Revisiting our Multiple Linear Regression model for All-NBA teams… were we right?

Greetings. A couple of months ago, I created a Machine Learning model that used MLR (Multiple Linear Regression) which was trained on a dataset that took into consideration a variety of counting and advanced statistics of the last 10 years of All-NBA teams. We then used the current 2023–24 season as a test set and tested the model against it, which outputted the 15 players that would make the All-NBA 1st, 2nd and 3rd team. This was 2 months ago, and like many things in this medley we call life, things change quick. So, now that the NBA regular season has wrapped up for another year, let’s revisit our predictions, and see which players have dropped out of contention, and which players have managed to end the season on a high, shoehorning themselves into an All-NBA team. (Hint: there are some surprises!) <a href="https://medium.com/@chARTsLTD/revisiting-our-multiple-linear-regression-model-for-all-nba-teams-were-we-right-9597eee31661">Read More</a>