Calculate running Elos for a series of matches.

elo.run(
  formula,
  data,
  na.action,
  subset,
  k = NULL,
  initial.elos = NULL,
  ...,
  prob.fun = elo.prob,
  update.fun = elo.update,
  verbose = TRUE
)

Arguments

formula	A formula. See the help page for formulas for details.
data	A `data.frame` in which to look for objects in `formula`.
na.action	A function which indicates what should happen when the data contain NAs.
subset	An optional vector specifying a subset of observations.
k	A constant k-value (or a vector, where appropriate).
initial.elos	An optional named vector containing initial Elo ratings for all teams in `formula`. If a single (unnamed) value is supplied, that value is applied to all teams. `NULL` (the default) sets all Elos to 1500.
...	Other arguments (not used at this time).
prob.fun	A function with at least 4 arguments: elo.A, elo.B, adjust.A, and adjust.B. It should return a predicted probability that team A wins. The values passed in will be scalars, and a scalar is expected as output.
update.fun	A function with at least 6 arguments: the same as `elo.update.default`. The function takes in the Elos, the win indicator, k, and any adjustments, and returns a value by which to update the Elos. The values passed in will be scalars, and a scalar is expected as output.
verbose	Should a message be issued when R is used (over C++)?

Value

An object of class "elo.run" or class "elo.run.regressed".

Details

elo.run is run two different ways: the first (default) uses C++ and may be up to 50 times faster, while the second (when prob.fun or update.fun are specified) uses R but also supports custom update functions. Prefer the first unless you really need a custom update function.

Examples

data(tournament)
elo.run(score(points.Home, points.Visitor) ~ team.Home + team.Visitor,
        data = tournament, k = 20)
#> 
#> An object of class 'elo.run', containing information on 8 teams and 56 matches.
#> 

# Create non-constant 'k'
elo.run(score(points.Home, points.Visitor) ~ team.Home + team.Visitor +
        k(20*log(abs(points.Home - points.Visitor) + 1)), data = tournament)
#> 
#> An object of class 'elo.run', containing information on 8 teams and 56 matches.
#> 

# Adjust Elo for, e.g., home-field advantage
elo.run(score(points.Home, points.Visitor) ~ adjust(team.Home, 30) + team.Visitor,
        data = tournament, k = 20)
#> 
#> An object of class 'elo.run', containing information on 8 teams and 56 matches.
#> 

tournament$home.field <- 30
elo.run(score(points.Home, points.Visitor) ~ adjust(team.Home, home.field) + team.Visitor,
        data = tournament, k = 20)
#> 
#> An object of class 'elo.run', containing information on 8 teams and 56 matches.
#> 

# Regress the Elos back toward 1500 at the end of the half-season
elo.run(score(points.Home, points.Visitor) ~ adjust(team.Home, 30) +
        team.Visitor + regress(half, 1500, 0.2), data = tournament, k = 20)
#> 
#> An object of class 'elo.run.regressed', containing information on 8 teams and 56 matches, with 2 regressions.
#>

Calculate running Elos for a series of matches.

Arguments

Value

Details

See also

Examples