1. Conclusions and areas for further analysis
Predicting a Player’s NBA Success Using Collegiate Statistics
Christopher Linn and Kushal Kathed
Oklahoma State University
Abstract
Method
Data Information
Each year NBA teams draft players to their teams
Average NBA career lasts 4.8 years
Players are drafted and evaluated based on college performance and physical attributes
My goal was to evaluate college players and find answers to the three questions below
Created comprehensive data set
Imputation of missing values; monitoring for outliers and skewness
Created 130 models including grouping by position
Subsets for validation
Collegiate Drafted Players from
25 Variables 391 Observations
Players no longer in NBA year 4, used Career Average PER
Players playing < 10 NBA games, used NBA PER mean of players no longer in NBA
Took position average for missing values
Will a player still be in the NBA 4 years after draft?
Will a player be an All-star within 4 years after draft?
What is a player’s projected Player Efficiency Rating (PER) after year 4?
References
ESPN.com/NBA
DraftExpress.com
Basketball-Reference.com
Sports-Reference.com
NBA.com
Kenpom.com
Conclusions and areas for further analysis

2. Will a Player be in the NBA 4 Years after NBA Draft?
PER Players still in NBA
14.74
PER Players no longer in NBA
9.66
Continue to Modeling

3. Will a Player Still be in the NBA 4 Years after NBA Draft?
Point Guard
Age
Win Shares
Strength of Schedule
Shooting Guard
Height
Steals per 40 Minutes
Small Forward
PER
Power Forward
Adj PER
Center
Body Fat%
Defensive Win Shares
Ordinal Logistic Model P-value Threshold .05 Mixed Stepwise
Misclassification Rate = 26%
Position Grouped Ordinal Logistic Models P-value Threshold .05 Mixed/Forward Stepwise
Misclassification Rate = 23%
Position Grouped Subsets for Validation
Subset 1: Misclassification Rate = 22.1%
Subset 2: Misclassification Rate = 24.1%

4. Will a Player be an All-Star within 4 Years after NBA Draft?
Ordinal Logistic Model (Stepwise P-value Threshold .05 Mixed)
Misclassification Rate = 5%
13.6% Sensitivity (% of players that become Allstar predicted correctly)
99.7% Specificity (% of players that are non Allstar predicted correctly)
Validation Subsets
Subset 1: Misclassification Rate = 5.6%
Subset 2: Misclassification Rate = 4.1%
All-star PER Non All-Star PER

5. What is a Player’s Projected Efficiency Rating after Year 4?
Point Guard
Age
Height
Win Shares
True Shooting %
Rebounds per 40 Min
Shooting Guard
Points per 40 Min
Blocks per 40 Min
Small Forward
Power Forward
Adj PER
Center
Wingspan
Assists per 40 Min
Standard Least Squares Model P-value Threshold .05 Mixed Stepwise
Rsquare Adjusted = 28%
Position Grouped Standard Least Square Model P-value Threshold .05 Mixed Stepwise
Rsquare Adjusted = 32%
Position Grouped Subsets for Validation
Subset 1: Rsquare Adjusted = 30.6%
Subset 2: Rsquare Adjusted = 30.1%

6. Conclusions and Areas for Further Analysis
Models should be used to assist teams when selecting players to draft
Best models were created when grouping players by position
These models can’t predict injuries, personality issues, and players not getting adequate opportunity
Players drafted at a younger age typically have better success
Further Analysis
Add variables (College Coach, Max Vertical, Bench Press, Lane Agility, etc)
Use scout and insider analysis to apply text mining/ sentiment analysis for predictive modeling
Explore segmentation due to teams needing certain types of skill sets that fit their system
Other Applications for Grouping Variables
Determining whether a customer will purchase based on demographics when grouped by geographic areas
Determine the number of emergency room visits per patient during flu season grouped by gender