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 2003 -2011 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

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

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%

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 22.09 12.26

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%

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