week my Metis Data Science summer cohort completed and presented our second project. Unlike our first project, this was a solo effort. We were coached up on Python scraping tools Selenium and Beautiful Soup, sent out to find meaningful conclusions from movie data using linear regression.
This past week I began a more intense journey as part of the Metis Data Science summer cohort, culminating in our first project: How can we use various data sources to find the ideal subway stations to promote the annual summer gala for WomenTechWomenYes (WTWY), a fictional non-profit focused on increasing the participation of women in technology?
This an analysis uses current MyFantasyLeague MFL10 ADP to determine which teams have the most opportunity for rookie wide receivers.
This analysis builds off the work from Chase Stuart at Football Perspective for determining a better calculation than traditional score differential.