Relaunching the Blog: Reintroduction & A New Project
Welcome to The Rishab Report- a blog about my thoughts on football, analytics, music, movies, college, and life in general. Every week I'll try to write two posts on these topics and hope to engage in a great discussion with all of you! Several years ago (mostly in high school), I used this blog to write about the NFL and electoral politics. Since then, life has taken me on quite a rollar coaster and I've had many experiences that I've wanted to discuss. I hope you'll find my perspectives interesting and enjoyable to read as I finish my last semester of college. The topics I write about may change over time, but if you have any suggestions, you can let me know via the comments section. With that in mind, let's begin!
As many of you may know, I've become an avid New York Giants fan- and my fandom really grew in college as I was away from home and wanted to find an outlet to stay close to it. With my passion for the team and analytics, I've spent a lot of time playing around for fun with available d
ata to measure quarterback performance, wide receiver efficiency, and other projects. To me, analytics have been a way of answering questions and telling stories that can explain phenomena that we might not ordinarily think about. Throughout my professional experiences in college, I've worked with very bright people and fantastic mentors who've helped me develop a skillset and several frameworks for analyzing data.
My latest project with football data has been examining the variable of "time of possession" to measure overall team offensive and defensive efficiency. Often discussed as a trait of "good" teams, time of possession can generally indicate how efficient a team is in scoring and in allowing points per game. With this in mind, and a small sample size from the 2018 NFL season so far (a little over 3 weeks of play), I began to examine which teams were scoring the most/allowing the most plays per minute of play. To do so, I collected ESPN's game-data to aggregate total minutes of possession per team and total minutes allowed along with overall points scored and allowed per game. Using other ESPN data to see how many points have been scored by each team so far, I was able to calculate total time of possession and the top teams in overall offensive and defensive efficiency with the perspective of time of possession.
Using the Arizona Cardinals (the highlighted team) here, the Cardinals have scored a total of 20 points through three games and average approximately .28 points per minute played while allowing .68 points per minute played when opposing teams have the ball. The image here is a limited version of my overall dataset that I've been working with so far.
,
With the available data we have so far, the most efficient offenses have been the Chiefs (scoring approx. 1.42 points per play), the Buccaneers, Rams, Saints, and Bengals. All of these teams average over 1 point per minute played whereas the average points scored by any team in the league is approximately .77 points per minute played. As the weeks continue, I will continue to update the model and will attempt to use this data to predict how many points a team will score and allow per game.
For those of you who dislike Excel or math in general, I promise that I'll be covering other topics that won't be as data-intense or crazy 😄. Just wanted to start the revival of the blog with a project I've been working on over the last two weeks. If you have any opinions or suggestions about the blog/the content/the project, let me know via the comments section here! You can also follow me on Twitter via @RishabNJain or @BigBlueThoughts to get my additional perspectives. Thanks so much for reading!!!
As many of you may know, I've become an avid New York Giants fan- and my fandom really grew in college as I was away from home and wanted to find an outlet to stay close to it. With my passion for the team and analytics, I've spent a lot of time playing around for fun with available data to measure quarterback performance, wide receiver efficiency, and other projects. To me, analytics have been a way of answering questions and telling stories that can explain phenomena that we might not ordinarily think about. Throughout my professional experiences in college, I've worked with very bright people and fantastic mentors who've helped me develop a skillset and several frameworks for analyzing data.
My latest project with football data has been examining the variable of "time of possession" to measure overall team offensive and defensive efficiency. Often discussed as a trait of "good" teams, time of possession can generally indicate how efficient a team is in scoring and in allowing points per game. With this in mind, and a small sample size from the 2018 NFL season so far (a little over 3 weeks of play), I began to examine which teams were scoring the most/allowing the most plays per minute of play. To do so, I collected ESPN's game-data to aggregate total minutes of possession per team and total minutes allowed along with overall points scored and allowed per game. Using other ESPN data to see how many points have been scored by each team so far, I was able to calculate total time of possession and the top teams in overall offensive and defensive efficiency with the perspective of time of possession.
Using the Arizona Cardinals (the highlighted team) here, the Cardinals have scored a total of 20 points through three games and average approximately .28 points per minute played while allowing .68 points per minute played when opposing teams have the ball. The image here is a limited version of my overall dataset that I've been working with so far.
,
With the available data we have so far, the most efficient offenses have been the Chiefs (scoring approx. 1.42 points per play), the Buccaneers, Rams, Saints, and Bengals. All of these teams average over 1 point per minute played whereas the average points scored by any team in the league is approximately .77 points per minute played. As the weeks continue, I will continue to update the model and will attempt to use this data to predict how many points a team will score and allow per game.
For those of you who dislike Excel or math in general, I promise that I'll be covering other topics that won't be as data-intense or crazy 😄. Just wanted to start the revival of the blog with a project I've been working on over the last two weeks. If you have any opinions or suggestions about the blog/the content/the project, let me know via the comments section here! You can also follow me on Twitter via @RishabNJain or @BigBlueThoughts to get my additional perspectives. Thanks so much for reading!!!
Comments
Post a Comment