Predict results of a game from a ELO or mELO model

Source: R/predict.mELO_rating.R

This function gives predictions of success probabilities for agent or player 1 from a fitted ELO or mELO model (a mELO_rating oject).

# S3 method for mELO_rating
predict(
  object,
  new_match_data,
  min_games = 15,
  default_ratings = NULL,
  p1_advantage = 0,
  thresh
)

Arguments

object

An object of class mELO_rating.
new_match_data

A data frame containing four columns: (1) a numeric vector denoting the time period in which the game took place (2) a numeric or character identifier for player one (3) a numeric or character identifier for player two.
min_games

A single value. If the number of games of either player is below this value, the prediction will be based on the default_ratings parameter.
default_ratings

The rating to be used for agents or players who have not yet played min_games.
p1_advantage

Player 1 advantage parameter. A single value or numeric vector with length equal to the number of rows in new_match_data. Can be though of as representing first move or home ground advantage.
thresh

A single value. If given, a binary vector is returned indicating whether the prediction is greater than this value.

Value

A numeric vector of predictions, which may contain missing values.

Examples

# Rock paper scissors
head(rps_df)
#> # A tibble: 6 x 4
#>   time_index throw_1  throw_2  outcome
#>        <int> <chr>    <chr>      <dbl>
#> 1          1 PAPER    ROCK           1
#> 2          2 ROCK     SCISSORS       1
#> 3          3 SCISSORS PAPER          1
#> 4          4 ROCK     PAPER          0
#> 5          5 SCISSORS ROCK           0
#> 6          6 PAPER    SCISSORS       0

# Note that ELO doesn't perform well
rps_ELO <- ELO(rps_df)
rps_ELO
#> 
#> ELO Ratings For 3 Players Playing 120 Games
#> 
#>     Player Rating Games Win Draw Loss Lag
#> 1 SCISSORS 2204.6    80  40    0   40   0
#> 2     ROCK 2204.6    80  40    0   40   1
#> 3    PAPER 2190.7    80  40    0   40   0
#> 
ELO_preds <- predict(
    rps_ELO,
    head(rps_df)
)
cbind(
    head(rps_df),
    ELO_preds
)
#>   time_index  throw_1  throw_2 outcome ELO_preds
#> 1          1    PAPER     ROCK       1 0.4799619
#> 2          2     ROCK SCISSORS       1 0.4999898
#> 3          3 SCISSORS    PAPER       1 0.5200482
#> 4          4     ROCK    PAPER       0 0.5200381
#> 5          5 SCISSORS     ROCK       0 0.5000102
#> 6          6    PAPER SCISSORS       0 0.4799518
    # Predictions are all ~0.5

# Fit a mELO model that can handle these types of interactions.
rps_mELO <- mELO(rps_df, k=1)
rps_mELO
#> 
#> mELO Ratings For 3 Players Playing 120 Games
#> 
#> k = 1.
#> 
#>     Player Rating Games Win Draw Loss Lag
#> 1 SCISSORS 2206.2    80  40    0   40   0
#> 2     ROCK 2197.2    80  40    0   40   1
#> 3    PAPER 2196.7    80  40    0   40   0
#> 
# Inspect advantage matrix
get_adv_mat(rps_mELO)
#>              PAPER      ROCK  SCISSORS
#> PAPER        0.000  1145.892 -1129.229
#> ROCK     -1145.892     0.000  1157.136
#> SCISSORS  1129.229 -1157.136     0.000
# Get predictioncs
mELO_preds <- predict(
    rps_mELO,
    head(rps_df)
)
cbind(
    head(rps_df),
    mELO_preds
)
#>   time_index  throw_1  throw_2 outcome  mELO_preds
#> 1          1    PAPER     ROCK       1 0.998632279
#> 2          2     ROCK SCISSORS       1 0.998654133
#> 3          3 SCISSORS    PAPER       1 0.998579105
#> 4          4     ROCK    PAPER       0 0.001367721
#> 5          5 SCISSORS     ROCK       0 0.001345867
#> 6          6    PAPER SCISSORS       0 0.001420895
    # Much better predictions!