The Importance of Being Open:What Player Tracking Data Says About NBA Field Goal Shooting<br />Sandy Weil<br />Sportsmetri...
What is Player Tracking Data?<br />Only the closest thing to the Data Holy Grail for basketball (and other sports) analysi...
What do we learn?<br />After controlling for shot type, shot distance, player historical norms, and defender proximity:<br...
Field goal shooting percentage drops by 1% for every 1.5 feet from the basket
A quick catch-and-shoot has value on top of its influence on defender proximity
NBA offensive / defensive positioning is an efficient market for space
This data is fun to work with</li></li></ul><li>Data Used for this Project<br />We integrated three different datasets:<br...
Player Tracking Data Features<br />STATS has already tagged certain events in the Tracking Database<br />Some event types ...
Building Dataset for this Project<br />The Chances database provides the how, who and when for the starting and ending eve...
Clean Data is Good Data<br />We started with 10,000 shots. But to look for subtle effects, we want to make sure that we ha...
Shot Distance Difference versus Clock DifferenceAll Shots <br />
Shot Distance Difference versus Clock DifferenceAll Shots <br />
Shot Distance Difference versus Clock DifferenceAfter Eliminating Certain Specific Time Differences<br />
Shot Distance Difference versus Clock DifferenceAfter Eliminating Shooting Player Distance > 5 ft<br />
Shot Distance Difference versus Clock DifferenceAfter Eliminating All Shots with Distance Difference between -3 and 6<br /...
The Basic Model<br />Shot Outcome (i.e., make=1, miss=0)<br />	= Constant Term<br />	+  Coefficient for Variable A * Varia...
What the Play-by-Play Data Alone Says…<br />Shot Types<br />(Indicator variables for Dunk, Layup, Finger Roll, Tip, Hook, ...
What the Play-by-Play Data Alone Says…<br />Starts How<br />(Indicator  variables for Turnover, DefReb, OffReb, Dead Ball ...
What the Play-by-Play Data Alone Says…<br /><ul><li>Probability of making a shot goes down 1% for every 4-5 feet of additi...
Point blank jump shots get converted at 42%</li></li></ul><li>What Career Averages Can Add…<br />Quiz # 1<br />Which of th...
What Career Averages Can Add…<br />Career FT%, 3pt FG% add nothing<br />eFG% adds little<br />FG% adds some to the model, ...
What Career Averages Can Add…<br /><ul><li>Little change to top three coefficient estimates
As a measure of the probability of converting a point-blank jumper, TS% looks a bit too high</li></li></ul><li>What Player...
What Player Tracking Says About…Opponent Proximity<br />Distance from ball to closest defender is significant, as is dista...
What Player Tracking Says About…Opponent Proximity<br />Including opponent proximity exposes are greater influence of shot...
What Player Tracking Says About…Opponent Proximity<br />True Shooting Percentage is an amazingly good estimate of the prob...
What Player Tracking Says About…Opponent Proximity<br /><ul><li>Tightly contested layup, moderately contested 3-pt jumper,...
Tightly contested 3-pointer is equivalent to a 13-foot jumper that is loosely contested</li></li></ul><li>What Player Trac...
What Player Tracking Says About…How the Ball Got There<br />Quiz # 3<br />The only of these variables that contains signif...
What Player Tracking Says About…How the Ball Got There<br /><ul><li>Interesting that these new passing variable and the “q...
Perhaps these new variables act as a proxy for how long the defender has been close…?
Nope: including how much farther away the defenders were 1 second earlier does not knock out these results
Perhaps this is the influence of shooting in rhythm</li></li></ul><li>What Player Tracking Says About…How the Ball Got The...
For a jumper, holding the ball for 1.5 seconds wipes out the benefit of having just received a pass</li></li></ul><li>What...
And getting away from the defender has a huge impact</li></ul>		   Is there an opportunity here?<br />
Statistical Arbitrage Opportunity?<br />Can an offensive player step back / away and get a better shot? (I.e., a higher pe...
Statistical Arbitrage Opportunity?<br />To get some idea what this quadratic looks like:<br />
Statistical Arbitrage Opportunity?<br />A tightly covered 24-foot 3-pointer is equivalent to an open 30-footer<br />Offens...
Defensive Elasticity<br />Offensive player steps back 1 foot: how much does the defender step forward? <br />Elasticities ...
Defensive Elasticity<br />What about in that darkest green section with an elasticity of 0.4? (~5 inches) <br />Median def...
Defensive Elasticity<br />We find no evidence that statistical arbitrage opportunities are available, given median defensi...
What are we missing?<br />Fouls<br />Not looking here at either propensity for drawing fouls or value of points from fouls...
What are we including?<br />Objective measures defender proximity at time of shot<br />Defender proximity prior to shot at...
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The Importance of Being Open: What Player Tracking Data Can Say About NBA Field Goal Shooting

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  • -- Events with potentially biased shot clock resets: FGMade (not in last two minutes), Off Reb (air ball?), certain events that have a possible reset to 14 seconds,
  • Value of the quicker double team: for every foot farther away that the second defender was away, adds about 0.5% to the FG Pct.
  • Value of the quicker double team: for every foot farther away that the second defender was away, adds about 0.5% to the FG Pct.
  • Value of the quicker double team: for every foot farther away that the second defender was away, adds about 0.5% to the FG Pct.
  • Value of the quicker double team: for every foot farther away that the second defender was away, adds about 0.5% to the FG Pct.
  • The Importance of Being Open: What Player Tracking Data Can Say About NBA Field Goal Shooting

    1. 1. The Importance of Being Open:What Player Tracking Data Says About NBA Field Goal Shooting<br />Sandy Weil<br />SportsmetriciansConsulting<br />Sandy.Weil@Sportsmetricians.com<br />Raw Play-by-Play and Player Tracking Data provided by STATS, LLC<br />
    2. 2. What is Player Tracking Data?<br />Only the closest thing to the Data Holy Grail for basketball (and other sports) analysis<br />Computer vision cameras capture video and data<br />Three cameras per half court allows for true 3D object tracking<br />Complex algorithms extract X,Y,Z positioning data of all objects on the court (players, referees, ball), 25 times per second<br />Available exclusively from STATS, LLC<br />
    3. 3. What do we learn?<br />After controlling for shot type, shot distance, player historical norms, and defender proximity:<br /><ul><li>Tight defense reduces shooting percentage around 12 percentage points
    4. 4. Field goal shooting percentage drops by 1% for every 1.5 feet from the basket
    5. 5. A quick catch-and-shoot has value on top of its influence on defender proximity
    6. 6. NBA offensive / defensive positioning is an efficient market for space
    7. 7. This data is fun to work with</li></li></ul><li>Data Used for this Project<br />We integrated three different datasets:<br />Player Tracking data<br />65 Games from 2010-2011 season<br />Home games for 3 teams<br />Almost 60,000,000 tracking records <br />Just over 10,000 Field Goal Attempts<br />Our Chances database<br />Built from play-by-play data<br />Provides easy access to the context of a play-by-play event<br />Player season / career totals<br />
    8. 8. Player Tracking Data Features<br />STATS has already tagged certain events in the Tracking Database<br />Some event types are also available in Play-by-Play / Chances<br />Field Goal Attempts<br />Free Throw Attempts<br />Rebounds (Offensive and Defensive)<br />Turnovers<br />Fouls<br />Blocked Shots<br />Assists<br />Other events were previously available only through manual charting<br />Dribbles<br />Passes<br />Start of Player Possession<br />
    9. 9. Building Dataset for this Project<br />The Chances database provides the how, who and when for the starting and ending events of each chance and for the shot.<br />Use this to slice the Player Tracking data into chances by matching up those events using as many of the following as applicable:<br />Is this a change of possession event in Player Tracking?<br />Do the two data sets agree on the player credited with the box score event (FGA, Reb, TO, Foul)?<br />Are the game clocks similar?<br />E.g., we expect the game clock from Player Tracking to generally register shots earlier—Player Tracking event tagging rules flag the shot release while play-by-play is entered by a human after observing the outcome of the shot<br />Do the two data sets agree on the number of shots already taken by the player to that point of the game?<br />To correctly match cases of player with multiple FGAs + Off Reb in quick succession<br />
    10. 10. Clean Data is Good Data<br />We started with 10,000 shots. But to look for subtle effects, we want to make sure that we have clean data—to limit impact of artifacts<br />What information is available to identify the most reliable records?<br />How many “other team” tracking events lie between start of chance and the shot (as we’ve identified them)?<br />How well do the shot distances match?<br />How well do the game clocks match?<br />How close to the ball is the shooting player when the shot goes up?<br />
    11. 11. Shot Distance Difference versus Clock DifferenceAll Shots <br />
    12. 12. Shot Distance Difference versus Clock DifferenceAll Shots <br />
    13. 13. Shot Distance Difference versus Clock DifferenceAfter Eliminating Certain Specific Time Differences<br />
    14. 14. Shot Distance Difference versus Clock DifferenceAfter Eliminating Shooting Player Distance > 5 ft<br />
    15. 15. Shot Distance Difference versus Clock DifferenceAfter Eliminating All Shots with Distance Difference between -3 and 6<br />Leaves 6767 shots for analysis<br />
    16. 16. The Basic Model<br />Shot Outcome (i.e., make=1, miss=0)<br /> = Constant Term<br /> + Coefficient for Variable A * Variable A<br /> + Coefficient for Variable B * Variable B<br /> … etc<br />Estimate the terms in blue over the set of clean shots<br />One way of looking at the constant term is that it represents the baseline FG shooting percentage for a shot that is characterized by “not A” and “not B”, etc.<br />
    17. 17. What the Play-by-Play Data Alone Says…<br />Shot Types<br />(Indicator variables for Dunk, Layup, Finger Roll, Tip, Hook, Jumper)<br />Dunks and finger-rolls have highest estimated coefficient; layups next highest<br />Shot performance on hooks indistinguishable from jumpers<br />Tips are terrible shot chances (22% lower FG% than a point-blank jumper) but are too infrequent to have a decent t-test<br /> [Good advice to players: if you can, catch the rebound]<br />Shot Distance and Angle<br />Distance significant through various models <br />Drops 1% for every 4-5 feet from basket<br />We find no significant effects from simple shot angle<br />
    18. 18. What the Play-by-Play Data Alone Says…<br />Starts How<br />(Indicator variables for Turnover, DefReb, OffReb, Dead Ball Inbounds, Inbounds after a Made Shot)<br />Shots on chances beginning with Def Reb or TO are still slightly better chances (+3%) after controlling for shot type and distance<br />These are rendered insignificant (aka “Knocked out”) by the inclusion of the shot clock variables. Indicators for Def Reb and TO must proxy for getting a better early shot<br />Most of the effect of Starts How is likely absorbed in the probability of getting a shot attempt and the type of shot one gets (which are outside the scope of this study)<br />Shot Clock<br />(Seconds remaining; Indicators for first 6 seconds, last 2 seconds)<br />Note that neither data set provides a shot clock. Our estimated shot clock can be biased, though we can tell where bias is likely and the direction of the bias.<br />When looking at unbiased records, the effect of a shot in first 6 seconds of shot clock is significant (+6%), last 2 seconds (-4.5%) is not.<br />When looking at all records, neither indicator appears significant<br />
    19. 19. What the Play-by-Play Data Alone Says…<br /><ul><li>Probability of making a shot goes down 1% for every 4-5 feet of additional shot distance
    20. 20. Point blank jump shots get converted at 42%</li></li></ul><li>What Career Averages Can Add…<br />Quiz # 1<br />Which of the following variables contain the most information regarding the probability of making the shot, given the previous model?<br />Career FG%<br />Career 3pt FG%<br />Career FT%<br />Career eFG%<br />Career TS%<br />
    21. 21. What Career Averages Can Add…<br />Career FT%, 3pt FG% add nothing<br />eFG% adds little<br />FG% adds some to the model, but knocks out the distance<br />(Likely due to negative correlation between historical shooting percentage and current shot distance. In general, inside players shoot higher rates while taking shorter shots.)<br />Chose to keep TS% in the model, in place of a constant term <br />
    22. 22. What Career Averages Can Add…<br /><ul><li>Little change to top three coefficient estimates
    23. 23. As a measure of the probability of converting a point-blank jumper, TS% looks a bit too high</li></li></ul><li>What Player Tracking Says About…Opponent Proximity<br />Quiz # 2<br />Which of the following variables contain the most information regarding the probability of making the shot?<br />Distance to closest defender<br />Distance to second-closest defender<br />Distance to third-closest defender<br />Distance to closest teammate<br />Number of defenders within X feet?<br />
    24. 24. What Player Tracking Says About…Opponent Proximity<br />Distance from ball to closest defender is significant, as is distance to second closest defender<br />Both distance-to-defender variables get knocked out by including a count of defenders within X feet—with number of defenders within 3 feet and within 5 feet seeming to contain the most information<br />Teammate proximity variables alone appear significant but are knocked out by inclusion of opponent proximity –when teammates are nearby, defenders usually are, too<br />
    25. 25. What Player Tracking Says About…Opponent Proximity<br />Including opponent proximity exposes are greater influence of shot distance<br />Shot distance coefficient estimate increases so that field goal shooting drops 1% per 1.7 feet away from basket (was 1% per 4.6 feet)<br />Distance from basket is negatively correlated to number of nearby defenders, which has strong effect on p(Make)<br />Adding defender proximity means that shot distance can no longer proxy for avoiding defenders. Now, we see a more accurate representation of the impact of shot distance on a jump shot<br />
    26. 26. What Player Tracking Says About…Opponent Proximity<br />True Shooting Percentage is an amazingly good estimate of the probability of making an open, point-blank jumper (i.e., after adjusting for shot type, shot distance, defender distance)<br />
    27. 27. What Player Tracking Says About…Opponent Proximity<br /><ul><li>Tightly contested layup, moderately contested 3-pt jumper, and open 19-foot jumper have similar expected values
    28. 28. Tightly contested 3-pointer is equivalent to a 13-foot jumper that is loosely contested</li></li></ul><li>What Player Tracking Says About…How the Ball Got There<br />Quiz # 3<br />Which of these variables contains the most information when added to the model?<br />Total touches during chance<br />Count of distinct players touching ball<br />Count of distinct players passing ball<br />Total number of dribbles during chance<br />Total number of passes during chance<br />At least one pass since last dribble<br />At least one dribble since last pass<br />Count of times ball switches side of floor*<br />
    29. 29. What Player Tracking Says About…How the Ball Got There<br />Quiz # 3<br />The only of these variables that contains significant new information is:<br />Total touches during chance<br />Count of distinct players touching ball<br />Count of distinct players passing ball<br />Total number of dribbles during chance<br />Total number of passes during chance<br />At least one pass since last dribble<br />At least one dribble since last pass<br />Count of times ball switches side of floor*<br />
    30. 30. What Player Tracking Says About…How the Ball Got There<br /><ul><li>Interesting that these new passing variable and the “quick action” variable are significant even after adjusting for defender proximity
    31. 31. Perhaps these new variables act as a proxy for how long the defender has been close…?
    32. 32. Nope: including how much farther away the defenders were 1 second earlier does not knock out these results
    33. 33. Perhaps this is the influence of shooting in rhythm</li></li></ul><li>What Player Tracking Says About…How the Ball Got There<br /><ul><li>For a non-jumper, holding the ball for 2.25 seconds wipes out the benefit of having just received a pass
    34. 34. For a jumper, holding the ball for 1.5 seconds wipes out the benefit of having just received a pass</li></li></ul><li>What Player Tracking Says About…How the Ball Got There<br /><ul><li>By including these new variables, we see a slightly reduced estimate of the defender proximity variables (about 1% each) for jumpers. Some of the defender proximity effect proxies for the “quick catch and shoot”. </li></li></ul><li>What Player Tracking Says About…How the Ball Got There<br /><ul><li>It appears that the shooter does not give up much for stepping back one foot
    35. 35. And getting away from the defender has a huge impact</li></ul> Is there an opportunity here?<br />
    36. 36. Statistical Arbitrage Opportunity?<br />Can an offensive player step back / away and get a better shot? (I.e., a higher percentage shot that is farther from the basket but with fewer proximal defenders.)<br />In academic finance literature, a "statistical arbitrage" conjectures statistical mispricings of price relationships that are true in expectation, in the long run when repeating a trading strategy<br />
    37. 37. Statistical Arbitrage Opportunity?<br />To get some idea what this quadratic looks like:<br />
    38. 38. Statistical Arbitrage Opportunity?<br />A tightly covered 24-foot 3-pointer is equivalent to an open 30-footer<br />Offensive player can retreat up to 6 feet to create space <br />But this is a dynamic equilibrium (the defender doesn’t have to stand still)<br />How much does the defender react?<br />
    39. 39. Defensive Elasticity<br />Offensive player steps back 1 foot: how much does the defender step forward? <br />Elasticities vary from 0.4 to about 1.1<br />Median defensive player steps up between 5 and 13 inches<br />Offensive player has created from 7 inches of space to -1 inch of space<br />
    40. 40. Defensive Elasticity<br />What about in that darkest green section with an elasticity of 0.4? (~5 inches) <br />Median defensive player is about 4.5 feet away, basket is 15 feet away <br />At that location and defender distance, player can retreat up to 2 feet<br />Backing up 1 foot provides the maximum benefit: an increase of just 0.1% <br />
    41. 41. Defensive Elasticity<br />We find no evidence that statistical arbitrage opportunities are available, given median defensive pressure<br />This finding suggests that the defenders and offensive players have already found a pretty reasonable equilibrium in the market for space on the floor<br />
    42. 42. What are we missing?<br />Fouls<br />Not looking here at either propensity for drawing fouls or value of points from fouls<br />Value of subsequent chances (e.g., subsequent offensive rebounds)<br />Fatigue / Minutes played in game<br />Individual player effects<br />Heights of players<br />Foul trouble (for neither defender nor shooter)<br />
    43. 43. What are we including?<br />Objective measures defender proximity at time of shot<br />Defender proximity prior to shot attempt<br />How the ball got to the shooter<br />What the team did as a group prior to the shot<br />What player did immediately prior to shooting<br />
    44. 44. What comes next?<br />How do these newly available variables influence the ability to get off a shot? Or influence the type of shot one gets?<br />How do the “how the ball got there” variables influence the defender proximity?<br />Finer measures of defensive elasticity<br />Individual defender elasticity terms by area of floor<br />Floor spacing influences (on ability to get a shot or ability to convert a shot)<br />
    45. 45. What do we learn?<br />After controlling for shot type, shot distance, player historical norms, and defender proximity:<br /><ul><li>Tight defense reduces shooting percentage around 12%
    46. 46. Field goal shooting percentage drops by 1% for every 1.5 feet from the basket
    47. 47. A quick catch-and-shoot has value on top of its influence on defender proximity
    48. 48. NBA offensive / defensive positioning is an efficient market for space
    49. 49. This data is fun to work with</li></li></ul><li>The Importance of Being Open:What Player Tracking Data Says About NBA Field Goal Shooting<br />Sandy Weil<br />SportsmetriciansConsulting<br />Sandy.Weil@Sportsmetricians.com<br />Raw Play-by-Play and Player Tracking Data provided by STATS, LLC<br />
    50. 50.
    51. 51. Expected Points by Floor LocationGiven the Mean Defender Distance and Ball Arrival Variables<br />

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