PbP.BDB | R Documentation |
In this play-by-play data frame (NBA 2017-2018 Championship), the cases (rows) are the events occurred during the analyzed games and the variables (columns) are descriptions of the events in terms of type, time, players involved, score, area of the court.
PbP.BDB
A data.frame with 37430 rows and 48 variables:
Identification code for the game
Season: years and type (Regular or Playoffs)
Date of the game
Five players on the court (away team; home team)
Quarter (>= 5: over-time)
Score of the away/home team
Time left in the quarter (h:mm:ss)
Time played in the quarter (h:mm:ss)
Time since the immediately preceding event (h:mm:ss)
Identification code for the play
Team responsible for the event
Type of event
Player who made the assist
Players for the jump ball
Player who blocked the shot
Player who entered/left the court
Sequence number of the free throw
Player who made the foul
Number of free throws accorded
Player responsible for the event
Scored points
Player who the jump ball is tipped to
Reason of the turnover
Result of the shot (made or missed)
Player who stole the ball
Type of play
Field shots: distance from the basket
Coordinates of the shooting player. original
: tracking coordinate system half court, (0,0) center of the basket; converted
: coordinates in feet full court, (0,0) bottom-left corner
Textual description of the event
This data set has been kindly made available by BigDataBall, a data provider which leverages computer-vision technologies to richen and extend sports datasets with lots of unique metrics. Since its establishment, BigDataBall has also supported many academic studies and is referred as a reliable source of validated and verified stats for NBA, MLB, NFL and WNBA.
The functions of BasketballAnalyzeR requiring play-by-play data as input need a data frame with some additional variables with respect to PbP.BDB. It can be obtained by means of the function PbPmanipulation
.
Marco Sandri, Paola Zuccolotto, Marica Manisera (basketballanalyzer.help@unibs.it)
https://github.com/sndmrc/BasketballAnalyzeR
P. Zuccolotto and M. Manisera (2020) Basketball Data Science: With Applications in R. CRC Press.
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