Okay, so I wanted to make my fantasy basketball team better, you know? I was kind of tired of just guessing who to pick each week. So I thought, “Why not use some tech to help me out here?” That’s how I started this little project to build a fantasy basketball team analyzer.
First, I needed to get all the player data. I found this cool site that has all the stats for NBA players. I used Python and this library called Beautiful Soup to scrape the data from the site. Basically, I wrote a script that goes through the website, grabs all the stats like points, rebounds, assists, and stuff, and puts them into a format I can use.
After getting all the data, I needed to clean it up. You know, some data was missing, some was not in the right format… just messy stuff. I used the Pandas library in Python to do this. It’s pretty good for handling data like this. I made sure all the player names were correct, the stats were numbers, and all that jazz.
Next, I wanted to analyze this data. I started by calculating some simple stuff – averages for each stat, totals, and so on. Again, Pandas was super helpful here. I could easily group the data by player and calculate these stats. I also made some graphs using Matplotlib to see which players were performing well in different categories.
Then came the tricky part. I wanted to create a score for each player that would tell me how good they are overall for fantasy basketball. I decided to use a weighted sum of their stats, where each stat like points or rebounds is multiplied by a weight. Figuring out these weights was tough. I did some research and found some common strategies that fantasy basketball players use. I also tweaked them a bit based on my own experience.
Once I had the scoring system, I applied it to my data. Each player got a score based on their stats and the weights I set. I could now rank the players based on this score, which was super helpful.
The final step was to make this all usable. I built a simple user interface using Streamlit. It’s this neat Python library that lets you create web apps really quickly. In this app, you can select different weights for the stats, and it will recalculate the player scores and show you the rankings. You can also filter by position and see the top players for each position. So I could easily see which players are good to pick up.
Here’s what I ended up with:
- Data Collection: Used Python with Beautiful Soup to scrape player stats from a website.
- Data Cleaning: Cleaned up the data with Pandas, handling missing values and formatting issues.
- Analysis: Calculated averages, totals, and made some graphs with Matplotlib to visualize performance.
- Scoring System: Created a weighted scoring system to rank players based on their overall fantasy value.
- User Interface: Built a simple web app with Streamlit where I can adjust weights, see player rankings, and filter by position.
It was a fun little project. Now, instead of just guessing, I have a tool that helps me make smarter decisions for my fantasy basketball team. It’s not perfect, but it’s way better than what I was doing before!