Package 'speedyBBT' reference manual

Package 'speedyBBT'

Title:	Efficient Bayesian Inference for the Bradley--Terry Model
Description:	A suite of functions that allow a full, fast, and efficient Bayesian treatment of the Bradley--Terry model. Prior assumptions about the model parameters can be encoded through a multivariate normal prior distribution. Inference is performed using a latent variable representation of the model.
Authors:	Rowland Seymour [aut, cre, cph] (ORCID: <https://orcid.org/0000-0002-8739-3921>), Christine Stawitz [aut] (ORCID: <https://orcid.org/0000-0003-3122-4501>), Heather Turner [aut] (ORCID: <https://orcid.org/0000-0002-1256-3375>)
Maintainer:	Rowland Seymour <[email protected]>
License:	GPL (>= 3)
Version:	1.0.0.9000
Built:	2026-09-23 17:34:14 UTC
Source:	https://github.com/rowlandseymour/speedybbt

Title:

Efficient Bayesian Inference for the Bradley--Terry Model

Description:

A suite of functions that allow a full, fast, and efficient Bayesian treatment of the Bradley--Terry model. Prior assumptions about the model parameters can be encoded through a multivariate normal prior distribution. Inference is performed using a latent variable representation of the model.

Authors:

Rowland Seymour [aut, cre, cph] (ORCID: <https://orcid.org/0000-0002-8739-3921>), Christine Stawitz [aut] (ORCID: <https://orcid.org/0000-0003-3122-4501>), Heather Turner [aut] (ORCID: <https://orcid.org/0000-0002-1256-3375>)

Maintainer:

Rowland Seymour <[email protected]>

License:

GPL (>= 3)

Version:

1.0.0.9000

Built:

2026-09-23 17:34:14 UTC

Source:

https://github.com/rowlandseymour/speedybbt

Help Index

Generalised Bradley-Terry model

Description

This function fits the Bradley-Terry model with comparison and player specific effects. Each comparison can be assigned a real value to allow for a specific effect for the comparison, such as bias, ordering or home/away effect. The value of this effect is denoted kappa. The player specific effects are described through a formula and data.frame containing the value. The function places a normal prior distribution on both kappa and the player specific parameters beta.

Usage

BBTm(
  outcome,
  player1,
  player2,
  lambda.initial = NULL,
  player.prior.var = NULL,
  beta.initial = NULL,
  n.iter = 1000,
  formula = NULL,
  data = NULL,
  advantage = NULL,
  kappa.initial = NULL,
  kappa.var = NULL,
  hyperparameter = TRUE,
  chi = 0.01,
  psi = 0.01,
  burn.in = 100,
  n.thin = 1,
  verbose = interactive()
)
BBTm(
  outcome,
  player1,
  player2,
  lambda.initial = NULL,
  player.prior.var = NULL,
  beta.initial = NULL,
  n.iter = 1000,
  formula = NULL,
  data = NULL,
  advantage = NULL,
  kappa.initial = NULL,
  kappa.var = NULL,
  hyperparameter = TRUE,
  chi = 0.01,
  psi = 0.01,
  burn.in = 100,
  n.thin = 1,
  verbose = interactive()
)

Arguments

outcome

Vector of outcomes. 1 if player2 is the winner, 0 if player1 is the winner.

player1

Vector of first players.

player2

Vector of second players.

lambda.initial

(optional) Vector containing the values of the player parameters for the first MCMC iteration.

player.prior.var

(optional) Matrix specifying the prior covariance of the player correlation parameters.

beta.initial

(optional) Vector containing the values of the player specific parameters for the first MCMC iteration.

n.iter

Number of MCMC samples to be drawn.

formula

Formula with no left-hand-side specifying the player specific effects.

data

Data frame with a row corresponding to each player and a column corresponding to each covariate.

advantage

(optional) A vector with the value of the comparisons specific effect for each comparison.

kappa.initial

(optional) An initial value for the comparison specific value kappa.

kappa.var

(optional) The prior variance of the comparison specific value kappa.

hyperparameter

Boolean indicating if inference should be performed for the prior variance hyperparameter. If TRUE the prior variance (main diagonal of the covariance matrix) must be set to 1.

chi

Rate parameter for the inverse-gamma prior distribution on the hyperparameter.

psi

Shape parameter for the inverse-gamma prior distribution on the hyperparameter.

burn.in

The number of iterations to use for a burn.in, default is 100.

n.thin

(optional) The number of iterations to thin the MCMC samples by. Default is 1.

verbose

(optional) A boolean indicating if progress should be printed to the console. Default is interactive() therefore progress is shown if run interactively, but disabled in non-interactive contexts.

Details

If player.prior.var is omitted, independent and identical N(0, 5^2) prior distributions are placed on each object quality parameter.

If beta.initialis omitted, it is set to a vector of zeroes.

If kappa.var is omitted, it is set to N(0, 5^2), if kappa.initial is omitted it is set to 0.5.

Value

A "mcmc" object containing samples from the posterior distribution.

Examples


#####################
## Wimbledon 2019 ##
####################
# Fit model where the quality of each player depends on their rank
# and the number of points they had immediately before the tournament.
# Allow an effect for a match being in the first or second week.
wimbledonModel <- BBTm(
  outcome = wimbledon$matches$outcome,
  player2 = wimbledon$matches$loser,
  player1 = wimbledon$matches$winner,
  advantage = wimbledon$matches$secondWeek,
  formula = ~ rank + points,
  data = wimbledon$players,
  n.iter = 1000, verbose = FALSE
)

# Plot posterior distributions
hist(parameter(wimbledonModel, "kappa"),
  main = "", xlab = expression(kappa), freq = FALSE
)

#####################
## Wimbledon 2019 ##
####################
# Fit model where the quality of each player depends on their rank
# and the number of points they had immediately before the tournament.
# Allow an effect for a match being in the first or second week.
wimbledonModel <- BBTm(
  outcome = wimbledon$matches$outcome,
  player2 = wimbledon$matches$loser,
  player1 = wimbledon$matches$winner,
  advantage = wimbledon$matches$secondWeek,
  formula = ~ rank + points,
  data = wimbledon$players,
  n.iter = 1000, verbose = FALSE
)

# Plot posterior distributions
hist(parameter(wimbledonModel, "kappa"),
  main = "", xlab = expression(kappa), freq = FALSE
)

Bayesian inference for the Bradley-Terry model with ties

Description

This function uses MCMC to sample from the posterior distribution of the Bradley–Terry model with ties.A multivariate normal prior distribution on the player quality parameters can be specified. An exponential prior distribution is placed on the tie parameter theta, and a Metropolis- Hasting random walk algorithm is used to update this parameter.

Usage

BBTm.ties(
  n.objects,
  outcome,
  player1,
  player2,
  player.prior.var = NULL,
  theta.initial = NULL,
  lambda.initial = NULL,
  n.iter = 1000,
  hyperparameter = TRUE,
  chi = 0.01,
  psi = 0.01,
  rw.sd = 0.1,
  theta.rate = 0.01,
  burn.in = 100,
  n.thin = 1,
  verbose = interactive()
)
BBTm.ties(
  n.objects,
  outcome,
  player1,
  player2,
  player.prior.var = NULL,
  theta.initial = NULL,
  lambda.initial = NULL,
  n.iter = 1000,
  hyperparameter = TRUE,
  chi = 0.01,
  psi = 0.01,
  rw.sd = 0.1,
  theta.rate = 0.01,
  burn.in = 100,
  n.thin = 1,
  verbose = interactive()
)

Arguments

n.objects

The number of objects in the study.

outcome

Vector of outcomes. 0 if player 1 is the winner, 1 if player 2 is the winner, and 2 if it is a tie.

player1

Vector of first players.

player2

Vector of second players.

player.prior.var

(optional) Matrix specifying the prior covariance of the player correlation parameters.

theta.initial

(optional) Value of the tied parameter there for the first MCMC iteration.

lambda.initial

(optional) Vector containing the values of the player parameters for the first MCMC iteration.

n.iter

Number of MCMC samples to be drawn.

hyperparameter

Boolean indicating if inference should be performed for the prior variance hyperparameter. If TRUE the prior variance (main diagonal of the covariance matrix) must be set to 1.

chi

(optional) Rate parameter for the inverse-gamma prior distribution on the hyperparameter.

psi

(optional) Shape parameter for the inverse-gamma prior distribution on the hyperparameter.

rw.sd

(optional) Number describing the standard deviation of normal distribution proposal distribution for theta.

theta.rate

(optional) The rate parameter of the exponential prior distribution placed on theta.

burn.in

(optional) The number of iterations to use as a burn-in period. Default is 100.

n.thin

(optional) The number of iterations to thin the MCMC samples by. Default is 1.

verbose

(optional) A boolean indicating if progress should be printed to the console. Default is interactive() therefore progress is shown if run interactively, but disabled in non-interactive contexts.

Details

If player.prior.var is omitted, independent and identical N(0, 5^2) prior distributions are placed on each object quality parameter.

If lambda.initial is omitted, it is set to a vector of zeroes.

Value

A "mcmc" object containing samples from the posterior distribution

Examples

############################################
## Deprivation in Dar es Salaam, Tanzania ##
## Seymour et al (2022)                   ##
############################################
# Construct covariance matrix based on spatial informartion
prior.var <- expm::expm(darEsSalaam$adjacencyMatrix)
prior.var <- diag(diag(prior.var)^-0.5) %*% prior.var %*% diag(diag(prior.var)^-0.5)


# Fit BT model with ties
# Using `n.iter = 3` here to reduce model runtime, you will need
# a larger number of iterations for valid inference.
darTiedModel <- BBTm.ties(
  n.objects = nrow(darEsSalaam$adjacencyMatrix),
  outcome = darEsSalaam$comparisons$outcome,
  player1 = darEsSalaam$comparisons$subward1,
  player2 = darEsSalaam$comparisons$subward2,
  player.prior.var = prior.var,
  hyperparameter = TRUE,
  rw.sd = 0.005, n.iter = 3, burn.in = 0, verbose = FALSE
)

# Get posterior means
darTiedModelLambda <- parameter(darTiedModel, "lambda") -
  colMeans(parameter(darTiedModel, "lambda"))
lambda.mean <- rowMeans(darTiedModelLambda)

# Generate trace plots
plot(lambda.mean)
plot(parameter(darTiedModel, "theta"), type = "l")
############################################
## Deprivation in Dar es Salaam, Tanzania ##
## Seymour et al (2022)                   ##
############################################
# Construct covariance matrix based on spatial informartion
prior.var <- expm::expm(darEsSalaam$adjacencyMatrix)
prior.var <- diag(diag(prior.var)^-0.5) %*% prior.var %*% diag(diag(prior.var)^-0.5)


# Fit BT model with ties
# Using `n.iter = 3` here to reduce model runtime, you will need
# a larger number of iterations for valid inference.
darTiedModel <- BBTm.ties(
  n.objects = nrow(darEsSalaam$adjacencyMatrix),
  outcome = darEsSalaam$comparisons$outcome,
  player1 = darEsSalaam$comparisons$subward1,
  player2 = darEsSalaam$comparisons$subward2,
  player.prior.var = prior.var,
  hyperparameter = TRUE,
  rw.sd = 0.005, n.iter = 3, burn.in = 0, verbose = FALSE
)

# Get posterior means
darTiedModelLambda <- parameter(darTiedModel, "lambda") -
  colMeans(parameter(darTiedModel, "lambda"))
lambda.mean <- rowMeans(darTiedModelLambda)

# Generate trace plots
plot(lambda.mean)
plot(parameter(darTiedModel, "theta"), type = "l")

Construct win matrix from comparisons

Description

This function constructs a win matrix from a data frame of comparisons. It is needed for the MCMC functions.

Usage

comparisons_to_matrix(n.objects, comparisons)
comparisons_to_matrix(n.objects, comparisons)

Arguments

n.objects

The number of areas in the study.

comparisons

An N x 2 data frame, where N is the number of comparisons. Each row should correspond to a judgment. The first column is the winning object, the second column is the more losing object. The areas should be labeled from 1 to n.objects.

Value

A matrix where the i, j th element is the number of times object i beat object j.

Examples


# Generate some sample comparisons
comparisons <- data.frame("winner" = c(1, 3, 2, 2), "loser" = c(3, 1, 1, 3))

# Create matrix from comparisons
win.matrix <- comparisons_to_matrix(3, comparisons)
# Generate some sample comparisons
comparisons <- data.frame("winner" = c(1, 3, 2, 2), "loser" = c(3, 1, 1, 3))

# Create matrix from comparisons
win.matrix <- comparisons_to_matrix(3, comparisons)

Comparative Judgment on Deprivation in Dar es Salaam, Tanzania

Description

A comparative judgment data set on deprivation in subwards in Dar es Salaam, Tanzania.Citizens were shown pairs of subwards at random and asked which was more deprived.If they said they were equal, one of the pair was chosen at random to be more deprived.The data was collected in August 2018. The sex of each judge is also included.

Usage

darEsSalaam
darEsSalaam

Format

A list with three elements.

comparisons

A dataframe containing the comparisons. Each row corresponds to a judgement made by a single judge. This differs from the data in the BBT package as it explicitly includes ties rather than randomly allocating a winner. The columns are:

outcome: The outcome of the comparison. 1 if item 2 is the winner, 0 if item 1 is the winner, and 2 if it was a tie (although there are no instances of this happening).
item1: The first item in the comparison.
item2: The second item in the comparison.
sex: The sex of the judge making the comparison.

wards

A dataframe containing the names and shapefiles of the subwards with columns:

subwardName: The name of the subward.
geometry: A list containing the shapefile of the subward.
adjacencyMatrix: An adjacency matrix of the subwards formed from the shapefiles.This considers subwards as nodes and places edges between adjacent subwards. Two additional edges have been manually included to allow for crossings of the Kurasini creek.

Source

This data set was collected by Madeleine Ellis, James Goulding, Bertrand Perrat, Gavin Smith and Gregor Engelmann. We gratefully acknowledge the Rights Lab at the University of Nottingham for supporting funding for the comprehensive ground truth survey. We also acknowledge HumanitarianStreet Mapping Team (HOT) for providing a team of experts in data collection to facilitate the surveys. This work was also supported by the EPSRC Horizon Centre for Doctoral Training - My Life in Data (EP/L015463/1) and EPSRC grant Neodemographics (EP/L021080/1).

Forced Marriage in Nottinghamshire

Description

A comparative judgment data set for risk of forced marriage at ward level in Nottinghamshire. There are 12 judges and 76 wards.

Usage

forcedMarriage
forcedMarriage

Format

A list with three elements.

comparisons

A dataframe containing the comparisons. Each row corresponds to a judgement made by a single judge. The columns are:

user: The ID of the judge.
time: The time the comparison was made.
win: The ID of the ward that was judged to have a higher risk of forced marriage.
lost: The ID of the ward that was judged to have a lower risk of forced marriage.

wards

A dataframe containing information about each of the wards. The columns are:

NAME: The name of the ward.
AREA_CODE: The code of the ward.
DESCRIPTIO: The electoral division of the ward.
FILE_NAME: The name of the file containing the shapefile for the ward.

adjacencyMatrix

The final element is an adjacency matrix, where the wards are nodes and edges are placed between adjacent wards.

Source

The data was collected using support from the Engineering and Physical Sciences Research Council (grant reference EP/R513283/1), the Economic and Social Sciences Research Council (ES/V015370/1) and the Research England Policy Support Fund. The data was collected following ethical approval from the University of Nottingham School of Politics and International Relations ethics committee.

Honour Based Abuse in Oxfordshire

Description

A comparative judgment data set for risk of honour based abuse in Oxford and Banbury

Usage

oxon.comparisons
oxon.comparisons

Format

A data frame with 1,167 comparisons.

comparison_id: The ID of the comparison.
user_id: The ID of the user who made the comparisons.
item_1_id: The ID of the first area involved in the comparison.
item_2_id: The ID of the second area involved in the comparison.
selected_item_id: The ID of the selected area. If the comparison was tied, the selected_item_id is NA.
state: The state of the outcome. selected indicates that a judgment was made, skipped indicates that the comparison was skipped, and tied indicates that the comparison was tied.

Source

The data was collected following ethical approval the University of Birmingham's Science, Engineering and Maths Ethics Committee.

Extract parameter draws from a model object

Description

This function helps the user extract relevant estimates from parameters fit using a Bayesian Bradley-Terry model.

Usage

parameter(model_output, parameter_name, indices_to_extract = NULL)
parameter(model_output, parameter_name, indices_to_extract = NULL)

Arguments

model_output

The mcmc object that contains the draws output from the BBT model.

parameter_name

The name of the parameter to extract draws for.

indices_to_extract

A single column index or a vector of column indices to extract from the draws matrix.

Value

A vector containing the draws of the parameter.

Examples


prior.var <- expm::expm(forcedMarriage$adjacencyMatrix)
prior.var <- diag(diag(prior.var)^-0.5) %*% prior.var %*% diag(diag(prior.var)^-0.5)

# Fit model
# Using `n.iter = 3, burn.in = 0` to reduce runtime
# - you should use more iterations for reliable posterior estimates
forcedMarriageModel <- speedyBBTm(
  outcome = rep(1, length(forcedMarriage$comparisons$win)),
  player1 = forcedMarriage$comparisons$win,
  player2 = forcedMarriage$comparisons$lost,
  player.prior.var = prior.var, n.iter = 3, burn.in = 0
)

parameter(forcedMarriageModel, "lambda", c(10, 20, 30, 40))
parameter(forcedMarriageModel, "alpha.sq")
prior.var <- expm::expm(forcedMarriage$adjacencyMatrix)
prior.var <- diag(diag(prior.var)^-0.5) %*% prior.var %*% diag(diag(prior.var)^-0.5)

# Fit model
# Using `n.iter = 3, burn.in = 0` to reduce runtime
# - you should use more iterations for reliable posterior estimates
forcedMarriageModel <- speedyBBTm(
  outcome = rep(1, length(forcedMarriage$comparisons$win)),
  player1 = forcedMarriage$comparisons$win,
  player2 = forcedMarriage$comparisons$lost,
  player.prior.var = prior.var, n.iter = 3, burn.in = 0
)

parameter(forcedMarriageModel, "lambda", c(10, 20, 30, 40))
parameter(forcedMarriageModel, "alpha.sq")

Plot Quality Parameter Posteriors

Description

Generates a plot of the posterior mean and 95% credible interval of the quality parameter estimates.

Usage

plot_qualities(
  model_output,
  player_names,
  flip = FALSE,
  main = "Posterior Mean and 95% Credible \n Interval of Player Quality",
  xlab = "Player",
  ylab = "Posterior Mean Quality",
  quality_label = NULL,
  ...
)
plot_qualities(
  model_output,
  player_names,
  flip = FALSE,
  main = "Posterior Mean and 95% Credible \n Interval of Player Quality",
  xlab = "Player",
  ylab = "Posterior Mean Quality",
  quality_label = NULL,
  ...
)

Arguments

model_output

The "mcmc" model output object containing the draws.

player_names

A character vector containing the names of the items (e.g., players or wards).

flip

Whether to flip the plot to put the quality estimates on the x-axis instead of the y-axis; defaults to FALSE.

main

The text to use for the plot title.

xlab

The text to use for the x-axis label.

ylab

The text to use for the y-axis label.

quality_label

A vector of three custom labels to use instead of numbers on the quality axis.

...

other arguments passed to plot

Examples


players <- wimbledon$players$name

wimbledonModel <- BBTm(
  outcome = wimbledon$matches$outcome,
  player2 = wimbledon$matches$loser,
  player1 = wimbledon$matches$winner,
  advantage = wimbledon$matches$secondWeek,
  formula = ~ rank + points,
  data = wimbledon$players,
  n.iter = 200
)

plot_qualities(model_output = wimbledonModel, player_names = players)
players <- wimbledon$players$name

wimbledonModel <- BBTm(
  outcome = wimbledon$matches$outcome,
  player2 = wimbledon$matches$loser,
  player1 = wimbledon$matches$winner,
  advantage = wimbledon$matches$secondWeek,
  formula = ~ rank + points,
  data = wimbledon$players,
  n.iter = 200
)

plot_qualities(model_output = wimbledonModel, player_names = players)

Standard Bayesian Bradley-Terry model

Description

This function uses MCMC to sample from the posterior distribution of the standard Bradley–Terry model. Standard model means that there are no tied comparisons and no player or comparison specific variables. This provides a fast implementation of the standard model. A multivariate normal prior distribution on the player quality parameters can be specified.

Usage

speedyBBTm(
  outcome = NULL,
  player1 = NULL,
  player2 = NULL,
  win.matrix = NULL,
  player.prior.var = NULL,
  lambda.initial = NULL,
  n.iter = 1000,
  hyperparameter = TRUE,
  chi = 0.01,
  psi = 0.01,
  burn.in = 100,
  n.thin = 1,
  verbose = interactive()
)
speedyBBTm(
  outcome = NULL,
  player1 = NULL,
  player2 = NULL,
  win.matrix = NULL,
  player.prior.var = NULL,
  lambda.initial = NULL,
  n.iter = 1000,
  hyperparameter = TRUE,
  chi = 0.01,
  psi = 0.01,
  burn.in = 100,
  n.thin = 1,
  verbose = interactive()
)

Arguments

outcome

Vector of outcomes. 1 if player 2 is the winner, 0 if player 1 is the winner.

player1

Vector of first players.

player2

Vector of second players.

win.matrix

(optional) A win-loss matrix where the i,j th element is the number of times object i beat object j.

player.prior.var

(optional) A matrix specifying the prior covariance of the player correlation parameters.

lambda.initial

(optional) A vector containing the values of the player correlation parameters for the first MCMC iteration.

n.iter

The number of MCMC samples to be drawn.

hyperparameter

A boolean indicating if inference should be performed for the prior variance hyperparameter. If TRUE the prior variance (main diagonal of the covariance matrix) must be set to 1.

chi

(Optional) The rate parameter for the inverse-gamma prior distribution on the hyperparameter. Default is 0.01.

psi

(Optional) The shape parameter for the inverse-gamma prior distribution on the hyperparameter. Default is 0.01.

burn.in

(optional) The number of iterations to discard as burn-in. Default is 100.

n.thin

(optional) The number of iterations to thin the MCMC samples by. Default is 1.

verbose

(optional) A boolean indicating if progress should be printed to the console. Default is interactive() therefore progress is shown if run interactively, but disabled in non-interactive contexts.

Details

If player.prior.var is omitted, independent and identical N(0, 1^2) prior distributions are placed on each object quality parameter.

If lambda.initial is ommitted, it is set to a vector of zeroes.

Value

An "mcmc" object containing samples from the posterior distribution.

Examples


########################################
## Forced Marriage in Nottinghamshire ##
########################################
# Construct covariance matrix based on spatial information
prior.var <- expm::expm(forcedMarriage$adjacencyMatrix)
prior.var <- diag(diag(prior.var)^-0.5) %*% prior.var %*% diag(diag(prior.var)^-0.5)

# Fit model
# Using `n.iter = 3` here to reduce runtime, you will need more
# iterations for inference.
forcedMarriageModel <- speedyBBTm(
  outcome = rep(1, length(forcedMarriage$comparisons$win)),
  player1 = forcedMarriage$comparisons$win,
  player2 = forcedMarriage$comparisons$lost,
  player.prior.var = prior.var, n.iter = 3, burn.in = 0
)

# Plot results

plot(forcedMarriageModel[, "lambda[1]"],
  xlab = "Iteration", ylab = expression(lambda[i])
)

########################################
## Forced Marriage in Nottinghamshire ##
########################################
# Construct covariance matrix based on spatial information
prior.var <- expm::expm(forcedMarriage$adjacencyMatrix)
prior.var <- diag(diag(prior.var)^-0.5) %*% prior.var %*% diag(diag(prior.var)^-0.5)

# Fit model
# Using `n.iter = 3` here to reduce runtime, you will need more
# iterations for inference.
forcedMarriageModel <- speedyBBTm(
  outcome = rep(1, length(forcedMarriage$comparisons$win)),
  player1 = forcedMarriage$comparisons$win,
  player2 = forcedMarriage$comparisons$lost,
  player.prior.var = prior.var, n.iter = 3, burn.in = 0
)

# Plot results

plot(forcedMarriageModel[, "lambda[1]"],
  xlab = "Iteration", ylab = expression(lambda[i])
)

FGM in South Yorkshire

Description

A comparative judgment data set for risk of female genital mutilation at ward level in South Yorkshire.

Usage

sy.comparisons
sy.comparisons

Format

A data frame with 877 comparisons.

comparison_id: The ID of each comparison.
user_id: The ID of the user who made the comparisons.
item_1_id: The ID of the first area involved in the comparison.
item_2_id: The ID of the second area involved in the comparison.
selected_item_id: The ID of the selected area. If the comparison was tied, the selected_item_id is NA.
state: The state of the outcome. selected indicates that a judgment was made, skipped indicates that the comparison was skipped, and tied indicates that the comparison was tied.

Source

The data was collected following ethical approval the University of Birmingham's Science, Engineering and Maths Ethics Committee.

Wimbledon Men's Singles Championship 2019

Description

The outcomes of all 127 men's singles matches in the 2019 Wimbledon champtionship.

Usage

wimbledon
wimbledon

Format

A list containing a dataframe with the outcomes of the matches and a dataframe describing the players.

matches

Each row of the matchs dataframe corresponds to a match. The columns are:

winner: The id of the winner of the match.
loser: The id of the loser of the match.
secondWeek: A boolean indicating if the match was played in the second week of the tournament.
outcome: The outcome of the match. 1 if the winner is item 2, 0 if the winner is item 1.

players

Data frame describing the players. The columns are:

name: The name of the player.
rank: The rank of the player in the ATP league table.
points: The number of points received so far in the ATP 2019 tour prior to Wimbledon starting.
id: The ID of the player.

Source

http://tennis-data.co.uk/alldata.php