hyper2 (version 2.0-1)

tennis: Match outcomes from repeated doubles tennis matches

Description

Match outcomes from repeated doubles tennis matches

Usage

data(tennis)

Format

A hyper2 object corresponding to the match outcomes listed below.

Details

There are four players, \(p_1\) to \(p_4\). These players play doubles tennis matches with the following results:

match	score
\(\lbrace p_1,p_2\rbrace\) vs \(\lbrace p_3,p_4\rbrace\)	9-2
\(\lbrace p_1,p_3\rbrace\) vs \(\lbrace p_2,p_4\rbrace\)	4-4
\(\lbrace p_1,p_4\rbrace\) vs \(\lbrace p_2,p_3\rbrace\)	6-7
\(\lbrace p_1\rbrace\) vs \(\lbrace p_3\rbrace\)	10-14
\(\lbrace p_2\rbrace\) vs \(\lbrace p_3\rbrace\)	12-14
\(\lbrace p_1\rbrace\) vs \(\lbrace p_4\rbrace\)	10-14
\(\lbrace p_2\rbrace\) vs \(\lbrace p_4\rbrace\)	11-10
\(\lbrace p_3\rbrace\) vs \(\lbrace p_4\rbrace\)	13-13

It is suspected that \(p_1\) and \(p_2\) have some form of team cohesion and play better when paired than when either solo or with other players. As the scores show, each player and, apart from p1-p2, each doubles partnership, is of approximately the same strength.

Dataset tennis gives the appropriate likelihood function for the players' strengths; and dataset tennis_ghost gives the appropriate likelihood function if the extra strength due to team cohesion of \(\lbrace p_1,p_2\rbrace\) is represented by a ghost player.

These objects can be generated by running script inst/tennis.Rmd, which includes some further discussion and technical documentation and creates file tennis.rda which resides in the data/ directory.

References

Robin K. S. Hankin (2010). “A Generalization of the Dirichlet Distribution”, Journal of Statistical Software, 33(11), 1-18, 10.18637/jss.v033.i11

Examples

# NOT RUN {
summary(tennis)

tennis %>% psubs(c("Federer","Laver","Graf","Navratilova"))

## Following line commented out because it takes too long:
# specificp.gt.test(tennis_ghost,"G",0)

# }