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CourtVisionPH A System for the Extraction of Field Goal Attempt Locations and Spatial Analysis of Shooting using Broadcast Basketball Videos Engr. Ben Hur S. Pintor Nikko Boy N. Cataniag Asst. Prof. Ma. Rosario Concepcion O. Ang University of

  1. CourtVisionPH A System for the Extraction of Field Goal Attempt Locations and Spatial Analysis of Shooting using Broadcast Basketball Videos Engr. Ben Hur S. Pintor Nikko Boy N. Cataniag Asst. Prof. Ma. Rosario Concepcion O. Ang University of the Philippines Department of Geodetic Engineering FOSS4G Seoul, South Korea | September 14-19, 2015

  2. OUTLINE I. INTRODUCTION II. PROBLEM STATEMENT III. OBJECTIVES IV. METHODOLOGY V. RESULTS AND DISCUSSION VI. CONCLUSIONS AND RECOMMENDATIONS CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  3. INTRODUCTION / INSPIRATION CourtVision -- New Visual and Spatial Analytics for the NBA (Dr. Kirk Goldsberry, MIT Sloan Sports Analytics Conference 2012 ) Scoring Area = 1248 shooting cells 1ft x 1ft per cell <images from CourtVision -- New Visual and Spatial Analytics for the NBA (Dr. Kirk Goldsberry, MIT Sloan Sports Analytics Conference 2012 )> > CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  4. INTRODUCTION / INSPIRATION CourtVision -- New Visual and Spatial Analytics for the NBA (Dr. Kirk Goldsberry, MIT Sloan Sports Analytics Conference 2012 ) n = 1248 Spread - number of unique shooting cells with at least one (1) FGA Spread% - Spread / n Range - number of unique shooting cells with an average of at least 1 Point-per-attempt (1 PPA) Range% - Range / n CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  5. INTRODUCTION / INSPIRATION SportVU Player Tracking System (STATS LLC.) <image from http://www.tvtechnology.com/news/0002/nba-to-follow-the-bouncing-data/226050> CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  6. INTRODUCTION / INSPIRATION Available Player Position and Tracking Data for the NBA <image from stats.nba.com/tracking> CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  7. INTRODUCTION / INSPIRATION Basketball is SPATIAL CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  8. INTRODUCTION / INSPIRATION Basketball is #1 Sport in the Philippines - History - Culture - Money CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  9. PROBLEM STATEMENT 1. No system in place to gather spatial information from basketball games 2. Basketball Analysis and Management in the Philippines is still very Traditional CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  10. OBJECTIVES 1. To develop a system that extracts field goal attempt locations from broadcast basketball videos, performs spatial analysis of shooting, and presents the results of the analysis with statistics and visualizations, using freely available resources. 2. To show the advantages of spatial analysis of shooting over the conventional non-spatial statistics currently used in the Philippines. CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  11. METHODOLOGY | DEVELOPMENT CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  12. METHODOLOGY | DEVELOPMENT Main Functionalities 1. Data Management 2. Field Goal Attempt Extraction (Manual) 3. Spatial Analysis CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  13. METHODOLOGY | DEVELOPMENT Data Management CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  14. METHODOLOGY | DEVELOPMENT Data Management CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  15. METHODOLOGY | DEVELOPMENT Field Goal Attempt Extraction CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  16. METHODOLOGY | DEVELOPMENT Field Goal Attempt Extraction 2D Projective Coordinate Transformation The scoring area is defined as a 15mx10m grid composed of 1mx1m cells (150 total cells). CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  17. METHODOLOGY | DEVELOPMENT Field Goal Attempt Extraction CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  18. METHODOLOGY | DEVELOPMENT Spatial Analysis CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  19. METHODOLOGY | DEVELOPMENT Spatial Analysis CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  20. METHODOLOGY | APPLICATION After the development of the system, it was used to study the UP Fighting Maroons and the DLSU Green Archers during the 2nd Round of the University Athletics Association of the Philippines (UAAP) Season 76 (2013-2014). Data used: - Videos publicly available on youtube.com (Low Quality) - Box-scores and play-by-play available online CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  21. METHODOLOGY | APPLICATION Field Goal Attempts excluded from Extraction: - FGA outside the scoring area - FGA with bad RMSE or back-substitution results CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  22. RESULTS AND DISCUSSION 20% (10-12 FGA/game) difference between the number of extracted FGA and those from box-scores. - Personal limitation of the user - Exclusion of shots beyond the scoring area CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  23. RESULTS AND DISCUSSION Statistics UP Fightng UP Opponents DLSU Green DLSU Opponents Maroons Archers CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  24. RESULTS AND DISCUSSION Visualizations - Range% (UP, UP Opp) CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  25. RESULTS AND DISCUSSION Visualizations - Range% (DLSU, DLSU Opp) CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  26. RESULTS AND DISCUSSION Visualizations - Range% (Marata - UP, Teng - DLSU) CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  27. RESULTS AND DISCUSSION Observations - UP and DLSU have relatively the same distribution of FGA in terms of distance from the basket - UP has a slight advantage over DLSU on shots at the rim (< 1m from basket) and three-pointers - DLSU has a significant advantage on close-range (1m - 3m) and mid-range (3m - 5m) CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  28. RESULTS AND DISCUSSION Observations - UP has difficulties converting and defending high- percentage shots (< 3m from basket) - Only 36% of UP’s FGA were taken within 3m from the basket at an average rate of 0.99 PPA - UP’s opponents took 52% of their shots within 3m from the basket at a rate of 1.15 PPA CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  29. RESULTS AND DISCUSSION Observations - DLSU only allowed their opponents to take 36.61% of their shots within 3 meters at a rate of 1.10 PPA - DLSU was able force their opponents to a lot of mid-range shots (17.85%) and long 2-pointers (14.15%) where these opponents scored just 0.62 PPA CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  30. RESULTS AND DISCUSSION Observations - J. Marata (UP) takes shots from all over the court (peppered look of his Range% map) but is effective only at certain areas of the court - J. Teng’s (DLSU) FGA are concentrated in the paint which he converts at a high rate, he seldom takes long two- pointers or three-point shots CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  31. CONCLUSIONS A system that can extract field goal attempt locations from broadcast basketball videos and perform spatial analysis of shooting was successfully developed and applied using freely available resources and data. It was demonstrated that spatial analysis provides a better characterization and appreciation of shooting as compared to conventional non-spatial shooting statistics. CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  32. CONCLUSIONS Aside from the quality of the videos used, the system is limited by the ability of the user to completely and correctly extract all the field goal attempts taken during a game . CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  33. RECOMMENDATIONS - expansion of the database - better video and image processing algorithms - automatic court detection and tracking - automatic shot and position determination - use of own cameras/video capture devices or multiple cameras (i.e. GoPro, off-the-shelf video cameras) CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  34. RECOMMENDATIONS - data fed into the system must complete and correct - use supplementary sources such as play-by-play data and regular checking and counter-checking of the contents of the database against known values from boxscores - allow a person who has intimate knowledge of basketball to perform the extraction CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

  35. REFERENCES Goldsberry, K. 2012. CourtVision – New Visual and Spatial Analytics for the NBA. MIT Sloan Sports Analytics Conference, Boston. MA, USA, 2-3 March 2012. CourtVisionPH FOSS4G Seoul, South Korea | September 14-19, 2015

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