- I am working on the topic of the rise of the three-pointer in basketball because I want to find out whether the trend will continue or the style of play will evolve back to the dominant big in order to help the reader understand better the trends of basketball’s style of play. I am interested in this because each year three-point records continue to get broken, and I am curious to see whether teams will continue to chuck threes or if eventually they will hit a breaking point and try something new to counteract the three. For example, back in the 1990s and 2000s, there used to be dominant big men, but teams started to counter this by signing big men who could shoot and stretch the floor. The dominant big men had a lot of difficulty defending these players, and slowly the low-post big man started being fazed out of the NBA. The major challenge would be finding statistics that would help me predict the three-point trends for the next few years, which should be very difficult and may make this topic close to impossible.
- I am working on the topic of saves in baseball because I want to find out whether advanced statistics have devalued saves and closers in order to help the reader better understand what statistics are important and which statistics are becoming obsolete. I am interested in this topic because of the recent free agency of Craig Kimbrel, who has yet to be signed despite having a lot of saves, as well as the debate about the value of closers in the MLB. This would be challenging because it is hard to distinguish a players success from the teams success as a whole.
- I am working on the topic of the growth of the sports statistics and analytics industry because I want to find out how it is affecting the sport, league, and players in order to help the reader better understand the significance that analyzing sports data has. This is the most interesting topic I have come up with because it directly relates to what I want to do. I think the biggest challenge would be finding direct correlation between the rise of statistics and analytics in sports and who the players, the league, and the sport as a whole are affected.
- I am working on the topic of advanced statistics and analytics because I want to find out how they are found and why they were created in order to help the reader better understand advanced statistics as a whole (how they are found, why they are useful, how they are implemented, etc.) This is interesting to me because there are definitely advanced statistics that I ahem never heard of, and I am curious to find out all of the things I hope to help the reader understand. The biggest challenge is finding all of the advanced statistics for a particular sport and figuring out why it was created, as well as the formula used to find the stat.
- I am working on the topic of the difference of effort from the regular season to the playoffs because I want to find out whether certain teams and players try harder, statistically, during the playoffs in order to help the reader better understand the difference in play between regular season and playoffs. I am interested in this because I hate when LeBron James coasts through the regular season and then turns it on in the playoffs, and I wanted to know how many teams and players actively do this as well (and why). I don’t think this would be possible to measure, which is a major challenge and will probably prevent me from choosing this topic.
Applied Project Brainstorms:
- Coming up with an Applied Project is difficult for me because most of the things I could do I am already getting credit for within my internship. The one idea I had would be to create my own statistic that could be tracked using live statistics here at PSU. This statistic could either be an advanced statistic that PSU doesn’t track, but that could be found, or a statistic created from scratch that may have some benefit. I have learned how to create formulas and algorithms on the computer using Python, and I would be creating a formula that would take specific inputs (data that we already track) to find a new statistic (data we don’t track).