Dynamic and Allocative Efficiency in NBA             Decision MakingPart of a longer, forthcoming paper entitled:“He Got Gam...
Introduction and OverviewWinning is All About Efficiency        Teams have roughly equal possessions per game. The more effici...
The Anatomy of a PossessionOptimal Stopping Under the Pressure of the Shot Clock         At each shot clock interval the c...
Two Requirements of Optimal Shot Selection  We model a half-court possession as a dynamic optimal stopping  problem, this ...
IntuitionChoosing From Feasible Combinations of Efficiency and Usage        Intuition: We observe ei,t and ui,t for each pla...
IntuitionChoosing From Feasible Combinations of Efficiency and Usage        Intuition: We observe ei,t and ui,t for each pla...
IntuitionChoosing From Feasible Combinations of Efficiency and Usage        Intuition: We observe ei,t and ui,t for each pla...
IntuitionChoosing From Feasible Combinations of Efficiency and Usage        Intuition: We observe ei,t and ui,t for each pla...
IntuitionChoosing From Feasible Combinations of Efficiency and Usage        Intuition: We observe ei,t and ui,t for each pla...
IntuitionChoosing From Feasible Combinations of Efficiency and Usage        Intuition: We observe ei,t and ui,t for each pla...
IntuitionChoosing From Feasible Combinations of Efficiency and Usage        Intuition: We observe ei,t and ui,t for each pla...
IntuitionChoosing From Feasible Combinations of Efficiency and Usage        Intuition: We observe ei,t and ui,t for each pla...
ModelStochastic Shot Arrivals         In each one-second period, with t seconds remaining, player i         observes poten...
ModelStochastic Shot Arrivals         In each one-second period, with t seconds remaining, player i         observes poten...
ModelStochastic Shot Arrivals         In each one-second period, with t seconds remaining, player i         observes poten...
ModelStochastic Shot Arrivals         In each one-second period, with t seconds remaining, player i         observes poten...
Dynamic Efficiency     All player’s should chose ci,t equal to the value of continuing the     possession.     If they don’t...
Dynamic Efficiency     All player’s should chose ci,t equal to the value of continuing the     possession.     If they don’t...
Dynamic Efficiency     All player’s should chose ci,t equal to the value of continuing the     possession.     If they don’t...
Dynamic Efficiency: Results     Figure shows that, on average, this is nearly the case for ALL     periods.     NBA players ...
Undershooting and Overshooting     We preformed a t-test of every player’s adherence to Dynamic     Efficiency (negative is ...
Undershooting and Overshooting     We preformed a t-test of every player’s adherence to Dynamic     Efficiency (negative is ...
Who Overshoots? Who Undershoots?        Top 7 Overshooters and Undershooters (by t-statistic)      Overshooter          t ...
Allocative EfficiencyOur Statistical Test         All teammates in a given line-up should choose a ’worst shot’ (ci,t )     ...
Allocative EfficiencyResults          A team that Allocates perfectly will have Spread = 0 in expectation.                  ...
Allocative EfficiencyResults          A team that Allocates perfectly will have Spread = 0 in expectation.                  ...
Allocative EfficiencyResults          A team that Allocates perfectly will have Spread = 0 in expectation.                  ...
Player Usage Curves     The results of our parametric procedure yield estimates of each     player’s abillity to create sh...
Player Usage Curves     The results of our parametric procedure yield estimates of each     player’s abillity to create sh...
Player Usage Curves     The results of our parametric procedure yield estimates of each     player’s abillity to create sh...
Conclusion     Thanks for your time!     If you liked our presentation, come check out our blog at     www.hooptheory.com ...
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Allocation and Dynamic Efficiency in NBA Decision Making

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2011 5th MIT Sloan Sports Analytics Conference

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Allocation and Dynamic Efficiency in NBA Decision Making

  1. 1. Dynamic and Allocative Efficiency in NBA Decision MakingPart of a longer, forthcoming paper entitled:“He Got Game Theory” Matt Goldman1 and Justin M. Rao2 1 Department of Economics University of California, San Diego 2 Yahoo! Research Labs March 5, 2005 MIT Sloan Sports Analytics Conference Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  2. 2. Introduction and OverviewWinning is All About Efficiency Teams have roughly equal possessions per game. The more efficient team will always win (Oliver (2002), James(1977)). Team’s problem: allocate scarce possessions between teammates and across the shot clock to maximize points. We attack this problem using game theory and optimal stopping. To do so, we need estimates of what would happen to a particular player’s efficiency if he chose to increase or decrease his usage. Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  3. 3. The Anatomy of a PossessionOptimal Stopping Under the Pressure of the Shot Clock At each shot clock interval the choice is between using and waiting for a better opportunity As shot clock goes to 0, the value of continuing the possession declines and players must shoot much more frequently. Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  4. 4. Two Requirements of Optimal Shot Selection We model a half-court possession as a dynamic optimal stopping problem, this leads to two fundamental requirements: 1 Dynamic efficiency : a shot is realized only if its expected value exceeds the continuation value of a possession 2 Allocative efficiency : The frequency at which each player shoots generates equal marginal productivity Effective randomization over who shoots is a best-response to selective defensive pressure It ensures the team could not reallocate to more efficient players on the margin (which would increase output) Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  5. 5. IntuitionChoosing From Feasible Combinations of Efficiency and Usage Intuition: We observe ei,t and ui,t for each player in 18 different periods of the shot clock. The fitted red line is an estimate of what efficiency-usage combinations are possible for the given player. The green line indicates the value of the marginal shot a player must be willing to take to reach any given level of usage. Figure 1: ’Usage Curve’ for the aggregate of all NBA Centers Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  6. 6. IntuitionChoosing From Feasible Combinations of Efficiency and Usage Intuition: We observe ei,t and ui,t for each player in 18 different periods of the shot clock. The fitted red line is an estimate of what efficiency-usage combinations are possible for the given player. The green line indicates the value of the marginal shot a player must be willing to take to reach any given level of usage. Figure 1: ’Usage Curve’ for the aggregate of all NBA Centers Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  7. 7. IntuitionChoosing From Feasible Combinations of Efficiency and Usage Intuition: We observe ei,t and ui,t for each player in 18 different periods of the shot clock. The fitted red line is an estimate of what efficiency-usage combinations are possible for the given player. The green line indicates the value of the marginal shot a player must be willing to take to reach any given level of usage. Figure 1: ’Usage Curve’ for the aggregate of all NBA Centers Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  8. 8. IntuitionChoosing From Feasible Combinations of Efficiency and Usage Intuition: We observe ei,t and ui,t for each player in 18 different periods of the shot clock. The fitted red line is an estimate of what efficiency-usage combinations are possible for the given player. The green line indicates the value of the marginal shot a player must be willing to take to reach any given level of usage. Figure 1: ’Usage Curve’ for the aggregate of all NBA Centers Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  9. 9. IntuitionChoosing From Feasible Combinations of Efficiency and Usage Intuition: We observe ei,t and ui,t for each player in 18 different periods of the shot clock. The fitted red line is an estimate of what efficiency-usage combinations are possible for the given player. The green line indicates the value of the marginal shot a player must be willing to take to reach any given level of usage. Figure 1: ’Usage Curve’ for the aggregate of all NBA Power Forwards Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  10. 10. IntuitionChoosing From Feasible Combinations of Efficiency and Usage Intuition: We observe ei,t and ui,t for each player in 18 different periods of the shot clock. The fitted red line is an estimate of what efficiency-usage combinations are possible for the given player. The green line indicates the value of the marginal shot a player must be willing to take to reach any given level of usage. Figure 1: ’Usage Curve’ for the aggregate of all NBA Small Forwards Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  11. 11. IntuitionChoosing From Feasible Combinations of Efficiency and Usage Intuition: We observe ei,t and ui,t for each player in 18 different periods of the shot clock. The fitted red line is an estimate of what efficiency-usage combinations are possible for the given player. The green line indicates the value of the marginal shot a player must be willing to take to reach any given level of usage. Figure 1: ’Usage Curve’ for the aggregate of all NBA Shooting Guards Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  12. 12. IntuitionChoosing From Feasible Combinations of Efficiency and Usage Intuition: We observe ei,t and ui,t for each player in 18 different periods of the shot clock. The fitted red line is an estimate of what efficiency-usage combinations are possible for the given player. The green line indicates the value of the marginal shot a player must be willing to take to reach any given level of usage. Figure 1: ’Usage Curve’ for the aggregate of all NBA Point Guards Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  13. 13. ModelStochastic Shot Arrivals In each one-second period, with t seconds remaining, player i observes potential shot value: η ∼ Uniform(Bi , Ai ). We assume this distribution is constant across all periods of the shot clock. Player i shoots if and only if η > ci,t (the cut threshold). From such a player we should observe: A+ci,t Efficiency: ei,t = 2 Usage Hazard Rate (Probability that player i uses the possession, A−ci,t given that his team has the ball): ui,t = Ai −Bi dei −(Ai −Bi ) ’Usage Curve’ Slope: dui = 2 Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  14. 14. ModelStochastic Shot Arrivals In each one-second period, with t seconds remaining, player i observes potential shot value: η ∼ Uniform(Bi , Ai ). We assume this distribution is constant across all periods of the shot clock. Player i shoots if and only if η > ci,t (the cut threshold). From such a player we should observe: A+ci,t Efficiency: ei,t = 2 Usage Hazard Rate (Probability that player i uses the possession, A−ci,t given that his team has the ball): ui,t = Ai −Bi dei −(Ai −Bi ) ’Usage Curve’ Slope: dui = 2 Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  15. 15. ModelStochastic Shot Arrivals In each one-second period, with t seconds remaining, player i observes potential shot value: η ∼ Uniform(Bi , Ai ). We assume this distribution is constant across all periods of the shot clock. Player i shoots if and only if η > ci,t (the cut threshold). From such a player we should observe: A+ci,t Efficiency: ei,t = 2 Usage Hazard Rate (Probability that player i uses the possession, A−ci,t given that his team has the ball): ui,t = Ai −Bi dei −(Ai −Bi ) ’Usage Curve’ Slope: dui = 2 Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  16. 16. ModelStochastic Shot Arrivals In each one-second period, with t seconds remaining, player i observes potential shot value: η ∼ Uniform(Bi , Ai ). We assume this distribution is constant across all periods of the shot clock. Player i shoots if and only if η > ci,t (the cut threshold). From such a player we should observe: A+ci,t Efficiency: ei,t = 2 Usage Hazard Rate (Probability that player i uses the possession, A−ci,t given that his team has the ball): ui,t = Ai −Bi dei −(Ai −Bi ) ’Usage Curve’ Slope: dui = 2 Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  17. 17. Dynamic Efficiency All player’s should chose ci,t equal to the value of continuing the possession. If they don’t, they are throwing away points for their team. Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  18. 18. Dynamic Efficiency All player’s should chose ci,t equal to the value of continuing the possession. If they don’t, they are throwing away points for their team. Figure 2: OVERSHOOTING! Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  19. 19. Dynamic Efficiency All player’s should chose ci,t equal to the value of continuing the possession. If they don’t, they are throwing away points for their team. Figure 2: UNDERSHOOTING! Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  20. 20. Dynamic Efficiency: Results Figure shows that, on average, this is nearly the case for ALL periods. NBA players understand Dynamic Efficiency very well. Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  21. 21. Undershooting and Overshooting We preformed a t-test of every player’s adherence to Dynamic Efficiency (negative is overshooting; positive is undershooting) Overshooting is very rare, undershooting is more common. Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  22. 22. Undershooting and Overshooting We preformed a t-test of every player’s adherence to Dynamic Efficiency (negative is overshooting; positive is undershooting) Overshooting is very rare, undershooting is more common. Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  23. 23. Who Overshoots? Who Undershoots? Top 7 Overshooters and Undershooters (by t-statistic) Overshooter t Undershooter t Russell Westbrook -2.97838 Chris Paul 5.341594 Tyrus Thomas -1.983194 Brandon Roy 5.127828 Lamar Odom -1.942873 LeBron James 4.931574 Monta Ellis -1.908993 Al Jefferson 4.623833 Larry Hughes -1.802382 Joe Johnson 4.437453 Drew Gooden -1.790609 Amare Stoudemire 4.061478 Tracy McGrady -1.770439 Vince Carter 4.0012 Undershooters are primarily elite offensive players who may be sacrificing immediate preformance in order to conserve energy. High salary players are more likely to undershoot (t=2.75). Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  24. 24. Allocative EfficiencyOur Statistical Test All teammates in a given line-up should choose a ’worst shot’ (ci,t ) of the same value in each shot clock period. This ensures the team cannot reallocate to more productive players (on the margin) and increase output. Deviation from this standard is measured by Spread (defined in the paper). We use concept of 3-man (“most important 3 of 5-man line-up”) and 4-man cores to increase statistical power. Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  25. 25. Allocative EfficiencyResults A team that Allocates perfectly will have Spread = 0 in expectation. The majority of line-ups achieve Allocative Efficiency, but it is very clear that some core’s are imperfect(t = 8.05). Additionally less experienced (t = 2.70) and higher salary (t = 3.96) ’3-man cores’ Figure 3: Histogram of observed spread amongst all ’3 man cores’ demonstrate larger deviations from Allocative Efficiency. Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  26. 26. Allocative EfficiencyResults A team that Allocates perfectly will have Spread = 0 in expectation. The majority of line-ups achieve Allocative Efficiency, but it is very clear that some core’s are imperfect(t = 8.05). Additionally less experienced (t = 2.70) and higher salary (t = 3.96) ’3-man cores’ Figure 3: Histogram of observed spread amongst all ’3 man cores’ demonstrate larger deviations from Allocative Efficiency. Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  27. 27. Allocative EfficiencyResults A team that Allocates perfectly will have Spread = 0 in expectation. The majority of line-ups achieve Allocative Efficiency, but it is very clear that some core’s are imperfect(t = 8.05). Additionally less experienced (t = 2.70) and higher salary (t = 3.96) ’3-man cores’ Figure 3: Histogram of observed spread amongst all ’3 man cores’ demonstrate larger deviations from Allocative Efficiency. Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  28. 28. Player Usage Curves The results of our parametric procedure yield estimates of each player’s abillity to create shots on the margin: ’Usage Curve’ Slope: duii = −(Ai2−Bi ) de What kinds of players have flatter usage curves? Table: Robust OLS Regression Explaining the Usage Curve LHS/RHS PG SG SF PF C Usage % ˆ ˆ A i −B i 2 2.51* 2.87* 2.80* 3.15* 3.26* -4.32* s.e. 0.26 0.30 0.29 0.31 0.33 0.97 R 2 = 0.8465, ∗p <0.01 Perimeter players and high-usage players have significantly flatter usgae curves. A player who wants to use an additional 1% of his team’s half-court offense possessions, will see his overall efficiency drop by .0025 − .006 points per possession. This estimate holds teammates constant. Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  29. 29. Player Usage Curves The results of our parametric procedure yield estimates of each player’s abillity to create shots on the margin: ’Usage Curve’ Slope: duii = −(Ai2−Bi ) de What kinds of players have flatter usage curves? Table: Robust OLS Regression Explaining the Usage Curve LHS/RHS PG SG SF PF C Usage % ˆ ˆ A i −B i 2 2.51* 2.87* 2.80* 3.15* 3.26* -4.32* s.e. 0.26 0.30 0.29 0.31 0.33 0.97 R 2 = 0.8465, ∗p <0.01 Perimeter players and high-usage players have significantly flatter usgae curves. A player who wants to use an additional 1% of his team’s half-court offense possessions, will see his overall efficiency drop by .0025 − .006 points per possession. This estimate holds teammates constant. Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  30. 30. Player Usage Curves The results of our parametric procedure yield estimates of each player’s abillity to create shots on the margin: ’Usage Curve’ Slope: duii = −(Ai2−Bi ) de What kinds of players have flatter usage curves? Table: Robust OLS Regression Explaining the Usage Curve LHS/RHS PG SG SF PF C Usage % ˆ ˆ A i −B i 2 2.51* 2.87* 2.80* 3.15* 3.26* -4.32* s.e. 0.26 0.30 0.29 0.31 0.33 0.97 R 2 = 0.8465, ∗p <0.01 Perimeter players and high-usage players have significantly flatter usgae curves. A player who wants to use an additional 1% of his team’s half-court offense possessions, will see his overall efficiency drop by .0025 − .006 points per possession. This estimate holds teammates constant. Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making
  31. 31. Conclusion Thanks for your time! If you liked our presentation, come check out our blog at www.hooptheory.com Our contact info: Matt Goldman: mrgoldman@ucsd.edu Justin M. Rao jmrao@yahoo-inc.com Goldman and Rao Dynamic and Allocative Efficiency in NBA Decision Making

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