Package 'ovlytics' reference manual

Title:	Functions and Algorithms for Volleyball Analytics
Description:	Analytical functions for volleyball analytics, to be used in conjunction with the datavolley and peranavolley packages.
Authors:	Ben Raymond [aut, cre], Adrien Ickowicz [aut], openvolley.org [org]
Maintainer:	Ben Raymond <[email protected]>
License:	MIT + file LICENSE
Version:	0.3.3
Built:	2024-10-23 05:31:39 UTC
Source:	https://github.com/openvolley/ovlytics

Various skill performance indicators

Description

attack_eff: (number of kills - number of errors and blocked attacks) / (number of attacks)
serve_eff: (number of aces and positive serves - number of errors and poor serves) / (number of serves)
reception_eff: (number of perfect and positive passes - number of errors and overpasses) / (number of passes)

Usage

attack_eff(evaluation, skill)

serve_eff(evaluation, skill)

reception_eff(evaluation, skill)
attack_eff(evaluation, skill)

serve_eff(evaluation, skill)

reception_eff(evaluation, skill)

Arguments

`evaluation`	character: vector of skill evaluations ("Winning attack", "Error", etc)
`skill`	character: (optional) vector of skill values ("Attack", "Block", etc). If provided, it will be used to filter the `evaluation` vector to the elements corresponding to the correct skill. If not provided, all elements of `evaluation` will be used

Value

A numeric scalar

Examples

## Not run: 
 library(dplyr)
 x <- ovdata_example("mlafin_braslovce_nkbm", as = "parsed")
 plays(x) %>% dplyr::filter(skill == "Attack") %>% group_by(player_name) %>%
   dplyr::summarize(N_attacks = n(), att_eff = attack_eff(evaluation))

## End(Not run)

## Not run: 
 library(dplyr)
 x <- ovdata_example("mlafin_braslovce_nkbm", as = "parsed")
 plays(x) %>% dplyr::filter(skill == "Attack") %>% group_by(player_name) %>%
   dplyr::summarize(N_attacks = n(), att_eff = attack_eff(evaluation))

## End(Not run)

Calculate attack evenness

Description

Attack evenness is a measure of how balanced a team's attack is. Teams that rely heavily on one or two attackers will have a relatively low evenness value, whereas teams that use all of their attackers will be higher. See https://untan.gl/attack-evenness.html for further background. Evenness is calculated for each lineup used by a team and averaged (weighting by the number of attacks that each lineup made) to get an overall evenness value. Liberos are not expected to attack, and setter attacks (dumps) are ignored. Evenness can be calculated on grouped data: by default, it is calculated by match but averaged over matches when reporting the final result.

Usage

ov_aev(
  x,
  team,
  rotation,
  reference_props = NULL,
  calculate_by = "match_id",
  report_by,
  min_N_attacks = 10,
  detail = FALSE
)
ov_aev(
  x,
  team,
  rotation,
  reference_props = NULL,
  calculate_by = "match_id",
  report_by,
  min_N_attacks = 10,
  detail = FALSE
)

Arguments

`x`	data.frame: the `plays` data.frame as returned by `datavolley::read_dv()` or `peranavolley::pv_read()`
`team`	string: the team to calculate attack evenness for. If not provided, it will be calculated separately for all teams in `x`
`rotation`	string: the calculation needs to know what position each player is playing, so that it can work out how many attacks that player should have made under a "perfectly even attack" scenario. This is important for middle hitters, who are usually replaced by the libero in back court and therefore cannot make attacks at those times. The `rotation` parameter can be one of: "player_role": use the player roles as specified in the "player_role" column in `x`. These are typically the player roles defined in the roster, but it is left to the user to populate this column "SHM": assume a setter-hitter-middle rotation order (i.e. outside hitter is in position 2 when the setter is in 1) and infer the player roles from that "SMH": assume a setter-middle-hitter rotation order and infer the player roles from that "none": don't assume player roles, which will mean that under a "perfectly even attack" scenario, each player (excluding the setter) will be expected to make 20% of attacks. This option is probably of limited use
`reference_props`	data.frame or NULL: if `NULL`, the default attack profile as returned by `ov_aev_reference_props()` will be used (recommended). Otherwise, provide a data.frame with the same format as that returned by `ov_aev_reference_props()`
`calculate_by`	character: names variables in `x` to group by when doing the calculations. Note that "lineup" is always used as a calculation grouping variable (it is populated inside the function). See Details
`report_by`	character: names of variables in `x` to group by for the final results. Note that results are always effectively grouped by team. Any `report_by` variables will be used in addition to team. Note that: `report_by` can include "lineup" and/or "setter_id" (these variables are used internally in calculations) `report_by` variables must also be present in `calculate_by`, with the exception of "lineup" and "setter_id"
`min_N_attacks`	integer: minimum number of attacks that must be made in order to be included in the calculations. Attacks are counted by lineup and `calculate_by` variables (if any). If `calculate_by` is empty and `min_N_attacks` is 10, then only lineups that made 10 or more attacks (in total) will be included in the calculations. If `min_N_attacks` is 10 and `calculate_by` is "match_id", then calculations will be done match by match, using only lineups in a match that made 10 or more attacks in that match
`detail`	logical: if `TRUE`, the returned data frame will include additional columns: "aev_sd" (standard deviation of aev across the calculate_by groups), "rally_win_rate", "kill_rate", "rec_eff" (reception efficiency), "N_rallies" (number of rallies played). Note that including these details makes the calculation noticeably slower

Details

Note that calculation by group and averaging will not generally give the same results as calculating the average result in one step (e.g. calculating for several matches and averaging those results will probably not give the same answer as calculating for all matches pooled). This is expected: say that my team under-utilizes a particular hitter in one match, and over-utilizes her in another. In both of those matches my team's attack was uneven, and so the average of those two results should return a low evenness value indicating that on average my team's attack was uneven. But if the two matches are pooled and the data analyzed all together, the under-utilization in the first match might be balanced by the over-utilization in the second match, giving a higher attack evenness.

Value

A tibble with at least the columns "team", "aev", and "N_attacks". If detail was TRUE, additional columns will also be present (see the detail parameter)

References

https://untan.gl/attack-evenness.html

Examples


px <- plays(dv_read(ovdata_example()))

## for a single team
ov_aev(px, rotation = "SHM", team = "GKS Katowice")

## for all teams in px, and with extra detail
ov_aev(px, rotation = "SHM", detail = TRUE)

## for a single team, calculated by set number but aggregate results when reporting
ov_aev(px, team = "GKS Katowice", rotation = "SHM", calculate_by = "set_number")

## for a single team, calculated and reported by match and set number
ov_aev(px, team = "GKS Katowice", rotation = "SHM", calculate_by = c("match_id", "set_number"),
       report_by = c("match_id", "set_number"))

## for a single team, calculated by match and setter position, and reported by setter position
px <- ov_augment_plays(px, to_add = "setters", use_existing = FALSE)
ov_aev(px, team = "GKS Katowice", rotation = "SHM",
       calculate_by = c("match_id", "setter_position"), report_by = "setter_position")

px <- plays(dv_read(ovdata_example()))

## for a single team
ov_aev(px, rotation = "SHM", team = "GKS Katowice")

## for all teams in px, and with extra detail
ov_aev(px, rotation = "SHM", detail = TRUE)

## for a single team, calculated by set number but aggregate results when reporting
ov_aev(px, team = "GKS Katowice", rotation = "SHM", calculate_by = "set_number")

## for a single team, calculated and reported by match and set number
ov_aev(px, team = "GKS Katowice", rotation = "SHM", calculate_by = c("match_id", "set_number"),
       report_by = c("match_id", "set_number"))

## for a single team, calculated by match and setter position, and reported by setter position
px <- ov_augment_plays(px, to_add = "setters", use_existing = FALSE)
ov_aev(px, team = "GKS Katowice", rotation = "SHM",
       calculate_by = c("match_id", "setter_position"), report_by = "setter_position")

Reference proportions used in attack evenness calculations

Description

Reference proportions used in attack evenness calculations

Usage

ov_aev_reference_props()
ov_aev_reference_props()

Value

A tibble with columns player_role ("middle", "outside", "opposite", "setter", "libero"), player_front_back (whether that player is front or back court), and p_expected (the proportion of attacks that the player is expected to make under a perfectly even attack scenario)

Add some extra columns to a plays object

Description

Add some extra columns to a plays object

Usage

ov_augment_plays(
  x,
  to_add = c("receiving_team", "touch_summaries", "setters"),
  rotation = "SHM",
  use_existing = TRUE
)
ov_augment_plays(
  x,
  to_add = c("receiving_team", "touch_summaries", "setters"),
  rotation = "SHM",
  use_existing = TRUE
)

Arguments

`x`	data.frame: the `plays` data.frame as returned by `datavolley::read_dv()` or `peranavolley::pv_read()`
`to_add`	character: columns to add "receiving_team" adds the columns "receiving_team" (team name) and "receiving_team_id" "winners" adds the columns "set_won_by", "set_won_by_id" (the name and ID of the team that won the current set), "match_won_by", "match_won_by_id" (the name and ID of the team that won the current match), "team_won_set" and "team_won_match" (did the team making the action in the current row win the set/match), and "home_sets_won" and "visiting_sets_won" (the number of sets won by the home and visiting teams) "touch_summaries" adds a number of columns named "ts_*" that summarize a team touch (e.g. columns "ts_pass_quality", "ts_pass_evaluation_code" give the pass quality and pass evaluation code of the reception or dig associated with a given team touch). "touch_summaries" also adds a column named `freeball_over`, which disambiguates the action of putting a freeball over the net from the action of digging such a ball. Many scouts code both of these as a "Freeball". The `freeball_over` column will be `TRUE` if it was a freeball being put over the net, and `FALSE` otherwise (including freeball digs). Freeballs over and freeball digs will still both have "Freeball" in their `skill` column "setters" adds the columns "home_setter_id", "visiting_setter_id" (the player IDs of the home and visiting setter on court), and "setter_id", "setter_position", and "setter_front_back" (the player ID and position of the setter from the team performing the current action) "followed" adds the columns "followed_timeout", "followed_technical_timeout", and "followed_sub" "player_role" add the column "player_role" which gives the role (outside, middle, opposite, setter) for the active player on each row of `x`. This assumes a standard rotation as specified by `rotation`. Note that `player_role` does NOT include libero, although this can be inferred from the `meta` component of a full datavolley object "all" is a shortcut for all of the above
`rotation`	string: (only relevant when `to_add` includes "player_role") either "SHM" (assume a setter-hitter-middle rotation order, i.e. outside hitter is in position 2 when the setter is in 1), or "SMH" (setter-middle-hitter)
`use_existing`	logical: if `TRUE` and all of the columns associated with a given `to_add` choice are already present in `x`, they won't be re-generated

Value

x with the extra columns added

Create a prior table from a dvw or a directory of dvw files

Description

Create a prior table from a dvw or a directory of dvw files

Usage

ov_create_history_table(
  dvw,
  play_phase = c("Reception", "Transition"),
  attack_by = "code",
  setter_position_by = "rotation",
  exclude_attacks = c("PR"),
  normalize_parameters = TRUE
)
ov_create_history_table(
  dvw,
  play_phase = c("Reception", "Transition"),
  attack_by = "code",
  setter_position_by = "rotation",
  exclude_attacks = c("PR"),
  normalize_parameters = TRUE
)

Arguments

`dvw`	string: path to one or more datavolley files, a list of one or more datavolley objects, or a directory containing datavolley files
`play_phase`	character: one or both of "Reception", "Transition"
`attack_by`	string: either "code", "zone", "tempo" or "setter call"
`setter_position_by`	string: either "rotation", or "front_back"
`exclude_attacks`	character: vector of attack codes to exclude
`normalize_parameters`	logical: reduce the prior parameter values

Value

A list, currently with one component named "prior_table"

Examples

## use this file to create the priors
hist_dvw <- ovdata_example("NCA-CUB")
history_table <- ov_create_history_table(dvw = hist_dvw, attack_by = "attacker_name",
                                   setter_position_by = "front_back")

## use it on another file (here, the same file for demo purposes)
## usually the history would be from a reference set of previous matches

dvw <- ovdata_example("NCA-CUB")
setter <- ov_simulate_setter_distribution(dvw = dvw, play_phase = "Reception", n_sim = 100,
                                  attack_by = "attacker_name", attack_options = "use_history",
                                  setter_position_by = "front_back",
                                  history_table = history_table, filter_sim = TRUE)

## plot the results
ov_plot_ssd(setter, overlay_set_number = TRUE)
ov_plot_distribution(setter)

## use this file to create the priors
hist_dvw <- ovdata_example("NCA-CUB")
history_table <- ov_create_history_table(dvw = hist_dvw, attack_by = "attacker_name",
                                   setter_position_by = "front_back")

## use it on another file (here, the same file for demo purposes)
## usually the history would be from a reference set of previous matches

dvw <- ovdata_example("NCA-CUB")
setter <- ov_simulate_setter_distribution(dvw = dvw, play_phase = "Reception", n_sim = 100,
                                  attack_by = "attacker_name", attack_options = "use_history",
                                  setter_position_by = "front_back",
                                  history_table = history_table, filter_sim = TRUE)

## plot the results
ov_plot_ssd(setter, overlay_set_number = TRUE)
ov_plot_distribution(setter)

Example DataVolley files provided as part of the ovlytics package

Description

Example DataVolley files provided as part of the ovlytics package

Usage

ov_example_file(choice = "190301_kats_beds")
ov_example_file(choice = "190301_kats_beds")

Arguments

choice

string: which data file to return?

"190301_kats_beds" - a match between GKS Katowice and MKS Bedzin during the 2018/19 Polish Plus Liga

Value

path to the file

Examples

myfile <- ov_example_file()
x <- dv_read(myfile)
summary(x)

myfile <- ov_example_file()
x <- dv_read(myfile)
summary(x)

Kernel density estimates for volleyball heatmaps

Description

Kernel density estimates for volleyball heatmaps

Usage

ov_heatmap_kde(
  x,
  y,
  N = NULL,
  resolution = "coordinates",
  bw,
  n,
  court = "full",
  auto_flip = FALSE
)
ov_heatmap_kde(
  x,
  y,
  N = NULL,
  resolution = "coordinates",
  bw,
  n,
  court = "full",
  auto_flip = FALSE
)

Arguments

`x`	: either a numeric vector of x-locations, or a three-column data.frame or matrix with columns `x`, `y`, and optionally `N`. If `x` is a grouped tibble, the kernel density estimates will be calculated separately for group
`y`	numeric: (unless `x` is a data.frame or matrix) a numeric vector of y-locations
`N`	numeric: (unless `x` is a data.frame or matrix) a numeric vector of counts associated with each location (the corresponding location was observed `N` times)
`resolution`	string: the resolution of the locations, either "coordinates" or "subzones"
`bw`	numeric: a vector of bandwidths to use in the x- and y-directions (see `MASS::kde2d()`). If not provided, default values will be used based on the location resolution
`n`	integer: (scalar or a length-2 integer vector) the number of grid points in each direction. If not provided, 60 points in the x-direction and 60 (for half-court) or 120 points in the y-direction will be used
`court`	string: "full" (generate the kernel density estimate for the full court) or "lower" or "upper" (only the lower or upper half of the court)
`auto_flip`	logical: if `TRUE`, and `court` is either "lower" or "upper", then locations corresponding to the non-selected half of the court will be flipped. This might be appropriate if, for example, the heatmap represents attack end locations that were scouted with coordinates (because these aren't necessarily all aligned to the same end of the court by default)

Value

A data.frame with columns x, y, and density

Examples


library(ggplot2)
library(datavolley)

## Example 1 - by coordinates
## generate some fake coordinate data
Na <- 20
set.seed(17)
px <- data.frame(x = c(runif(Na, min = 0.4, max = 1.2), runif(Na, min = 2, max = 3)),
                 y = c(runif(Na, min = 4.5, max = 6.6), runif(Na, min = 4.9, max = 6.6)))

## plot as points
ggplot(px, aes(x, y)) + ggcourt(labels = NULL, court = "upper") +
       geom_point(colour = "dodgerblue")

## or as a heatmap
hx <- ov_heatmap_kde(px, resolution = "coordinates", court = "upper")
ggplot(hx, aes(x, y, fill = density)) +
       scale_fill_distiller(palette = "Purples", direction = 1, labels = NULL,
                            name = "Attack\ndensity") +
       geom_raster() + ggcourt(labels = NULL, court = "upper")

## Example 2 - by subzones, with data from two attackers
## generate some fake data
Na <- 20
set.seed(17)
px <- data.frame(zone = sample(c(1, 5:9), Na * 2, replace = TRUE),
                 subzone = sample(c("A", "B", "C", "D"), Na * 2, replace = TRUE),
                 attacker = c(rep("Attacker 1", Na), rep("Attacker 2", Na)))

## convert to x, y coordinates
px <- cbind(px, dv_xy(zones = px$zone, end = "upper", subzone = px$subzone))

## plot as tiles
library(dplyr)
ggplot(count(px, attacker, x, y), aes(x, y, fill = n)) + geom_tile() +
       facet_wrap(~attacker) + ggcourt(labels = NULL, court = "upper")

## or as a heatmap, noting that we group the data by attacker first
hx <- ov_heatmap_kde(group_by(px, attacker), resolution = "subzones", court = "upper")
ggplot(hx, aes(x, y, fill = density)) + facet_wrap(~attacker) +
       scale_fill_distiller(palette = "Purples", direction = 1, labels = NULL,
                            name = "Attack\ndensity") +
       geom_raster() + ggcourt(labels = NULL, court = "upper")

library(ggplot2)
library(datavolley)

## Example 1 - by coordinates
## generate some fake coordinate data
Na <- 20
set.seed(17)
px <- data.frame(x = c(runif(Na, min = 0.4, max = 1.2), runif(Na, min = 2, max = 3)),
                 y = c(runif(Na, min = 4.5, max = 6.6), runif(Na, min = 4.9, max = 6.6)))

## plot as points
ggplot(px, aes(x, y)) + ggcourt(labels = NULL, court = "upper") +
       geom_point(colour = "dodgerblue")

## or as a heatmap
hx <- ov_heatmap_kde(px, resolution = "coordinates", court = "upper")
ggplot(hx, aes(x, y, fill = density)) +
       scale_fill_distiller(palette = "Purples", direction = 1, labels = NULL,
                            name = "Attack\ndensity") +
       geom_raster() + ggcourt(labels = NULL, court = "upper")

## Example 2 - by subzones, with data from two attackers
## generate some fake data
Na <- 20
set.seed(17)
px <- data.frame(zone = sample(c(1, 5:9), Na * 2, replace = TRUE),
                 subzone = sample(c("A", "B", "C", "D"), Na * 2, replace = TRUE),
                 attacker = c(rep("Attacker 1", Na), rep("Attacker 2", Na)))

## convert to x, y coordinates
px <- cbind(px, dv_xy(zones = px$zone, end = "upper", subzone = px$subzone))

## plot as tiles
library(dplyr)
ggplot(count(px, attacker, x, y), aes(x, y, fill = n)) + geom_tile() +
       facet_wrap(~attacker) + ggcourt(labels = NULL, court = "upper")

## or as a heatmap, noting that we group the data by attacker first
hx <- ov_heatmap_kde(group_by(px, attacker), resolution = "subzones", court = "upper")
ggplot(hx, aes(x, y, fill = density)) + facet_wrap(~attacker) +
       scale_fill_distiller(palette = "Purples", direction = 1, labels = NULL,
                            name = "Attack\ndensity") +
       geom_raster() + ggcourt(labels = NULL, court = "upper")

Infer the role of each player

Description

Infer the role of each player

Usage

ov_infer_player_roles(
  x,
  target_team,
  method,
  fall_back = TRUE,
  setter_tip_codes = c("PP")
)
ov_infer_player_roles(
  x,
  target_team,
  method,
  fall_back = TRUE,
  setter_tip_codes = c("PP")
)

Arguments

`x`	: a datavolley object (as returned by `datavolley::dv_read()`), a list of datavolley objects, or the `plays` component of a datavolley object
`target_team`	string or function: team to report on. If this is a function, it should return `TRUE` when passed the target team name
`method`	string: "meta" (rely on player metadata), "SHM" (assume a setter-hitter-middle rotation order), "SMH" (setter-middle-hitter), or "data" (figure out positions from scouting data). Method "meta" is the default if a datavolley object or list of objects is provided
`fall_back`	logical: if TRUE and `method` is "meta" and x is a single datavolley object BUT player roles are not provided in the DataVolley file metadata section, fall back to method="data"
`setter_tip_codes`	character: vector of attack combination codes that correspond to setter tips

Value

A data.frame

Examples

x <- ovdata_example("mlafin_braslovce_nkbm", as = "parsed")
## guess roles according to the actions that the players made
rx <- ov_infer_player_roles(x, target_team = "Nova KBM Branik", method = "data")

x <- ovdata_example("mlafin_braslovce_nkbm", as = "parsed")
## guess roles according to the actions that the players made
rx <- ov_infer_player_roles(x, target_team = "Nova KBM Branik", method = "data")

Court plot of a real and simulated setter distribution

Description

Court plot of a real and simulated setter distribution

Usage

ov_plot_distribution(
  ssd,
  label_setters_by = "id",
  font_size = 11,
  title_wrap = NA,
  output = "plot"
)
ov_plot_distribution(
  ssd,
  label_setters_by = "id",
  font_size = 11,
  title_wrap = NA,
  output = "plot"
)

Arguments

`ssd`	simulated setter distribution output as returned by `ov_simulate_setter_distribution()`
`label_setters_by`	string: either "id" or "name"
`font_size`	numeric: font size
`title_wrap`	numeric: if non-`NA`, use `strwrap()` to break the title into lines of this width
`output`	string: either "plot" or "list"

Examples

dvw <- ovdata_example("NCA-CUB")
setter <- ov_simulate_setter_distribution(dvw = dvw, play_phase = c("Reception", "Transition"),
                                          n_sim = 100, attack_by = "code")
ov_plot_distribution(setter)
dvw <- ovdata_example("NCA-CUB")
setter <- ov_simulate_setter_distribution(dvw = dvw, play_phase = c("Reception", "Transition"),
                                          n_sim = 100, attack_by = "code")
ov_plot_distribution(setter)

Plot the prior table

Description

Plot the prior table

Usage

ov_plot_history_table(history_table, team, setter_id)
ov_plot_history_table(history_table, team, setter_id)

Arguments

`history_table`	data.frame: the `prior_table` component of the object returned by `ov_create_history_table()`
`team`	string: team name
`setter_id`	string: setter_id

Examples

hist_dvw <- ovdata_example("NCA-CUB")
history_table <- ov_create_history_table(dvw = hist_dvw, setter_position_by = "front_back",
                                                    normalize_parameters = FALSE)
team = unique(history_table$prior_table$team)[1]
setter_id = unique(history_table$prior_table$setter_id)[1]
ov_plot_history_table(history_table, team, setter_id)
hist_dvw <- ovdata_example("NCA-CUB")
history_table <- ov_create_history_table(dvw = hist_dvw, setter_position_by = "front_back",
                                                    normalize_parameters = FALSE)
team = unique(history_table$prior_table$team)[1]
setter_id = unique(history_table$prior_table$setter_id)[1]
ov_plot_history_table(history_table, team, setter_id)

Plot the rates

Description

Plot the rates

Usage

ov_plot_rate(ssd, team, setter_id, range = c(0.05, 0.95))
ov_plot_rate(ssd, team, setter_id, range = c(0.05, 0.95))

Arguments

`ssd`	simulated setter distribution output as returned by `ov_simulate_setter_distribution()`
`team`	string: team name
`setter_id`	string: setter_id
`range`	vector of maximum and minimum quantile value description

Examples

dvw <- ovdata_example("NCA-CUB")
system.time({
  ssd <- ov_simulate_setter_distribution(dvw = dvw, play_phase = c("Reception", "Transition"),
                                         n_sim = 150, setter_position_by = "front_back", 
                                          attack_by = "zone")
  team <- ssd$raw_data$meta$teams$team[1]
  setter_id <- ssd$raw_data$meta$players_h$player_id[which(ssd$raw_data$meta$players_h$role == "setter")][2]
  ov_plot_rate(ssd, team, setter_id)
})
dvw <- ovdata_example("NCA-CUB")
system.time({
  ssd <- ov_simulate_setter_distribution(dvw = dvw, play_phase = c("Reception", "Transition"),
                                         n_sim = 150, setter_position_by = "front_back", 
                                          attack_by = "zone")
  team <- ssd$raw_data$meta$teams$team[1]
  setter_id <- ssd$raw_data$meta$players_h$player_id[which(ssd$raw_data$meta$players_h$role == "setter")][2]
  ov_plot_rate(ssd, team, setter_id)
})

Plot a simulated setter distribution sequence

Description

Plot a simulated setter distribution sequence

Usage

ov_plot_sequence_distribution(
  ssd,
  label_setters_by = "id",
  font_size = 11,
  title_wrap = NA,
  split_set = FALSE,
  output = "plot"
)
ov_plot_sequence_distribution(
  ssd,
  label_setters_by = "id",
  font_size = 11,
  title_wrap = NA,
  split_set = FALSE,
  output = "plot"
)

Arguments

`ssd`	simulated setter distribution output as returned by `ov_simulate_setter_distribution()`
`label_setters_by`	string: either "id" or "name"
`font_size`	numeric: font size
`title_wrap`	numeric: if non-`NA`, use `strwrap()` to break the title into lines of this width
`split_set`	boolean: if `TRUE`, separate the distribution sequence by set
`output`	string: either "plot" or "list"

Examples

dvw <- ovdata_example("NCA-CUB")
ssd <- ov_simulate_setter_distribution(dvw = dvw, play_phase = c("Reception"),
                                       n_sim = 100, attack_by = "zone",
                                       setter_position_by = "front_back")
ov_plot_sequence_distribution(ssd)

dvw <- ovdata_example("NCA-CUB")
ssd <- ov_simulate_setter_distribution(dvw = dvw, play_phase = c("Reception"),
                                       n_sim = 100, attack_by = "zone",
                                       setter_position_by = "front_back")
ov_plot_sequence_distribution(ssd)

Plot a simulated setter distribution

Description

Plot a simulated setter distribution

Usage

ov_plot_ssd(
  ssd,
  overlay_set_number = FALSE,
  label_setters_by = "name",
  font_size = 11
)
ov_plot_ssd(
  ssd,
  overlay_set_number = FALSE,
  label_setters_by = "name",
  font_size = 11
)

Arguments

`ssd`	simulated setter distribution output as returned by `ov_simulate_setter_distribution()`
`overlay_set_number`	boolean: if `TRUE`, overlay set number and score in the plot
`label_setters_by`	string: either "id" or "name"
`font_size`	numeric: font size

Examples

dvw <- ovdata_example("NCA-CUB")
setter <- ov_simulate_setter_distribution(dvw = dvw,
                                          n_sim = 150, attack_by = "zone")
ov_plot_ssd(setter, overlay_set_number = TRUE)
dvw <- ovdata_example("NCA-CUB")
setter <- ov_simulate_setter_distribution(dvw = dvw,
                                          n_sim = 150, attack_by = "zone")
ov_plot_ssd(setter, overlay_set_number = TRUE)

Print the prior table

Description

Print the prior table

Usage

ov_print_history_table(history_table, team, setter_id)
ov_print_history_table(history_table, team, setter_id)

Arguments

`history_table`	data.frame: the `prior_table` component of the object returned by `ov_create_history_table()`
`team`	string: team name
`setter_id`	string: setter_id

Examples

hist_dvw <- ovdata_example("NCA-CUB")
history_table <- ov_create_history_table(dvw = hist_dvw, attack_by = "zone")
team = history_table$prior_table$team[1]
setter_id = history_table$prior_table$setter_id[1]
ov_print_history_table(history_table, team, setter_id)
hist_dvw <- ovdata_example("NCA-CUB")
history_table <- ov_create_history_table(dvw = hist_dvw, attack_by = "zone")
team = history_table$prior_table$team[1]
setter_id = history_table$prior_table$setter_id[1]
ov_print_history_table(history_table, team, setter_id)

Print the rate table

Description

Print the rate table

Usage

ov_print_rate_table(ssd, team, setter_id)
ov_print_rate_table(ssd, team, setter_id)

Arguments

`ssd`	simulated setter distribution output as returned by `ov_simulate_setter_distribution()`
`team`	string: team name
`setter_id`	string: setter_id

Examples

dvw <- ovdata_example("NCA-CUB")
system.time({
  ssd <- ov_simulate_setter_distribution(dvw = dvw, play_phase = "Reception",
                                         n_sim = 100, setter_position_by = "front_back")
  team <- ssd$raw_data$meta$teams$team[1]
  setter_id <- ssd$raw_data$meta$players_h$player_id[which(ssd$raw_data$meta$players_h$role == "setter")][2]
  ov_print_rate_table(ssd, team, setter_id)
})
dvw <- ovdata_example("NCA-CUB")
system.time({
  ssd <- ov_simulate_setter_distribution(dvw = dvw, play_phase = "Reception",
                                         n_sim = 100, setter_position_by = "front_back")
  team <- ssd$raw_data$meta$teams$team[1]
  setter_id <- ssd$raw_data$meta$players_h$player_id[which(ssd$raw_data$meta$players_h$role == "setter")][2]
  ov_print_rate_table(ssd, team, setter_id)
})

Create a summary table of a team's matches in a season

Description

Create a summary table of a team's matches in a season

Usage

ov_season_table(xl, target_team, target_team_id, show_by = "match date")
ov_season_table(xl, target_team, target_team_id, show_by = "match date")

Arguments

`xl`	list: list of datavolley objects (each as returned by `datavolley::dv_read()`
`target_team`	string: the name of the target team. Only one of `target_team` or `target_team_id` is required
`target_team_id`	string: the team ID of the target team. Ignored if `target_team` has been provided
`show_by`	string: either "match date" (show each match according to its date) or "filename" (show each match according to its filename. This might be useful if the match dates are being parsed incorrectly by `datavolley::dv_read()`)

Value

A tibble with columns "Opponent", "Date" (or "File"), "Result", "Set scores", and one column for sets 1 to 5

Examples

## trivial example of a single-match "season"
library(datavolley)
x <- dv_read(dv_example_file())
ov_season_table(list(x), target_team = home_team(x))

## trivial example of a single-match "season"
library(datavolley)
x <- dv_read(dv_example_file())
ov_season_table(list(x), target_team = home_team(x))

Tabulate setter repeat patterns

Description

Note: analysis is done on the basis of attack actions, and simply assumes that the setter on court made the set.

Usage

ov_setter_repetition(
  x,
  setter_id,
  setter_name,
  exclude_attacks = c("PP", "PR", "P2"),
  exclude_negative_reception = TRUE,
  exclude_highballs = FALSE
)
ov_setter_repetition(
  x,
  setter_id,
  setter_name,
  exclude_attacks = c("PP", "PR", "P2"),
  exclude_negative_reception = TRUE,
  exclude_highballs = FALSE
)

Arguments

`x`	data.frame: the `plays` data.frame as returned by `datavolley::read_dv()` or `peranavolley::pv_read()`
`setter_id`	string: (optional) the player ID of the setter to analyze (or provide `setter_name`). If neither `setter_id` nor `setter_name` are provided, all setters will be analyzed separately, and collated results returned
`setter_name`	string: (optional) the name of the setter to analyze (ignored if `setter_id` is provided). If neither `setter_id` nor `setter_name` are provided, all setters will be analyzed separately, and collated results returned
`exclude_attacks`	character: vector of attack codes to exclude
`exclude_negative_reception`	logical: if `TRUE`, exclude attacks following poor reception (likely to be out-of-system and therefore might not represent attacks on which the setter had genuine options)
`exclude_highballs`	logical: if `TRUE`, exclude highball attacks (likely to be out-of-system and therefore might not represent attacks on which the setter had genuine options)

Value

A data.frame with columns "team", "setter_name", "setter_id", "player_name", "player_id", "category", "opportunities", "repeats", "repeat%"

Examples


x <- plays(ovdata_example("NCA-CUB", as = "parsed"))
set_reps <- ov_setter_repetition(x, setter_name = "LOLETTE RODRIGUEZ")

library(ggplot2)
ggplot(set_reps, aes(x = player_name, y = `repeat%`)) + geom_col() +
    geom_text(aes(x = player_name, label = paste0("N=", opportunities)),
              angle = 90, y = 100, hjust = 1, inherit.aes = FALSE) +
    facet_wrap(~category) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 60, vjust = 1, hjust = 1)) +
    labs(x = NULL, y = "Repeat percentage")

x <- plays(ovdata_example("NCA-CUB", as = "parsed"))
set_reps <- ov_setter_repetition(x, setter_name = "LOLETTE RODRIGUEZ")

library(ggplot2)
ggplot(set_reps, aes(x = player_name, y = `repeat%`)) + geom_col() +
    geom_text(aes(x = player_name, label = paste0("N=", opportunities)),
              angle = 90, y = 100, hjust = 1, inherit.aes = FALSE) +
    facet_wrap(~category) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 60, vjust = 1, hjust = 1)) +
    labs(x = NULL, y = "Repeat percentage")

Simulate a Bayesian Bandit choice for a given set of probabilities and a number of points for multiple games

Description

Simulate a Bayesian Bandit choice for a given set of probabilities and a number of points for multiple games

Usage

ov_simulate_multiple_setter_distribution(
  list_dv,
  play_phase = c("Reception", "Transition"),
  n_sim = 500,
  priors = list(name = "beta", par1 = 1, par2 = 1),
  epsilon = 1,
  filter_sim = FALSE,
  attack_options = "use_data",
  killRate_grouping = NULL,
  setter_position_by = "rotation",
  history_table = NULL,
  attack_by = "code",
  exclude_attacks = c("PR"),
  rotation = "SHM",
  shiny_progress = NULL
)
ov_simulate_multiple_setter_distribution(
  list_dv,
  play_phase = c("Reception", "Transition"),
  n_sim = 500,
  priors = list(name = "beta", par1 = 1, par2 = 1),
  epsilon = 1,
  filter_sim = FALSE,
  attack_options = "use_data",
  killRate_grouping = NULL,
  setter_position_by = "rotation",
  history_table = NULL,
  attack_by = "code",
  exclude_attacks = c("PR"),
  rotation = "SHM",
  shiny_progress = NULL
)

Arguments

`list_dv`	list: list of datavolley object as returned by `datavolley::dv_read()`
`play_phase`	character: one or both of "Reception", "Transition"
`n_sim`	integer: number of simulations
`priors`	numeric: prior distribution of the kill rate for the different attacking options
`epsilon`	numeric: reward size
`filter_sim`	logical:
`attack_options`	string: either "use_data" or "use_history"
`killRate_grouping`	string: Default to NULL, it will use 'attack by' grouping variables. Otherwise a set of additional grouping variables to calculate the kill rate.
`setter_position_by`	string: either "rotation" or "front_back"
`history_table`	list: (only if `attack_options` is "use_history") the object returned by `ov_create_history_table()`
`attack_by`	string: either "code", "zone", "tempo", "setter call", "attacker_name", "player_role"
`exclude_attacks`	character: vector of attack codes to exclude
`rotation`	string: (only relevant when `attack_by` is "player_role") either "SHM" (assume a setter-hitter-middle rotation order), or "SMH" (setter-middle-hitter)
`shiny_progress`	numeric: an optional two-element vector. If not `NULL` or `NA`, `shiny::setProgress()` calls will be made during simulation with `value`s in this range

Examples

list_dv <- list(dv_read(ovdata_example("NCA-CUB")), dv_read(ovdata_example("NCA-CUB")))
system.time({
  mssd <- ov_simulate_multiple_setter_distribution(list_dv = list_dv, play_phase = "Reception",
               n_sim = 100, setter_position_by = "front_back")
})
list_dv <- list(dv_read(ovdata_example("NCA-CUB")), dv_read(ovdata_example("NCA-CUB")))
system.time({
  mssd <- ov_simulate_multiple_setter_distribution(list_dv = list_dv, play_phase = "Reception",
               n_sim = 100, setter_position_by = "front_back")
})

Simulate a Bayesian Bandit choice for a given set of probabilities and a number of points

Description

Simulate a Bayesian Bandit choice for a given set of probabilities and a number of points

Usage

ov_simulate_setter_distribution(
  dvw,
  play_phase = c("Reception", "Transition"),
  n_sim = 500,
  priors = list(name = "beta", par1 = 1, par2 = 1),
  epsilon = 1,
  filter_sim = FALSE,
  attack_options = "use_data",
  killRate_grouping = NULL,
  setter_position_by = "rotation",
  history_table = NULL,
  attack_by = "code",
  exclude_attacks = c("PR"),
  rotation = "SHM",
  shiny_progress = NULL
)
ov_simulate_setter_distribution(
  dvw,
  play_phase = c("Reception", "Transition"),
  n_sim = 500,
  priors = list(name = "beta", par1 = 1, par2 = 1),
  epsilon = 1,
  filter_sim = FALSE,
  attack_options = "use_data",
  killRate_grouping = NULL,
  setter_position_by = "rotation",
  history_table = NULL,
  attack_by = "code",
  exclude_attacks = c("PR"),
  rotation = "SHM",
  shiny_progress = NULL
)

Arguments

`dvw`	string or datavolley: a datavolley object as returned by `datavolley::dv_read()` or a path to datavolley file
`play_phase`	character: one or both of "Reception", "Transition"
`n_sim`	integer: number of simulations
`priors`	numeric: prior distribution of the kill rate for the different attacking options
`epsilon`	numeric: reward size
`filter_sim`	logical:
`attack_options`	string: either "use_data" or "use_history"
`killRate_grouping`	string: Default to NULL, it will use 'attack by' grouping variables. Otherwise a set of additional grouping variables to calculate the kill rate.
`setter_position_by`	string: either "rotation" or "front_back"
`history_table`	list: (only if `attack_options` is "use_history") the object returned by `ov_create_history_table()`
`attack_by`	string: either "code", "zone", "tempo", "setter call", "attacker_name", "player_role"
`exclude_attacks`	character: vector of attack codes to exclude
`rotation`	string: (only relevant when `attack_by` is "player_role") either "SHM" (assume a setter-hitter-middle rotation order), or "SMH" (setter-middle-hitter)
`shiny_progress`	numeric: an optional two-element vector. If not `NULL` or `NA`, `shiny::setProgress()` calls will be made during simulation with `value`s in this range

Examples

dvw <- ovdata_example("NCA-CUB")
system.time({
  ssd <- ov_simulate_setter_distribution(dvw = dvw, play_phase = "Reception",
                                         n_sim = 100, attack_by = "setter call",
                                         setter_position_by = "front_back", filter_sim = TRUE)
})
dvw <- ovdata_example("NCA-CUB")
system.time({
  ssd <- ov_simulate_setter_distribution(dvw = dvw, play_phase = "Reception",
                                         n_sim = 100, attack_by = "setter call",
                                         setter_position_by = "front_back", filter_sim = TRUE)
})

Sort DataVolley attack codes

Description

Sort DataVolley attack codes

Usage

ov_sort_attack_codes(ac, by = "XV", na.last = NA)
ov_sort_attack_codes(ac, by = "XV", na.last = NA)

Arguments

`ac`	character: character vector of attack codes to sort
`by`	string: method to use, currently only "XV" (any other value will default back to using `sort` without modification). "XV" will place X and V codes first (in numerical order, with each X preceding its matching V) then everything else in alphabetical order after that
`na.last`	logical: passed to `sort`

Value

Sorted character vector

Examples

ov_sort_attack_codes(c("V5", "V1", "X6", "CF", "X5"))

## Not run: 
  ## sorting might be useful for controlling the plot order when facetting
  ##  a `ggplot` by attack code
  mydata$attack_code <- factor(mydata$attack_code,
            levels = ov_sort_attack_codes(unique(na.omit(mydata$attack_code))))
  ggplot(mydata, ...) + facet_wrap(~attack_code)

## End(Not run)

ov_sort_attack_codes(c("V5", "V1", "X6", "CF", "X5"))

## Not run: 
  ## sorting might be useful for controlling the plot order when facetting
  ##  a `ggplot` by attack code
  mydata$attack_code <- factor(mydata$attack_code,
            levels = ov_sort_attack_codes(unique(na.omit(mydata$attack_code))))
  ggplot(mydata, ...) + facet_wrap(~attack_code)

## End(Not run)

Table of a simulated multi-game setter distribution sequence

Description

Table of a simulated multi-game setter distribution sequence

Usage

ov_table_mssd(
  mssd,
  label_setters_by = "name",
  team = NULL,
  nrows = 50,
  groupBy = TRUE
)
ov_table_mssd(
  mssd,
  label_setters_by = "name",
  team = NULL,
  nrows = 50,
  groupBy = TRUE
)

Arguments

`mssd`	simulated multi-game setter distribution output as returned by `ov_simulate_multiple_setter_distribution()`
`label_setters_by`	string: either "id" or "name"
`team`	NULL or string: if non-NULL, show sequence just for this team name
`nrows`	integer: number of rows per page in the table
`groupBy`	boolean: if TRUE, will group the rows by Opponent

Examples

## Not run: 
 list_dv <- list(dv_read(ovdata_example("NCA-CUB"))) # would normally be multiple games
 mssd <- ov_simulate_multiple_setter_distribution(list_dv = list_dv,
             play_phase = c("Reception", "Transition"), attack_by = "attacker_name",
             n_sim = 100, setter_position_by = "front_back")

 res <- ov_table_mssd(mssd, team = "NICARAGUA")

## End(Not run)
## Not run: 
 list_dv <- list(dv_read(ovdata_example("NCA-CUB"))) # would normally be multiple games
 mssd <- ov_simulate_multiple_setter_distribution(list_dv = list_dv,
             play_phase = c("Reception", "Transition"), attack_by = "attacker_name",
             n_sim = 100, setter_position_by = "front_back")

 res <- ov_table_mssd(mssd, team = "NICARAGUA")

## End(Not run)

ovlytics

Description

Analytical functions for volleyball analytics, to be used in conjunction with the datavolley and peranavolley packages.

Author(s)

Maintainer: Ben Raymond [email protected]

Authors:

Adrien Ickowicz

Other contributors:

openvolley.org [originator]

Package 'ovlytics'

Help Index

Various skill performance indicators

Description

Usage

Arguments

Value

Examples

Calculate attack evenness

Description

Usage

Arguments

Details

Value

References

Examples

Reference proportions used in attack evenness calculations

Description

Usage

Value

See Also

Add some extra columns to a plays object

Description

Usage

Arguments

Value

Create a prior table from a dvw or a directory of dvw files

Description

Usage

Arguments

Value

Examples

Example DataVolley files provided as part of the ovlytics package

Description

Usage

Arguments

Value

Examples

Kernel density estimates for volleyball heatmaps

Description

Usage

Arguments

Value

Examples

Infer the role of each player

Description

Usage

Arguments

Value

Examples

Court plot of a real and simulated setter distribution

Description

Usage

Arguments

Examples

Plot the prior table

Description

Usage

Arguments

Examples

Plot the rates

Description

Usage

Arguments

Examples

Plot a simulated setter distribution sequence

Description

Usage

Arguments

Examples

Plot a simulated setter distribution

Description

Usage

Arguments

Examples

Print the prior table

Description

Usage

Arguments

Examples