NHL scritchy scratchy

Going to look at some things using the nhl data ala nhlscrapr

If it's the first time ever...

install.packages("nhlscrapr")
install.packages("ggplot2)")
library(nhlscrapr)
#compile.all.games() takes a few hours 
compile.all.games()
#or you could git http://goggable.areteh.co:3000/RotBlauer/ZamboniR.git

Load up dat core data and setup

library(ggplot2)
load("source-data/nhlscrapr-core.RData")

Checkin number of unique players by positions, for example:

 theme_set(theme_bw(24))
 c <- ggplot(subset(roster.unique,pos!=''), aes(x=pos))
 c + geom_bar() +xlab("Player Position")

Lets load up 2014 - 2015

load('source-data//nhlscrapr-20142015.RData')

Check out event types

table(grand.data$etype)

## 
##  BLOCK CHANGE  EGPID    EGT  EIEND  EISTR    FAC   GIVE   GOAL    HIT 
##  38545 132296      1      1      4      4  81082  22193   7377  67418 
##   MISS   PEND   PENL   SHOT    SOC   TAKE 
##  30887   4459  10563  72659    173  18004

Neat events, plot time

plot(grand.data$xcoord,grand.data$ycoord)

It's a rink!

Look at density of events

ggplot(grand.data, aes(x = xcoord, y = ycoord)) +  geom_point() + geom_density2d()

And just the goals

newDat =grand.data[ which(grand.data$etype=='GOAL'), ]
ggplot(newDat, aes(x = xcoord, y = ycoord)) +  geom_point() + geom_density2d()

And just the shots

newDat =grand.data[ which(grand.data$etype=='SHOT'), ]
ggplot(newDat, aes(x = xcoord, y = ycoord)) +  geom_point() + geom_density2d()

The penalties

newDat =grand.data[ which(grand.data$etype=='PENL'), ]
ggplot(newDat, aes(x = xcoord, y = ycoord)) +  geom_point() + geom_density2d()

And short handed/pp shots

newDat =grand.data[ which(grand.data$etype=='SHOT' & grand.data$away.skaters != grand.data$home.skaters), ]
ggplot(newDat, aes(x = xcoord, y = ycoord)) +  geom_point() + geom_density2d()

And short handed/pp goals

newDat =grand.data[ which(grand.data$etype=='GOAL' & grand.data$away.skaters != grand.data$home.skaters), ]
ggplot(newDat, aes(x = xcoord, y = ycoord)) +  geom_point() + geom_density2d()

And our favorite, hits!

newDat =grand.data[ which(grand.data$etype=='HIT'), ]
ggplot(newDat, aes(x = xcoord, y = ycoord)) +  geom_point() + geom_density2d()

And the types of shots

newDat =grand.data[ which(grand.data$etype=='SHOT'), ]
ggplot(newDat, aes(x = xcoord, y = ycoord, color=as.factor(type))) +  geom_point() +theme(legend.position="bottom",legend.title=element_blank()) 

Now, wrist shot distances

newDat =grand.data[ which(grand.data$etype=='SHOT' & grand.data$type=='Wrist'), ]
ggplot(newDat, aes(x=distance)) + geom_histogram(binwidth=1)

Compared to the distance of wrist shot goals

newDat =grand.data[ which(grand.data$etype=='GOAL' & grand.data$type=='Wrist'), ]
ggplot(newDat, aes(x=distance)) + geom_histogram(binwidth=1)

Slappers

newDat =grand.data[ which(grand.data$etype=='SHOT' & grand.data$type=='Slap'), ]
ggplot(newDat, aes(x=distance)) + geom_histogram(binwidth=1)

Slapper goals

newDat =grand.data[ which(grand.data$etype=='GOAL' & grand.data$type=='Slap'), ]
ggplot(newDat, aes(x=distance)) + geom_histogram(binwidth=1)