expectedpts: Plots expected points of shots as a function of the distance...

In BasketballAnalyzeR: Analysis and Visualization of Basketball Data

Description

Plots expected points of shots as a function of the distance from the basket (default) or another variable

Usage

expectedpts(
  data,
  var = "shot_distance",
  players = NULL,
  bw = 10,
  period.length = 12,
  palette = gg_color_hue,
  team = TRUE,
  col.team = "gray",
  col.hline = "black",
  xlab = NULL,
  x.range = "auto",
  title = NULL,
  legend = TRUE
)

Arguments

data	a data frame whose rows are field shots and with the following columns: points, event_type, player (only if the players argument is not NULL) and at least one of playlength, periodTime, totalTime, shot_distance (the column specified in var, see Details).
var	character, a string giving the name of the numerical variable according to which the expected points are estimated; available options "playlength", "periodTime", "totalTime", "shot_distance" (default).
players	subset of players to be displayed (optional; it can be used only if the player column is present in data).
bw	numeric, smoothing bandwidth of the kernel density estimator (see ksmooth).
period.length	numeric, the length of a quarter in minutes (default: 12 minutes as in NBA).
palette	color palette.
team	logical; if TRUE, draws the expected points for all the shots in data.
col.team	character, color of the expected points line for all the shots in data (default "gray").
col.hline	character, color of the dashed horizontal line (default "black") denoting the expected points for all the shots in data, not conditional to the variable in the x-axis.
xlab	character, x-axis label.
x.range	numerical vector or character; available options: NULL (x-axis range defined by ggplot2, the default), "auto" (internally defined x-axis range), or a 2-component numerical vector (user-defined x-axis range).
title	character, plot title.
legend	logical, if TRUE, color legend is displayed (only when players is not NULL).

Details

The data data frame could also be a play-by-play dataset provided that rows corresponding to events different from field shots have values different from "shot" or "miss" in the even_type variable.

Required columns:

• event_type, a factor with the following levels: "shot" for made field shots and "miss" for missed field shots

• player, a factor with the name of the player who made the shot

• points, a numeric variable (integer) with the points scored by made shots and 0 for missed shots

• playlength, a numeric variable with time between the shot and the immediately preceding event

• periodTime, a numeric variable with seconds played in the quarter when the shot is attempted

• totalTime, a numeric variable with seconds played in the whole match when the shot is attempted

• shot_distance, a numeric variable with the distance of the shooting player from the basket (in feet)

Value

A ggplot2 plot

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([e-mail])

References

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

Examples

PbP <- PbPmanipulation(PbP.BDB)
PbP.GSW <- subset(PbP, team=="GSW" & !is.na(shot_distance))
plrys <- c("Stephen Curry","Kevin Durant")
expectedpts(data=PbP.GSW, bw=10, players=plrys, col.team='dodgerblue',
        palette=colorRampPalette(c("gray","black")), col.hline="red")

BasketballAnalyzeR documentation built on July 2, 2020, 2:14 a.m.