Extracting data from fotmob

Overview

This package is designed to allow users to extract various world football results and player statistics from the following popular football (soccer) data sites:

Installation

You can install the CRAN version of worldfootballR with:

install.packages("worldfootballR")

You can install the released version of worldfootballR from GitHub with:

# install.packages("devtools")
devtools::install_github("JaseZiv/worldfootballR")

library(worldfootballR)
library(dplyr)
library(tidyr)

Usage

Package vignettes have been built to help you get started with the package.

For functions to extract data from FBref, see here
For functions to extract data from Transfermarkt, see here
For functions to extract data from Understat, see here
For functions to extract data for international matches from FBref, see here
For functions to load pre-scraped data, see here

This vignette will cover the functions to extract data from fotmob.com

fotmob Helper Functions

There are currently no helper functions for fotmob functions.

League Season-Level Data

fotmob has data for just about every league that you can think of, including all of the Big 5 leagues.

Team Stats

Use fotmob_get_season_stats to retrieve values for a specified stat, season, and league. See the docs for the function for a complete list of stat_names. Note that some stats are only available for either team or player, and that some names are different for teams and players (e.g. "Expected goals" for "team" and "Expected goals (xG)" for "player").

epl_team_xg_2021 <- fotmob_get_season_stats(
  country = "ENG",
  league_name = "Premier League",
  season_name = "2020/2021",
  stat_name = "Expected goals",
  team_or_player = "team"
)

epl_team_xg_2021 %>%
  dplyr::select(
    league_id,
    league_name,
    season_id,
    season_name,
    team_id,
    team_name = participant_name,
    matches_played,
    xg = stat_value,
    g = sub_stat_value
  ) %>%
  dplyr::glimpse()
#> Rows: 20
#> Columns: 9
#> $ league_id      <int> 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47,…
#> $ league_name    <chr> "Premier League", "Premier League", "Premier League", "…
#> $ season_id      <chr> "15382", "15382", "15382", "15382", "15382", "15382", "…
#> $ season_name    <chr> "2020/2021", "2020/2021", "2020/2021", "2020/2021", "20…
#> $ team_id        <int> 8456, 8650, 8455, 10260, 8654, 8463, 8197, 8586, 10252,…
#> $ team_name      <chr> "Manchester City", "Liverpool", "Chelsea", "Manchester …
#> $ matches_played <int> 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38,…
#> $ xg             <dbl> 68.9, 68.4, 62.9, 60.9, 56.7, 56.4, 56.1, 53.8, 53.8, 5…
#> $ g              <dbl> 83, 68, 58, 73, 62, 62, 68, 68, 55, 55, 40, 47, 46, 47,…

If you know the league_id, you can use it instead of passing in values for country and league_name.

fotmob_get_season_stats(
  league_id = 47,
  season_name = "2020/2021",
  stat_name = "Expected goals",
  team_or_player = "team"
)
#> # A tibble: 20 × 20
#>    country league_name    league_id season_name season_id stat_league_name
#>    <chr>   <chr>              <int> <chr>       <chr>     <chr>           
#>  1 ENG     Premier League        47 2020/2021   15382     Premier League  
#>  2 ENG     Premier League        47 2020/2021   15382     Premier League  
#>  3 ENG     Premier League        47 2020/2021   15382     Premier League  
#>  4 ENG     Premier League        47 2020/2021   15382     Premier League  
#>  5 ENG     Premier League        47 2020/2021   15382     Premier League  
#>  6 ENG     Premier League        47 2020/2021   15382     Premier League  
#>  7 ENG     Premier League        47 2020/2021   15382     Premier League  
#>  8 ENG     Premier League        47 2020/2021   15382     Premier League  
#>  9 ENG     Premier League        47 2020/2021   15382     Premier League  
#> 10 ENG     Premier League        47 2020/2021   15382     Premier League  
#> 11 ENG     Premier League        47 2020/2021   15382     Premier League  
#> 12 ENG     Premier League        47 2020/2021   15382     Premier League  
#> 13 ENG     Premier League        47 2020/2021   15382     Premier League  
#> 14 ENG     Premier League        47 2020/2021   15382     Premier League  
#> 15 ENG     Premier League        47 2020/2021   15382     Premier League  
#> 16 ENG     Premier League        47 2020/2021   15382     Premier League  
#> 17 ENG     Premier League        47 2020/2021   15382     Premier League  
#> 18 ENG     Premier League        47 2020/2021   15382     Premier League  
#> 19 ENG     Premier League        47 2020/2021   15382     Premier League  
#> 20 ENG     Premier League        47 2020/2021   15382     Premier League  
#> # ℹ 14 more variables: stat_name <chr>, stat <tibble[,1]>,
#> #   participant_name <chr>, particiant_id <int>, team_id <int>,
#> #   team_color <chr>, stat_value <dbl>, sub_stat_value <dbl>,
#> #   minutes_played <int>, matches_played <int>, stat_value_count <int>,
#> #   rank <int>, participant_country_code <chr>, team_name <lgl>

You can retrieve data for multiple leagues, seasons, or stat types at the same time.

team_xgs_2021 <- fotmob_get_season_stats(
  country =        c("ITA",     "ESP"),
  league_name =    c("Serie A", "LaLiga"),
  season_name =    c("2020/2021", "2021/2022"),
  stat_name =      c("Expected goals", "xG conceded"),
  team_or_player = "team"
)

## 2 leagues x 20 teams x 2 seasons x 2 stats = 160 rows
team_xgs_2021 %>% nrow()
#> [1] 160

International tournament data can be retrieved, during the right time of year.

## Can only check on CL after group stages and briefly after the final.
m <- lubridate::month(Sys.Date())
if(m >= 1 && m <= 5) {
  fotmob_get_season_stats(
    league_id = 42,
    season_name = "2020/2021",
    stat_name = "Expected goals",
    team_or_player = "team"
  )
}
#> # A tibble: 32 × 20
#>    country league_name      league_id season_name season_id stat_league_name
#>    <chr>   <chr>                <int> <chr>       <chr>     <chr>           
#>  1 INT     Champions League        42 2020/2021   15237     Champions League
#>  2 INT     Champions League        42 2020/2021   15237     Champions League
#>  3 INT     Champions League        42 2020/2021   15237     Champions League
#>  4 INT     Champions League        42 2020/2021   15237     Champions League
#>  5 INT     Champions League        42 2020/2021   15237     Champions League
#>  6 INT     Champions League        42 2020/2021   15237     Champions League
#>  7 INT     Champions League        42 2020/2021   15237     Champions League
#>  8 INT     Champions League        42 2020/2021   15237     Champions League
#>  9 INT     Champions League        42 2020/2021   15237     Champions League
#> 10 INT     Champions League        42 2020/2021   15237     Champions League
#> # ℹ 22 more rows
#> # ℹ 14 more variables: stat_name <chr>, stat <tibble[,1]>,
#> #   participant_name <chr>, particiant_id <int>, team_id <int>,
#> #   team_color <chr>, stat_value <dbl>, sub_stat_value <dbl>,
#> #   minutes_played <int>, matches_played <int>, stat_value_count <int>,
#> #   rank <int>, participant_country_code <chr>, team_name <lgl>

You can potentially get cup data for leagues in the future, although at this time Fotmob does not have this data for most leagues, seasons, or stats.

## Not run to avoid error.
fotmob_get_season_stats(
  league_id = 47,
  season_name = "2020/2021",
  stat_name = "Expected goals",
  team_or_player = "team",
  stat_league_name = "FA Cup"
)

At this time, only one team_or_player can be retrieved at a time.

Player Stats

You can also use fotmob_get_season_stats to retrieve stats for players.

epl_player_xg_2021 <- fotmob_get_season_stats(
  country = "ENG",
  league_name = "Premier League",
  season = "2020/2021",
  stat_name = "Expected goals (xG)",
  team_or_player = "player"
)

epl_player_xg_2021 %>%
  dplyr::select(
    league_id,
    league_name,
    season_id,
    season_name,
    team_id,
    ## NOTE: particiant_id is a typo on behalf of fotmob! We leave it as is.
    player_id = particiant_id,
    player_name = participant_name,
    minutes_played,
    matches_played,
    xg = stat_value,
    g = sub_stat_value
  ) %>%
  dplyr::glimpse()
#> Rows: 433
#> Columns: 11
#> $ league_id      <int> 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47,…
#> $ league_name    <chr> "Premier League", "Premier League", "Premier League", "…
#> $ season_id      <chr> "15382", "15382", "15382", "15382", "15382", "15382", "…
#> $ season_name    <chr> "2020/2021", "2020/2021", "2020/2021", "2020/2021", "20…
#> $ team_id        <int> 8586, 8197, 8650, 8463, 8668, 10260, 10252, 8650, 8654,…
#> $ player_id      <int> 194165, 286119, 292462, 302241, 612150, 422685, 540088,…
#> $ player_name    <chr> "Harry Kane", "Jamie Vardy", "Mohamed Salah", "Patrick …
#> $ minutes_played <int> 3085, 2844, 3079, 3062, 2874, 3105, 3329, 2810, 1983, 2…
#> $ matches_played <int> 35, 34, 37, 38, 33, 37, 37, 35, 26, 33, 33, 26, 31, 36,…
#> $ xg             <dbl> 20.2, 19.9, 19.3, 18.3, 16.3, 16.1, 14.6, 14.2, 13.6, 1…
#> $ g              <dbl> 23, 15, 22, 17, 16, 18, 14, 11, 10, 8, 12, 12, 13, 9, 6…

Match Results

Matches for one or multiple leagues can be retrieved with fotmob_get_league_matches.

league_matches <- fotmob_get_league_matches(
  country =     c("ENG",            "ESP"   ),
  league_name = c("Premier League", "LaLiga")
)

league_matches_unnested <- league_matches %>%
  dplyr::select(match_id = id, home, away) %>%
  tidyr::unnest_wider(c(home, away), names_sep = "_")
dplyr::glimpse(league_matches_unnested)
#> Rows: 760
#> Columns: 7
#> $ match_id       <chr> "3900932", "3900933", "3900934", "3900935", "3900937", …
#> $ home_name      <chr> "Crystal Palace", "Fulham", "AFC Bournemouth", "Leeds U…
#> $ home_shortName <chr> "Crystal Palace", "Fulham", "Bournemouth", "Leeds", "Ne…
#> $ home_id        <chr> "9826", "9879", "8678", "8463", "10261", "8586", "8668"…
#> $ away_name      <chr> "Arsenal", "Liverpool", "Aston Villa", "Wolverhampton W…
#> $ away_shortName <chr> "Arsenal", "Liverpool", "Aston Villa", "Wolves", "Nottm…
#> $ away_id        <chr> "9825", "8650", "10252", "8602", "10203", "8466", "8455…

Data can even be retrieved for past seasons.

fotmob_get_league_matches(
  country = "GER",
  league_name = "1. Bundesliga",
  season = "2020/2021"
)
#> # A tibble: 306 × 7
#>    round round_name page_url            id    home$name away$name status$utcTime
#>    <lgl> <lgl>      <chr>               <chr> <chr>     <chr>     <chr>         
#>  1 NA    NA         /match/3399144/mat… 3399… Bayern M… Schalke … 2020-09-18T16…
#>  2 NA    NA         /match/3399150/mat… 3399… 1. FC Kö… TSG Hoff… 2020-09-19T11…
#>  3 NA    NA         /match/3399148/mat… 3399… Eintrach… Arminia … 2020-09-19T11…
#>  4 NA    NA         /match/3399151/mat… 3399… Werder B… Hertha B… 2020-09-19T11…
#>  5 NA    NA         /match/3399149/mat… 3399… Union Be… FC Augsb… 2020-09-19T11…
#>  6 NA    NA         /match/3399152/mat… 3399… VfB Stut… SC Freib… 2020-09-19T11…
#>  7 NA    NA         /match/3399145/mat… 3399… Borussia… Borussia… 2020-09-19T14…
#>  8 NA    NA         /match/3399146/mat… 3399… RB Leipz… Mainz 05  2020-09-20T11…
#>  9 NA    NA         /match/3399147/mat… 3399… Wolfsburg Bayer Le… 2020-09-20T14…
#> 10 NA    NA         /match/3399169/mat… 3399… Hertha B… Eintrach… 2020-09-25T16…
#> # ℹ 296 more rows
#> # ℹ 9 more variables: home$shortName <chr>, $id <chr>, away$shortName <chr>,
#> #   $id <chr>, status$finished <lgl>, $started <lgl>, $cancelled <lgl>,
#> #   $scoreStr <chr>, $reason <df[,2]>

fotmob also lists data for all leagues by date. Use fotmob_get_matches_by_date to select matches occurring on specific day(s) and filter down to the league(s) that you care about. Since fotmob_get_league_matches does not allow for selecting seasons, the only way to get matches from prior seasons is to use fotmob_get_matches_by_date

results <- fotmob_get_matches_by_date(date = c("20210925", "20210926"))
dplyr::glimpse(results)
#> Rows: 1,011
#> Columns: 34
#> $ ccode                    <chr> "ENG", "ENG", "ENG", "ENG", "ENG", "ENG", "EN…
#> $ id                       <int> 47, 47, 47, 47, 47, 47, 47, 87, 87, 87, 87, 5…
#> $ primary_id               <int> 47, 47, 47, 47, 47, 47, 47, 87, 87, 87, 87, 5…
#> $ name                     <chr> "Premier League", "Premier League", "Premier …
#> $ match_id                 <int> 3609981, 3609986, 3609983, 3609984, 3609985, …
#> $ match_league_id          <int> 47, 47, 47, 47, 47, 47, 47, 87, 87, 87, 87, 5…
#> $ match_time               <chr> "25.09.2021 13:30", "25.09.2021 13:30", "25.0…
#> $ home_id                  <int> 8455, 10260, 8668, 8463, 8197, 9817, 9937, 98…
#> $ home_score               <int> 0, 0, 2, 1, 2, 1, 3, 1, 1, 2, 0, 1, 1, 3, 6, …
#> $ home_name                <chr> "Chelsea", "Man United", "Everton", "Leeds", …
#> $ home_long_name           <chr> "Chelsea", "Manchester United", "Everton", "L…
#> $ home_pen_score           <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ away_id                  <int> 8456, 10252, 9850, 8654, 8191, 10261, 8650, 9…
#> $ away_score               <int> 1, 1, 0, 2, 2, 1, 3, 0, 1, 0, 0, 0, 1, 1, 0, …
#> $ away_name                <chr> "Man City", "Aston Villa", "Norwich", "West H…
#> $ away_long_name           <chr> "Manchester City", "Aston Villa", "Norwich Ci…
#> $ away_pen_score           <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ match_eliminated_team_id <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ match_status_id          <int> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, …
#> $ match_tournament_stage   <chr> "6", "6", "6", "6", "6", "6", "6", "7", "7", …
#> $ match_status_utc_time    <chr> "2021-09-25T11:30:00.000Z", "2021-09-25T11:30…
#> $ match_status_finished    <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRU…
#> $ match_status_started     <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRU…
#> $ match_status_cancelled   <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
#> $ match_status_score_str   <chr> "0 - 1", "0 - 1", "2 - 0", "1 - 2", "2 - 2", …
#> $ match_status_awarded     <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ match_time_ts            <dbl> 1.632569e+12, 1.632569e+12, 1.632578e+12, 1.6…
#> $ internal_rank            <int> 30, 30, 30, 30, 30, 30, 30, 10, 10, 10, 10, 2…
#> $ live_rank                <int> 101, 101, 101, 101, 101, 101, 101, 100, 100, …
#> $ simple_league            <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
#> $ parent_league_id         <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ is_group                 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ group_name               <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ parent_league_name       <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…

You should see international tournaments in the output from fotmob_get_matches_by_date().

results <- fotmob_get_matches_by_date("20220412")
results %>%
  dplyr::filter(name == "Champions League Final Stage", ccode == "INT")
#> # A tibble: 2 × 32
#>   ccode     id primary_id name       match_id match_league_id match_time home_id
#>   <chr>  <int>      <int> <chr>         <int>           <int> <chr>        <int>
#> 1 INT   875880         42 Champions…  3846346          875880 12.04.202…    9823
#> 2 INT   875880         42 Champions…  3846342          875880 12.04.202…    8633
#> # ℹ 24 more variables: home_score <int>, home_name <chr>, home_long_name <chr>,
#> #   away_id <int>, away_score <int>, away_name <chr>, away_long_name <chr>,
#> #   match_eliminated_team_id <int>, match_status_id <int>,
#> #   match_tournament_stage <chr>, match_status_utc_time <chr>,
#> #   match_status_finished <lgl>, match_status_started <lgl>,
#> #   match_status_cancelled <lgl>, match_status_score_str <chr>,
#> #   match_status_aggregated_str <chr>, match_time_ts <dbl>, …

Standings

Standings for one or multiple leagues can be retrieved with fotmob_get_league_tables. Currently only the current season’s standings can be retrieved.

league_tables <- fotmob_get_league_tables(
  country =     c("ENG",            "ESP"   ),
  league_name = c("Premier League", "LaLiga")
)
# or
# league_tables <- fotmob_get_league_tables(league_id = c(47, 87))

away_league_tables <- league_tables %>%
  dplyr::filter(table_type == "away")
dplyr::glimpse(away_league_tables)
#> Rows: 40
#> Columns: 18
#> $ league_id           <int> 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47…
#> $ page_url            <chr> "/leagues/47/overview/premier-league", "/leagues/4…
#> $ table_type          <chr> "away", "away", "away", "away", "away", "away", "a…
#> $ table_name          <chr> "Arsenal", "Manchester City", "Newcastle United", …
#> $ table_short_name    <chr> "Arsenal", "Man City", "Newcastle", "Brighton", "M…
#> $ table_id            <int> 9825, 8456, 10261, 10204, 10260, 10252, 9879, 8586…
#> $ table_page_url      <chr> "/teams/9825/overview/arsenal", "/teams/8456/overv…
#> $ table_deduction     <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ table_played        <int> 18, 16, 18, 16, 18, 18, 17, 17, 17, 17, 18, 17, 18…
#> $ table_wins          <int> 12, 10, 8, 7, 7, 6, 6, 5, 5, 5, 4, 5, 4, 2, 4, 4, …
#> $ table_draws         <int> 3, 3, 7, 4, 3, 4, 2, 5, 4, 4, 7, 2, 4, 8, 2, 2, 3,…
#> $ table_losses        <int> 3, 3, 3, 5, 8, 8, 9, 7, 8, 8, 7, 10, 10, 8, 12, 12…
#> $ table_scores_str    <chr> "35-17", "30-14", "30-17", "30-26", "21-33", "17-2…
#> $ table_goal_con_diff <int> 18, 16, 13, 4, -12, -7, -1, -7, -4, -6, -7, -23, -…
#> $ table_pts           <int> 39, 33, 31, 25, 24, 22, 20, 20, 19, 19, 19, 17, 16…
#> $ table_idx           <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,…
#> $ table_qual_color    <chr> "#2AD572", "#2AD572", "#2AD572", "#2AD572", "#0046…
#> $ table_ongoing       <df[,14]> <data.frame[26 x 14]>

Note that the output of fotmob_get_league_tables for a tournament has more columns.

m <- lubridate::month(Sys.Date())
if(m >= 1 && m <= 5) {
  cl_table <- fotmob_get_league_tables(league_id = 42)
  
  cl_table %>%
    dplyr::filter(table_type == "all") %>% 
    dplyr::glimpse()
}
#> Rows: 32
#> Columns: 23
#> $ league_id           <int> 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42…
#> $ page_url            <chr> "/leagues/42/overview/champions-league", "/leagues…
#> $ ccode               <chr> "INT", "INT", "INT", "INT", "INT", "INT", "INT", "…
#> $ group_id            <int> 879535, 879535, 879535, 879535, 879536, 879536, 87…
#> $ group_page_url      <chr> "/leagues/879535/overview/grp-a?season=2022-2023",…
#> $ group_name          <chr> "Grp. A", "Grp. A", "Grp. A", "Grp. A", "Grp. B", …
#> $ ongoing             <list> [], [], [], [], [], [], [], [], [], [], [], [], […
#> $ table_type          <chr> "all", "all", "all", "all", "all", "all", "all", "…
#> $ table_name          <chr> "Napoli", "Liverpool", "Ajax", "Rangers", "FC Port…
#> $ table_short_name    <chr> "Napoli", "Liverpool", "Ajax", "Rangers", "FC Port…
#> $ table_id            <int> 9875, 8650, 8593, 8548, 9773, 8342, 8178, 9906, 98…
#> $ table_page_url      <chr> "/teams/9875/overview/napoli", "/teams/8650/overvi…
#> $ table_deduction     <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ table_ongoing       <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ table_played        <int> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,…
#> $ table_wins          <int> 5, 5, 2, 0, 4, 3, 1, 1, 6, 3, 2, 0, 3, 3, 2, 2, 4,…
#> $ table_draws         <int> 0, 0, 0, 0, 0, 2, 2, 2, 0, 1, 1, 0, 2, 1, 1, 0, 1,…
#> $ table_losses        <int> 1, 1, 4, 6, 2, 1, 3, 3, 0, 2, 3, 6, 1, 2, 3, 4, 1,…
#> $ table_scores_str    <chr> "20-6", "17-6", "11-16", "2-22", "12-7", "7-4", "4…
#> $ table_goal_con_diff <int> 14, 11, -5, -20, 5, 3, -4, -4, 16, 3, 0, -19, 2, -…
#> $ table_pts           <int> 15, 15, 6, 0, 12, 11, 5, 5, 18, 10, 7, 0, 11, 10, …
#> $ table_idx           <int> 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1,…
#> $ table_qual_color    <chr> "#2AD572", "#2AD572", "#FFD908", NA, "#2AD572", "#…

Match-Level Data

To get general info for a match, use the fotmob_get_match_info() function.

fotmob_matches <- c(3609994, 3610132)
match_info <- fotmob_get_match_info(fotmob_matches)
match_info %>%
  dplyr::select(match_id, match_time_utc:dplyr::last_col()) %>%
  dplyr::glimpse()
#> Rows: 2
#> Columns: 24
#> $ match_id                     <int> 3609994, 3610132
#> $ match_time_utc               <chr> "Sun, Oct 3, 2021, 15:30 UTC", "Sun, Jan …
#> $ home_team_id                 <int> 8650, 8455
#> $ home_team                    <chr> "Liverpool", "Chelsea"
#> $ away_team_id                 <int> 8456, 8650
#> $ away_team                    <chr> "Manchester City", "Liverpool"
#> $ match_date_utc_time          <chr> "2021-10-03T15:30:00.000Z", "2022-01-02T1…
#> $ tournament_id                <int> 47, 47
#> $ tournament_parent_league_id  <int> 47, 47
#> $ tournament_link              <chr> "/leagues/47/overview/premier-league?seas…
#> $ tournament_league_name       <chr> "Premier League", "Premier League"
#> $ tournament_round_name        <chr> "Round 7", "Round 21"
#> $ tournament_round             <chr> "7", "21"
#> $ tournament_selected_season   <chr> "2021/2022", "2021/2022"
#> $ tournament_is_current_season <lgl> FALSE, FALSE
#> $ stadium_name                 <chr> "Anfield", "Stamford Bridge"
#> $ stadium_city                 <chr> "Liverpool", "London"
#> $ stadium_country              <chr> "England", "England"
#> $ stadium_lat                  <dbl> 53.43083, 51.48171
#> $ stadium_long                 <dbl> -2.960853, -0.190975
#> $ referee_img_url              <chr> "https://images.fotmob.com/image_resource…
#> $ referee_text                 <chr> "Paul Tierney", "Anthony Taylor"
#> $ referee_country              <chr> "England", "England"
#> $ attendance                   <int> 53102, 40072

Team-level Stats

To get team-level team stats for a match, use the fotmob_get_match_team_stats() function.

match_team_stats <- fotmob_get_match_team_stats(fotmob_matches)
match_team_stats %>%
  dplyr::select(match_id, title:dplyr::last_col()) %>%
  dplyr::glimpse()
#> Rows: 92
#> Columns: 9
#> $ match_id          <int> 3609994, 3609994, 3609994, 3609994, 3609994, 3609994…
#> $ title             <chr> "Top stats", "Top stats", "Top stats", "Top stats", …
#> $ key               <chr> "top_stats", "top_stats", "top_stats", "top_stats", …
#> $ stats_title       <chr> "Ball possession", "Expected goals (xG)", "Total sho…
#> $ stats_key         <chr> "BallPossesion", "expected_goals", "total_shots", "b…
#> $ home_value        <chr> "48", "0.90", "6", "3", "1", "423 (84%)", "10", "2",…
#> $ away_value        <chr> "52", "1.24", "12", "1", "1", "443 (84%)", "12", "5"…
#> $ stats_type        <chr> "graph", "text", "text", "text", "text", "text", "te…
#> $ stats_highlighted <chr> "away", "away", "away", "home", "equal", "away", "ho…

Stats are categorized into one of several groups, by title.

match_team_stats %>%
  dplyr::filter(match_id == dplyr::first(match_id)) %>%
  dplyr::count(title)
#> # A tibble: 7 × 2
#>   title                   n
#>   <chr>               <int>
#> 1 Defence                 6
#> 2 Discipline              3
#> 3 Duels                   5
#> 4 Expected goals (xG)     7
#> 5 Passes                  8
#> 6 Shots                   8
#> 7 Top stats               9

Match Shooting Locations

To get shooting locations for an individual match along with expected goals (xG), expected goals on target (xGoT), etc., use the fotmob_get_match_details() function.

match_details <- fotmob_get_match_details(fotmob_matches)
dplyr::glimpse(match_details)
#> Rows: 43
#> Columns: 40
#> $ match_id                 <int> 3609994, 3609994, 3609994, 3609994, 3609994, …
#> $ match_round              <chr> "7", "7", "7", "7", "7", "7", "7", "7", "7", …
#> $ league_id                <int> 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 4…
#> $ league_name              <chr> "Premier League", "Premier League", "Premier …
#> $ league_round_name        <chr> "Round 7", "Round 7", "Round 7", "Round 7", "…
#> $ parent_league_id         <int> 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 4…
#> $ parent_league_season     <chr> "2021/2022", "2021/2022", "2021/2022", "2021/…
#> $ match_time_utc           <chr> "Sun, Oct 3, 2021, 15:30 UTC", "Sun, Oct 3, 2…
#> $ home_team_id             <int> 8650, 8650, 8650, 8650, 8650, 8650, 8650, 865…
#> $ home_team                <chr> "Liverpool", "Liverpool", "Liverpool", "Liver…
#> $ away_team_id             <int> 8456, 8456, 8456, 8456, 8456, 8456, 8456, 845…
#> $ away_team                <chr> "Manchester City", "Manchester City", "Manche…
#> $ id                       <dbl> 2338869639, 2338876703, 2338881401, 233888382…
#> $ event_type               <chr> "AttemptSaved", "AttemptSaved", "AttemptSaved…
#> $ team_id                  <int> 8650, 8456, 8456, 8456, 8456, 8456, 8456, 845…
#> $ player_id                <int> 292462, 312765, 815006, 169200, 169200, 16920…
#> $ player_name              <chr> "Mohamed Salah", "Jack Grealish", "Phil Foden…
#> $ x                        <dbl> 92.50000, 91.50000, 100.35345, 97.57017, 97.0…
#> $ y                        <dbl> 50.72688, 49.53580, 42.00375, 46.31286, 28.95…
#> $ min                      <int> 8, 15, 21, 24, 34, 38, 40, 40, 50, 59, 63, 67…
#> $ min_added                <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ is_blocked               <lgl> TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, TRUE, …
#> $ is_on_target             <lgl> TRUE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE, F…
#> $ blocked_x                <dbl> 94.48246, 93.40000, 101.87069, 104.05172, NA,…
#> $ blocked_y                <dbl> 48.20459, 47.57401, 39.54500, 0.00000, NA, 28…
#> $ goal_crossed_y           <dbl> 34.6100000, 34.8387500, 32.7800000, 0.3279621…
#> $ goal_crossed_z           <dbl> 1.21999999, 1.21999999, 0.36600000, 0.5329473…
#> $ expected_goals           <dbl> 0.01901395, 0.02792695, 0.42046532, 0.0984095…
#> $ expected_goals_on_target <dbl> NA, NA, 0.6931, NA, NA, NA, NA, NA, 0.0412, 0…
#> $ shot_type                <chr> "LeftFoot", "RightFoot", "LeftFoot", "LeftFoo…
#> $ situation                <chr> "FromCorner", "RegularPlay", "RegularPlay", "…
#> $ period                   <chr> "FirstHalf", "FirstHalf", "FirstHalf", "First…
#> $ is_own_goal              <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
#> $ on_goal_shot_x           <dbl> 0.8386243, 0.7781085, 1.3227513, 2.0000000, 1…
#> $ on_goal_shot_y           <dbl> 0.32275132, 0.32275132, 0.09682540, 0.0158275…
#> $ on_goal_shot_zoom_ratio  <dbl> 1.0000000, 1.0000000, 1.0000000, 0.1122593, 0…
#> $ first_name               <chr> "Mohamed", "Jack", "Phil", "Kevin", "Kevin", …
#> $ last_name                <chr> "Salah", "Grealish", "Foden", "De Bruyne", "D…
#> $ full_name                <chr> "Mohamed Salah", "Jack Grealish", "Phil Foden…
#> $ team_color               <chr> "#d3171e", "#69A8D8", "#69A8D8", "#69A8D8", "…

Be very careful with interpreting x and y from on_goal_shot. They’re not on the same scale as x and y in the top-level of the result returned from fotmob_get_match_details!

Players

You can also extract players from matches with the fotmob_get_match_players() function.

players <- fotmob_get_match_players(fotmob_matches)
dplyr::glimpse(players)
#> Rows: 80
#> Columns: 77
#> $ match_id                             <int> 3609994, 3609994, 3609994, 360999…
#> $ team_id                              <int> 8650, 8650, 8650, 8650, 8650, 865…
#> $ team_name                            <chr> "Liverpool", "Liverpool", "Liverp…
#> $ id                                   <chr> "319784", "38807", "171698", "209…
#> $ using_opta_id                        <lgl> FALSE, FALSE, FALSE, FALSE, FALSE…
#> $ first_name                           <chr> "Alisson", "James", "Joel", "Virg…
#> $ last_name                            <chr> "Becker", "Milner", "Matip", "van…
#> $ image_url                            <chr> "https://images.fotmob.com/image_…
#> $ page_url                             <chr> "/players/319784/alisson-becker",…
#> $ shirt                                <chr> "1", "7", "32", "4", "26", "14", …
#> $ is_home_team                         <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRU…
#> $ time_subbed_on                       <int> NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ time_subbed_off                      <int> NA, 78, NA, NA, NA, NA, NA, NA, N…
#> $ usual_position                       <int> 0, 1, 1, 1, 1, 2, 2, 2, 3, 3, 3, …
#> $ position_row                         <int> 0, 1, 1, 1, 1, 3, 3, 3, 5, 5, 5, …
#> $ role                                 <chr> "Keeper", "Defender", "Defender",…
#> $ is_captain                           <lgl> FALSE, FALSE, FALSE, FALSE, FALSE…
#> $ subbed_out                           <int> NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ g                                    <int> NA, NA, NA, NA, NA, NA, NA, NA, 1…
#> $ rating_num                           <chr> "5.7", "7.2", "7.3", "6.6", "7.2"…
#> $ rating_bgcolor                       <chr> "#f08022", "#1ec853", "#1ec853", …
#> $ is_top_rating                        <lgl> FALSE, FALSE, FALSE, FALSE, FALSE…
#> $ is_match_finished                    <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRU…
#> $ fantasy_score_num                    <chr> "1", "1", "1", "1", "1", "2", "1"…
#> $ fantasy_score_bgcolor                <chr> "purple", "purple", "purple", "pu…
#> $ home_team_id                         <int> 8650, 8650, 8650, 8650, 8650, 865…
#> $ home_team_color                      <chr> "#d3171e", "#d3171e", "#d3171e", …
#> $ away_team_id                         <int> 8456, 8456, 8456, 8456, 8456, 845…
#> $ away_team_color                      <chr> "#69A8D8", "#69A8D8", "#69A8D8", …
#> $ stats_fot_mob_rating                 <chr> "5.67", "7.17", "7.32", "6.55", "…
#> $ stats_minutes_played                 <chr> "90", "78", "90", "90", "90", "90…
#> $ stats_saves                          <chr> "1", NA, NA, NA, NA, NA, NA, NA, …
#> $ stats_goals_conceded                 <chr> "2", NA, NA, NA, NA, NA, NA, NA, …
#> $ stats_x_got_faced                    <chr> "1.35", NA, NA, NA, NA, NA, NA, N…
#> $ stats_accurate_passes                <chr> "48/55 (87%)", "27/34 (79%)", "81…
#> $ stats_accurate_long_balls            <chr> "9/15 (60%)", "1/3 (33%)", "4/8 (…
#> $ stats_diving_save                    <chr> "0", NA, NA, NA, NA, NA, NA, NA, …
#> $ stats_saves_inside_box               <chr> "1", NA, NA, NA, NA, NA, NA, NA, …
#> $ stats_acted_as_sweeper               <chr> "0", NA, NA, NA, NA, NA, NA, NA, …
#> $ stats_punches                        <chr> "1", NA, NA, NA, NA, NA, NA, NA, …
#> $ stats_throws                         <chr> "4", NA, NA, NA, NA, NA, NA, NA, …
#> $ stats_high_claim                     <chr> "1", NA, NA, NA, NA, NA, NA, NA, …
#> $ stats_recoveries                     <chr> "5", " 6", " 5", " 4", " 7", " 9"…
#> $ stats_fantasy_points                 <chr> "1", " 1", " 1", " 1", " 1", " 2"…
#> $ stats_touches                        <chr> "64", "68", "101", "100", "70", "…
#> $ stats_goals                          <chr> NA, " 0", " 0", " 0", " 0", " 0",…
#> $ stats_assists                        <chr> NA, " 0", " 0", " 0", " 0", " 0",…
#> $ stats_total_shots                    <chr> NA, " 0", " 0", " 0", " 0", " 0",…
#> $ stats_chances_created                <chr> NA, " 1", " 1", " 0", " 0", " 0",…
#> $ stats_expected_assists_x_a           <chr> NA, "0.04", "0.03", "0.01", "0.01…
#> $ stats_passes_into_final_third        <chr> NA, " 6", "11", " 9", " 8", "10",…
#> $ stats_accurate_crosses               <chr> NA, "2/8 (25%)", NA, NA, "0/4 (0%…
#> $ stats_corners                        <chr> NA, " 2", NA, NA, " 1", NA, NA, N…
#> $ stats_dispossessed                   <chr> NA, " 0", " 0", " 0", " 0", " 2",…
#> $ stats_tackles_won                    <chr> NA, " 0", "3/3 (100%)", " 0", "2/…
#> $ stats_blocks                         <chr> NA, " 1", " 2", " 1", " 1", NA, N…
#> $ stats_clearances                     <chr> NA, " 2", " 1", " 2", " 5", NA, N…
#> $ stats_interceptions                  <chr> NA, " 2", " 4", " 1", " 3", " 2",…
#> $ stats_ground_duels_won               <chr> NA, "3/6 (50%)", "3/3 (100%)", "1…
#> $ stats_aerial_duels_won               <chr> NA, "1/1 (100%)", " 0", " 0", "2/…
#> $ stats_was_fouled                     <chr> NA, " 3", " 0", " 0", " 1", " 0",…
#> $ stats_fouls_committed                <chr> NA, " 3", " 0", " 0", " 0", " 0",…
#> $ stats_headed_clearance               <chr> NA, NA, " 1", " 1", " 2", NA, NA,…
#> $ stats_successful_dribbles            <chr> NA, NA, NA, "1/1 (100%)", NA, "1/…
#> $ stats_dribbled_past                  <chr> NA, NA, NA, " 1", NA, " 3", " 5",…
#> $ stats_offsides                       <chr> NA, NA, NA, NA, " 1", " 1", NA, N…
#> $ stats_shotmap                        <chr> NA, NA, NA, NA, NA, NA, "TRUE", N…
#> $ stats_expected_goals_x_g             <chr> NA, NA, NA, NA, NA, NA, "0.23", N…
#> $ stats_shot_accuracy                  <chr> NA, NA, NA, NA, NA, NA, "1/1 (100…
#> $ stats_big_chance_missed              <chr> NA, NA, NA, NA, NA, NA, " 1", NA,…
#> $ stats_expected_goals_on_target_x_got <chr> NA, NA, NA, NA, NA, NA, NA, NA, "…
#> $ stats_blocked_shots                  <chr> NA, NA, NA, NA, NA, NA, NA, NA, "…
#> $ stats_clearance_off_the_line         <chr> NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ shotmap                              <list> <NULL>, <NULL>, <NULL>, <NULL>, …
#> $ is_starter                           <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRU…
#> $ stats_error_led_to_goal              <chr> NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ stats_last_man_tackle                <chr> NA, NA, NA, NA, NA, NA, NA, NA, N…

salah <- players %>% dplyr::filter(id == "292462")
salah_shotmap <- salah %>% 
  dplyr::select(player_id = id, shotmap) %>% 
  tidyr::unnest(shotmap)
dplyr::glimpse(salah_shotmap)
#> Rows: 7
#> Columns: 24
#> $ player_id             <chr> "292462", "292462", "292462", "292462", "292462"…
#> $ id                    <dbl> 2338869639, 2338938757, 2338947917, 2371191153, …
#> $ eventType             <chr> "AttemptSaved", "AttemptSaved", "Goal", "Attempt…
#> $ teamId                <int> 8650, 8650, 8650, 8650, 8650, 8650, 8650
#> $ playerId              <int> 292462, 292462, 292462, 292462, 292462, 292462, …
#> $ playerName            <chr> "Mohamed Salah", "Mohamed Salah", "Mohamed Salah…
#> $ x                     <dbl> 92.50000, 80.57039, 101.87069, 90.00000, 101.396…
#> $ y                     <dbl> 50.72688, 45.26191, 24.27949, 33.31375, 24.13936…
#> $ min                   <int> 8, 63, 76, 6, 26, 36, 57
#> $ minAdded              <lgl> NA, NA, NA, NA, NA, NA, NA
#> $ isBlocked             <lgl> TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE
#> $ isOnTarget            <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE
#> $ blockedX              <dbl> 94.48246, 90.80000, NA, 97.76316, NA, 93.10000, …
#> $ blockedY              <dbl> 48.20459, 40.97381, NA, 33.69500, NA, 30.22000, …
#> $ goalCrossedY          <dbl> 34.61000, 33.39000, 37.27875, 34.68625, 32.01750…
#> $ goalCrossedZ          <dbl> 1.220000, 1.220000, 1.136526, 0.854000, 1.014526…
#> $ expectedGoals         <dbl> 0.01901395, 0.05450234, 0.13671082, 0.17167990, …
#> $ expectedGoalsOnTarget <dbl> NA, NA, 0.9744, 0.1107, 0.2217, NA, 0.2100
#> $ shotType              <chr> "LeftFoot", "LeftFoot", "RightFoot", "RightFoot"…
#> $ situation             <chr> "FromCorner", "FreeKick", "RegularPlay", "Regula…
#> $ period                <chr> "FirstHalf", "SecondHalf", "SecondHalf", "FirstH…
#> $ isOwnGoal             <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE
#> $ onGoalShot            <df[,3]> <data.frame[7 x 3]>
#> $ teamColor             <chr> "#d3171e", "#d3171e", "#d3171e", "#d3171e", "#d3…

Note that stats_ columns are returned as characters (changed in 0.5.9).

salah_stats <- salah %>% 
  dplyr::select(player_id = id, tidyselect::vars_select_helpers$starts_with("stats_")) %>% 
  janitor::remove_empty(which = "cols")
dplyr::glimpse(salah_stats)
#> Rows: 2
#> Columns: 32
#> $ player_id                            <chr> "292462", "292462"
#> $ stats_fot_mob_rating                 <chr> "8.85", "7.99"
#> $ stats_minutes_played                 <chr> "90", "90"
#> $ stats_accurate_passes                <chr> "16/19 (84%)", "27/30 (90%)"
#> $ stats_recoveries                     <chr> " 1", " 2"
#> $ stats_fantasy_points                 <chr> "10 + 3", " 7"
#> $ stats_touches                        <chr> "49", "41"
#> $ stats_goals                          <chr> " 1", " 1"
#> $ stats_assists                        <chr> " 1", " 0"
#> $ stats_total_shots                    <chr> " 3", " 4"
#> $ stats_chances_created                <chr> " 2", " 2"
#> $ stats_expected_assists_x_a           <chr> "0.29", "0.04"
#> $ stats_passes_into_final_third        <chr> " 2", " 1"
#> $ stats_accurate_crosses               <chr> "1/2 (50%)", NA
#> $ stats_dispossessed                   <chr> " 1", " 1"
#> $ stats_tackles_won                    <chr> "3/4 (75%)", " 0"
#> $ stats_clearances                     <chr> " 1", NA
#> $ stats_interceptions                  <chr> " 1", " 1"
#> $ stats_ground_duels_won               <chr> "10/15 (67%)", "1/5 (20%)"
#> $ stats_aerial_duels_won               <chr> "1/2 (50%)", " 0"
#> $ stats_was_fouled                     <chr> " 1", " 0"
#> $ stats_fouls_committed                <chr> " 0", " 1"
#> $ stats_headed_clearance               <chr> " 1", NA
#> $ stats_successful_dribbles            <chr> "5/8 (63%)", "1/3 (33%)"
#> $ stats_dribbled_past                  <chr> " 1", NA
#> $ stats_offsides                       <chr> NA, " 1"
#> $ stats_shotmap                        <chr> "TRUE", "TRUE"
#> $ stats_expected_goals_x_g             <chr> "0.21", "0.71"
#> $ stats_shot_accuracy                  <chr> "1/1 (100%)", "3/3 (100%)"
#> $ stats_big_chance_missed              <chr> NA, " 1"
#> $ stats_expected_goals_on_target_x_got <chr> "0.97", "0.54"
#> $ stats_blocked_shots                  <chr> " 2", " 1"

Momentum

You can extract momentum data with the fotmob_get_match_momentum() function. At time of writing, momentum is provided in two sets: (1) "main" (“Using xT SA-version”) and (2) "alternateModels" (“Using xT SA-version without rolling”).

momentum <- fotmob_get_match_momentum(3901251)
dplyr::glimpse(momentum)
#> Rows: 186
#> Columns: 5
#> $ match_id    <int> 3901251, 3901251, 3901251, 3901251, 3901251, 3901251, 3901…
#> $ minute      <dbl> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, …
#> $ value       <int> 0, 5, 5, -5, 38, 76, 100, 54, 33, 16, 16, 11, 54, 38, 33, …
#> $ debug_title <chr> "Using xT SA-version", "Using xT SA-version", "Using xT SA…
#> $ type        <chr> "main", "main", "main", "main", "main", "main", "main", "m…

Note that matches before the 2022/23 season do not have momentum available. (And, for the 2022/23 season, momentum only seems available for matches going back to Oct. 18, 2022.)

Tony ElHabr

2023-05-13