We are continuing on with Kevin Pelton's 10 questions to solve in the NBA

Question 8: How do statistics translate from other leagues to the NBA?

Luck would have it we are looking at this exact problem. We have been asked in the past if one could determine which players in the European leagues would do well in the NBA. However, we are currently examining which players at the collegiate level will do well in the NBA. I cannot yet get into the specifics, but we are able to determine with high accuracy which players fit into specified categories (level of NBA talent) based solely on box score data in college.

Pelton discusses the use of translations to determine how college players will do in the NBA. He notes that while this is successfully used in baseball, the ability to translate statistics is much more difficult. We are taking a somewhat different look at the problem.

Every NBA player has been categorized based on their talent/success in the NBA. This could be by statistics, influence or any number of subjective measures. We then break these groups up by positions so that we do not measure centers the same as point guards (Pelton did the same). Each position possesses their own unique set of statistics that explain each player (common techniques such as principal component analysis or factor analysis can be applied here).

After capturing the right statistics for each group, we can then apply various prediction algorithms to determine which players belong in which groups. The beauty behind our algorithms is that they "see" things we could easily have missed!

I look forward to sharing our results. With this information, we take the guessing out of the draft and provide insight into the true floppers of the NBA.

A fantastic question by Kevin Pelton -- which should be number 1 on the list. If you claim to love Moneyball, this question should just scream potential to you!

Question 8: How do statistics translate from other leagues to the NBA?

Luck would have it we are looking at this exact problem. We have been asked in the past if one could determine which players in the European leagues would do well in the NBA. However, we are currently examining which players at the collegiate level will do well in the NBA. I cannot yet get into the specifics, but we are able to determine with high accuracy which players fit into specified categories (level of NBA talent) based solely on box score data in college.

Pelton discusses the use of translations to determine how college players will do in the NBA. He notes that while this is successfully used in baseball, the ability to translate statistics is much more difficult. We are taking a somewhat different look at the problem.

Every NBA player has been categorized based on their talent/success in the NBA. This could be by statistics, influence or any number of subjective measures. We then break these groups up by positions so that we do not measure centers the same as point guards (Pelton did the same). Each position possesses their own unique set of statistics that explain each player (common techniques such as principal component analysis or factor analysis can be applied here).

After capturing the right statistics for each group, we can then apply various prediction algorithms to determine which players belong in which groups. The beauty behind our algorithms is that they "see" things we could easily have missed!

I look forward to sharing our results. With this information, we take the guessing out of the draft and provide insight into the true floppers of the NBA.

A fantastic question by Kevin Pelton -- which should be number 1 on the list. If you claim to love Moneyball, this question should just scream potential to you!

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