I recently used machine learning to create a new system for classifying NBA players. Now, this system can be used to analyze and optimize five-man lineups.
Which NBA players are the most similar to one another? This question can be answered through the use of hierarchical agglomerative clustering (HAC).
The traditional five position system of classifying NBA players is getting outdated. Let’s use machine learning to learn more about what sets players apart.
The traditional five position system of classifying NBA players is getting outdated. Let’s use machine learning to learn more about what sets players apart.
Which NBA players are earning disproportionately more money in the 2019-20 season than their performance warrants?
Which NBA players signed contracts that are proving to be the best bargains for their teams in the 2019-20 regular season?
Which players increase and decrease their teammates’ shooting efficiency the most through their passes?
Instead of assuming that it’s all the same, let’s determine the true probability of completion for every single pass attempt in the NFL since 2006.
Which NBA players cover the most ground in their games? What about teams? Are these numbers meaningful? Let’s dive into the data and find out.