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NBA_Champion

➭ Predicting the last 3 (2020-2022) NBA Champions using Machine Learning.

The NBA playoffs is the postseason tournament of the National Basketball Association (NBA) held to determine the league's Champion. An annual best-of-seven elimination tournament, the NBA playoffs are held after the league's regular season and its preliminary postseason tournament, the NBA Play-In Tournament.
I use historical data on each regular season played by a team to predict the last 3 Champions (2020-2022).
My regression model combined with adjusted ranking metrics correctly predicted ALL 3 Champions!
But what are the stats (features) that have allowed my model to perform so well?

The following picture shows all the work steps that are carried out. I usually combine these steps in a fully automated pipeline, but since this is a side project and my free time is limited, the pipeline is split into 3 files that are executed sequentially.

➤ 1 'nba_html_crawler.ipynb':
  • Parse selected Basketball-Reference (Website) pages and save all relevant pages in html-format.
  • Basketball-Reference
➤ 2 'nba_html_to_mongodb.ipynb':
  • Aggregate the data from the html pages and upload it to my MongoDB Cloud account.
➤ 3 'nba_ml.ipynb':
  • Predict the last 3 (2020-2022) NBA Champions with Machine Learning.
➤ Additional 'dashboard.pbix':
  • PowerBI file with a three charts, all three are featured in the 'nba_ml.ipynb' file.

Article on Medium:

Medium

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nba_champion's Issues

2023 Prediction

Hey there!

I watched a YouTube video by jxmyhighroller featuring your repo and read your Medium article. I must say, I found your work quite intriguing, especially the unique feature classes you've identified. It's a breath of fresh air compared to the typical models and statistical analyses out there, particularly in terms of weight and importance.

I had this idea and thought it'd be cool to test your model on this season's playoffs, even though we're already halfway through. It'd be interesting to see if it holds up and predicts the champion. I'm thinking of tweaking your code and giving it a shot myself, but I believe your expertise would make the process more efficient.

Let me know if you're interested in collaborating on this project. I'd be more than happy to contribute. Thanks again for sharing the article and repo—I truly appreciate it!

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