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.
Category: Classification
Predicting the 2020 MVP with linear models
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.
Introducing true win shares: estimating team win probability given player stats
In evaluating players, teams look for who will give them the highest chance to win. Let’s create a metric based on the win probability given a player’s stat line.
Determining the 2010s NBA All-Decade team with machine learning
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.
Using machine learning to predict the 2019 MVP and All-NBA teams: end of season predictions
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.
Predicting the 2019 All-NBA teams with machine learning
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.
Using machine learning to predict hall of famers and all stars from the 2017 draft
Out of the first round in every draft class, on average, only 1 player makes the hall of fame, and about 2-3 players make an All-Star game at some point in their career. Let’s use classification models to see who those players are from the 2017 draft class.