tennis | R Documentation |
Match outcomes from repeated doubles tennis matches
data(tennis)
A hyper2 object corresponding to the match outcomes listed below.
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.
Doubles tennis matches at NOCS, Jan-May 2008
Robin K. S. Hankin (2010). “A Generalization of the Dirichlet Distribution”, Journal of Statistical Software, 33(11), 1-18
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)
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