Package 'BasketballAnalyzeR' reference manual

Title:	Analysis and Visualization of Basketball Data
Description:	Contains data and code to accompany the book P. Zuccolotto and M. Manisera (2020) Basketball Data Science. Applications with R. CRC Press. ISBN 9781138600799.
Authors:	Marco Sandri [aut, cre] (ORCID: <https://orcid.org/0000-0002-1422-5695>), Paola Zuccolotto [aut] (ORCID: <https://orcid.org/0000-0003-4399-7018>), Marica Manisera [aut] (ORCID: <https://orcid.org/0000-0002-2982-0243>)
Maintainer:	Marco Sandri <[email protected]>
License:	GPL (>= 2.0)
Version:	0.8.1
Built:	2026-05-28 08:25:05 UTC
Source:	https://github.com/sndmrc/basketballanalyzer

Investigates the network of assists-shots in a team

Description

The assistnet command provides a comprehensive analysis of a team's assist-shot network, revealing crucial insights into player interactions and on-court dynamics.

Usage

assistnet(
  data,
  assist = "assist",
  player = "player",
  points = "points",
  event.type = "event_type",
  normalize = FALSE,
  period.length = 12,
  time.thr = 0
)
assistnet(
  data,
  assist = "assist",
  player = "player",
  points = "points",
  event.type = "event_type",
  normalize = FALSE,
  period.length = 12,
  time.thr = 0
)

Arguments

data

a data frame whose rows are field shots and columns are variables to be specified in assist, player, points, event.type (see Details).

assist

character, indicating the name of the variable with players who made the assists, if any.

player

character, indicating the name of the variable with players who made the shot.

points

character, indicating the name of the variable with points.

event.type

character, indicating the name of the variable with type of event (mandatory categories are "miss" for missed field shots and "shot" for field goals).

normalize

logical, if TRUE the number of assists is normalized (default normalize=FALSE, see Details).

period.length

numerical, the length of a quarter in minutes (default: 12 minutes as in NBA)

time.thr

numerical, Minimum number of minutes played together by a pair of players required for computing their normalized assist count. Pairs below time.thr are excluded to avoid inflation due to small denominators (default: time.thr = 0).

Details

The data data frame could also be a play-by-play dataset provided that rows corresponding to events different from field shots are not coded as "shot" in the event.type variable. (To be completed)

Normalization: \[4 \cdot \text{(period.length)} \cdot \frac{(\text{number of assists})}{\text{(minutes played in attack by each couple of players)}}\]

Value

A list with 3 elements, assistTable (a table), nodeStats (a data frame), and assistNet (a network object). See Details.

assistTable, the cross-table of assists made and received by the players.

nodeStats, a data frame with the following variables:

FGM (fields goals made),

FGM_AST (field goals made thanks to a teammate's assist),

FGM_ASTp (percentage of FGM_AST over FGM),

FGPTS (points scored with field goals),

FGPTS_AST (points scored thanks to a teammate's assist),

FGPTS_ASTp (percentage of FGPTS_AST over FGPTS),

AST (assists made),

ASTPTS (point scored by assist's teammates).

minTable, a square matrix with the total number of minutes played in attack by each pair of players; the elements on the principal diagonal are set to zero.

assistminTable, a matrix showing the assist frequency between player pairs, adjusted for minutes played together in attack and expressed per 4*period.length minutes.

assistNet, an object of class network that can be used for further network analysis with specific R packages (see network)

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

P. Zuccolotto, M. Manisera and M. Sandri (2026) Advanced Basketball Data Science: With Applications in R. CRC Press.

Examples

PbP <- PbPmanipulation(PbP.BDB)
PbP.GSW <- subset(PbP, team=="GSW")
out <- assistnet(PbP.GSW)
plot(out)
## Not run: 
  out <- assistnet(PbP.GSW, normalize=TRUE, time.thr=50)
  plot(out, edge.thr=5)

## End(Not run)
PbP <- PbPmanipulation(PbP.BDB)
PbP.GSW <- subset(PbP, team=="GSW")
out <- assistnet(PbP.GSW)
plot(out)
## Not run: 
  out <- assistnet(PbP.GSW, normalize=TRUE, time.thr=50)
  plot(out, edge.thr=5)

## End(Not run)

Draws a bar-line plot

Description

Draws a bar-line plot

Usage

barline(
  data,
  id,
  bars,
  line,
  order.by = id,
  decreasing = TRUE,
  labels.bars = NULL,
  label.line = NULL,
  position.bars = "stack",
  title = NULL
)
barline(
  data,
  id,
  bars,
  line,
  order.by = id,
  decreasing = TRUE,
  labels.bars = NULL,
  label.line = NULL,
  position.bars = "stack",
  title = NULL
)

Arguments

data

a data frame.

id

character, name of the ID variable.

bars

character vector, names of the bar variables.

line

character, name of the line variable.

order.by

character, name of the variable used to order bars (on the x-axis).

decreasing

logical; if TRUE, decreasing order.

labels.bars

character vector, labels for the bar variables.

label.line

character, label for the line variable on the second y-axis (on the right).

position.bars

character, used to adjust the positioning of the bars in the plot; there are four main options: stack, fill, dodge, and identity.

title

character, plot title.

Value

A ggplot2 object

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

dts <- subset(Pbox, Team=="Houston Rockets" & MIN>=500)
barline(data=dts, id="Player", bars=c("P2p","P3p","FTp"),
        line="MIN", order.by="Player",
        labels.bars=c("2P","3P","FT"), title="Houston Rockets")
dts <- subset(Pbox, Team=="Houston Rockets" & MIN>=500)
barline(data=dts, id="Player", bars=c("P2p","P3p","FTp"),
        line="MIN", order.by="Player",
        labels.bars=c("2P","3P","FT"), title="Houston Rockets")

Draws a bubble plot

Description

Draws a bubble plot

Usage

bubbleplot(
  data,
  id,
  x,
  y,
  col,
  size,
  text.col = NULL,
  text.size = 2.5,
  scale.size = TRUE,
  labels = NULL,
  mx = NULL,
  my = NULL,
  mcol = NULL,
  title = NULL,
  repel = TRUE,
  text.legend = TRUE,
  hline = TRUE,
  vline = TRUE
)
bubbleplot(
  data,
  id,
  x,
  y,
  col,
  size,
  text.col = NULL,
  text.size = 2.5,
  scale.size = TRUE,
  labels = NULL,
  mx = NULL,
  my = NULL,
  mcol = NULL,
  title = NULL,
  repel = TRUE,
  text.legend = TRUE,
  hline = TRUE,
  vline = TRUE
)

Arguments

data

a data frame.

id

character, name of the ID variable.

x

character, name of the x-axis variable.

y

character, name of the y-axis variable.

col

character, name of variable on the color axis.

size

character, name of variable on the size axis.

text.col

character, name of variable for text colors.

text.size

numeric, text font size (default 2.5).

scale.size

logical; if TRUE, size variable is rescaled between 0 and 100.

labels

character vector, variable labels (on legend and axis).

mx

numeric, x-coordinate of the vertical axis; default is the mean value of x variable.

my

numeric, y-coordinate of the horizontal axis; default is the mean value of y variable.

mcol

numeric, midpoint of the diverging scale (see scale_colour_gradient2); default is the mean value of col variable.

title

character, plot title.

repel

logical; if TRUE, activate text repelling.

text.legend

logical; if TRUE, show the legend for text color.

hline

logical; if TRUE, a horizontal line is drawn with y intercept at the mean value of the variable on the y axis.

vline

logical; if TRUE, a vertical line is drawn with x intercept at the mean value of the variable on the x axis.

Value

A ggplot2 object

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

X <- with(Tbox, data.frame(T=Team, P2p=P2p, P3p=P3p, FTp=FTp, AS=P2A+P3A+FTA))
labs <- c("2-point shots (% made)","3-point shots (% made)",
          "free throws (% made)","Total shots attempted")
bubbleplot(X, id="T", x="P2p", y="P3p", col="FTp",
           size="AS", labels=labs)
X <- with(Tbox, data.frame(T=Team, P2p=P2p, P3p=P3p, FTp=FTp, AS=P2A+P3A+FTA))
labs <- c("2-point shots (% made)","3-point shots (% made)",
          "free throws (% made)","Total shots attempted")
bubbleplot(X, id="T", x="P2p", y="P3p", col="FTp",
           size="AS", labels=labs)

Correlation analysis

Description

Correlation analysis

Usage

corranalysis(data, threshold = 0, sig.level = 0.95)
corranalysis(data, threshold = 0, sig.level = 0.95)

Arguments

data

a numeric matrix or data frame (see cor).

threshold

numeric, correlation cutoff (default 0); correlations in absolute value below threshold are set to 0.

sig.level

numeric, significance level (default 0.95); correlations with p-values greater that 1-sig.level are set to 0.

Value

A list with the following elements:

corr.mtx (the complete correlation matrix)
corr.mtx.trunc (the truncated correlation matrix)
cor.mtest (the output of the significance test on correlations; see cor.mtest)
threshold correlation cutoff
sig.level significance level

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

data <- data.frame(Pbox$PTS,Pbox$P3M,Pbox$P2M,
                   Pbox$OREB + Pbox$DREB,Pbox$AST,
                   Pbox$TOV,Pbox$STL,Pbox$BLK)/Pbox$MIN
names(data) <- c("PTS","P3M","P2M","REB","AST","TOV","STL","BLK")
data <- subset(data, Pbox$MIN >= 500)
out <- corranalysis(data, threshold = 0.5)
data <- data.frame(Pbox$PTS,Pbox$P3M,Pbox$P2M,
                   Pbox$OREB + Pbox$DREB,Pbox$AST,
                   Pbox$TOV,Pbox$STL,Pbox$BLK)/Pbox$MIN
names(data) <- c("PTS","P3M","P2M","REB","AST","TOV","STL","BLK")
data <- subset(data, Pbox$MIN >= 500)
out <- corranalysis(data, threshold = 0.5)

R function CreateRadialPlot by William D. Vickers, freely downloadable from the web

Description

R function CreateRadialPlot by William D. Vickers, freely downloadable from the web

Usage

CreateRadialPlot(
  plot.data,
  axis.labels = colnames(plot.data)[-1],
  grid.min = -0.5,
  grid.mid = 0,
  grid.max = 0.5,
  centre.y = grid.min - ((1/9) * (grid.max - grid.min)),
  plot.extent.x.sf = 1.2,
  plot.extent.y.sf = 1.2,
  x.centre.range = 0.02 * (grid.max - centre.y),
  label.centre.y = FALSE,
  grid.line.width = 0.5,
  gridline.min.linetype = "longdash",
  gridline.mid.linetype = "longdash",
  gridline.max.linetype = "longdash",
  gridline.min.colour = "grey",
  gridline.mid.colour = "blue",
  gridline.max.colour = "grey",
  grid.label.size = 4,
  gridline.label.offset = -0.02 * (grid.max - centre.y),
  label.gridline.min = TRUE,
  axis.label.offset = 1.15,
  axis.label.size = 2.5,
  axis.line.colour = "grey",
  group.line.width = 1,
  group.point.size = 4,
  background.circle.colour = "yellow",
  background.circle.transparency = 0.2,
  plot.legend = if (nrow(plot.data) > 1) TRUE else FALSE,
  legend.title = "Player",
  legend.text.size = grid.label.size,
  titolo = FALSE
)
CreateRadialPlot(
  plot.data,
  axis.labels = colnames(plot.data)[-1],
  grid.min = -0.5,
  grid.mid = 0,
  grid.max = 0.5,
  centre.y = grid.min - ((1/9) * (grid.max - grid.min)),
  plot.extent.x.sf = 1.2,
  plot.extent.y.sf = 1.2,
  x.centre.range = 0.02 * (grid.max - centre.y),
  label.centre.y = FALSE,
  grid.line.width = 0.5,
  gridline.min.linetype = "longdash",
  gridline.mid.linetype = "longdash",
  gridline.max.linetype = "longdash",
  gridline.min.colour = "grey",
  gridline.mid.colour = "blue",
  gridline.max.colour = "grey",
  grid.label.size = 4,
  gridline.label.offset = -0.02 * (grid.max - centre.y),
  label.gridline.min = TRUE,
  axis.label.offset = 1.15,
  axis.label.size = 2.5,
  axis.line.colour = "grey",
  group.line.width = 1,
  group.point.size = 4,
  background.circle.colour = "yellow",
  background.circle.transparency = 0.2,
  plot.legend = if (nrow(plot.data) > 1) TRUE else FALSE,
  legend.title = "Player",
  legend.text.size = grid.label.size,
  titolo = FALSE
)

Arguments

plot.data

plot.data

axis.labels

axis.labels

grid.min

grid.min

grid.mid

grid.mid

grid.max

grid.max

centre.y

centre.y

plot.extent.x.sf

plot.extent.x.sf

plot.extent.y.sf

plot.extent.y.sf

x.centre.range

x.centre.range

label.centre.y

label.centre.y

grid.line.width

grid.line.width

gridline.min.linetype

gridline.min.linetype

gridline.mid.linetype

gridline.mid.linetype

gridline.max.linetype

gridline.max.linetype

gridline.min.colour

gridline.min.colour

gridline.mid.colour

gridline.mid.colour

gridline.max.colour

gridline.max.colour

grid.label.size

grid.label.size

gridline.label.offset

gridline.label.offset

label.gridline.min

label.gridline.min

axis.label.offset

axis.label.offset

axis.label.size

axis.label.size

axis.line.colour

axis.line.colour

group.line.width

group.line.width

group.point.size

group.point.size

background.circle.colour

background.circle.colour

background.circle.transparency

background.circle.transparency

plot.legend

plot.legend

legend.title

legend.title

legend.text.size

legend.text.size

titolo

plot title

Details

A description of the function can be found at the following link: http://rstudio-pubs-static.s3.amazonaws.com/5795_e6e6411731bb4f1b9cc7eb49499c2082.html

References

Vickers D.W. (2006) Multi-Level Integrated Classifications Based on the 2001 Census, PhD Thesis, School of Geography, The University of Leeds

Computes and plots kernel density estimation of shots with respect to a concurrent variable

Description

Computes and plots kernel density estimation of shots with respect to a concurrent variable

Usage

densityplot(
  data,
  var,
  shot.type = "field",
  thresholds = NULL,
  best.scorer = FALSE,
  period.length = 12,
  bw = NULL,
  title = NULL
)
densityplot(
  data,
  var,
  shot.type = "field",
  thresholds = NULL,
  best.scorer = FALSE,
  period.length = 12,
  bw = NULL,
  title = NULL
)

Arguments

data

a data frame whose rows are shots and with the following columns: ShotType, player, points 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 shot density is estimated. Available options: "playlength", "periodTime", "totalTime", "shot_distance".

shot.type

character, a string giving the type of shots to be analyzed. Available options: "2P", "3P", "FT", "field".

thresholds

numerical vector with two thresholds defining the range boundaries that divide the area under the density curve into three regions. If NULL default values are used.

best.scorer

logical; if TRUE, displays the player who scored the highest number of points in the corresponding interval.

period.length

numeric, the length of a quarter in minutes (default: 12 minutes as in NBA).

bw

numeric, the value for the smoothing bandwidth of the kernel density estimator or a character string giving a rule to choose the bandwidth (see density).

title

character, plot title.

Details

The data data frame could also be a play-by-play dataset provided that rows corresponding to events different from shots have NA in the ShotType variable.

Required columns:

ShotType, a factor with the following levels: "2P", "3P", "FT" (and NA for events different from 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 ([email protected])

References

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

Examples

PbP <- PbPmanipulation(PbP.BDB)
data.team  <- subset(PbP, team=="GSW" & result!="")
densityplot(data=data.team, shot.type="2P", var="playlength", best.scorer=TRUE)
data.opp <- subset(PbP, team!="GSW" & result!="")
densityplot(data=data.opp, shot.type="2P", var="shot_distance", best.scorer=TRUE)
PbP <- PbPmanipulation(PbP.BDB)
data.team  <- subset(PbP, team=="GSW" & result!="")
densityplot(data=data.team, shot.type="2P", var="playlength", best.scorer=TRUE)
data.opp <- subset(PbP, team!="GSW" & result!="")
densityplot(data=data.opp, shot.type="2P", var="shot_distance", best.scorer=TRUE)

Add lines of NBA court to an existing ggplot2 plot

Description

Add lines of NBA court to an existing ggplot2 plot

Usage

drawNBAcourt(p, size = 1.5, col = "black", full = FALSE)
drawNBAcourt(p, size = 1.5, col = "black", full = FALSE)

Arguments

p

a ggplot2 object.

size

numeric, line size.

col

line color.

full

logical; if TRUE draws a complete NBA court; if FALSE draws a half court.

Value

A ggplot2 object

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

Examples

library(ggplot2)
p <- ggplot(data.frame(x=0, y=0), aes(x,y)) + coord_fixed()
drawNBAcourt(p)
library(ggplot2)
p <- ggplot(data.frame(x=0, y=0), aes(x,y)) + coord_fixed()
drawNBAcourt(p)

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

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
)
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 ([email protected])

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")
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")

Calculates possessions, pace, offensive and defensive rating, and Four Factors

Description

Calculates possessions, pace, offensive and defensive rating, and Four Factors

Usage

fourfactors(TEAM, OPP)
fourfactors(TEAM, OPP)

Arguments

TEAM

a data frame whose rows are the analyzed teams and with columns referred to the team achievements in the considered games (a box score); required variables: Team, P2A, P2M, P3A, P3M, FTA, FTM, OREB, DREB, TOV, MIN (see Details).

OPP

a data frame whose rows are the analyzed teams and with columns referred to the achievements of the opponents of each team in the considered game; required variables: Team, P2A, P2M, P3A, P3M, FTA, FTM, OREB, DREB, TOV, MIN (see Details).

Details

The rows of the TEAM and the OPP data frames must be referred to the same teams in the same order.

Required columms:

Team, a factor with the name of the analyzed team

P2A, a numeric variable (integer) with the number of 2-points shots attempted

P2M, a numeric variable (integer) with the number of 2-points shots made

P3A, a numeric variable (integer) with the number of 3-points shots attempted

P3M, a numeric variable (integer) with the number of 3-points shots made

FTA, a numeric variable (integer) with the number of free throws attempted

FTM, a numeric variable (integer) with the number of free throws made

OREB, a numeric variable (integer) with the number of offensive rebounds

DREB, a numeric variable (integer) with the number of defensive rebounds

TOV, a numeric variable (integer) with the number of turnovers

MIN, a numeric variable (integer) with the number of minutes played

Value

An object of class fourfactors, i.e. a data frame with the following columns:

Team, a factor with the name of the analyzed team

POSS.Off, a numeric variable with the number of possessions of each team calculated with the formula $POSS=(P2A+P3A)+0.44*FTA-OREB+TOV$

POSS.Def, a numeric variable with the number of possessions of the opponents of each team calculated with the formula $POSS=(P2A+P3A)+0.44*FTA-OREB+TOV$

PACE.Off, a numeric variable with the pace of each team (number of possessions per minute played)

PACE.Def, a numeric variable with the pace of the opponents of each team (number of possessions per minute played)

ORtg, a numeric variable with the offensive rating (the points scored by each team per 100 possessions)

DRtg, a numeric variable with the defensive rating (the points scored by the opponents of each team per 100 possessions)

F1.Off, a numeric variable with the offensive first factor (effective field goal percentage)

F2.Off, a numeric variable with the offensive second factor (turnovers per possession)

F3.Off, a numeric variable with the offensive third factor (rebouding percentage)

F4.Off, a numeric variable with the offensive fourth factor (free throw rate)

F1.Def, a numeric variable with the defensive first factor (effective field goal percentage)

F2.Def, a numeric variable with the defensive second factor (turnovers per possession)

F3.Def, a numeric variable with the defensive third factor (rebouding percentage)

F4.Def, a numeric variable with the defensive fourth factor (free throw rate)

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

selTeams <- c(2,6,10,11)
FF <- fourfactors(Tbox[selTeams,], Obox[selTeams,])
plot(FF)
selTeams <- c(2,6,10,11)
FF <- fourfactors(Tbox[selTeams,], Obox[selTeams,])
plot(FF)

Agglomerative hierarchical clustering

Description

Agglomerative hierarchical clustering

Usage

hclustering(data, k = NULL, nclumax = 10, labels = NULL, linkage = "ward.D")
hclustering(data, k = NULL, nclumax = 10, labels = NULL, linkage = "ward.D")

Arguments

data

numeric data frame.

k

integer, number of clusters.

nclumax

integer, maximum number of clusters (when k=NULL).

labels

character, row labels.

linkage

character, the agglomeration method to be used in hclust (see method in hclust).

Details

The hclustering function performs a preliminary standardization of columns in data.

Value

A hclustering object.

If k is NULL, the hclustering object is a list of 3 elements:

k NULL

clusterRange integer vector, values of k (from 1 to nclumax) at which the variance between of the clusterization is evaluated

VarianceBetween numeric vector, values of the variance between evaluated for k in clusterRange

If k is not NULL, the hclustering object is a list of 5 elements:

k integer, number of clusters

Subjects data frame, subjects' cluster identifiers

ClusterList list, clusters' composition

Profiles data frame, clusters' profiles, i.e. the average of the variables within clusters and the cluster eterogeineity index (CHI)

Hclust an object of class hclust, see hclust

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

data <- with(Pbox, data.frame(PTS, P3M, REB=OREB+DREB, AST, TOV, STL, BLK, PF))
data <- subset(data, Pbox$MIN >= 1500)
ID <- Pbox$Player[Pbox$MIN >= 1500]
hclu1 <- hclustering(data)
plot(hclu1)
hclu2 <- hclustering(data, labels=ID, k=7)
plot(hclu2)
data <- with(Pbox, data.frame(PTS, P3M, REB=OREB+DREB, AST, TOV, STL, BLK, PF))
data <- subset(data, Pbox$MIN >= 1500)
ID <- Pbox$Player[Pbox$MIN >= 1500]
hclu1 <- hclustering(data)
plot(hclu1)
hclu2 <- hclustering(data, labels=ID, k=7)
plot(hclu2)

Inequality analysis

Description

Inequality analysis

Usage

inequality(data, nplayers)
inequality(data, nplayers)

Arguments

data

numeric vector containing the achievements (e.g. scored points) of the players whose inequality has to be analyzed.

nplayers

integer, number of players to include in the analysis (ranked in nondecreasing order according to the values in data).

Value

A list with the following elements: Lorenz (cumulative distributions used to plot the Lorenz curve) and Gini (Gini coefficient).

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

Pbox.BN <- subset(Pbox, Team=="Brooklyn Nets")
out <- inequality(Pbox.BN$PTS, nplayers=8)
print(out)
plot(out)
Pbox.BN <- subset(Pbox, Team=="Brooklyn Nets")
out <- inequality(Pbox.BN$PTS, nplayers=8)
print(out)
plot(out)

Reports whether x is a 'networkdata' object

Description

Reports whether x is a 'networkdata' object

Usage

is.assistnet(x)
is.assistnet(x)

Arguments

x

an object to test.

Value

Returns TRUE if its argument is of class networkdata and FALSE otherwise.

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

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" & player!="")
out <- assistnet(PbP.GSW)
is.assistnet(out)
PbP <- PbPmanipulation(PbP.BDB)
PbP.GSW <- subset(PbP, team=="GSW" & player!="")
out <- assistnet(PbP.GSW)
is.assistnet(out)

Reports whether x is a 'corranalysis' object

Description

Reports whether x is a 'corranalysis' object

Usage

is.corranalysis(x)
is.corranalysis(x)

Arguments

x

an object to test.

Value

Returns TRUE if its argument is of class corranalysis and FALSE otherwise.

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

data <- data.frame(Pbox$PTS,Pbox$P3M,Pbox$P2M,
                   Pbox$OREB + Pbox$DREB,Pbox$AST,
                   Pbox$TOV,Pbox$STL,Pbox$BLK)/Pbox$MIN
names(data) <- c("PTS","P3M","P2M","REB","AST","TOV","STL","BLK")
data <- subset(data, Pbox$MIN >= 500)
out <- corranalysis(data)
is.corranalysis(out)
data <- data.frame(Pbox$PTS,Pbox$P3M,Pbox$P2M,
                   Pbox$OREB + Pbox$DREB,Pbox$AST,
                   Pbox$TOV,Pbox$STL,Pbox$BLK)/Pbox$MIN
names(data) <- c("PTS","P3M","P2M","REB","AST","TOV","STL","BLK")
data <- subset(data, Pbox$MIN >= 500)
out <- corranalysis(data)
is.corranalysis(out)

Reports whether x is a 'fourfactors' object

Description

Reports whether x is a 'fourfactors' object

Usage

is.fourfactors(x)
is.fourfactors(x)

Arguments

x

an object to test.

Value

Returns TRUE if its argument is of class fourfactors and FALSE otherwise.

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

selTeams <- c(2,6,10,11)
out <- fourfactors(Tbox[selTeams,], Obox[selTeams,])
is.fourfactors(out)
selTeams <- c(2,6,10,11)
out <- fourfactors(Tbox[selTeams,], Obox[selTeams,])
is.fourfactors(out)

Reports whether x is a 'hclustering' object

Description

Reports whether x is a 'hclustering' object

Usage

is.hclustering(x)
is.hclustering(x)

Arguments

x

an object to test.

Value

Returns TRUE if its argument is of class hclustering and FALSE otherwise.

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

data <- data.frame(Pbox$PTS,Pbox$P3M,
                   Pbox$OREB + Pbox$DREB, Pbox$AST,
                   Pbox$TOV, Pbox$STL, Pbox$BLK,Pbox$PF)
names(data) <- c("PTS","P3M","REB","AST","TOV","STL","BLK","PF")
data <- subset(data, Pbox$MIN >= 1500)
ID <- Pbox$Player[Pbox$MIN >= 1500]
hclu <- hclustering(data, labels=ID, k=7)
is.hclustering(hclu)
data <- data.frame(Pbox$PTS,Pbox$P3M,
                   Pbox$OREB + Pbox$DREB, Pbox$AST,
                   Pbox$TOV, Pbox$STL, Pbox$BLK,Pbox$PF)
names(data) <- c("PTS","P3M","REB","AST","TOV","STL","BLK","PF")
data <- subset(data, Pbox$MIN >= 1500)
ID <- Pbox$Player[Pbox$MIN >= 1500]
hclu <- hclustering(data, labels=ID, k=7)
is.hclustering(hclu)

Reports whether x is a 'inequality' object.

Description

Reports whether x is a 'inequality' object.

Usage

is.inequality(x)
is.inequality(x)

Arguments

x

an object to test.

Value

Returns TRUE if its argument is of class inequality and FALSE otherwise.

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

Pbox.BN <- subset(Pbox, Team=="Brooklyn Nets")
out <- inequality(Pbox.BN$PTS, npl=8)
is.inequality(out)
Pbox.BN <- subset(Pbox, Team=="Brooklyn Nets")
out <- inequality(Pbox.BN$PTS, npl=8)
is.inequality(out)

Reports whether x is a 'kclustering' object

Description

Reports whether x is a 'kclustering' object

Usage

is.kclustering(x)
is.kclustering(x)

Arguments

x

an object to test.

Value

Returns TRUE if its argument is of class kclustering and FALSE otherwise.

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

FF <- fourfactors(Tbox,Obox)
X <- with(FF, data.frame(OD.Rtg=ORtg/DRtg,
               F1.r=F1.Def/F1.Off, F2.r=F2.Off/F2.Def,
               F3.O=F3.Def, F3.D=F3.Off))
X$P3M <- Tbox$P3M
X$STL.r <- Tbox$STL/Obox$STL
kclu <- kclustering(X)
is.kclustering(kclu)
FF <- fourfactors(Tbox,Obox)
X <- with(FF, data.frame(OD.Rtg=ORtg/DRtg,
               F1.r=F1.Def/F1.Off, F2.r=F2.Off/F2.Def,
               F3.O=F3.Def, F3.D=F3.Off))
X$P3M <- Tbox$P3M
X$STL.r <- Tbox$STL/Obox$STL
kclu <- kclustering(X)
is.kclustering(kclu)

Reports whether x is a 'MDSmap' object

Description

Reports whether x is a 'MDSmap' object

Usage

is.MDSmap(x)
is.MDSmap(x)

Arguments

x

an object to test.

Value

Returns TRUE if its argument is of class MDSmap and FALSE otherwise.

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

data <- subset(Pbox, MIN >= 1500)
data <- data.frame(data$PTS, data$P3M, data$P2M, data$OREB + data$DREB, data$AST,
                   data$TOV,data$STL, data$BLK)
mds <- MDSmap(data)
is.MDSmap(mds)
data <- subset(Pbox, MIN >= 1500)
data <- data.frame(data$PTS, data$P3M, data$P2M, data$OREB + data$DREB, data$AST,
                   data$TOV,data$STL, data$BLK)
mds <- MDSmap(data)
is.MDSmap(mds)

Reports whether x is a 'simplereg' object

Description

Reports whether x is a 'simplereg' object

Usage

is.simplereg(x)
is.simplereg(x)

Arguments

x

an object to test.

Value

Returns TRUE if its argument is of class simplereg and FALSE otherwise.

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

Pbox.sel <- subset(Pbox, MIN >= 500)
X <- Pbox.sel$AST/Pbox.sel$MIN
Y <- Pbox.sel$TOV/Pbox.sel$MIN
Pl <- Pbox.sel$Player
out <- simplereg(x=X, y=Y, type="lin")
is.simplereg(out)
Pbox.sel <- subset(Pbox, MIN >= 500)
X <- Pbox.sel$AST/Pbox.sel$MIN
Y <- Pbox.sel$TOV/Pbox.sel$MIN
Pl <- Pbox.sel$Player
out <- simplereg(x=X, y=Y, type="lin")
is.simplereg(out)

Reports whether x is a 'variability' object

Description

Reports whether x is a 'variability' object

Usage

is.variability(x)
is.variability(x)

Arguments

x

an object to test.

Value

Returns TRUE if its argument is of class variability and FALSE otherwise.

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

Pbox.BC <- subset(Pbox, Team=="Oklahoma City Thunder" & MIN >= 500,
                    select=c("P2p","P3p","FTp","P2A","P3A","FTA"))
out <- variability(data=Pbox.BC, data.var=c("P2p","P3p","FTp"),
                   size.var=c("P2A","P3A","FTA"), weight=TRUE)
is.variability(out)
Pbox.BC <- subset(Pbox, Team=="Oklahoma City Thunder" & MIN >= 500,
                    select=c("P2p","P3p","FTp","P2A","P3A","FTA"))
out <- variability(data=Pbox.BC, data.var=c("P2p","P3p","FTp"),
                   size.var=c("P2A","P3A","FTA"), weight=TRUE)
is.variability(out)

K-means cluster analysis

Description

K-means cluster analysis

Usage

kclustering(
  data,
  k = NULL,
  labels = NULL,
  nclumax = 10,
  nruns = 10,
  iter.max = 50,
  algorithm = "Hartigan-Wong"
)
kclustering(
  data,
  k = NULL,
  labels = NULL,
  nclumax = 10,
  nruns = 10,
  iter.max = 50,
  algorithm = "Hartigan-Wong"
)

Arguments

data

numeric data frame.

k

integer, number of clusters.

labels

character, row labels.

nclumax

integer, maximum number of clusters (when k=NULL) used for calculating the explained variance as function of the number of clusters.

nruns

integer, run the k-means algorithm nruns times and chooses the best solution according to a maximum explained variance criterion.

iter.max

integer, maximum number of iterations allowed in k-means clustering (see kmeans).

algorithm

character, the algorithm used in k-means clustering (see kmeans).

Details

The kclustering function performs a preliminary standardization of columns in data.

Value

A kclustering object.

If k is NULL, the kclustering object is a list of 3 elements:

k NULL

clusterRange integer vector, values of k (from 1 to nclumax) at which the variance between of the clusterization is evaluated

VarianceBetween numeric vector, values of the variance between evaluated for k in clusterRange

If k is not NULL, the kclustering object is a list of 4 elements:

k integer, number of clusters

Subjects data frame, subjects' cluster identifiers

ClusterList list, clusters' composition

Profiles data frame, clusters' profiles, i.e. the average of the variables within clusters and the cluster eterogeineity index (CHI)

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

FF <- fourfactors(Tbox,Obox)
X <- with(FF, data.frame(OD.Rtg=ORtg/DRtg,
               F1.r=F1.Def/F1.Off, F2.r=F2.Off/F2.Def,
               F3.O=F3.Def, F3.D=F3.Off))
X$P3M <- Tbox$P3M
X$STL.r <- Tbox$STL/Obox$STL
kclu1 <- kclustering(X)
plot(kclu1)
kclu2 <- kclustering(X, k=9)
plot(kclu2)
FF <- fourfactors(Tbox,Obox)
X <- with(FF, data.frame(OD.Rtg=ORtg/DRtg,
               F1.r=F1.Def/F1.Off, F2.r=F2.Off/F2.Def,
               F3.O=F3.Def, F3.D=F3.Off))
X$P3M <- Tbox$P3M
X$STL.r <- Tbox$STL/Obox$STL
kclu1 <- kclustering(X)
plot(kclu1)
kclu2 <- kclustering(X, k=9)
plot(kclu2)

Multidimensional scaling (MDS) in 2 dimensions

Description

Multidimensional scaling (MDS) in 2 dimensions

Usage

MDSmap(data, std = TRUE)
MDSmap(data, std = TRUE)

Arguments

data

a numeric matrix, data frame or "dist" object (see dist).

std

logical; if TRUE, data columns are standardized (centered and scaled).

Details

If data is an object of class "dist", std is not active and data is directly inputted into MASS::isoMDS.

Value

An object of class MDSmap, i.e. a list with 4 objects:

points, a 2-column vector of the fitted configuration (see isoMDS);

stress, the final stress achieved in percent (see isoMDS);

data, the input data frame;

std, the logical std input.

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

data <- with(Pbox, data.frame(PTS, P3M, P2M, REB=OREB+DREB, AST, TOV, STL, BLK))
selp <- which(Pbox$MIN >= 1500)
data <- data[selp, ]
id <- Pbox$Player[selp]
mds <- MDSmap(data)
plot(mds, labels=id, z.var="P2M", level.plot=FALSE, palette=rainbow)
data <- with(Pbox, data.frame(PTS, P3M, P2M, REB=OREB+DREB, AST, TOV, STL, BLK))
selp <- which(Pbox$MIN >= 1500)
data <- data[selp, ]
id <- Pbox$Player[selp]
mds <- MDSmap(data)
plot(mds, labels=id, z.var="P2M", level.plot=FALSE, palette=rainbow)

Opponents box scores dataset - NBA 2017-2018

Description

In this data frame cases (rows) are teams and variables (columns) are referred to achievements of the opponents in the NBA 2017-2018 Championship

Usage

Obox
Obox

Format

A data frame with 30 rows and 23 variables:

Team: Analyzed team, character
GP: Games Played, numeric
MIN: Minutes Played, numeric
PTS: Points Made, numeric
W: Games won, numeric
L: Games lost, numeric
P2M: 2-Point Field Goals (Made), numeric
P2A: 2-Point Field Goals (Attempted), numeric
P2p: 2-Point Field Goals (Percentage), numeric
P3M: 3-Point Field Goals (Made), numeric
P3A: 3-Point Field Goals (Attempted), numeric
P3p: 3-Point Field Goals (Percentage), numeric
FTM: Free Throws (Made), numeric
FTA: Free Throws (Attempted), numeric
FTp: Free Throws (Percentage), numeric
OREB: Offensive Rebounds, numeric
DREB: Defensive Rebounds, numeric
AST: Assists, numeric
TOV: Turnovers, numeric
STL: Steals, numeric
BLK: Blocks, numeric
PF: Personal Fouls, numeric
PM: Plus/Minus, numeric

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Players box scores dataset - NBA 2017-2018

Description

In this data frame, cases (rows) are players and variables (columns) are referred to the individual achievements in the NBA 2017-2018 Championship

Usage

Pbox
Pbox

Format

A data.frame with 605 rows and 22 variables:

Team: Analyzed team, character
Player: Analyzed player, character
GP: Games Played, numeric
MIN: Minutes Played, numeric
PTS: Points Made, numeric
P2M: 2-Point Field Goals (Made), numeric
P2A: 2-Point Field Goals (Attempted), numeric
P2p: 2-Point Field Goals (Percentage), numeric
P3M: 3-Point Field Goals (Made), numeric
P3A: 3-Point Field Goals (Attempted), numeric
P3p: 3-Point Field Goals (Percentage), numeric
FTM: Free Throws (Made), numeric
FTA: Free Throws (Attempted), numeric
FTp: Free Throws (Percentage), numeric
OREB: Offensive Rebounds, numeric
DREB: Defensive Rebounds, numeric
AST: Assists, numeric
TOV: Turnovers, numeric
STL: Steals, numeric
BLK: Blocks, numeric
PF: Personal Fouls, numeric
PM: Plus/Minus, numeric

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Play-by-play dataset - NBA 2017-2018

Description

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.

Usage

PbP.BDB
PbP.BDB

Format

A data.frame with 37430 rows and 48 variables:

game_id: Identification code for the game
data_set: Season: years and type (Regular or Playoffs)
date: Date of the game
a1 ... a5; h1 ... h5: Five players on the court (away team; home team)
period: Quarter (>= 5: over-time)
away_score; home_score: Score of the away/home team
remaining_time: Time left in the quarter (h:mm:ss)
elapsed: Time played in the quarter (h:mm:ss)
play_length: Time since the immediately preceding event (h:mm:ss)
play_id: Identification code for the play
team: Team responsible for the event
event_type: Type of event
assist: Player who made the assist
away; home: Players for the jump ball
block: Player who blocked the shot
entered; left: Player who entered/left the court
num: Sequence number of the free throw
opponent: Player who made the foul
outof: Number of free throws accorded
player: Player responsible for the event
points: Scored points
possession: Player who the jump ball is tipped to
reason: Reason of the turnover
result: Result of the shot (made or missed)
steal: Player who stole the ball
type: Type of play
shot_distance: Field shots: distance from the basket
original_x ; original_y; converted_x ; converted_y: 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
description: Textual description of the event

Details

This data set has been kindly made available by BigDataBall (www.bigdataball.com), 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.

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

Source

https://github.com/sndmrc/BasketballAnalyzeR

References

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

Adapts the standard file supplied by BigDataBall to the format required by BasketballAnalyzeR

Description

Adapts the standard file supplied by BigDataBall to the format required by BasketballAnalyzeR

Usage

PbPmanipulation(data, period.length = 12, overtime.length = 5)
PbPmanipulation(data, period.length = 12, overtime.length = 5)

Arguments

data

a play-by-play data frame supplied by BigDataBall (www.bigdataball.com).

period.length

numeric, the length of a quarter in minutes (default: 12 minutes as in NBA)

overtime.length

numeric, the length of an overtime period in minutes (default: 5 minutes as in NBA)

Value

A play-by-play data frame.

The data frame generated by PbPmanipulation has the same variables of PbP.BDB (when necessary, coerced from one data type to another, e.g from factor to numeric) plus the following five additional variables:

periodTime, time played in the quarter (in seconds)

totalTime, time played in the match (in seconds)

playlength, time since the immediately preceding event (in seconds)

ShotType, type of shot (FT, 2P, 3P)

oppTeam, name of the opponent team

hometeam, name of the home team (generated conditionally on the presence of the variable home_score)

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

PbP <- PbPmanipulation(PbP.BDB)
PbP <- PbPmanipulation(PbP.BDB)

Plots a network from a 'assistnet' object

Description

Plots a network from a 'assistnet' object

Usage

## S3 method for class 'assistnet'
plot(
  x,
  layout = "kamadakawai",
  layout.par = list(),
  edge.thr = 0,
  edge.col.lim = NULL,
  edge.col.lab = NULL,
  node.size = NULL,
  node.size.lab = NULL,
  node.col = NULL,
  node.col.lim = NULL,
  node.col.lab = NULL,
  node.pal = colorRampPalette(c("white", "blue", "red")),
  edge.pal = colorRampPalette(c("white", "blue", "red")),
  ...
)
## S3 method for class 'assistnet'
plot(
  x,
  layout = "kamadakawai",
  layout.par = list(),
  edge.thr = 0,
  edge.col.lim = NULL,
  edge.col.lab = NULL,
  node.size = NULL,
  node.size.lab = NULL,
  node.col = NULL,
  node.col.lim = NULL,
  node.col.lab = NULL,
  node.pal = colorRampPalette(c("white", "blue", "red")),
  edge.pal = colorRampPalette(c("white", "blue", "red")),
  ...
)

Arguments

x

an object of class assistnet.

layout

character, network vertex layout algorithm (see gplot.layout) such as "kamadakawai" (the default).

layout.par

a list of parameters for the network vertex layout algorithm (see gplot.layout).

edge.thr

numeric, threshold for edge values; values below the threshold are set to 0.

edge.col.lim

numeric vector of length two providing limits of the scale for edge color.

edge.col.lab

character, label for edge color legend.

node.size

character, indicating the name of the variable for node size (one of the columns of the nodeStats data frame in the x object, see assistnet).

node.size.lab

character, label for node size legend.

node.col

character, indicating the name of the variable for node color (one of the columns of the nodeStats data frame in the x object, see assistnet).

node.col.lim

numeric vector of length two providing limits of the scale for node color.

node.col.lab

character, label for node color legend.

node.pal

color palette for node colors.

edge.pal

color palette for edge colors.

...

other graphical parameters.

Value

A ggplot2 object

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

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" & player!="")
out <- assistnet(PbP.GSW)
plot(out, layout="circle", edge.thr=30, node.col="FGM_ASTp", node.size="ASTPTS")
PbP <- PbPmanipulation(PbP.BDB)
PbP.GSW <- subset(PbP, team=="GSW" & player!="")
out <- assistnet(PbP.GSW)
plot(out, layout="circle", edge.thr=30, node.col="FGM_ASTp", node.size="ASTPTS")

Plots the correlation matrix and the correlation network from a 'corranalysis' object

Description

Plots the correlation matrix and the correlation network from a 'corranalysis' object

Usage

## S3 method for class 'corranalysis'
plot(x, horizontal = TRUE, title = NULL, ...)
## S3 method for class 'corranalysis'
plot(x, horizontal = TRUE, title = NULL, ...)

Arguments

x

an object of class corranalysis.

horizontal

logical; if TRUE, the two plots are arranged horizontally.

title

character, plot title.

...

other graphical parameters

Value

A ggplot2 object

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

data <- data.frame(Pbox$PTS,Pbox$P3M,Pbox$P2M,
                   Pbox$OREB + Pbox$DREB,Pbox$AST,
                   Pbox$TOV,Pbox$STL,Pbox$BLK)/Pbox$MIN
names(data) <- c("PTS","P3M","P2M","REB","AST","TOV","STL","BLK")
data <- subset(data, Pbox$MIN >= 500)
out <- corranalysis(data, threshold=0.5)
plot(out)
data <- data.frame(Pbox$PTS,Pbox$P3M,Pbox$P2M,
                   Pbox$OREB + Pbox$DREB,Pbox$AST,
                   Pbox$TOV,Pbox$STL,Pbox$BLK)/Pbox$MIN
names(data) <- c("PTS","P3M","P2M","REB","AST","TOV","STL","BLK")
data <- subset(data, Pbox$MIN >= 500)
out <- corranalysis(data, threshold=0.5)
plot(out)

Plot possessions, pace, offensive and defensive rating, and Four Factors from a 'fourfactors' object

Description

Plot possessions, pace, offensive and defensive rating, and Four Factors from a 'fourfactors' object

Usage

## S3 method for class 'fourfactors'
plot(x, title = NULL, ...)
## S3 method for class 'fourfactors'
plot(x, title = NULL, ...)

Arguments

x

an object of class fourfactors.

title

character, plot title.

...

other graphical parameters.

Details

The height of the bars in the two four factor plots are given by the difference between the team value and the average on the analyzed teams.

Value

A list of four ggplot2 plots.

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

selTeams <- c(2,6,10,11)
FF <- fourfactors(Tbox[selTeams,], Obox[selTeams,])
plot(FF)
selTeams <- c(2,6,10,11)
FF <- fourfactors(Tbox[selTeams,], Obox[selTeams,])
plot(FF)

Plots hierarchical clustering from a 'hclustering' object

Description

Plots hierarchical clustering from a 'hclustering' object

Usage

## S3 method for class 'hclustering'
plot(
  x,
  title = NULL,
  profiles = FALSE,
  ncol.arrange = NULL,
  circlize = FALSE,
  horiz = TRUE,
  cex.labels = 0.7,
  colored.labels = TRUE,
  colored.branches = FALSE,
  rect = FALSE,
  lower.rect = NULL,
  min.mid.max = NULL,
  ...
)
## S3 method for class 'hclustering'
plot(
  x,
  title = NULL,
  profiles = FALSE,
  ncol.arrange = NULL,
  circlize = FALSE,
  horiz = TRUE,
  cex.labels = 0.7,
  colored.labels = TRUE,
  colored.branches = FALSE,
  rect = FALSE,
  lower.rect = NULL,
  min.mid.max = NULL,
  ...
)

Arguments

x

an object of class hclustering.

title

character or vector of characters (when plotting radial plots of cluster profiles; see Value), plot title(s).

profiles

logical; if TRUE, displays radial plots of cluster profiles (active if x$k is not NULL; see Value).

ncol.arrange

integer, number of columns when arranging multiple grobs on a page (active when plotting radial plots of cluster profiles; see Value).

circlize

logical; if TRUE, plots a circular dendrogram (active when plotting a dendrogram; see Value).

horiz

logical; if TRUE, plots an horizontal dendrogram (active when plotting a non circular dendrogram; see Value).

cex.labels

numeric, the magnification to be used for labels (active when plotting a dendrogram; see Value).

colored.labels

logical; if TRUE, assigns different colors to labels of different clusters (active when plotting a dendrogram; see Value).

colored.branches

logical; if TRUE, assigns different colors to branches of different clusters (active when plotting a dendrogram; see Value).

rect

logical; if TRUE, draws rectangles around the branches in order to highlight the corresponding clusters (active when plotting a dendrogram; see Value).

lower.rect

numeric, a value of how low should the lower part of the rect be (active when plotting a dendrogram; see option lower_rect of rect.dendrogram).

min.mid.max

numeric vector with 3 elements: lower bound, middle dashed line, upper bound for radial axis (active when plotting radial plots of cluster profiles; see Value).

...

other graphical parameters.

Value

If x$k is NULL, plot.hclustering returns a single ggplot2 object, displaying the pattern of the explained variance vs the number of clusters.

If x$k is not NULL and profiles=FALSE, plot.hclustering returns a single ggplot2 object, displaying the dendrogram.

If x$k is not NULL and profiles=TRUE, plot.hclustering returns a list of ggplot2 objects, displaying the radial plots of the cluster profiles.

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

data <- with(Pbox, data.frame(PTS, P3M, REB=OREB+DREB, AST, TOV, STL, BLK, PF))
data <- subset(data, Pbox$MIN >= 1500)
ID <- Pbox$Player[Pbox$MIN >= 1500]
hclu1 <- hclustering(data)
plot(hclu1)
hclu2 <- hclustering(data, labels=ID, k=7)
plot(hclu2)
data <- with(Pbox, data.frame(PTS, P3M, REB=OREB+DREB, AST, TOV, STL, BLK, PF))
data <- subset(data, Pbox$MIN >= 1500)
ID <- Pbox$Player[Pbox$MIN >= 1500]
hclu1 <- hclustering(data)
plot(hclu1)
hclu2 <- hclustering(data, labels=ID, k=7)
plot(hclu2)

Plot Lorenz curve from a 'inequality' object

Description

Plot Lorenz curve from a 'inequality' object

Usage

## S3 method for class 'inequality'
plot(x, title = NULL, ...)
## S3 method for class 'inequality'
plot(x, title = NULL, ...)

Arguments

x

an object of class inequality.

title

character, plot title.

...

other graphical parameters.

Value

A ggplot2 object.

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

Pbox.BN <- subset(Pbox, Team=="Brooklyn Nets")
out <- inequality(Pbox.BN$PTS, nplayers=8)
print(out)
plot(out)
Pbox.BN <- subset(Pbox, Team=="Brooklyn Nets")
out <- inequality(Pbox.BN$PTS, nplayers=8)
print(out)
plot(out)

Plot k-means clustering from a 'kclustering' object

Description

Plot k-means clustering from a 'kclustering' object

Usage

## S3 method for class 'kclustering'
plot(
  x,
  title = NULL,
  ncol.arrange = NULL,
  min.mid.max = NULL,
  label.size = 2.5,
  ...
)
## S3 method for class 'kclustering'
plot(
  x,
  title = NULL,
  ncol.arrange = NULL,
  min.mid.max = NULL,
  label.size = 2.5,
  ...
)

Arguments

x

an object of class kclustering.

title

character or vector of characters (when plotting radial plots of cluster profiles; see Value), plot title(s).

ncol.arrange

integer, number of columns when arranging multiple grobs on a page (active when plotting radial plots of cluster profiles; see Value).

min.mid.max

numeric vector with 3 elements: lower bound, middle dashed line, upper bound for radial axis (active when plotting radial plots of cluster profiles; see Value).

label.size

numeric; label font size (default 2.5).

...

other graphical parameters.

Value

If x$k is NULL, plot.kclustering returns a single ggplot2 object, displaying the pattern of the explained variance vs the number of clusters.

If x$k is not NULL, plot.kclustering returns a list of ggplot2 objects, displaying the radial plots of the cluster profiles.

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

FF <- fourfactors(Tbox,Obox)
X <- with(FF, data.frame(OD.Rtg=ORtg/DRtg,
               F1.r=F1.Def/F1.Off, F2.r=F2.Off/F2.Def,
               F3.O=F3.Def, F3.D=F3.Off))
X$P3M <- Tbox$P3M
X$STL.r <- Tbox$STL/Obox$STL
kclu1 <- kclustering(X)
plot(kclu1)
kclu2 <- kclustering(X, k=9)
plot(kclu2)
FF <- fourfactors(Tbox,Obox)
X <- with(FF, data.frame(OD.Rtg=ORtg/DRtg,
               F1.r=F1.Def/F1.Off, F2.r=F2.Off/F2.Def,
               F3.O=F3.Def, F3.D=F3.Off))
X$P3M <- Tbox$P3M
X$STL.r <- Tbox$STL/Obox$STL
kclu1 <- kclustering(X)
plot(kclu1)
kclu2 <- kclustering(X, k=9)
plot(kclu2)

Draws two-dimensional plots for multidimensional scaling (MDS) from a 'MDSmap' object

Description

Draws two-dimensional plots for multidimensional scaling (MDS) from a 'MDSmap' object

Usage

## S3 method for class 'MDSmap'
plot(
  x,
  z.var = NULL,
  level.plot = TRUE,
  title = NULL,
  labels = NULL,
  repel_labels = FALSE,
  text_label = TRUE,
  label_size = 3,
  subset = NULL,
  col.subset = "gray50",
  zoom = NULL,
  palette = NULL,
  contour = FALSE,
  ncol.arrange = NULL,
  ...
)
## S3 method for class 'MDSmap'
plot(
  x,
  z.var = NULL,
  level.plot = TRUE,
  title = NULL,
  labels = NULL,
  repel_labels = FALSE,
  text_label = TRUE,
  label_size = 3,
  subset = NULL,
  col.subset = "gray50",
  zoom = NULL,
  palette = NULL,
  contour = FALSE,
  ncol.arrange = NULL,
  ...
)

Arguments

x

an object of class MDSmap.

z.var

character vector; defines the set of variables (available in the data data frame of MDSmap) used to color-coding the points in the map (for scatter plots) or, alternatively, overlap to the map a colored level plot.

level.plot

logical; if TRUE, draws a level plot, otherwise draws a scatter plot (not active if zvar=NULL).

title

character, plot title.

labels

character vector, labels for (x, y) points (only for single scatter plot).

repel_labels

logical; if TRUE, draw text labels using repelling (not for highlighted points) (see geom_text_repel).

text_label

logical; if TRUE, draw a rectangle behind the text labels (not active if subset=NULL).

label_size

numeric; label font size (default label_size=3, for scatter plots).

subset

logical vector, to select a subset of points to be highlighted.

col.subset

character, color for the subset of points.

zoom

numeric vector with 4 elements; c(xmin,xmax,ymin,ymax) for the x- and y-axis limits of the plot.

palette

color palette.

contour

logical; if TRUE, contour lines are plotted (not active if level.plot=FALSE).

ncol.arrange

integer, number of columns when arranging multiple grobs on a page.

...

other graphical parameters.

Value

A single ggplot2 plot or a list of ggplot2 plots

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

data <- data.frame(Pbox$PTS, Pbox$P3M, Pbox$P2M, Pbox$OREB + Pbox$DREB, Pbox$AST,
Pbox$TOV,Pbox$STL, Pbox$BLK)
names(data) <- c('PTS','P3M','P2M','REB','AST','TOV','STL','BLK')
selp <- which(Pbox$MIN >= 1500)
data <- data[selp,]
id <- Pbox$Player[selp]
mds <- MDSmap(data)
plot(mds, labels=id, z.var="P2M", level.plot=FALSE, palette=rainbow)
data <- data.frame(Pbox$PTS, Pbox$P3M, Pbox$P2M, Pbox$OREB + Pbox$DREB, Pbox$AST,
Pbox$TOV,Pbox$STL, Pbox$BLK)
names(data) <- c('PTS','P3M','P2M','REB','AST','TOV','STL','BLK')
selp <- which(Pbox$MIN >= 1500)
data <- data[selp,]
id <- Pbox$Player[selp]
mds <- MDSmap(data)
plot(mds, labels=id, z.var="P2M", level.plot=FALSE, palette=rainbow)

Plot simple regression from a 'simplereg' object

Description

Plot simple regression from a 'simplereg' object

Usage

## S3 method for class 'simplereg'
plot(
  x,
  labels = NULL,
  subset = NULL,
  Lx = 0.01,
  Ux = 0.99,
  Ly = 0.01,
  Uy = 0.99,
  title = "Simple regression",
  xtitle = NULL,
  ytitle = NULL,
  repel = TRUE,
  ...
)
## S3 method for class 'simplereg'
plot(
  x,
  labels = NULL,
  subset = NULL,
  Lx = 0.01,
  Ux = 0.99,
  Ly = 0.01,
  Uy = 0.99,
  title = "Simple regression",
  xtitle = NULL,
  ytitle = NULL,
  repel = TRUE,
  ...
)

Arguments

x

an object of class simplereg.

labels

character, labels for subjects.

subset

an optional vector specifying a subset of observations to be highlighted in the graph or subset='quant' to highligh observations with coordinates above and below the upper and lower quantiles of the variables on the x- and y-axis (Lx, Ux, Ly, Uy).

Lx

numeric; if subset='quant', lower quantile for the variable on the x-axis (default = 0.01).

Ux

numeric; if subset='quant', upper quantile for the variable on the x-axis (default = 0.99).

Ly

numeric; if subset='quant', lower quantile for the variable on the y-axis (default = 0.01).

Uy

numeric; if subset='quant', upper quantile for the variable on the y-axis (default = 0.99).

title

character, plot title.

xtitle

character, x-axis label.

ytitle

character, y-axis label.

repel

logical, if TRUE (the default) text labels repel away from each other.

...

other graphical parameters.

Value

A ggplot2 object

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

Pbox.sel <- subset(Pbox, MIN >= 500)
X <- Pbox.sel$AST/Pbox.sel$MIN
Y <- Pbox.sel$TOV/Pbox.sel$MIN
Pl <- Pbox.sel$Player
mod <- simplereg(x=X, y=Y, type="lin")
plot(mod)
Pbox.sel <- subset(Pbox, MIN >= 500)
X <- Pbox.sel$AST/Pbox.sel$MIN
Y <- Pbox.sel$TOV/Pbox.sel$MIN
Pl <- Pbox.sel$Player
mod <- simplereg(x=X, y=Y, type="lin")
plot(mod)

Plots a variability diagram from a 'variability' object

Description

Plots a variability diagram from a 'variability' object

Usage

## S3 method for class 'variability'
plot(
  x,
  title = "Variability diagram",
  ylim = NULL,
  ylab = NULL,
  size.lim = NULL,
  max.circle = 25,
  n.circle = 4,
  leg.brk = NULL,
  leg.pos = "right",
  leg.just = "left",
  leg.nrow = NULL,
  leg.title = NULL,
  leg.title.pos = "top",
  ...
)
## S3 method for class 'variability'
plot(
  x,
  title = "Variability diagram",
  ylim = NULL,
  ylab = NULL,
  size.lim = NULL,
  max.circle = 25,
  n.circle = 4,
  leg.brk = NULL,
  leg.pos = "right",
  leg.just = "left",
  leg.nrow = NULL,
  leg.title = NULL,
  leg.title.pos = "top",
  ...
)

Arguments

x

an aobject of class variability.

title

character, plot title.

ylim

numeric vector of length two, y-axis limits.

ylab

character, y-axis label.

size.lim

numeric vector of length two, set limits of the bubbles' size scale (see limits of scale_size).

max.circle

numeric, maximum size of the size plotting symbol (see range of scale_size).

n.circle

integer; if leg.brk=NULL, set a sequence of about n.circle+1 equally spaced 'round' values which cover the range of the values used to set the bubbles' size.

leg.brk

numeric vector, breaks for bubbles' size legend (see breaks of scale_size).

leg.pos

character or numeric vector of length two, legend position; available options "none", "left", "right" (default), "bottom", "top", or a c(x,y) numeric vector (x and y are coordinates of the legend box; their values should be between 0 and 1; c(0,0) corresponds to the bottom-left and c(1,1) corresponds to the top-right position).

leg.just

character or numeric vector of length two; anchor point for positioning legend inside plot ("left" (default), "center", "right" or two-element numeric vector) or the justification according to the plot area when positioned outside the plot.

leg.nrow

integer, number of rows of the bubbles' size legend.

leg.title

character, title of the bubbles' size legend.

leg.title.pos

character, position of the legend title; available options: "top" (default for a vertical legend), "bottom", "left" (default for a horizontal legend), or "right".

...

other graphical parameters.

Value

A ggplot2 object

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

Pbox.BC <- subset(Pbox, Team=="Oklahoma City Thunder" & MIN >= 500,
                    select=c("P2p","P3p","FTp","P2A","P3A","FTA"))
out <- variability(data=Pbox.BC, data.var=c("P2p","P3p","FTp"),
                   size.var=c("P2A","P3A","FTA"), weight=TRUE)
plot(out, leg.brk=c(10,25,50,100,500,1000), max.circle=30)
Pbox.BC <- subset(Pbox, Team=="Oklahoma City Thunder" & MIN >= 500,
                    select=c("P2p","P3p","FTp","P2A","P3A","FTA"))
out <- variability(data=Pbox.BC, data.var=c("P2p","P3p","FTp"),
                   size.var=c("P2A","P3A","FTA"), weight=TRUE)
plot(out, leg.brk=c(10,25,50,100,500,1000), max.circle=30)

Draws radial plots for player profiles

Description

Draws radial plots for player profiles

Usage

radialprofile(
  data,
  perc = FALSE,
  std = TRUE,
  title = NULL,
  ncol.arrange = NULL,
  min.mid.max = NULL,
  label.size = 2.5
)
radialprofile(
  data,
  perc = FALSE,
  std = TRUE,
  title = NULL,
  ncol.arrange = NULL,
  min.mid.max = NULL,
  label.size = 2.5
)

Arguments

data

a data frame.

perc

logical; if perc=TRUE, std=FALSE and min.mid.max=NULL, set axes range between 0 and 100 and set the middle dashed line at 50.

std

logical; if std=TRUE, variables are preliminarily standardized.

title

character vector, titles for radial plots.

ncol.arrange

integer, number of columns in the grid of arranged plots.

min.mid.max

numeric vector with 3 elements: lower bound, middle dashed line, upper bound for radial axis.

label.size

numeric; label font size (default 2.5).

Value

A list of ggplot2 radial plots or, if ncol.arrange=NULL, a single ggplot2 plot of arranged radial plots

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

data("Pbox")
Pbox.PG <- Pbox[1:6,]
X <- data.frame(Pbox.PG$P2M, Pbox.PG$P3M, Pbox.PG$OREB+Pbox.PG$DREB,
                Pbox.PG$AST, Pbox.PG$TO)/Pbox.PG$MIN
names(X) <- c("P2M","P3M","REB","AST","TO")
radialprofile(data=X, ncol.arrange=3, title=Pbox.PG$Player)
data("Pbox")
Pbox.PG <- Pbox[1:6,]
X <- data.frame(Pbox.PG$P2M, Pbox.PG$P3M, Pbox.PG$OREB+Pbox.PG$DREB,
                Pbox.PG$AST, Pbox.PG$TO)/Pbox.PG$MIN
names(X) <- c("P2M","P3M","REB","AST","TO")
radialprofile(data=X, ncol.arrange=3, title=Pbox.PG$Player)

Draws a scatter plot or a matrix of scatter plots

Description

Draws a scatter plot or a matrix of scatter plots

Usage

scatterplot(
  data,
  data.var,
  z.var = NULL,
  palette = NULL,
  labels = NULL,
  repel_labels = FALSE,
  text_label = TRUE,
  label_size = 3,
  subset = NULL,
  col.subset = "gray50",
  zoom = NULL,
  title = NULL,
  legend = TRUE,
  upper = list(continuous = "cor", combo = "box_no_facet", discrete = "facetbar", na =
    "na"),
  lower = list(continuous = "points", combo = "facethist", discrete = "facetbar", na =
    "na"),
  diag = list(continuous = "densityDiag", discrete = "barDiag", na = "naDiag")
)
scatterplot(
  data,
  data.var,
  z.var = NULL,
  palette = NULL,
  labels = NULL,
  repel_labels = FALSE,
  text_label = TRUE,
  label_size = 3,
  subset = NULL,
  col.subset = "gray50",
  zoom = NULL,
  title = NULL,
  legend = TRUE,
  upper = list(continuous = "cor", combo = "box_no_facet", discrete = "facetbar", na =
    "na"),
  lower = list(continuous = "points", combo = "facethist", discrete = "facetbar", na =
    "na"),
  diag = list(continuous = "densityDiag", discrete = "barDiag", na = "naDiag")
)

Arguments

data

an object of class data.frame.

data.var

character or numeric vector, name or column number of variables (in data object) used on the axes of scatter plot(s).

z.var

character or number, name or column number of variable (in data object) used to assign colors to points (see Details).

palette

color palette (active when plotting a single scatter plot; see Value).

labels

character vector, labels for points (active when plotting a single scatter plot, see Value).

repel_labels

logical; if TRUE, draws text labels of not highlighted points using repelling (active when plotting a single scatter plot; see Value).

text_label

logical; if TRUE, draws a rectangle behind the labels of highlighted points (active when plotting a single scatter plot; see Value).

label_size

numeric; label font size (default label_size=3).

subset

logical or numeric vector, to select a subset of points to be highlighted (active when plotting a single scatter plot; see Value).

col.subset

character, color for the labels and rectangles of highlighted points (active when plotting a single scatter plot; see Value).

zoom

numeric vector with 4 elements; c(xmin,xmax,ymin,ymax) for the x- and y-axis limits of the plot (active when plotting a single scatter plot; see Value).

title

character, plot title.

legend

logical, if legend=FALSE legend is removed (active when plotting a single scatter plot with z.var not NULL; see Value).

upper

list, may contain the variables continuous, combo, discrete, and na (active when plotting a matrix of scatter plot; see Value and upper in ggpairs)

lower

list, may contain the variables continuous, combo, discrete, and na (active when plotting a matrix of scatter plot; see Value and lower in ggpairs)

diag

list, may contain the variables continuous, discrete, and na (active when plotting a matrix of scatter plot; see Value and diag in ggpairs)

Details

If length(data.var)=2, the variable specified in z.var can be numeric or factor; if length(data.var)>2, the variable specified in z.var must be a factor.

Value

A ggplot2 object with a single scatter plot if length(data.var)=2 or a matrix of scatter plots if length(data.var)>2.

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

# Single scatter plot
Pbox.sel <- subset(Pbox, MIN>= 500)
X <- data.frame(AST=Pbox.sel$AST/Pbox.sel$MIN,TOV=Pbox.sel$TOV/Pbox.sel$MIN)
X$PTSpm <- Pbox.sel$PTS/Pbox.sel$MIN
mypal <- colorRampPalette(c("blue","yellow","red"))
scatterplot(X, data.var=c("AST","TOV"), z.var="PTSpm", labels=1:nrow(X), palette=mypal)
# Matrix of scatter plots
data <- Pbox[1:50, c("PTS","P3M","P2M","OREB","Team")]
scatterplot(data, data.var=1:4, z.var="Team")
# Single scatter plot
Pbox.sel <- subset(Pbox, MIN>= 500)
X <- data.frame(AST=Pbox.sel$AST/Pbox.sel$MIN,TOV=Pbox.sel$TOV/Pbox.sel$MIN)
X$PTSpm <- Pbox.sel$PTS/Pbox.sel$MIN
mypal <- colorRampPalette(c("blue","yellow","red"))
scatterplot(X, data.var=c("AST","TOV"), z.var="PTSpm", labels=1:nrow(X), palette=mypal)
# Matrix of scatter plots
data <- Pbox[1:50, c("PTS","P3M","P2M","OREB","Team")]
scatterplot(data, data.var=1:4, z.var="Team")

Plots scoring probability of shots as a function of a given variable

Description

Plots scoring probability of shots as a function of a given variable

Usage

scoringprob(
  data,
  var,
  shot.type,
  players = NULL,
  bw = 20,
  period.length = 12,
  xlab = NULL,
  x.range = "auto",
  title = NULL,
  palette = gg_color_hue,
  team = TRUE,
  col.team = "dodgerblue",
  legend = TRUE
)
scoringprob(
  data,
  var,
  shot.type,
  players = NULL,
  bw = 20,
  period.length = 12,
  xlab = NULL,
  x.range = "auto",
  title = NULL,
  palette = gg_color_hue,
  team = TRUE,
  col.team = "dodgerblue",
  legend = TRUE
)

Arguments

data

a data frame whose rows are shots and with the following columns: result, ShotType, 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, the string giving the name of the numerical variable according to which the scoring probability is estimated. Available options: "playlength", "periodTime", "totalTime", "shot_distance".

shot.type

character, the type of shots to be analyzed; available options: "2P", "3P", "FT", "field".

players

subset of players to be displayed (optional; it can be used only if the player column is present in data).

bw

numeric, the 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).

xlab

character, x-axis label.

x.range

title

character, plot title.

palette

color palette.

team

character; if TRUE draws the scoring probability for all the shots in data.

col.team

character, color of the scoring probability line for all the shots in data.

legend

character; 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 shots have NA in the ShotType variable.

Required columns:

result, a factor with the following levels: "made" for made shots, "miss" for missed shots, and "" for events different from shots

ShotType, a factor with the following levels: "2P", "3P", "FT" (and NA for events different from shots)

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

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 ([email protected])

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" & result!="")
players <- c("Kevin Durant","Draymond Green","Klay Thompson")
scoringprob(data=PbP.GSW, shot.type="2P", players=players,
            var="shot_distance", col.team="gray")
PbP <- PbPmanipulation(PbP.BDB)
PbP.GSW <- subset(PbP, team=="GSW" & result!="")
players <- c("Kevin Durant","Draymond Green","Klay Thompson")
scoringprob(data=PbP.GSW, shot.type="2P", players=players,
            var="shot_distance", col.team="gray")

Plots different kinds of charts based on shot coordinates

Description

Plots different kinds of charts based on shot coordinates

Usage

shotchart(
  data,
  x,
  y,
  z = NULL,
  z.fun = median,
  result = NULL,
  type = NULL,
  scatter = FALSE,
  num.sect = 7,
  n = 1000,
  col.limits = c(NA, NA),
  courtline.col = "black",
  bg.col = "white",
  sectline.col = "white",
  text.col = "white",
  legend = FALSE,
  drop.levels = TRUE,
  pt.col = "black",
  pt.alpha = 0.5,
  nbins = 25,
  palette = "mixed"
)
shotchart(
  data,
  x,
  y,
  z = NULL,
  z.fun = median,
  result = NULL,
  type = NULL,
  scatter = FALSE,
  num.sect = 7,
  n = 1000,
  col.limits = c(NA, NA),
  courtline.col = "black",
  bg.col = "white",
  sectline.col = "white",
  text.col = "white",
  legend = FALSE,
  drop.levels = TRUE,
  pt.col = "black",
  pt.alpha = 0.5,
  nbins = 25,
  palette = "mixed"
)

Arguments

data

A data frame whose rows are field shots and columns are half-court shot coordinates x and y, and optionally additional variables to be specified in z and/or result (see Details).

x

character, indicating the variable name of the x coordinate.

y

character, indicating the variable name of the y coordinate.

z

character, indicating the name of the variable used to color the points (if type=NULL) or the sectors (if type="sectors", in this case z must be a numeric variable).

z.fun

function (active when type="sectors"), used to summarize the values of z variable within each sector (recommended: mean, median).

result

character (active when type="sectors" and scatter=FALSE), indicating the name of the factor with the shot result (allowed categories made and missed).

type

character, indicating the plot type; available option are NULL, "sectors", "density-polygons", "density-raster", "density-hexbin".

scatter

logical, if TRUE a scatter plot of the shots is added to the plot.

num.sect

integer (active when type="sectors"), number of sectors.

n

integer (active when type="sectors"), number of points used to draw arcs (must be > 500).

col.limits

numeric vector, (active when z is a numeric variable), limits c(min, max) for the gradient color scale of z variable.

courtline.col

color of court lines.

bg.col

background color.

sectline.col

color of sector lines (active when type="sectors").

text.col

color of text annotation within sectors (active when type="sectors").

legend

logical, if TRUE a legend for z is plotted.

drop.levels

logical, if TRUE unused levels of the z variable are dropped.

pt.col

color of points in the scatter plot.

pt.alpha

numeric, transparency of points in the scatter plot.

nbins

integer (active when type="density-hexbin"), number of bins.

palette

color palette; available options "main", "cool", "hot", "mixed", "grey", "bwr" (blue, white, red).

Details

The data dataframe could also be a play-by-play dataset provided that rows corresponding to events different from field shots have missing x and y coordinates.

x and y coordinates must be expressed in feets; the origin of the axes is positioned at the center of the field.

Value

A ggplot2 object.

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

PbP <- PbPmanipulation(PbP.BDB)
subdata <- subset(PbP, player=="Kevin Durant")
subdata$xx <- subdata$original_x/10
subdata$yy <- subdata$original_y/10-41.75
shotchart(data=subdata, x="xx", y="yy", scatter=TRUE)
shotchart(data=subdata, x="xx", y="yy", scatter=TRUE, z="result")
shotchart(data=subdata, x="xx", y="yy", scatter=TRUE, z="result",
          bg.col="black", courtline.col="white", palette="hot")
shotchart(data=subdata, x="xx", y="yy", result="result",
          type="sectors", sectline.col="gray", text.col="red")
shotchart(data=subdata, x="xx", y="yy", z="playlength", result="result",
          type="sectors",  num.sect=5)
shotchart(data=subdata, x="xx", y="yy", type="density-polygons", palette="bwr")
shotchart(data=subdata, x="xx", y="yy", type="density-raster",
          scatter=TRUE, pt.col="tomato", pt.alpha=0.1)
shotchart(data=subdata, x="xx", y="yy", type="density-hexbin", nbins=30)
PbP <- PbPmanipulation(PbP.BDB)
subdata <- subset(PbP, player=="Kevin Durant")
subdata$xx <- subdata$original_x/10
subdata$yy <- subdata$original_y/10-41.75
shotchart(data=subdata, x="xx", y="yy", scatter=TRUE)
shotchart(data=subdata, x="xx", y="yy", scatter=TRUE, z="result")
shotchart(data=subdata, x="xx", y="yy", scatter=TRUE, z="result",
          bg.col="black", courtline.col="white", palette="hot")
shotchart(data=subdata, x="xx", y="yy", result="result",
          type="sectors", sectline.col="gray", text.col="red")
shotchart(data=subdata, x="xx", y="yy", z="playlength", result="result",
          type="sectors",  num.sect=5)
shotchart(data=subdata, x="xx", y="yy", type="density-polygons", palette="bwr")
shotchart(data=subdata, x="xx", y="yy", type="density-raster",
          scatter=TRUE, pt.col="tomato", pt.alpha=0.1)
shotchart(data=subdata, x="xx", y="yy", type="density-hexbin", nbins=30)

Simple linear and nonparametric regression

Description

Simple linear and nonparametric regression

Usage

simplereg(x, y, type = "lin", sp = NULL)
simplereg(x, y, type = "lin", sp = NULL)

Arguments

x

numerical vector, input x values.

y

numerical vector, input y values.

type

character, type of regression; available options are: lin (linear regression, the default), pol (local polynomial regression of degree 2), ks (nonparametric kernel smoothing).

sp

numeric, parameter to control the degree of smoothing; span for local polynomial regression and bandwidth for ksmooth.

Value

An object of class simplereg, i.e. a list with the following objects:

Model, the output model (linear regression, local polynomial regression, or kernel smoothing)

R2, (in-sample) coefficient of determination

x, input x values

y, input y values

type, type of regression

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Examples

Pbox.sel <- subset(Pbox, MIN >= 500)
X <- Pbox.sel$AST/Pbox.sel$MIN
Y <- Pbox.sel$TOV/Pbox.sel$MIN
Pl <- Pbox.sel$Player
mod <- simplereg(x=X, y=Y, type="lin")
Pbox.sel <- subset(Pbox, MIN >= 500)
X <- Pbox.sel$AST/Pbox.sel$MIN
Y <- Pbox.sel$TOV/Pbox.sel$MIN
Pl <- Pbox.sel$Player
mod <- simplereg(x=X, y=Y, type="lin")

Tadd dataset - NBA 2017-2018

Description

In this data frame, the cases (rows) are the analyzed teams and the variables (columns) are qualitative information such as Conference, Division, final rank, qualification for Playoffs for the NBA 2017-2018 Championship.

Usage

Tadd
Tadd

Format

A data frame with 30 rows and 6 variables:

Team: Analyzed team (long name), factor
team: Analyzed team (short name), factor
Conference: Conference, factor
Division: Division, factor
Rank: Rank (end season), numeric
Playoff: Playoff qualification (Yes or No), factor

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Teams box scores dataset - NBA 2017-2018

Description

In this data frame, cases (rows) are teams and variables (columns) are referred to team achievements in the different games in the NBA 2017-2018 Championship.

Usage

Tbox
Tbox

Format

A data frame with 30 rows and 23 variables:

Team: Analyzed team, character
GP: Games Played, numeric
MIN: Minutes Played, numeric
PTS: Points Made, numeric
W: Games won, numeric
L: Games lost, numeric
P2M: 2-Point Field Goals (Made), numeric
P2A: 2-Point Field Goals (Attempted), numeric
P2p: 2-Point Field Goals (Percentage), numeric
P3M: 3-Point Field Goals (Made), numeric
P3A: 3-Point Field Goals (Attempted), numeric
P3p: 3-Point Field Goals (Percentage), numeric
FTM: Free Throws (Made), numeric
FTA: Free Throws (Attempted), numeric
FTp: Free Throws (Percentage), numeric
OREB: Offensive Rebounds, numeric
DREB: Defensive Rebounds, numeric
AST: Assists, numeric
TOV: Turnovers, numeric
STL: Steals, numeric
BLK: Blocks, numeric
PF: Personal Fouls, numeric
PM: Plus/Minus, numeric

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

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

Calculate Team, Opponents and Players box scores (Tbox, Obox and Pbox)

Description

Calculate Team, Opponents and Players box scores (Tbox, Obox and Pbox)

Usage

TOPboxes(data, team)
TOPboxes(data, team)

Arguments

data

a data frame containing play-by-play data (see Details)

team

character, indicating the name of the team

Details

To compute Tbox and Obox, the function needs the following variables: game_id, playlength, ShotType, points, result, team, oreb, dreb, PF, turnover, assist, block and steal. If any of these variables is missing, an error message is displayed.

To compute Pbox, also the variables player, a1 . . . a5, h1 . . . h5 and hometeam are needed. If any is omitted, only Tbox and Obox are given in output.

Note that the variables assist, block and steal can contain the logical indicator of whether the corresponding event has occurred (TRUE/FALSE or numerical 0/1) or the name of the involved player (character). In the former case, Tbox and Obox are fully computed, while the variables AST, BLK and STL are missing in the Pbox data frame. In the latter case, all the data frames Tbox, Obox and Pbox are fully computed.

TOPboxes omits the computation of the variables W (Games won) and L (Games lost). In fact, since we aim at computing box scores starting from whatever portion of play-by-play data (e.g., only a part of a game), in some cases, calculating the number of won and lost games does not make sense.

Value

A list with the following elements

Tbox, the data frame of team box scores

Obox, the data frame of opponents box scores

Pbox, the data frame of player box scores

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

References

P. Zuccolotto, M. Manisera and M. Sandri (2026) Advanced Basketball Data Science: With Applications in R. CRC Press.

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

Examples

library(operators)
library(dplyr)
PbP <- PbPmanipulation(PbP.BDB)
PbP <- PbP %>%
  mutate(oreb = type %~% "rebound offensive",
         dreb = type %~% "rebound defensive",
                turnover = event_type=="turnover",
                PF = (event_type == "foul") & !(type %~% "technical") ) %>%
        mutate(across(c(player, assist, steal, block, h1:h5, a1:a5), as.character)) %>%
 as.data.frame()
out <- TOPboxes(PbP, team="GSW")
library(operators)
library(dplyr)
PbP <- PbPmanipulation(PbP.BDB)
PbP <- PbP %>%
  mutate(oreb = type %~% "rebound offensive",
         dreb = type %~% "rebound defensive",
                turnover = event_type=="turnover",
                PF = (event_type == "foul") & !(type %~% "technical") ) %>%
        mutate(across(c(player, assist, steal, block, h1:h5, a1:a5), as.character)) %>%
 as.data.frame()
out <- TOPboxes(PbP, team="GSW")

Variability analysis

Description

Variability analysis

Usage

variability(data, data.var, size.var, VC = TRUE, weight = FALSE)
variability(data, data.var, size.var, VC = TRUE, weight = FALSE)

Arguments

data

a data frame.

data.var

a vector of variable names or of column numbers defining (numeric) variables whose variability will be analyzed by variability.

size.var

a vector of variable names or of column numbers defining variables for weights (active only if weight=TRUE).

VC

logical; if TRUE, calculates variation coefficients of variables in data.var.

weight

logical; if TRUE, calculates weighted variation coefficients and standard deviations.

Value

A list with the following elements: ranges, standard deviations, variation coefficients, and two dataframes (data, size).

Author(s)

Marco Sandri, Paola Zuccolotto, Marica Manisera ([email protected])

Examples

Pbox.BC <- subset(Pbox, Team=="Oklahoma City Thunder" & MIN >= 500,
                    select=c("P2p","P3p","FTp","P2A","P3A","FTA"))
list_variability <- variability(data=Pbox.BC, data.var=c("P2p","P3p","FTp"),
                                size.var=c("P2A","P3A","FTA"), weight=TRUE)
print(list_variability)
plot(list_variability, leg.brk=c(10,25,50,100,500,1000), max.circle=30)
Pbox.BC <- subset(Pbox, Team=="Oklahoma City Thunder" & MIN >= 500,
                    select=c("P2p","P3p","FTp","P2A","P3A","FTA"))
list_variability <- variability(data=Pbox.BC, data.var=c("P2p","P3p","FTp"),
                                size.var=c("P2A","P3A","FTA"), weight=TRUE)
print(list_variability)
plot(list_variability, leg.brk=c(10,25,50,100,500,1000), max.circle=30)

Package 'BasketballAnalyzeR'

Help Index

Investigates the network of assists-shots in a team

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Draws a bar-line plot

Description

Usage

Arguments

Value

Author(s)

References

Examples

Draws a bubble plot

Description

Usage

Arguments

Value

Author(s)

References

Examples

Correlation analysis

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

R function CreateRadialPlot by William D. Vickers, freely downloadable from the web

Description

Usage

Arguments

Details

References

Computes and plots kernel density estimation of shots with respect to a concurrent variable

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Add lines of NBA court to an existing ggplot2 plot

Description

Usage

Arguments

Value

Author(s)

Examples

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

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Calculates possessions, pace, offensive and defensive rating, and Four Factors

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Agglomerative hierarchical clustering

Description

Usage