comperank provides
tools for computing ranking and rating based on competition results. It
is tightly connected to its data infrastructure package comperes. Basic
knowledge about creating valid
competition results and Head-to-Head
expressions with comperes is needed in order to
efficiently use comperank.
Understanding of competition is quite general: it is a set of games (abstract event) in which players (abstract entity) gain some abstract scores (typically numeric). The most natural example is sport results, however not the only one. Product rating can also be considered as a competition between products as “players”. Here a “game” is a customer that reviews a set of products by rating them with numerical “score” (stars, points, etc.).
Rating is a list (in the ordinary sense) of numerical values, one for each player, or the numerical value itself. Its interpretation depends on rating method: either bigger value indicates better player performance or otherwise.
Ranking is a rank-ordered list (in the ordinary sense) of players: rank 1 indicates player with best performance.
comperank leverages the tidyverse ecosystem of R packages.
Among other things, it means that the main output format is tibble.
comperank gets inspiration from the book “Who’s
#1” by Langville and Meyer. It provides functionality for the
following rating algorithms:
rate_massey() and
rank_massey().rate_colley() and
rank_colley().rate_keener() and
rank_keener().rate_markov() and
rank_markov().rate_od() and
rank_od().rate_iterative(),
rank_iterative(), and
add_iterative_ratings().rate_elo(), rank_elo(),
and add_elo_ratings().As you can see, there are three sets of functions:
rate_*(). Its output is a tibble with columns
player (player identifier) and at least one
rating_* (rating value). Names of rating columns depend on
rating method.rank_*(). Its default output is similar to previous
one, but with ranking_* instead of rating columns. It runs
rate_*() and does ranking with correct direction. One can
use option keep_rating = TRUE to keep rating columns in the
output.add_*_ratings(). These functions are present only for
algorithms with iterative nature and competition results with games only
between two players. They return tibble with row corresponding to a game
and extra columns indicating ratings of players before and after the
game.This vignette provides examples of basic usage of these functions. To learn more about algorithms behind them, see corresponding help pages.
For this vignette we will need the following packages:
All functions in comperank expect competition results in
one of the formats from comperes package. That is either
long or wide format.
Long format is the most abstract way of presenting
competition results. Basically, it is a data frame (or tibble) with
columns game (game identifier), player (player
identifier) and score where each row represents the
score of particular player in particular game. One game can consist
from variable number of players which makes this format
more usable. Inside a game all players are treated equally.
Programmatically long format is represented with longcr
S3 class which should be created with as_longcr() function
from comperes.
For examples we will use ncaa2005 data set from
comperes package, which is already of longcr
class. It is an example competition results of an isolated group of
Atlantic Coast Conference teams provided in book “Who’s
#1”:
ncaa2005
#> # A longcr object:
#> # A tibble: 20 × 3
#> game player score
#> <int> <chr> <int>
#> 1 1 Duke 7
#> 2 1 Miami 52
#> 3 2 Duke 21
#> 4 2 UNC 24
#> 5 3 Duke 7
#> 6 3 UVA 38
#> 7 4 Duke 0
#> 8 4 VT 45
#> 9 5 Miami 34
#> 10 5 UNC 16
#> 11 6 Miami 25
#> 12 6 UVA 17
#> 13 7 Miami 27
#> 14 7 VT 7
#> 15 8 UNC 7
#> 16 8 UVA 5
#> 17 9 UNC 3
#> 18 9 VT 30
#> 19 10 UVA 14
#> 20 10 VT 52Wide format is a more convenient way to store
results with fixed number of players in a game.
Each row represents scores of all players in particular game.
Data should be organized in pairs of columns “player”-“score”.
Identifier of a pair should go after respective keyword and consist only
from digits. For example: player1, score1,
player2, score2. Order doesn’t matter. Column
game is optional.
Programmatically wide format is represented with widecr
S3 class which should be created with as_widecr() function
from comperes:
comperes::as_widecr(ncaa2005)
#> # A widecr object:
#> # A tibble: 10 × 5
#> game player1 score1 player2 score2
#> <int> <chr> <int> <chr> <int>
#> 1 1 Duke 7 Miami 52
#> 2 2 Duke 21 UNC 24
#> 3 3 Duke 7 UVA 38
#> 4 4 Duke 0 VT 45
#> 5 5 Miami 34 UNC 16
#> 6 6 Miami 25 UVA 17
#> 7 7 Miami 27 VT 7
#> 8 8 UNC 7 UVA 5
#> 9 9 UNC 3 VT 30
#> 10 10 UVA 14 VT 52All comperank functions expect either a data
frame with long format structure, or longcr object, or
widecr object.
Massey and Colley methods were initially designed for competitions where:
Idea of Massey method is that difference in ratings should be proportional to score difference in direct confrontations. Bigger value indicates better player competition performance.
rate_massey(ncaa2005)
#> # A tibble: 5 × 2
#> player rating_massey
#> <chr> <dbl>
#> 1 Duke -24.8
#> 2 Miami 18.2
#> 3 UNC -8
#> 4 UVA -3.4
#> 5 VT 18
rank_massey(ncaa2005)
#> # A tibble: 5 × 2
#> player ranking_massey
#> <chr> <dbl>
#> 1 Duke 5
#> 2 Miami 1
#> 3 UNC 4
#> 4 UVA 3
#> 5 VT 2
rank_massey(ncaa2005, keep_rating = TRUE)
#> # A tibble: 5 × 3
#> player rating_massey ranking_massey
#> <chr> <dbl> <dbl>
#> 1 Duke -24.8 5
#> 2 Miami 18.2 1
#> 3 UNC -8 4
#> 4 UVA -3.4 3
#> 5 VT 18 2Idea of Colley method is that ratings should be proportional to share of player’s won games. Bigger value indicates better player performance.
All algorithms with variable Head-to-Head structure depend on user supplying custom Head-to-Head expression for computing quality of direct confrontations between all pairs of players of interest.
Computation of Head-to-Head values is done with functionality of
comperes package. Programmatically it is implemented as
summary of players’ matchups - mini-“games” in widecr
format between pair of players. In other words, for every directed pair
(order matters) of players (including “pair” of player with
oneself):
game, player1, score1,
player2, score2.For more robust usage comperes provides
h2h_funs - a list of the most common Head-to-Head expressions
which are designed to be used with rlang’s unquoting
mechanism. All comperank functions are designed to be used
smoothly with it.
Examples of computing Head-to-Head values for more clarity:
# Examples of h2h_funs elements
names(h2h_funs)
#> [1] "mean_score_diff" "mean_score_diff_pos" "mean_score"
#> [4] "sum_score_diff" "sum_score_diff_pos" "sum_score"
#> [7] "num_wins" "num_wins2" "num"
h2h_funs[1:3]
#> $mean_score_diff
#> mean(score1 - score2)
#>
#> $mean_score_diff_pos
#> max(mean(score1 - score2), 0)
#>
#> $mean_score
#> mean(score1)
# Computing Head-to-Head values with unquoting
comperes::h2h_long(ncaa2005, !!! h2h_funs)
#> # A long format of Head-to-Head values:
#> # A tibble: 25 × 11
#> player1 player2 mean_score_diff mean_score_diff_pos mean_score sum_score_diff
#> <chr> <chr> <dbl> <dbl> <dbl> <int>
#> 1 Duke Duke 0 0 8.75 0
#> 2 Duke Miami -45 0 7 -45
#> 3 Duke UNC -3 0 21 -3
#> 4 Duke UVA -31 0 7 -31
#> 5 Duke VT -45 0 0 -45
#> 6 Miami Duke 45 45 52 45
#> 7 Miami Miami 0 0 34.5 0
#> 8 Miami UNC 18 18 34 18
#> 9 Miami UVA 8 8 25 8
#> 10 Miami VT 20 20 27 20
#> # ℹ 15 more rows
#> # ℹ 5 more variables: sum_score_diff_pos <dbl>, sum_score <int>,
#> # num_wins <dbl>, num_wins2 <dbl>, num <int>
comperes::h2h_mat(ncaa2005, !!! h2h_funs["mean_score"])
#> # A matrix format of Head-to-Head values:
#> Duke Miami UNC UVA VT
#> Duke 8.75 7.0 21.0 7.0 0.0
#> Miami 52.00 34.5 34.0 25.0 27.0
#> UNC 24.00 16.0 12.5 7.0 3.0
#> UVA 38.00 17.0 5.0 18.5 14.0
#> VT 45.00 7.0 30.0 52.0 33.5
# Computing Head-to-Head values manually
comperes::h2h_mat(ncaa2005, mean(score1))
#> # A matrix format of Head-to-Head values:
#> Duke Miami UNC UVA VT
#> Duke 8.75 7.0 21.0 7.0 0.0
#> Miami 52.00 34.5 34.0 25.0 27.0
#> UNC 24.00 16.0 12.5 7.0 3.0
#> UVA 38.00 17.0 5.0 18.5 14.0
#> VT 45.00 7.0 30.0 52.0 33.5
# To account for self play use `if-else`
comperes::h2h_mat(ncaa2005, if(player1[1] == player2[1]) 0 else mean(score1))
#> # A matrix format of Head-to-Head values:
#> Duke Miami UNC UVA VT
#> Duke 0 7 21 7 0
#> Miami 52 0 34 25 27
#> UNC 24 16 0 7 3
#> UVA 38 17 5 0 14
#> VT 45 7 30 52 0All functions for methods with variable Head-to-Head structure are designed with this rule in mind: the more Head-to-Head value the better player1 performed than player2.
Keener method is based on the idea of “relative strength” - the strength of the player relative to the strength of the players he/she has played against. This is computed based on provided Head-to-Head values and some flexible algorithmic adjustments to make method more robust. Bigger value indicates better player performance.
The main idea of Markov method is that players “vote” for other players’ performance. Voting is done with Head-to-Head values and the more value the more “votes” gives player2 to player1. For example, if Head-to-Head value is “number of wins” then player2 “votes” for player1 proportionally to number of times player1 won in a matchup with player2. Beware of careful consideration of Head-to-Head values for self plays.
Actual “voting” is done in Markov chain fashion: Head-to-Head values are organized in stochastic matrix which vector of stationary probabilities is declared to be output ratings. Bigger value indicates better player performance.
As stochastic matrices can be averaged (with weights), this is the only method capable of direct averaging ratings for different Head-to-Head expressions.
rank_markov(ncaa2005, !!! h2h_funs["num_wins"], keep_rating = TRUE)
#> # A tibble: 5 × 3
#> player rating_markov ranking_markov
#> <chr> <dbl> <dbl>
#> 1 Duke 0.0991 5
#> 2 Miami 0.407 1
#> 3 UNC 0.154 3
#> 4 UVA 0.120 4
#> 5 VT 0.220 2
rank_markov(
ncaa2005,
!!! h2h_funs[c("num_wins", "mean_score_diff_pos")],
weights = c(0.2, 0.8),
keep_rating = TRUE
)
#> # A tibble: 5 × 3
#> player rating_markov ranking_markov
#> <chr> <dbl> <dbl>
#> 1 Duke 0.0994 5
#> 2 Miami 0.408 1
#> 3 UNC 0.115 4
#> 4 UVA 0.120 3
#> 5 VT 0.257 2The idea of Offense-Defense (OD) method is to account for different abilities of players by combining different ratings:
Offensive and defensive ratings describe different skills of players. In order to fully rate players, OD ratings are computed: offensive ratings divided by defensive. The more OD rating the better player performance.
rank_od(
ncaa2005,
if (player1[1] == player2[1]) 0 else mean(score1),
keep_rating = TRUE
)
#> # A tibble: 5 × 7
#> player rating_off rating_def rating_od ranking_off ranking_def ranking_od
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Duke 34.0 1.69 20.1 5 5 5
#> 2 Miami 152. 0.803 189. 1 2 2
#> 3 UNC 48.7 1.16 41.8 4 4 4
#> 4 UVA 82.0 0.967 84.8 3 3 3
#> 5 VT 115. 0.411 280. 2 1 1Rating methods with iterative nature assume that games occur in some
particular order. All players have some initial ratings which are
updated after every game in order they appear. Although, it is possible
to consider games with more than two players, comperank
only supports competition results with all games between two
players.
Iterative ratings represent the general approach to ratings with
iterative nature. It needs custom rating function and initial player
ratings to perform iterative ratings computation. Rating function should
accept four arguments: rating1 (scalar rating of the first
player before the game), score1 (his score),
rating2 and score2 for the data about second
player’s performance. It should return a numeric vector of length 2 with
elements respectively representing ratings of players after the
game.
All functions assume that the order in which games were played is
identical to order of values in column game (if present) or
is defined by the row order.
Arguably, the most useful function is
add_iterative_ratings(), which adds to widecr
format of competition results information about game ratings before and
after the game.
rate_iterative() and rank_iterative()
return ratings after the last game.
# Adds 1 to winner's rating and subtracts 1 from loser's rating
test_rate_fun <- function(rating1, score1, rating2, score2) {
c(rating1, rating2) + ((score1 >= score2) * 2 - 1) * c(1, -1)
}
add_iterative_ratings(ncaa2005, test_rate_fun)
#> # A widecr object:
#> # A tibble: 10 × 9
#> game player1 score1 player2 score2 rating1Before rating2Before rating1After
#> <int> <chr> <int> <chr> <int> <dbl> <dbl> <dbl>
#> 1 1 Duke 7 Miami 52 0 0 -1
#> 2 2 Duke 21 UNC 24 -1 0 -2
#> 3 3 Duke 7 UVA 38 -2 0 -3
#> 4 4 Duke 0 VT 45 -3 0 -4
#> 5 5 Miami 34 UNC 16 1 1 2
#> 6 6 Miami 25 UVA 17 2 1 3
#> 7 7 Miami 27 VT 7 3 1 4
#> 8 8 UNC 7 UVA 5 0 0 1
#> 9 9 UNC 3 VT 30 1 0 0
#> 10 10 UVA 14 VT 52 -1 1 -2
#> # ℹ 1 more variable: rating2After <dbl>
# Revert the order of games
ncaa2005_rev <- ncaa2005
ncaa2005_rev$game <- 11 - ncaa2005_rev$game
add_iterative_ratings(ncaa2005_rev, test_rate_fun)
#> # A widecr object:
#> # A tibble: 10 × 9
#> game player1 score1 player2 score2 rating1Before rating2Before rating1After
#> <dbl> <chr> <int> <chr> <int> <dbl> <dbl> <dbl>
#> 1 1 UVA 14 VT 52 0 0 -1
#> 2 2 UNC 3 VT 30 0 1 -1
#> 3 3 UNC 7 UVA 5 -1 -1 0
#> 4 4 Miami 27 VT 7 0 2 1
#> 5 5 Miami 25 UVA 17 1 -2 2
#> 6 6 Miami 34 UNC 16 2 0 3
#> 7 7 Duke 0 VT 45 0 1 -1
#> 8 8 Duke 7 UVA 38 -1 -3 -2
#> 9 9 Duke 21 UNC 24 -2 -1 -3
#> 10 10 Duke 7 Miami 52 -3 3 -4
#> # ℹ 1 more variable: rating2After <dbl>
# Rating after the last game
rank_iterative(ncaa2005, test_rate_fun, keep_rating = TRUE)
#> # A tibble: 5 × 3
#> player rating_iterative ranking_iterative
#> <chr> <dbl> <dbl>
#> 1 Duke -4 5
#> 2 Miami 4 1
#> 3 UNC 0 3
#> 4 UVA -2 4
#> 5 VT 2 2Elo method is, basically, an iterative rating method with fixed Elo rating function. General idea is that rating increase for winner should be the bigger the more is rating difference between players. In other words, win over a better player should lead to more rating increase and win over a considerably weaker player shouldn’t affect rating that much.
add_elo_ratings(ncaa2005)
#> # A widecr object:
#> # A tibble: 10 × 9
#> game player1 score1 player2 score2 rating1Before rating2Before rating1After
#> <int> <chr> <int> <chr> <int> <dbl> <dbl> <dbl>
#> 1 1 Duke 7 Miami 52 0 0 -15
#> 2 2 Duke 21 UNC 24 -15 0 -29.4
#> 3 3 Duke 7 UVA 38 -29.4 0 -43.1
#> 4 4 Duke 0 VT 45 -43.1 0 -56.2
#> 5 5 Miami 34 UNC 16 15 14.4 30.0
#> 6 6 Miami 25 UVA 17 30.0 13.7 44.3
#> 7 7 Miami 27 VT 7 44.3 13.1 57.9
#> 8 8 UNC 7 UVA 5 -0.619 -0.564 14.4
#> 9 9 UNC 3 VT 30 14.4 -0.511 -1.26
#> 10 10 UVA 14 VT 52 -15.6 15.1 -29.2
#> # ℹ 1 more variable: rating2After <dbl>
add_elo_ratings(ncaa2005_rev)
#> # A widecr object:
#> # A tibble: 10 × 9
#> game player1 score1 player2 score2 rating1Before rating2Before rating1After
#> <dbl> <chr> <int> <chr> <int> <dbl> <dbl> <dbl>
#> 1 1 UVA 14 VT 52 0 0 -15
#> 2 2 UNC 3 VT 30 0 15 -14.4
#> 3 3 UNC 7 UVA 5 -14.4 -15 0.619
#> 4 4 Miami 27 VT 7 0 29.4 16.3
#> 5 5 Miami 25 UVA 17 16.3 -30.0 29.3
#> 6 6 Miami 34 UNC 16 29.3 0.619 43.0
#> 7 7 Duke 0 VT 45 0 13.1 -14.4
#> 8 8 Duke 7 UVA 38 -14.4 -43.0 -30.7
#> 9 9 Duke 21 UNC 24 -30.7 -13.1 -44.9
#> 10 10 Duke 7 Miami 52 -44.9 43.0 -56.2
#> # ℹ 1 more variable: rating2After <dbl>
rank_elo(ncaa2005, keep_rating = TRUE)
#> # A tibble: 5 × 3
#> player rating_elo ranking_elo
#> <chr> <dbl> <dbl>
#> 1 Duke -56.2 5
#> 2 Miami 57.9 1
#> 3 UNC -1.26 3
#> 4 UVA -29.2 4
#> 5 VT 28.8 2
rank_elo(ncaa2005_rev, keep_rating = TRUE)
#> # A tibble: 5 × 3
#> player rating_elo ranking_elo
#> <chr> <dbl> <dbl>
#> 1 Duke -56.2 5
#> 2 Miami 54.3 1
#> 3 UNC 1.10 3
#> 4 UVA -26.8 4
#> 5 VT 27.5 2