Buli1718 {BTLLasso}

Bundesliga Data 2017/18 (Buli1718)

Description

Data from the German Bundesliga from the season 2017/18. The data contain all 306 matches of the season treated as paired comparisons with 5 (Y5) or 3 (Y3) different response categories. Additionally, different match-specific covariates are given as, for example, the percentage of ball possession or the total running distance per team and per match.

Format

A list containing data from the German Bundesliga with 306 observations. The list contains both information on the response (paired comparisons) and different covariates.

Y5

A response.BTLLasso object with 5 response categories for the Buli1516 data including

• response: Ordinal paired comparison response vector

• first.object: Vector containing the first-named team per paired comparison (home team)

• second.object: Vector containing the second-named team per paired comparison (away team)

• subject: Vector containing a match-day identifier per paired comparison

• with.order Vector containing information that each match has to be considered including an order effect.

Y3

A response.BTLLasso object with 3 response categories for the Buli1516 data including

• response: Ordinal paired comparison response vector

• first.object: Vector containing the first-named team per paired comparison (home team)

• second.object: Vector containing the second-named team per paired comparison (away team)

• subject: Vector containing a match-day identifier per paired comparison

• with.order Vector containing information that each match has to be considered including an order effect.

Z1

Matrix containing all team-match-specific covariates

• Distance: Total amount of km run

• BallPossession: Percentage of ball possession

• TacklingRate: Rate of won tacklings

• ShotsonGoal: Total number of shots on goal

• CompletionRate: Percentage of passes reaching teammates

• FoulsSuffered: Number of fouls suffered

• Offside: Number of offsides (in attack)

• Corners: Number of corners (in attack)

@references Schauberger, Gunther and Tutz, Gerhard (2019): BTLLasso - A Common Framework and Software Package for the Inclusion and Selection of Covariates in Bradley-Terry Models, Journal of Statistical Software, to appear

Schauberger, Gunther and Tutz, Gerhard (2017): Subject-specific modelling of paired comparison data: A lasso-type penalty approach, Statistical Modelling, 17(3), 223 - 243

Schauberger, Gunther, Groll Andreas and Tutz, Gerhard (2018): Analysis of the importance of on-field covariates in the German Bundesliga, Journal of Applied Statistics, 45(9), 1561 - 1578

Source

https://www.kicker.de/

See Also

Buli1415, Buli1516, Buli1617

Examples

## Not run: 
op <- par(no.readonly = TRUE)

data(Buli1718)

Y <- Buli1718$Y5
Z1 <- scale(Buli1718$Z1, scale = FALSE)

ctrl.buli <- ctrl.BTLLasso(object.order.effect = TRUE, 
                           name.order = "Home", 
                           penalize.order.effect.diffs = TRUE, 
                           penalize.order.effect.absolute = FALSE,
                           order.center = TRUE, lambda2 = 1e-2)

set.seed(1860)
m.buli <- cv.BTLLasso(Y = Y, Z1 = Z1, control = ctrl.buli)
m.buli

par(xpd = TRUE, mar = c(5,4,4,6))
plot(m.buli)

par(op)

## End(Not run)