Damian Lillard’s Game-Winner in Context

Damian Lillard of the Portland Trailblazers hit a crazy game-winner the other night. The game was tied, the clock was winding down, and Lillard pulled up from a thousand feet away for the win. Lillard’s straight-faced reaction was as good as the shot.

Here’s how that shot matches up with all of the other threes Lillard has made during his playoff career:

Just nuts.

The R code, in case you’re interested:

library(png)
library(plotrix)

# Load data.
makes3 <- read.csv("https://flowingdata.com/projects/2019/lillard/threes_lillard.tsv", sep="\t")

# Plot all made threes
par(mar=c(0,0,0,0))
plot(-makes3$loc_x[-dim(makes3)[1]], makes3$loc_y[-dim(makes3)[1]],
     cex=.7, pch=19, col="#888888", 
     asp=1, bty="n", axes=FALSE, xlab="", ylab="",  
     xlim=c(-25, 25), ylim=c(0, 50))
segments(-makes3$loc_x[-dim(makes3)[1]], makes3$loc_y[-dim(makes3)[1]], 
         rep(0, dim(makes3)[1]), rep(5.25, dim(makes3)[1]), 
         lwd=.4, col="#888888")
draw.arc(0, 5.25, 9/12, angle1=0, angle2=2*pi, col="black", lwd=2)

# Game winner
x_win <- -makes3$loc_x[dim(makes3)[1]]
y_win <- makes3$loc_y[dim(makes3)[1]]
segments(x_win, y_win, 0, 5.25, lwd=3, col="#CF082C")

# Note: Download file at https://flowingdata.com/projects/2019/lillard/lillard_face.png
img <- readPNG("lillard_face.png")
rasterImage(img, xleft=x_win-2, xright=x_win+2, ybottom = y_win-2, ytop=y_win+2)
symbols(x_win, y_win, squares = 4, add=TRUE, inches=FALSE, lwd=3, fg="#CF082C")
text(x_win+2.25, y_win, "Bye, OKC.", pos=4, family="Georgia", font=3, cex=.9)

Become a member. Support an independent site. Get extra visualization goodness.

See What You Get

Favorites

10 Best Data Visualization Projects of 2017

It was a rough year, which brought about a lot of good work. Here are my favorite data visualization projects of the year.

The Most Unisex Names in US History

Moving on from the most trendy names in US history, …

Divorce and Occupation, in 2015

Some jobs tend towards higher divorce rates. Some towards lower. Salary also probably plays a role.

Marrying Age

People get married at various ages, but there are definite trends that vary across demographic groups. What do these trends look like?