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The Importance of Being Open: What Player Tracking Data Can Say About NBA Field Goal Shooting
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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.
  • Transcript

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