fisdata makes it easy to download data from the FIS webpage into tibbles.
You can install the development version of fisdata from GitHub with:
# install.packages("remotes")
remotes::install_github("stibu81/fisdata")In order to use the functionality of this package and understand the results it returns, it is important to be familiar with its terminology. It follows mostly the terminology used on the FIS page. Other than the web page, it uses terms consistently and never uses the same term for different things.
The following are the relevant terms with an example value for each. Explanations for terms with obvious meaning are omitted.
| Term | Explanation | Examples |
|---|---|---|
| athlete | Didier Cuche, Lara Gut-Behrami | |
| sector | the major groups of disciplines | Alpine Skiing, Cross-Country |
| discipline | a specific discipline | Downhill, Large Hill, Aerials |
| brand | manufacturer of an athlete’s skis or snowboards | Stöckli, Head |
| competition | a competition in a single discipline and sector | Wengen Downhill 2025 |
| event | a set of one or more competitions in one or more disciplines taking place during one or more days in a specific location during a season | Wengen 2025 |
When filtering for sector, discipline, or category, a code is used
instead of the full description. See the datasets sectors,
disciplines, and categories, respectively, for a list of available
codes with a description.
fisdata currently offers four functions to query different types of data:
query_athletes(): query athletes by name and other attributes and obtain a table of matching athletes.query_results(): query all competition results for a single athlete.query_race(): get the full results for a single competition.query_events(): query events by date, place, and other attributes.query_competitions(): obtain all individual competitions that are associated with an event.
Each function returns a tibble that contains instances of a given type
as its rows. The following diagram shows the types of tables that can be
created. Each node stands for a table with the label of the node
indicating the type of objects contained in the table. Each arrow is
labelled by the name of the function that returns the table that the
arrow is pointing to. Some functions require an instance of another type
as input, as indicated by the arrows. The two exceptions,
query_athletes() and query_events() are thus the usual entry points
to start a query.
graph LR;
S1[ ]-->|"query_athletes()"|A(athletes);
A-->|"query_results()"|AR(athlete<br>results);
AR-->|"query_race()"|CR(competition<br>results);
S2[ ]-->|"query_events()"|E(events);
E-->|"query_competitions()"|C(competitions);
C-->|"query_race()"|CR;
CR-->|"query_results()"|AR;
style S1 fill:#FFFFFF00, stroke:#FFFFFF00;
style S2 fill:#FFFFFF00, stroke:#FFFFFF00;
Since athletes are identified by a Competitor-ID and races by a Race-ID that are usually not known to the user, it is generally easiest to start a query by finding an athlete. For example, the following call gets the information for Didier Cuche:
library(fisdata)
library(dplyr)
cuche <- query_athletes("cuche", "didier")
cuche %>%
select(active:nation, sector, brand, competitor_id)
#> # A tibble: 1 × 7
#> active fis_code name nation sector brand competitor_id
#> <lgl> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 FALSE 510030 Cuche Didier SUI AL Head 11795The result includes the competitor_id, which is required as input in
order to query an athlete’s results.
With the object returned by query_athletes(), it is now possible to
query all the race result of an athlete. Note that query_results()
must be called with a single athlete. The following query only obtains
the results in downhill races:
cuche_res <- query_results(cuche, discipline = "DH")
cuche_res %>%
select(athlete:place, category:rank, race_id)
#> # A tibble: 303 × 7
#> athlete date place category discipline rank race_id
#> <chr> <date> <chr> <chr> <chr> <int> <chr>
#> 1 Cuche Didier 2012-03-14 Schladming World Cup Downhill 17 66746
#> 2 Cuche Didier 2012-03-13 Schladming Training Downhill 1 66744
#> 3 Cuche Didier 2012-03-03 Kvitfjell World Cup Downhill 10 66811
#> 4 Cuche Didier 2012-03-01 Kvitfjell Training Downhill 2 66809
#> 5 Cuche Didier 2012-02-29 Kvitfjell Training Downhill 3 66810
#> 6 Cuche Didier 2012-02-11 Sochi World Cup Downhill 12 66801
#> 7 Cuche Didier 2012-02-10 Sochi Training Downhill 7 66800
#> 8 Cuche Didier 2012-02-08 Sochi Training Downhill 2 66798
#> 9 Cuche Didier 2012-02-04 Chamonix World Cup Downhill 7 66795
#> 10 Cuche Didier 2012-02-03 Chamonix World Cup Downhill 3 66767
#> # ℹ 293 more rowsThe result includes the race_id, which is required as input in order
to query the full results of a race.
By extracting one of the races from the table, one can now obtain the full results for that race. The following code gets the full result for Didier Cuche’s last victory in Wengen:
wengen_res <- cuche_res %>%
filter(place == "Wengen", rank == 1) %>%
head(n = 1) %>%
query_race()
wengen_res %>%
select(rank, bib, name:diff_time)
#> # A tibble: 70 × 7
#> rank bib name birth_year nation time diff_time
#> <int> <int> <chr> <int> <chr> <Period> <Period>
#> 1 1 18 Cuche Didier 1974 SUI 1M 50.31S 0S
#> 2 2 12 Heel Werner 1982 ITA 1M 51.17S 0.86S
#> 3 3 15 Janka Carlo 1986 SUI 1M 51.52S 1.21S
#> 4 4 1 Jerman Andrej 1978 SLO 1M 51.58S 1.27S
#> 5 5 3 Nyman Steven 1982 USA 1M 51.67S 1.36S
#> 6 6 14 Scheiber Mario 1983 AUT 1M 51.76S 1.45S
#> 7 7 28 Streitberger Georg 1981 AUT 1M 51.77S 1.46S
#> 8 8 16 Walchhofer Michael 1975 AUT 1M 51.79S 1.48S
#> 9 8 11 Kroell Klaus 1980 AUT 1M 51.79S 1.48S
#> 10 10 25 Innerhofer Christof 1984 ITA 1M 51.85S 1.54S
#> # ℹ 60 more rowsAlternatively, you may start by querying for an event. This finds the Wengen races from the season 2024/25:
wengen2025 <- query_events(sector = "AL", place = "wengen", season = 2025)
wengen2025 %>%
select(start_date:genders)
#> # A tibble: 1 × 8
#> start_date end_date place nation sector categories disciplines genders
#> <date> <date> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 2025-01-14 2025-01-19 Wengen SUI AL TRA / WC 4xDH / SL / SG MThe result includes the event_id, which is required as input in order
to query all the competitions that took place during this event:
wengen_races <- query_competitions(wengen2025)
wengen_races %>%
select(place:cancelled)
#> # A tibble: 6 × 8
#> place date time competition sector category gender cancelled
#> <chr> <date> <chr> <chr> <chr> <chr> <chr> <lgl>
#> 1 Wengen 2025-01-14 12:30 Downhill Training AL TRA M FALSE
#> 2 Wengen 2025-01-15 12:30 Downhill Training AL TRA M FALSE
#> 3 Wengen 2025-01-16 <NA> Downhill Training AL TRA M TRUE
#> 4 Wengen 2025-01-17 12:30 Super G AL WC M FALSE
#> 5 Wengen 2025-01-18 12:45 Downhill AL WC M FALSE
#> 6 Wengen 2025-01-19 10:15 Slalom AL WC M FALSEThis table now allows to query for the full race results (similar to the
output from query_results()). For example, the results for the Super G
can be obtained as follows:
sg_res <- wengen_races %>%
filter(competition == "Super G") %>%
query_race()
sg_res %>%
select(rank, bib, name:diff_time)
#> # A tibble: 47 × 8
#> rank bib name brand birth_year nation time diff_time
#> <int> <int> <chr> <chr> <int> <chr> <Period> <Period>
#> 1 1 3 Von Allmen Franjo Head 2001 SUI 1M 47.65S 0S
#> 2 2 6 Kriechmayr Vincent Head 1991 AUT 1M 47.75S 0.1S
#> 3 3 11 Rogentin Stefan Fischer 1994 SUI 1M 48.23S 0.58S
#> 4 4 1 Crawford James Head 1997 CAN 1M 48.27S 0.62S
#> 5 5 9 Paris Dominik Nordica 1989 ITA 1M 48.28S 0.63S
#> 6 6 8 Casse Mattia Rossign… 1990 ITA 1M 48.57S 0.92S
#> 7 7 13 Odermatt Marco Stoeckli 1997 SUI 1M 48.69S 1.04S
#> 8 8 16 Alexander Cameron Rossign… 1997 CAN 1M 48.78S 1.13S
#> 9 9 2 Monney Alexis Stoeckli 2000 SUI 1M 48.92S 1.27S
#> 10 10 26 Feurstein Lukas Head 2001 AUT 1M 48.97S 1.32S
#> # ℹ 37 more rowsNote that the objects created by the querying functions always include the URL that was downloaded. This URL can be obtained as follows:
show_url(cuche_res)
#> https://www.fis-ski.com/DB/general/athlete-biography.html?sectorcode=AL&seasoncode=&competitorid=11795&type=result&categorycode=&sort=&place=&disciplinecode=DH&position=&limit=2000You can copy this address into a browser in order to see the same data
as they are presented on the FIS web site. To make this as easy as
possible, show_url() automatically copies the URL into the clip board,
when called from an interactive R session.
Querying athletes and results is expected to work for all sectors and disciplines. Querying races, however, is more difficult, since different types of races have their results rendered in different form on the FIS web site. fisdata is able to handle the results of many common types of races but it is expected to fail for some types that are not yet covered. For example, it usually cannot handle team races:
query_athletes("von allmen", "franjo") %>%
query_results(category = "WSC") %>%
filter(discipline == "Team Combined") %>%
query_race()
#> Warning in query_race(.): ! The data contains some fields unknown to fisdata.
#> ℹ Affected column: 'name'
#> ! These columns might not be processed as expected.
#> Warning in process_race_column(name, race_df): NAs introduced by coercion
#> Warning in process_race_column(name, race_df): NAs introduced by coercion
#> Warning in process_race_column(name, race_df): NAs introduced by coercion
#> Error in `dplyr::as_tibble()`:
#> ! Column names `nation` and `time` must not be duplicated.
#> Use `.name_repair` to specify repair.
#> Caused by error in `repaired_names()`:
#> ! Names must be unique.
#> ✖ These names are duplicated:
#> * "nation" at locations 7 and 8.
#> * "time" at locations 9 and 10.