hockey: NHL hockey data

In gamlr: Gamma Lasso Regression

NHL hockey data

Description

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

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.

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(s)

Matt Taddy, [e-mail]

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,])

gamlr documentation built on April 17, 2023, 1:06 a.m.