Last year, we created machine learning models that nailed the All-NBA teams. This year, we’re using even better methods to predict the All-NBA teams.
Last season, we correctly predicted the MVP with four machine learning models. This year, we’ll take a better approach to predict the 2020 MVP.
Following the end of the 2018-19 NBA season, many released their All-Decade teams for the 2010s. Different authors weight longevity and peak differently, creating some subjectivity. Let’s use machine learning to approach this objectively.
Over the past few months, we’ve been using machine learning to predict the MVP and All-NBA teams. Now that the season is done, let’s update these predictions and predict our final MVP and All-NBA teams.
Now that we’re over halfway done with the regular season, the All-NBA picture is clearing up. By creating accurate models which examine the factors that lead each historical All-Star and top player to make (or miss) an All-NBA team, we can predict each player on all 3 All-NBA teams.
This is the second installment of predicting the 2019 MVP with machine learning. Since the first installment, James Harden has strengthened his MVP case, while Giannis Antetokounmpo has continued to lead the NBA-leading Bucks. Meanwhile, Paul George’s hot streak has shaken up the rest of the pack.
With the halfway point in the year passing, the MVP discussion is heating up. James Harden and Giannis Antetokounmpo find themselves in a tight 2-man race for MVP. Let’s see who should win based off historical precedent for MVP vote share.
Every few years, there’s a highly controversial MVP pick. Most recently, Russell Westbrook won MVP over James Harden on the back of a historic season where he average a triple-double. Let’s look at the historical precedent for MVP vote share and determine the least and most deserving MVPs.