gamlr (version 1.13-8)

hockey: NHL hockey data

Description

Every NHL goal from fall 2002 through the 2014 cup finals.

Value

goal

Info about each goal scored, including homegoal -- an indicator for the home team scoring.

player

Sparse Matrix with entries for who was on the ice for each goal: +1 for a home team player, -1 for an away team player, zero otherwise.

team

Sparse Matrix with indicators for each team*season interaction: +1 for home team, -1 for away team.

config

Special teams info. For example, S5v4 is a 5 on 4 powerplay, +1 if it is for the home-team and -1 for the away team.

Author

Matt Taddy,

Details

The data comprise of information about play configuration and the players on ice (including goalies) for every goal from 2002-03 to 2012-14 NHL seasons. Collected using A. C. Thomas's nlhscrapr package. See the Chicago hockey analytics project at github.com/mataddy/hockey.

References

Gramacy, Jensen, and Taddy (2013): "Estimating Player Contribution in Hockey with Regularized Logistic Regression", the Journal of Quantitative Analysis in Sport.

Gramacy, Taddy, and Tian (2015): "Hockey Player Performance via Regularized Logistic Regression", the Handbook of statistical methods for design and analysis in sports.

See Also

gamlr

Examples

## design 
data(hockey)
x <- cbind(config,team,player)
y <- goal$homegoal

## fit the plus-minus regression model
## (non-player effects are unpenalized)

fit <- gamlr(x, y, 
  lambda.min.ratio=0.05, nlambda=40, ## just so it runs in under 5 sec
  free=1:(ncol(config)+ncol(team)),
  standardize=FALSE, family="binomial")
plot(fit)

## look at estimated player [career] effects
B <- coef(fit)[colnames(player),]
sum(B!=0) # number of measurable effects (AICc selection)
B[order(-B)[1:10]] # 10 biggest

## convert to 2013-2014 season partial plus-minus
now <- goal$season=="20132014"
pm <- colSums(player[now,names(B)]*c(-1,1)[y[now]+1]) # traditional plus minus
ng <- colSums(abs(player[now,names(B)])) # total number of goals
# The individual effect on probability that a
# given goal is for vs against that player's team
p <- 1/(1+exp(-B)) 
# multiply ng*p - ng*(1-p) to get expected plus-minus
ppm <- ng*(2*p-1)

# organize the data together and print top 20
effect <- data.frame(b=round(B,3),ppm=round(ppm,3),pm=pm)
effect <- effect[order(-effect$ppm),]
print(effect[1:20,])