Analysis Ideas

  1. Which country (other than the U.S.) produce the most number of NBA players?
  2. Which school produce the most number of NBA players?
  3. Does teams with larger overall size (in terms of height and weight) win more games?
  4. When do players reach their maximum potential after being drafted?
  5. Home court advantage over the years
  6. Does home court advantage vary by location?
  7. Which players are associated more wins? Identifying MVPs programmatically
  8. Can we use ELO rating to predict the Playoffs winners?
  9. Do teams win by a smaller margin when winning at away vs. at home?
  10. Can we cluster different teams into categories/groups based on their playing styles and performance output?
  11. What’s the effect of playing back-to-back games on game outcomes?
  12. What’s the effect of distance traveled for games on game outcomes?
  13. What happens if a fast-paced team plays against a slow-paced team? Do fast-paced teams outperform slow-paced teams?
  14. Is team’s average age of players a good indicator of game outcomes? (How do teams of veteran starting players fare against teams of young athletic starters?)
  15. Is team’s average height/weight of players a good indicator of game outcomes? (Do bigger, taller teams dominate teams with smaller players?)
  16. Suppose there are three teams, A, B, and C. A beats B. B beats C. C beats A. How should you rank these teams? Is there rock-paper-scissors relationship amongst basketball teams? (
  17. Is there a way to visualize the different areas of strengths of a team? (See
  18. Is there a way to quantify/visualize the talent difference between East and West Conference teams?
  19. Has game pace changed over time?
  20. Are there foul call favors for home teams?
  21. Which players/teams take the most number of forced shots?
  22. Which teams move the ball around the most?
  23. Which teams run the most?
  24. Which are the most team-oriented teams, as measured by the usage percentage or assist ratio?
  25. Do teams have a harder time covering the spread when they are on the 2nd game of a back to back or if they have to travel far to their next game?
  26. Is there a system that assigns players with an offensive/defensive value to see how it will affect the teams scoring and defensive output?
  27. Is there a good relationship between a team’s pace of play versus whether or not they hit the Over/Under?
  28. What exactly is the KenPom equation and can we apply it to the NBA?
  29. Can we identify prolific-scoring but low-efficiency players (i.e. players who accumulate points simply by taking a lot of shots)?
  30. Size in the NBA, what ways do height and wingspan affect defense?
  31. The effects of rest and is there a critical point of rest?
  32. The 5 conventional positions are outdated. Can we reclassify positions with machine learning?
  33. Machine learning: classifying the true contenders every season.
  34. Can we use machine learning to predict the MVP halfway through a season?
  35. Identifying the importance of rhythm in the NBA. Can we identify players who are ready at a moment’s notice and the players who play better with some amount of minutes?
  36. Is isolation basketball really that bad? Comparing win percentages, isolation percentages, and effects on low usage teammates.