Extracting data from fotmob
Tony ElHabr
2023-01-28
Source:vignettes/extract-fotmob-data.Rmd
extract-fotmob-data.RmdOverview
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")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
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 leagu…¹ seaso…² seaso…³ stat_…⁴ stat_…⁵ stat$…⁶ parti…⁷
#> <chr> <chr> <int> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 ENG Premier Leag… 47 2020/2… 15382 Premie… Expect… expect… Manche…
#> 2 ENG Premier Leag… 47 2020/2… 15382 Premie… Expect… expect… Liverp…
#> 3 ENG Premier Leag… 47 2020/2… 15382 Premie… Expect… expect… Chelsea
#> 4 ENG Premier Leag… 47 2020/2… 15382 Premie… Expect… expect… Manche…
#> 5 ENG Premier Leag… 47 2020/2… 15382 Premie… Expect… expect… West H…
#> 6 ENG Premier Leag… 47 2020/2… 15382 Premie… Expect… expect… Leeds …
#> 7 ENG Premier Leag… 47 2020/2… 15382 Premie… Expect… expect… Leices…
#> 8 ENG Premier Leag… 47 2020/2… 15382 Premie… Expect… expect… Totten…
#> 9 ENG Premier Leag… 47 2020/2… 15382 Premie… Expect… expect… Aston …
#> 10 ENG Premier Leag… 47 2020/2… 15382 Premie… Expect… expect… Arsenal
#> 11 ENG Premier Leag… 47 2020/2… 15382 Premie… Expect… expect… Bright…
#> 12 ENG Premier Leag… 47 2020/2… 15382 Premie… Expect… expect… Everton
#> 13 ENG Premier Leag… 47 2020/2… 15382 Premie… Expect… expect… Newcas…
#> 14 ENG Premier Leag… 47 2020/2… 15382 Premie… Expect… expect… Southa…
#> 15 ENG Premier Leag… 47 2020/2… 15382 Premie… Expect… expect… Fulham
#> 16 ENG Premier Leag… 47 2020/2… 15382 Premie… Expect… expect… Burnley
#> 17 ENG Premier Leag… 47 2020/2… 15382 Premie… Expect… expect… Wolver…
#> 18 ENG Premier Leag… 47 2020/2… 15382 Premie… Expect… expect… West B…
#> 19 ENG Premier Leag… 47 2020/2… 15382 Premie… Expect… expect… Crysta…
#> 20 ENG Premier Leag… 47 2020/2… 15382 Premie… Expect… expect… Sheffi…
#> # … with 11 more variables: particiant_id <int>, team_id <int>,
#> # team_color <chr>, stat_value <dbl>, sub_stat_value <dbl>,
#> # minutes_played <int>, matches_played <int>, stat_value_count <int>,
#> # rank <int>, participant_country_code <chr>, team_name <lgl>, and
#> # abbreviated variable names ¹league_id, ²season_name, ³season_id,
#> # ⁴stat_league_name, ⁵stat_name, ⁶stat$stat, ⁷participant_nameYou 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] 160International 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 leagu…¹ seaso…² seaso…³ stat_…⁴ stat_…⁵ stat$…⁶ parti…⁷
#> <chr> <chr> <int> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 INT Champions Le… 42 2020/2… 15237 Champi… Expect… expect… Chelsea
#> 2 INT Champions Le… 42 2020/2… 15237 Champi… Expect… expect… Manche…
#> 3 INT Champions Le… 42 2020/2… 15237 Champi… Expect… expect… Bayern…
#> 4 INT Champions Le… 42 2020/2… 15237 Champi… Expect… expect… Paris …
#> 5 INT Champions Le… 42 2020/2… 15237 Champi… Expect… expect… Barcel…
#> 6 INT Champions Le… 42 2020/2… 15237 Champi… Expect… expect… Real M…
#> 7 INT Champions Le… 42 2020/2… 15237 Champi… Expect… expect… Juvent…
#> 8 INT Champions Le… 42 2020/2… 15237 Champi… Expect… expect… Liverp…
#> 9 INT Champions Le… 42 2020/2… 15237 Champi… Expect… expect… Boruss…
#> 10 INT Champions Le… 42 2020/2… 15237 Champi… Expect… expect… Lazio
#> # … with 22 more rows, 11 more variables: particiant_id <int>, team_id <int>,
#> # team_color <chr>, stat_value <dbl>, sub_stat_value <dbl>,
#> # minutes_played <int>, matches_played <int>, stat_value_count <int>,
#> # rank <int>, participant_country_code <chr>, team_name <lgl>, and
#> # abbreviated variable names ¹league_id, ²season_name, ³season_id,
#> # ⁴stat_league_name, ⁵stat_name, ⁶stat$stat, ⁷participant_nameYou 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. Only the current season’s
matches can be retrieved with this function.
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…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…¹ match…² match…³ home_id home_…⁴ home_…⁵
#> <chr> <int> <int> <chr> <int> <int> <chr> <int> <int> <chr>
#> 1 INT 875880 42 Champ… 3846346 875880 12.04.… 9823 1 Bayern…
#> 2 INT 875880 42 Champ… 3846342 875880 12.04.… 8633 2 Real M…
#> # … with 22 more variables: 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", "Tottenham Hotspur", "Newcastle United"…
#> $ table_short_name <chr> "Arsenal", "Tottenham", "Newcastle", "Man City", "…
#> $ table_id <int> 9825, 8586, 10261, 8456, 10204, 10260, 9879, 8455,…
#> $ table_page_url <chr> "/teams/9825/overview/arsenal", "/teams/8586/overv…
#> $ table_deduction <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ table_ongoing <df[,13]> <data.frame[26 x 13]>
#> $ table_played <int> 10, 11, 10, 9, 10, 11, 10, 11, 10, 10, 9, 10, …
#> $ table_wins <int> 8, 5, 4, 5, 5, 5, 4, 3, 2, 3, 2, 3, 3, 2, 1, 1, 1,…
#> $ table_draws <int> 1, 3, 5, 2, 2, 2, 1, 3, 5, 2, 3, 0, 0, 2, 4, 3, 3,…
#> $ table_losses <int> 1, 3, 1, 2, 3, 4, 5, 5, 3, 5, 4, 7, 7, 5, 5, 6, 6,…
#> $ table_scores_str <chr> "20-6", "19-16", "15-6", "15-8", "21-17", "15-20",…
#> $ table_goal_con_diff <int> 14, 3, 9, 7, 4, -5, 0, -5, -6, -8, -4, -6, -10, -5…
#> $ table_pts <int> 25, 18, 17, 17, 17, 17, 13, 12, 11, 11, 9, 9, 9, 8…
#> $ 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…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> "", "", "", "", "", "", "", "", "", "", "", "", ""…
#> $ group_id <int> 879535, 879535, 879535, 879535, 879536, 879536, 87…
#> $ group_page_url <chr> "/leagues/879535/overview/grp-a", "/leagues/879535…
#> $ 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: 22
#> $ match_id <int> 3609994, 3610132
#> $ match_time_utc <chr> "Sun, Oct 3, 2021, 15:30 UTC", "Sun, Jan 2, 202…
#> $ home_team_id <int> 8650, 8455
#> $ home_team <chr> "Liverpool", "Chelsea"
#> $ home_team_color <chr> "#d3171e", "#064b95"
#> $ away_team_id <int> 8456, 8650
#> $ away_team <chr> "Manchester City", "Liverpool"
#> $ away_team_color <chr> "#69A8D8", "#d3171e"
#> $ match_date_utc_time <chr> "2021-10-03T15:30:00.000Z", "2022-01-02T16:30:0…
#> $ tournament_id <int> 47, 47
#> $ tournament_link <chr> "/leagues/47/overview/premier-league", "/league…
#> $ tournament_league_name <chr> "Premier League", "Premier League"
#> $ tournament_round <chr> "Round 7", "Round 21"
#> $ 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_resources/logo…
#> $ referee_text <chr> "Paul Tierney", "Anthony Taylor"
#> $ referee_country <chr> "England", "England"
#> $ attendance <int> 53102, 40072Team-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: 7
#> $ match_id <int> 3609994, 3609994, 3609994, 3609994, 3609994, 3609994…
#> $ title <chr> "Top stats", "Top stats", "Top stats", "Top stats", …
#> $ stats_title <chr> "Ball possession", "Expected goals (xG)", "Total sho…
#> $ home_value <chr> "48", "0.78", "6", "3", "1", "423 (84%)", "10", "2",…
#> $ away_value <chr> "52", "1.66", "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 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: 41
#> $ 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> "2022/2023", "2022/2023", "2022/2023", "2022/…
#> $ 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…
#> $ home_team_color <chr> "#d3171e", "#d3171e", "#d3171e", "#d3171e", "…
#> $ away_team_id <int> 8456, 8456, 8456, 8456, 8456, 8456, 8456, 845…
#> $ away_team <chr> "Manchester City", "Manchester City", "Manche…
#> $ away_team_color <chr> "#69A8D8", "#69A8D8", "#69A8D8", "#69A8D8", "…
#> $ 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> 0.04540855, 0.04835442, 0.63251555, 0.0870914…
#> $ 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…
#> $ 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: 76
#> $ 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, 2, 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", "Midfielder", "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_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, "0.04", 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> 0.04540855, 0.04966947, 0.29863000, 0.17881796, …
#> $ 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: 31
#> $ 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_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> "0.39", "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"