- Documentación en español
The handbaloner package has useful function for the visualization of
handball data.
The development version can be installed from GitHub with:
# install.packages("devtools")
devtools::install_github("telaroz/handbaloner")Also, the plot_paces function has as dependency the package ggflags,
which is not in CRAN. So in order to use that function, that dependency
should be installed (not needed to use the rest of the functions)
install.packages("ggflags", repos = c(
"https://jimjam-slam.r-universe.dev",
"https://cloud.r-project.org"))In this example, we can draw a basic court.
library(handbaloner)
court()
We can change the colours, rotate vertically and mirror the court with the functions’ parameters
court(vertical = TRUE, flip = TRUE, court_color = "orange",
area_color = "#3431A2", lines_color = "black")
As the plots are generated with ggplot, we can describe the colours with their HEX code, rgb, number or by its name in english; see: use of colours in ggplot2
We can also draw half a court
half_court(vertical = TRUE, court_color = colors()[36],
area_color = rgb(red = 0.2, green = 0.4, blue = 0.6),
lines_color = "yellow")
Another useful function is distance_to_goal which measures the
distance from a point of the field to its closest goal, given some
coordinates ([-40, 40] in the x axis and [-20, 20] in the y axis):
distance_to_goal(x = 10, y = 3)
#> [1] 10.11187Also, as this is a ggplot, we can add some additional layers. For
example, let’s generate a data.frame with some coordinates of shots
and whether they were goals or not. We will add the distance to goal as
a column and plot in green and red if the shots were goal or not.
shots <- dplyr::tibble(x = c(-13, -12, 11, -11, 9.5),
y = c(2, 5, -3, -1, 0),
gol = c(1, 0, 1, 1, 0))
dplyr::mutate(shots, distance_to_goal = distance_to_goal(x, y))
#> # A tibble: 5 × 4
#> x y gol distance_to_goal
#> <dbl> <dbl> <dbl> <dbl>
#> 1 -13 2 1 7.02
#> 2 -12 5 0 8.73
#> 3 11 -3 1 9.12
#> 4 -11 -1 1 9
#> 5 9.5 0 0 10.5court() +
ggplot2::geom_point(data = shots, ggplot2::aes(x, y),
color = ifelse(shots$gol == 1, 'Green', 'Red'),
size = 4)
In this example, we can draw a handball goal.
library(handbaloner)
draw_goal()
We can change the colour of the goal. It is red by default.
library(handbaloner)
draw_goal("blue")
Now, let’s draw some shots, just as we did with the court:
goal_shots <- dplyr::tibble(x = c(-2, -1, 0.5, 0.7, 1.4),
y = c(0.2, 2, -0.5, 0.3, 0.9),
gol = c(0, 0, 1, 1, 1))
draw_goal() +
ggplot2::geom_point(data = goal_shots, ggplot2::aes(x, y),
color = ifelse(goal_shots$gol == 1, 'Green', 'Red'),
size = 4)
First, you need to download the PBP pdf file. You can use the
scrape_from_ihf function to do so. Find the link for the match
information and set the folder to download the file.
For the first match of the 2023 World Men’s Handball Championship, you can download all PDFs as follows:
scrape_from_ihf(link = "https://www.ihf.info/competitions/men/308/28th-ihf-men039s-world-championship-2023-polandsweden/101253/match-center/118963",
folder = "ejemplo")Now, use the generate_tidy_pbp to generate a data.frame in a tidy
format.
tidy <- generate_tidy_pbp("ejemplo/47PBP.PDF")
#> Column 2 ['V3'] of item 2 is missing in item 1. Use fill=TRUE to fill with NA (NULL for list columns), or use.names=FALSE to ignore column names. use.names='check' (default from v1.12.2) emits this message and proceeds as if use.names=FALSE for backwards compatibility. See news item 5 in v1.12.2 for options to control this message.tidy
#> match_id teams gender time numeric_time half
#> <num> <char> <char> <char> <num> <num>
#> 1: 47 USA - EGY M 0:00 0 1
#> 2: 47 USA - EGY M 0:00 0 1
#> 3: 47 USA - EGY M 0:39 39 1
#> 4: 47 USA - EGY M 0:39 39 1
#> 5: 47 USA - EGY M 0:47 47 1
#> ---
#> 169: 47 USA - EGY M 57:33 3453 2
#> 170: 47 USA - EGY M 58:28 3508 2
#> 171: 47 USA - EGY M 59:11 3551 2
#> 172: 47 USA - EGY M 59:45 3585 2
#> 173: 47 USA - EGY M 59:55 3595 2
#> action number
#> <char> <char>
#> 1: ROBINSON N Goalkeeper 99
#> 2: HENDAWY K Goalkeeper 88
#> 3: STROMBERG J Turnover 6
#> 4: HENDAWY K Steal 88
#> 5: SAAD A Goal right wing top left (48 ABDELHAK M), 86 km/h 53
#> ---
#> 169: ELDERAA Y Goal centre 9m bottom right 39
#> 170: AMITOVIC A Goal centre 9m bottom left (7 CHAN BLANCO A), 79 km/h 5
#> 171: ELDERAA S Goal centre 9m bottom left 45
#> 172: AMITOVIC A Shot centre 9m post 5
#> 173: SHEBIB M Technical Fault (FB) 89
#> team goalkeeper opponent_goalkeeper assist_number goal_number
#> <char> <char> <char> <char> <char>
#> 1: USA 99 88 <NA> <NA>
#> 2: EGY 88 99 <NA> <NA>
#> 3: USA 99 88 <NA> <NA>
#> 4: EGY 88 99 <NA> <NA>
#> 5: EGY 88 99 48 53
#> ---
#> 169: EGY 92 99 <NA> 39
#> 170: USA 99 92 7 5
#> 171: EGY 92 99 <NA> 45
#> 172: USA 99 92 <NA> <NA>
#> 173: EGY 92 99 <NA> <NA>
#> shot_number goal shot_speed in_goal_position shot_position post saved
#> <char> <num> <num> <char> <char> <num> <num>
#> 1: <NA> 0 NA <NA> <NA> NA NA
#> 2: <NA> 0 NA <NA> <NA> NA NA
#> 3: <NA> 0 NA <NA> <NA> NA NA
#> 4: <NA> 0 NA <NA> <NA> NA NA
#> 5: <NA> 1 86 top left right wing NA NA
#> ---
#> 169: <NA> 1 NA bottom right centre 9m NA NA
#> 170: <NA> 1 79 bottom left centre 9m NA NA
#> 171: <NA> 1 NA bottom left centre 9m NA NA
#> 172: 5 0 NA post centre 9m 1 NA
#> 173: <NA> 0 NA <NA> <NA> NA NA
#> vertical_goal_position horizontal_goal_position causes_7m_number
#> <char> <char> <char>
#> 1: <NA> <NA> <NA>
#> 2: <NA> <NA> <NA>
#> 3: <NA> <NA> <NA>
#> 4: <NA> <NA> <NA>
#> 5: top left <NA>
#> ---
#> 169: bottom right <NA>
#> 170: bottom left <NA>
#> 171: bottom left <NA>
#> 172: <NA> <NA> <NA>
#> 173: <NA> <NA> <NA>
#> receives_7m_number turnover technical_foul steal suspension is_home
#> <char> <char> <char> <char> <char> <lgcl>
#> 1: <NA> <NA> <NA> <NA> <NA> TRUE
#> 2: <NA> <NA> <NA> <NA> <NA> FALSE
#> 3: <NA> 6 <NA> <NA> <NA> TRUE
#> 4: <NA> <NA> <NA> 88 <NA> FALSE
#> 5: <NA> <NA> <NA> <NA> <NA> FALSE
#> ---
#> 169: <NA> <NA> <NA> <NA> <NA> FALSE
#> 170: <NA> <NA> <NA> <NA> <NA> TRUE
#> 171: <NA> <NA> <NA> <NA> <NA> FALSE
#> 172: <NA> <NA> <NA> <NA> <NA> TRUE
#> 173: <NA> <NA> 89 <NA> <NA> FALSE
#> number_suspended no_goalkeeper number_court_players possession
#> <int> <num> <num> <char>
#> 1: 0 NA NA USA
#> 2: 0 NA NA USA
#> 3: 0 0 6 USA
#> 4: 0 0 6 USA
#> 5: 0 0 6 EGY
#> ---
#> 169: 0 0 6 EGY
#> 170: 0 0 6 USA
#> 171: 0 0 6 EGY
#> 172: 0 0 6 USA
#> 173: 0 0 6 EGY
#> number_of_possession start_of_possession end_of_possession score lead
#> <int> <char> <char> <char> <num>
#> 1: 1 <NA> <NA> 0 - 0 0
#> 2: 1 <NA> <NA> 0 - 0 0
#> 3: 1 0:00 0:39 0 - 0 0
#> 4: 1 0:00 0:39 0 - 0 0
#> 5: 2 0:39 0:47 0 - 1 -1
#> ---
#> 169: 115 57:22 57:33 15 - 34 -19
#> 170: 116 57:33 58:28 16 - 34 -18
#> 171: 117 58:28 59:11 16 - 35 -19
#> 172: 118 59:11 59:45 16 - 35 -19
#> 173: 119 59:45 60:00 16 - 35 -19
#> possession_length
#> <num>
#> 1: NA
#> 2: NA
#> 3: 39
#> 4: 39
#> 5: 8
#> ---
#> 169: 11
#> 170: 55
#> 171: 43
#> 172: 34
#> 173: 15To plot the paces of both teams in 5 minute intervals, we just need to
have the play by play data in a tidy format generated by the
generate_tidy_pbp. The plot_paces function takes the data and the
match ID we want to visualize and returns the plot.
plot_paces(tidy, 47)