scoringprobability: Computes the probability of scoring certain shot types in...

In sndmrc/BasketAnalyzeR: Analysis and Visualization of Basketball Data

Computes the probability of scoring certain shot types in certain conditions, by looking at the result of the shots in the PbP provided

Description

Computes the probability of scoring certain shot types in certain conditions, by looking at the result of the shots in the PbP provided

Usage

scoringprobability(
  PbP_data,
  team_name = "",
  shotclock_interval = c(0, 24),
  totaltime = 0,
  score_difference = c(-100, 100),
  shot_type = "field",
  team_data
)

Arguments

PbP_data	a play-by-play dataframe, previously handled by the PbPmanipulation function
team_name	character, if the play-by-play dataframe given as an input contains data for multiple teams, this parameters filters only the shots of the team we are interested in
shotclock_interval	vector of two numeric values or single numeric value, condition on the value of shotclock of the shots that will be considered
totaltime	numeric value, condition on the value of totalTime of the shots that will be considered
score_difference	vector of two numeric values or single numeric value, condition on the value of shotclock of the shots that will be considered
shot_type	character, the type of shots to be analyzed; available options: "2P", "3P", "FT", "field"
team_data	dataframe, contains several data regarding the teams in the NBA. Inside this function it is used only to check if team_name corresponds to a team in the NBA. If the teams in the play-by-play data studied are the same as in the 2017-18 season, Tadd (the dataframe contained in the BasketballAnalyzeR package, regarding the 2017-18 season) can be used

Value

numeric value, indicating the probability that a shots which respects all the conditions defined is made

Author(s)

Andrea Fox

References

P. Zuccolotto and M. Manisera (2020) Basketball Data Science: With Applications in R. CRC Press.

P. Zuccolotto, M. Manisera and M. Sandri (2018) Big data analytics for modeling scoring probability in basketball: The effect of shooting under high pressure conditions. International Journal of Sports Science & Coaching.

Examples

# probability that a 2 point shot attempted by the Golden State Warriors
# in the last two seconds of an action is made
PbP <- PbPmanipulation(PbP.BDB)
PbP <- scoredifference(PbP, team_name = "GSW", player_data=Pbox, team_data=Tadd)
PbP <- shotclock(PbP,  sec_14_after_oreb = FALSE, team_data=Tadd)
scoringprobability(PbP, team_name = "GSW", shotclock_interval = c(0, 2),
                  shot_type = "2P", team_data=Tadd)

# probability that a 3 point shot attempted when the score difference is
# between -5 and 1 is made
PbP <- PbPmanipulation(PbP.BDB)
PbP <- scoredifference(PbP, team_name = "GSW", player_data=Pbox, team_data=Tadd)
PbP <- shotclock(PbP, sec_14_after_oreb = FALSE, team_data=Tadd)
scoringprobability(PbP, team_name = "GSW", score_difference = c(-5, 1),
                   shot_type = "3P", team_data=Tadd)

# probability that a free throw attempted in the last 5 minutes is made
PbP <- PbPmanipulation(PbP.BDB)
PbP <- scoredifference(PbP, team_name = "GSW", player_data=Pbox, team_data=Tadd)
PbP <- shotclock(PbP,  sec_14_after_oreb = FALSE, team_data=Tadd)
scoringprobability(PbP, team_name = "GSW", totaltime = 43, shot_type = "FT",
                  team_data=Tadd)

sndmrc/BasketAnalyzeR documentation built on June 6, 2023, 12:52 a.m.