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#' @demo Archetypal basketball players based on player statistics;
#' analysis from the manuscript "Archetypal athletes" by Eugster
#' (2011)
library("SportsAnalytics")
library("archetypes")
library("RColorBrewer")
col_pal <- brewer.pal(7, "Set1")
col_black <- rgb(0, 0, 0, 0.2)
### Data: ############################################################
data("NBAPlayerStatistics0910")
dat <- subset(NBAPlayerStatistics0910,
select = -c(Ejections, FlagrantFouls))
mat <- as.matrix(subset(dat, select = -c(League, Name, Team, Position)))
pcplot(mat, col = col_black, las = 2)
### Archetypes: ######################################################
set.seed(4321)
as <- stepArchetypes(mat, k = 1:10)
rss(as)
screeplot(as)
a4 <- bestModel(as[[4]])
### Archetypal basketball players:
parameters(a4)
barplot(a4, mat, percentiles = TRUE)
### Player interpretation: ###########################################
players <- function(which) {
players <- list()
players$which <- which
players$mat <- mat[which, ]
players$coef <- coef(a4, "alphas")[which, ]
players$dat <- dat[which, ]
players
}
### Archetypal players:
which <- apply(coef(a4, "alphas"), 2, which.max)
atypes <- players(which)
cbind(subset(atypes$dat, select = c(Name, Team, Position)),
atypes$coef)
### Good players:
good_players <- function(atype, threshold) {
which <- which(coef(a4, "alphas")[, atype] > threshold)
good_coef <- coef(a4, "alphas")[which, ]
good_dat <- subset(dat[which, ], select = c(Name, Team, Position))
good_dat <- cbind(good_dat, good_coef)
good_dat <- good_dat[order(-good_coef[, atype]), ]
good_dat
}
good_threshold <- 0.95
players <- lapply(2:4, good_players, good_threshold)
players
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