Let’s use college stats to predict All-Star probabilities for the top-10 picks in the 2019 draft.
As soon as a player is drafted, we compare them to historical stars based on their play styles. Let’s use their stats and some similarity metrics to create more concrete comparisons.
With the 2019 draft unclear looking inconsistent after the top 3, let’s try to predict the best scorers in the draft with machine learning. Who will be the hidden gem who could shine as a scorer?
Many claim tanking is worth it because players drafted earlier have a higher chance of being good. However, the draft is random. Let’s use Markov chains (a random process) to analyze if tanking is worth it.
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.
Though the top of the 2018 draft seems heavy on bigs and forwards, there’s bound to be several starting guards coming out of the draft class. Let’s see who has the best distributing ability by using historical college stats to predict NBA assists and turnovers.
With defensive freaks like Mohamed Bamba and Jaren Jackson Jr., this draft class seems strong defensively. Examining how historical players’ college stats predicted their NBA defense, we can predict the best defenders for this upcoming draft class.
With the NBA’s modern emphasis on 3-point shooting, draft good shooters has become more and more important. By looking at how historical players’ college shooting stats predicted their NBA PPG, we can predict the best shooters in the upcoming draft class.