What Inspired Us

As pretty avid sports fans, we were very curious about how teams can analyze the performance of their players. We stumbled upon the Pythagorean expectations formula which was originally meant for baseball statistics, and decided to implement it into a different sport. In sports, there are often frequent amount of upsets due to teams not knowing if they can perform better or not. Oftentimes, just because a team is winning a lot of games, does not mean they are performing to their full potential. Henceforth, we created this program for sports teams/ coaches to analyze their players better, so they could make necessary adjustments to players and ensure that they are playing to their best ability.

Programming and Implementation

We built our project after hours of brainstorming on an idea. We decided to create a program that would assist professionals in the sports industry or your general sports enthusiast with sports analytics. The basic process of any data science project was used to create this one as well. Firstly we searched the internet for a usable dataset with a large number of datapoints. Once we got that, we cleaned the dataset by removing the unwanted parts of the data and prepared it for analysis. Lastly, used the organized data to calculate, run, and visualize the data after it went through the statistical processes. We used the Pythagorean expectations formula to apply it to a topic that we have a lot of passion for. We were able to combine our love for soccer with technology to develop a program that applies classical statistical concepts to powerful data science. We can use the Pythagorean theorem to identify teams that have either overachieved or underachieved. When looking at a club with a surprisingly poor or surprisingly strong record early in the season, using the theory to determine a team's "expected" winning percentage for the remainder of the year can paint a more accurate picture of how things will play out than merely looking at actual winning percentage.

What We Learned

We had to revise the scope of our project many times, spending literally hours(11 pm-5 am) brainstorming and then figuring out if what we wanted to do was in our skill set. We learned to rein in our expectations after hitting roadblocks, pivoting some of our plans and ideas as we found coding too complex for us to do in 24 hours after quite literally changing our project idea less than 6 hours before it was due. We were able to work under pressure, adapt to strenuous situations, and still managed to create a successful project.

Built With

• python