vignettes/fdo_tutorial.md
In dashee87/footballR: R package to obtain football (soccer) data from APIs
title: "fdo_tutorial"
author: "David Sheehan"
date: "25 September 2016"
output: html_document
How to use the football-data.org API
This page will describe how to use the footballR package to perform calls to the football-data.org API. The data obtainable from these functions includes past/previous fixtures, league tables, team information for Europe's top leagues and major international competitions.
Before We start
The football-data.org API is free to use and (as of September 2016) appears to be regularly updated. An app key is not necessary, but it's recommeneded, as it's free and allows the API owner to manage and control API usage. It's a simple process; just enter your name and email address here.
The API is extensively documented, so please refer to the online documentation if you require any further information.
Code
Let's load the package (assuming you've already installed it).
# load package
require("footballR")
Let's start off by checking all the competitions that can be accessed via this API. Note: An app key is not necessary (though recommended), this tutorial will perform API calls without an app key. An app key can be incorporated quite easily, by appending the token argument to the function.
# without app key
fdo_listComps()
## _links.href
## 1 http://api.football-data.org/v1/competitions/424
## 2 http://api.football-data.org/v1/competitions/426
## 3 http://api.football-data.org/v1/competitions/427
## 4 http://api.football-data.org/v1/competitions/428
## 5 http://api.football-data.org/v1/competitions/430
## 6 http://api.football-data.org/v1/competitions/431
## 7 http://api.football-data.org/v1/competitions/432
## 8 http://api.football-data.org/v1/competitions/433
## 9 http://api.football-data.org/v1/competitions/434
## 10 http://api.football-data.org/v1/competitions/435
## 11 http://api.football-data.org/v1/competitions/436
## 12 http://api.football-data.org/v1/competitions/437
## 13 http://api.football-data.org/v1/competitions/438
## 14 http://api.football-data.org/v1/competitions/439
## 15 http://api.football-data.org/v1/competitions/440
## _links.href
## 1 http://api.football-data.org/v1/competitions/424/teams
## 2 http://api.football-data.org/v1/competitions/426/teams
## 3 http://api.football-data.org/v1/competitions/427/teams
## 4 http://api.football-data.org/v1/competitions/428/teams
## 5 http://api.football-data.org/v1/competitions/430/teams
## 6 http://api.football-data.org/v1/competitions/431/teams
## 7 http://api.football-data.org/v1/competitions/432/teams
## 8 http://api.football-data.org/v1/competitions/433/teams
## 9 http://api.football-data.org/v1/competitions/434/teams
## 10 http://api.football-data.org/v1/competitions/435/teams
## 11 http://api.football-data.org/v1/competitions/436/teams
## 12 http://api.football-data.org/v1/competitions/437/teams
## 13 http://api.football-data.org/v1/competitions/438/teams
## 14 http://api.football-data.org/v1/competitions/439/teams
## 15 http://api.football-data.org/v1/competitions/440/teams
## _links.href
## 1 http://api.football-data.org/v1/competitions/424/fixtures
## 2 http://api.football-data.org/v1/competitions/426/fixtures
## 3 http://api.football-data.org/v1/competitions/427/fixtures
## 4 http://api.football-data.org/v1/competitions/428/fixtures
## 5 http://api.football-data.org/v1/competitions/430/fixtures
## 6 http://api.football-data.org/v1/competitions/431/fixtures
## 7 http://api.football-data.org/v1/competitions/432/fixtures
## 8 http://api.football-data.org/v1/competitions/433/fixtures
## 9 http://api.football-data.org/v1/competitions/434/fixtures
## 10 http://api.football-data.org/v1/competitions/435/fixtures
## 11 http://api.football-data.org/v1/competitions/436/fixtures
## 12 http://api.football-data.org/v1/competitions/437/fixtures
## 13 http://api.football-data.org/v1/competitions/438/fixtures
## 14 http://api.football-data.org/v1/competitions/439/fixtures
## 15 http://api.football-data.org/v1/competitions/440/fixtures
## _links.href id
## 1 http://api.football-data.org/v1/competitions/424/leagueTable 424
## 2 http://api.football-data.org/v1/competitions/426/leagueTable 426
## 3 http://api.football-data.org/v1/competitions/427/leagueTable 427
## 4 http://api.football-data.org/v1/competitions/428/leagueTable 428
## 5 http://api.football-data.org/v1/competitions/430/leagueTable 430
## 6 http://api.football-data.org/v1/competitions/431/leagueTable 431
## 7 http://api.football-data.org/v1/competitions/432/leagueTable 432
## 8 http://api.football-data.org/v1/competitions/433/leagueTable 433
## 9 http://api.football-data.org/v1/competitions/434/leagueTable 434
## 10 http://api.football-data.org/v1/competitions/435/leagueTable 435
## 11 http://api.football-data.org/v1/competitions/436/leagueTable 436
## 12 http://api.football-data.org/v1/competitions/437/leagueTable 437
## 13 http://api.football-data.org/v1/competitions/438/leagueTable 438
## 14 http://api.football-data.org/v1/competitions/439/leagueTable 439
## 15 http://api.football-data.org/v1/competitions/440/leagueTable 440
## caption league year currentMatchday
## 1 European Championships France 2016 EC 2016 7
## 2 Premier League 2016/17 PL 2016 6
## 3 Championship 2016/17 ELC 2016 9
## 4 League One 2016/17 EL1 2016 9
## 5 1. Bundesliga 2016/17 BL1 2016 5
## 6 2. Bundesliga 2016/17 BL2 2016 7
## 7 DFB-Pokal 2016/17 DFB 2016 2
## 8 Eredivisie 2016/17 DED 2016 7
## 9 Ligue 1 2016/17 FL1 2016 7
## 10 Ligue 2 2016/17 FL2 2016 9
## 11 Primera Division 2016/17 PD 2016 6
## 12 Liga Adelante 2016/17 SD 2016 7
## 13 Serie A 2016/17 SA 2016 6
## 14 Primeira Liga 2016/17 PPL 2016 6
## 15 Champions League 2016/17 CL 2016 2
## numberOfMatchdays numberOfTeams numberOfGames lastUpdated
## 1 7 24 51 2016-07-10T21:32:20Z
## 2 38 20 380 2016-09-25T09:59:54Z
## 3 46 24 552 2016-09-25T10:05:15Z
## 4 46 24 552 2016-09-25T10:05:41Z
## 5 34 18 306 2016-09-25T00:02:13Z
## 6 34 18 306 2016-09-25T00:02:43Z
## 7 5 64 48 2016-08-28T07:00:07Z
## 8 34 18 306 2016-09-25T10:00:23Z
## 9 38 20 380 2016-09-25T10:00:44Z
## 10 38 20 380 2016-09-25T10:01:07Z
## 11 38 20 380 2016-09-25T10:01:41Z
## 12 42 22 462 2016-09-25T10:07:55Z
## 13 38 20 380 2016-09-25T10:08:20Z
## 14 34 18 306 2016-09-25T10:08:33Z
## 15 10 32 96 2016-09-17T10:00:15Z
# with app key
# appkey = "myappkey"
# fdo_listComps(token=appkey)
There's quite a bit of information in there, some of which we may not be interested in. We can streamline the output by including an appropriate response argument.
# default is reponse = "full"
fdo_listComps(response = "minified")
## id caption league year currentMatchday
## 1 424 European Championships France 2016 EC 2016 7
## 2 426 Premier League 2016/17 PL 2016 6
## 3 427 Championship 2016/17 ELC 2016 9
## 4 428 League One 2016/17 EL1 2016 9
## 5 430 1. Bundesliga 2016/17 BL1 2016 5
## 6 431 2. Bundesliga 2016/17 BL2 2016 7
## 7 432 DFB-Pokal 2016/17 DFB 2016 2
## 8 433 Eredivisie 2016/17 DED 2016 7
## 9 434 Ligue 1 2016/17 FL1 2016 7
## 10 435 Ligue 2 2016/17 FL2 2016 9
## 11 436 Primera Division 2016/17 PD 2016 6
## 12 437 Liga Adelante 2016/17 SD 2016 7
## 13 438 Serie A 2016/17 SA 2016 6
## 14 439 Primeira Liga 2016/17 PPL 2016 6
## 15 440 Champions League 2016/17 CL 2016 2
## numberOfMatchdays numberOfTeams numberOfGames lastUpdated
## 1 7 24 51 2016-07-10T21:32:20Z
## 2 38 20 380 2016-09-25T09:59:54Z
## 3 46 24 552 2016-09-25T10:05:15Z
## 4 46 24 552 2016-09-25T10:05:41Z
## 5 34 18 306 2016-09-25T00:02:13Z
## 6 34 18 306 2016-09-25T00:02:43Z
## 7 5 64 48 2016-08-28T07:00:07Z
## 8 34 18 306 2016-09-25T10:00:23Z
## 9 38 20 380 2016-09-25T10:00:44Z
## 10 38 20 380 2016-09-25T10:01:07Z
## 11 38 20 380 2016-09-25T10:01:41Z
## 12 42 22 462 2016-09-25T10:07:55Z
## 13 38 20 380 2016-09-25T10:08:20Z
## 14 34 18 306 2016-09-25T10:08:33Z
## 15 10 32 96 2016-09-17T10:00:15Z
And we can change the season that we're interested in (the package generally defaults to the current season).
fdo_listComps(season = "2015",response = "minified")
## id caption league year currentMatchday
## 1 394 1. Bundesliga 2015/16 BL1 2015 34
## 2 395 2. Bundesliga 2015/16 BL2 2015 34
## 3 396 Ligue 1 2015/16 FL1 2015 38
## 4 397 Ligue 2 2015/16 FL2 2015 38
## 5 398 Premier League 2015/16 PL 2015 38
## 6 399 Primera Division 2015/16 PD 2015 38
## 7 400 Segunda Division 2015/16 SD 2015 42
## 8 401 Serie A 2015/16 SA 2015 38
## 9 402 Primeira Liga 2015/16 PPL 2015 34
## 10 403 3. Bundesliga 2015/16 BL3 2015 38
## 11 404 Eredivisie 2015/16 DED 2015 34
## 12 405 Champions League 2015/16 CL 2015 10
## 13 425 League One 2015/16 EL1 2015 16
## numberOfMatchdays numberOfTeams numberOfGames lastUpdated
## 1 34 18 306 2016-06-15T08:09:51Z
## 2 34 18 306 2016-05-15T16:00:18Z
## 3 38 20 380 2016-05-13T07:00:02Z
## 4 38 20 380 2016-05-14T05:57:07Z
## 5 38 20 380 2016-05-19T15:12:55Z
## 6 38 20 380 2016-05-16T07:16:11Z
## 7 42 22 462 2016-07-04T21:35:12Z
## 8 38 20 380 2016-05-16T07:15:29Z
## 9 34 18 306 2016-05-15T19:23:33Z
## 10 38 20 380 2016-05-14T14:15:13Z
## 11 34 18 306 2016-05-08T18:39:50Z
## 12 10 32 125 2016-06-06T10:11:53Z
## 13 46 24 552 2016-05-19T19:00:12Z
If you require more information on a function, then run the function with a question mark e.g. ?fdo_listComps.
You'll notice from the above data frame that the league id for the 2016/17 season of the Premier League is 426. Let's have a look at all the teams in the that EPL season.
fdo_listCompTeams(id = "426",response = "minified")
## $count
## [1] 20
##
## $teams
## id name shortName squadMarketValue
## 1 322 Hull City FC Hull 122,250,000 <U+0080>
## 2 338 Leicester City FC Foxes 210,500,000 <U+0080>
## 3 340 Southampton FC Southampton 199,000,000 <U+0080>
## 4 346 Watford FC Watford 128,500,000 <U+0080>
## 5 343 Middlesbrough FC Middlesbrough 104,750,000 <U+0080>
## 6 70 Stoke City FC Stoke 172,250,000 <U+0080>
## 7 62 Everton FC Everton 239,250,000 <U+0080>
## 8 73 Tottenham Hotspur FC Spurs 365,500,000 <U+0080>
## 9 354 Crystal Palace FC Crystal 157,750,000 <U+0080>
## 10 74 West Bromwich Albion FC West Bromwich 107,600,000 <U+0080>
## 11 328 Burnley FC Burnley 66,500,000 <U+0080>
## 12 72 Swansea City FC Swans 106,100,000 <U+0080>
## 13 65 Manchester City FC ManCity 518,000,000 <U+0080>
## 14 71 Sunderland AFC Sunderland 92,500,000 <U+0080>
## 15 1044 AFC Bournemouth Bournemouth 121,750,000 <U+0080>
## 16 66 Manchester United FC ManU 534,250,000 <U+0080>
## 17 57 Arsenal FC Arsenal 468,500,000 <U+0080>
## 18 64 Liverpool FC Liverpool 387,200,000 <U+0080>
## 19 61 Chelsea FC Chelsea 514,800,000 <U+0080>
## 20 563 West Ham United FC West Ham 241,500,000 <U+0080>
## crestUrl
## 1 http://upload.wikimedia.org/wikipedia/de/a/a9/Hull_City_AFC.svg
## 2 http://upload.wikimedia.org/wikipedia/en/6/63/Leicester02.png
## 3 http://upload.wikimedia.org/wikipedia/de/c/c9/FC_Southampton.svg
## 4 https://upload.wikimedia.org/wikipedia/en/e/e2/Watford.svg
## 5 https://upload.wikimedia.org/wikipedia/en/2/2c/Middlesbrough_FC_crest.svg
## 6 http://upload.wikimedia.org/wikipedia/de/a/a3/Stoke_City.svg
## 7 http://upload.wikimedia.org/wikipedia/de/f/f9/Everton_FC.svg
## 8 http://upload.wikimedia.org/wikipedia/de/b/b4/Tottenham_Hotspur.svg
## 9 http://upload.wikimedia.org/wikipedia/de/b/bf/Crystal_Palace_F.C._logo_(2013).png
## 10 http://upload.wikimedia.org/wikipedia/de/8/8b/West_Bromwich_Albion.svg
## 11 https://upload.wikimedia.org/wikipedia/en/0/02/Burnley_FC_badge.png
## 12 http://upload.wikimedia.org/wikipedia/de/a/ab/Swansea_City_Logo.svg
## 13 https://upload.wikimedia.org/wikipedia/en/e/eb/Manchester_City_FC_badge.svg
## 14 http://upload.wikimedia.org/wikipedia/de/6/60/AFC_Sunderland.svg
## 15 https://upload.wikimedia.org/wikipedia/de/4/41/Afc_bournemouth.svg
## 16 http://upload.wikimedia.org/wikipedia/de/d/da/Manchester_United_FC.svg
## 17 http://upload.wikimedia.org/wikipedia/en/5/53/Arsenal_FC.svg
## 18 http://upload.wikimedia.org/wikipedia/de/0/0a/FC_Liverpool.svg
## 19 http://upload.wikimedia.org/wikipedia/de/5/5c/Chelsea_crest.svg
## 20 http://upload.wikimedia.org/wikipedia/de/e/e0/West_Ham_United_FC.svg
We can see the current league table for this competition.
fdo_leagueTable(id = "426",response = "minified")
## $leagueCaption
## [1] "Premier League 2016/17"
##
## $matchday
## [1] 6
##
## $standing
## rank team teamId playedGames
## 1 1 ManCity 65 6
## 2 2 Spurs 73 6
## 3 3 Arsenal 57 6
## 4 4 Liverpool 64 6
## 5 5 Everton 62 6
## 6 6 ManU 66 6
## 7 7 Crystal 354 6
## 8 8 Chelsea 61 6
## 9 9 West Bromwich 74 6
## 10 10 Watford 346 5
## 11 11 Foxes 338 6
## 12 12 Hull 322 6
## 13 13 Bournemouth 1044 6
## 14 14 Southampton 340 5
## 15 15 Middlesbrough 343 6
## 16 16 Swans 72 6
## 17 17 Burnley 328 5
## 18 18 West Ham 563 5
## 19 19 Stoke 70 6
## 20 20 Sunderland 71 6
## crestURI
## 1 https://upload.wikimedia.org/wikipedia/en/e/eb/Manchester_City_FC_badge.svg
## 2 http://upload.wikimedia.org/wikipedia/de/b/b4/Tottenham_Hotspur.svg
## 3 http://upload.wikimedia.org/wikipedia/en/5/53/Arsenal_FC.svg
## 4 http://upload.wikimedia.org/wikipedia/de/0/0a/FC_Liverpool.svg
## 5 http://upload.wikimedia.org/wikipedia/de/f/f9/Everton_FC.svg
## 6 http://upload.wikimedia.org/wikipedia/de/d/da/Manchester_United_FC.svg
## 7 http://upload.wikimedia.org/wikipedia/de/b/bf/Crystal_Palace_F.C._logo_%282013%29.png
## 8 http://upload.wikimedia.org/wikipedia/de/5/5c/Chelsea_crest.svg
## 9 http://upload.wikimedia.org/wikipedia/de/8/8b/West_Bromwich_Albion.svg
## 10 https://upload.wikimedia.org/wikipedia/en/e/e2/Watford.svg
## 11 http://upload.wikimedia.org/wikipedia/en/6/63/Leicester02.png
## 12 http://upload.wikimedia.org/wikipedia/de/a/a9/Hull_City_AFC.svg
## 13 https://upload.wikimedia.org/wikipedia/de/4/41/Afc_bournemouth.svg
## 14 http://upload.wikimedia.org/wikipedia/de/c/c9/FC_Southampton.svg
## 15 https://upload.wikimedia.org/wikipedia/en/2/2c/Middlesbrough_FC_crest.svg
## 16 http://upload.wikimedia.org/wikipedia/de/a/ab/Swansea_City_Logo.svg
## 17 https://upload.wikimedia.org/wikipedia/en/0/02/Burnley_FC_badge.png
## 18 http://upload.wikimedia.org/wikipedia/de/e/e0/West_Ham_United_FC.svg
## 19 http://upload.wikimedia.org/wikipedia/de/a/a3/Stoke_City.svg
## 20 http://upload.wikimedia.org/wikipedia/de/6/60/AFC_Sunderland.svg
## points goals goalsAgainst goalDifference
## 1 18 18 5 13
## 2 14 10 3 7
## 3 13 15 7 8
## 4 13 16 9 7
## 5 13 10 4 6
## 6 12 12 7 5
## 7 10 10 7 3
## 8 10 10 9 1
## 9 8 7 6 1
## 10 7 10 9 1
## 11 7 8 11 -3
## 12 7 7 12 -5
## 13 7 4 9 -5
## 14 5 4 6 -2
## 15 5 6 9 -3
## 16 4 5 10 -5
## 17 4 3 8 -5
## 18 3 7 13 -6
## 19 2 4 15 -11
## 20 1 5 12 -7
By default, it returns the current league table. Through the matchDay argument, we can also look at past league tables.
fdo_leagueTable(id = "426", matchDay = 3, response = "minified")
## $leagueCaption
## [1] "Premier League 2016/17"
##
## $matchday
## [1] 3
##
## $standing
## rank team teamId playedGames
## 1 1 ManCity 65 3
## 2 2 Chelsea 61 3
## 3 3 ManU 66 3
## 4 4 Everton 62 3
## 5 5 Hull 322 3
## 6 6 Middlesbrough 343 3
## 7 6 Spurs 73 3
## 8 8 Arsenal 57 3
## 9 9 Foxes 338 3
## 10 10 West Bromwich 74 3
## 11 11 Liverpool 64 3
## 12 12 West Ham 563 3
## 13 13 Burnley 328 3
## 14 13 Swans 72 3
## 15 15 Southampton 340 3
## 16 16 Sunderland 71 3
## 17 17 Crystal 354 3
## 18 18 Watford 346 3
## 19 19 Bournemouth 1044 3
## 20 20 Stoke 70 3
## crestURI
## 1 https://upload.wikimedia.org/wikipedia/en/e/eb/Manchester_City_FC_badge.svg
## 2 http://upload.wikimedia.org/wikipedia/de/5/5c/Chelsea_crest.svg
## 3 http://upload.wikimedia.org/wikipedia/de/d/da/Manchester_United_FC.svg
## 4 http://upload.wikimedia.org/wikipedia/de/f/f9/Everton_FC.svg
## 5 http://upload.wikimedia.org/wikipedia/de/a/a9/Hull_City_AFC.svg
## 6 https://upload.wikimedia.org/wikipedia/en/2/2c/Middlesbrough_FC_crest.svg
## 7 http://upload.wikimedia.org/wikipedia/de/b/b4/Tottenham_Hotspur.svg
## 8 http://upload.wikimedia.org/wikipedia/en/5/53/Arsenal_FC.svg
## 9 http://upload.wikimedia.org/wikipedia/en/6/63/Leicester02.png
## 10 http://upload.wikimedia.org/wikipedia/de/8/8b/West_Bromwich_Albion.svg
## 11 http://upload.wikimedia.org/wikipedia/de/0/0a/FC_Liverpool.svg
## 12 http://upload.wikimedia.org/wikipedia/de/e/e0/West_Ham_United_FC.svg
## 13 https://upload.wikimedia.org/wikipedia/en/0/02/Burnley_FC_badge.png
## 14 http://upload.wikimedia.org/wikipedia/de/a/ab/Swansea_City_Logo.svg
## 15 http://upload.wikimedia.org/wikipedia/de/c/c9/FC_Southampton.svg
## 16 http://upload.wikimedia.org/wikipedia/de/6/60/AFC_Sunderland.svg
## 17 http://upload.wikimedia.org/wikipedia/de/b/bf/Crystal_Palace_F.C._logo_%282013%29.png
## 18 https://upload.wikimedia.org/wikipedia/en/e/e2/Watford.svg
## 19 https://upload.wikimedia.org/wikipedia/de/4/41/Afc_bournemouth.svg
## 20 http://upload.wikimedia.org/wikipedia/de/a/a3/Stoke_City.svg
## points goals goalsAgainst goalDifference
## 1 9 9 3 6
## 2 9 7 2 5
## 3 9 6 1 5
## 4 7 4 2 2
## 5 6 4 2 2
## 6 5 3 2 1
## 7 5 3 2 1
## 8 4 6 5 1
## 9 4 3 3 0
## 10 4 2 2 0
## 11 4 5 6 -1
## 12 3 3 5 -2
## 13 3 2 4 -2
## 14 3 2 4 -2
## 15 2 2 4 -2
## 16 1 3 5 -2
## 17 1 1 3 -2
## 18 1 3 6 -3
## 19 1 2 5 -3
## 20 1 2 6 -4
And we can find out the upcoming/previous fixtures for this competition. By default, it returns all (380 in this case) fixtures for this competition. Like before, we can filter it by match day.
fdo_listCompFixtures(id = "426", matchDay = 3, response = "minified")
## $count
## [1] 10
##
## $fixtures
## id competitionId date status matchday
## 1 150822 426 2016-08-27T11:30:00Z FINISHED 3
## 2 150823 426 2016-08-27T14:00:00Z FINISHED 3
## 3 150815 426 2016-08-27T14:00:00Z FINISHED 3
## 4 150821 426 2016-08-27T14:00:00Z FINISHED 3
## 5 150819 426 2016-08-27T14:00:00Z FINISHED 3
## 6 150817 426 2016-08-27T14:00:00Z FINISHED 3
## 7 150816 426 2016-08-27T14:00:00Z FINISHED 3
## 8 150818 426 2016-08-27T16:30:00Z FINISHED 3
## 9 150824 426 2016-08-28T12:30:00Z FINISHED 3
## 10 150820 426 2016-08-28T15:00:00Z FINISHED 3
## homeTeamName homeTeamId awayTeamName awayTeamId
## 1 Tottenham Hotspur FC 73 Liverpool FC 64
## 2 Watford FC 346 Arsenal FC 57
## 3 Chelsea FC 61 Burnley FC 328
## 4 Southampton FC 340 Sunderland AFC 71
## 5 Leicester City FC 338 Swansea City FC 72
## 6 Everton FC 62 Stoke City FC 70
## 7 Crystal Palace FC 354 AFC Bournemouth 1044
## 8 Hull City FC 322 Manchester United FC 66
## 9 West Bromwich Albion FC 74 Middlesbrough FC 343
## 10 Manchester City FC 65 West Ham United FC 563
## result.goalsHomeTeam result.goalsAwayTeam odds.homeWin odds.draw
## 1 1 1 2.37 3.25
## 2 1 3 4.80 3.70
## 3 3 0 1.25 5.50
## 4 1 1 1.60 3.90
## 5 2 1 1.72 3.70
## 6 1 0 1.72 3.50
## 7 1 1 2.20 3.20
## 8 0 1 7.50 4.00
## 9 0 0 2.50 3.00
## 10 3 1 1.28 5.50
## odds.awayWin
## 1 3.00
## 2 1.72
## 3 15.00
## 4 5.80
## 5 4.75
## 6 5.50
## 7 3.40
## 8 1.50
## 9 3.00
## 10 11.00
Or if you're interested in what fixtures are taking place in the next 10 days
fdo_listCompFixtures(id = "426", timeFrame = "n10", response = "minified")
## $count
## [1] 12
##
## $fixtures
## id competitionId date status matchday
## 1 150794 426 2016-09-25T15:00:00Z TIMED 6
## 2 150787 426 2016-09-26T19:00:00Z TIMED 6
## 3 150776 426 2016-09-30T19:00:00Z TIMED 7
## 4 150781 426 2016-10-01T11:30:00Z TIMED 7
## 5 150780 426 2016-10-01T14:00:00Z TIMED 7
## 6 150783 426 2016-10-01T14:00:00Z TIMED 7
## 7 150784 426 2016-10-01T14:00:00Z TIMED 7
## 8 150777 426 2016-10-01T14:00:00Z TIMED 7
## 9 150779 426 2016-10-02T11:00:00Z TIMED 7
## 10 150778 426 2016-10-02T13:15:00Z TIMED 7
## 11 150782 426 2016-10-02T13:15:00Z TIMED 7
## 12 150775 426 2016-10-02T15:30:00Z TIMED 7
## homeTeamName homeTeamId awayTeamName awayTeamId
## 1 West Ham United FC 563 Southampton FC 340
## 2 Burnley FC 328 Watford FC 346
## 3 Everton FC 62 Crystal Palace FC 354
## 4 Swansea City FC 72 Liverpool FC 64
## 5 Sunderland AFC 71 West Bromwich Albion FC 74
## 6 Watford FC 346 AFC Bournemouth 1044
## 7 West Ham United FC 563 Middlesbrough FC 343
## 8 Hull City FC 322 Chelsea FC 61
## 9 Manchester United FC 66 Stoke City FC 70
## 10 Leicester City FC 338 Southampton FC 340
## 11 Tottenham Hotspur FC 73 Manchester City FC 65
## 12 Burnley FC 328 Arsenal FC 57
## result.goalsHomeTeam result.goalsAwayTeam odds.homeWin odds.draw
## 1 NA NA 3.00 3.25
## 2 NA NA 3.00 3.20
## 3 NA NA 1.72 4.00
## 4 NA NA 5.80 4.40
## 5 NA NA 2.45 3.25
## 6 NA NA 2.20 3.50
## 7 NA NA 2.10 3.40
## 8 NA NA 6.50 4.40
## 9 NA NA 1.30 5.00
## 10 NA NA 2.00 3.60
## 11 NA NA 3.60 3.50
## 12 NA NA 7.00 4.50
## odds.awayWin
## 1 2.40
## 2 2.45
## 3 4.33
## 4 1.50
## 5 2.90
## 6 3.10
## 7 3.40
## 8 1.50
## 9 12.00
## 10 3.60
## 11 2.00
## 12 1.44
Apologies to Liverpool fans (well, pretty much all non Man Utd fans), we're going to focus on Man Utd. You'll notice that their team id is 66. Let's use that id to get some team information.
# returns all Man Utd away fixtures in the previous 14 days
fdo_listTeamFixtures("66", timeFrame = "p14", venue="away", response = "minified")
## $timeFrameStart
## [1] "2016-09-11"
##
## $timeFrameEnd
## [1] "2016-09-25"
##
## $count
## [1] 1
##
## $fixtures
## id competitionId date status matchday homeTeamName
## 1 150803 426 2016-09-18T11:00:00Z FINISHED 5 Watford FC
## homeTeamId awayTeamName awayTeamId result.goalsHomeTeam
## 1 346 Manchester United FC 66 3
## result.goalsAwayTeam odds.homeWin odds.draw odds.awayWin
## 1 1 6.5 4 1.5
# general information on Man Utd
fdo_team("66")
## $`_links`
## $`_links`$self
## $`_links`$self$href
## [1] "http://api.football-data.org/v1/teams/66"
##
##
## $`_links`$fixtures
## $`_links`$fixtures$href
## [1] "http://api.football-data.org/v1/teams/66/fixtures"
##
##
## $`_links`$players
## $`_links`$players$href
## [1] "http://api.football-data.org/v1/teams/66/players"
##
##
##
## $name
## [1] "Manchester United FC"
##
## $code
## [1] "MUFC"
##
## $shortName
## [1] "ManU"
##
## $squadMarketValue
## [1] "534,250,000 <U+0080>"
##
## $crestUrl
## [1] "http://upload.wikimedia.org/wikipedia/de/d/da/Manchester_United_FC.svg"
Finally, we can find all the players currently playing with Man Utd. The data includes nationality and market value (not sure where those figures came from but Pogba's only worth €70m)
fdo_teamPlayers("66",response="minified")
## $count
## [1] 27
##
## $players
## id name position jerseyNumber dateOfBirth
## 1 487 Paul Pogba Central Midfield 6 1993-03-15
## 2 2593 Zlatan Ibrahimovic Centre Forward 9 1981-10-03
## 3 3664 Eric Bailly Centre Back 3 1994-04-12
## 4 4795 Henrikh Mkhitaryan Attacking Midfield 22 1989-01-21
## 5 6693 David de Gea Keeper 1 1990-11-07
## 6 6694 Sergio Romero Keeper 20 1987-02-22
## 7 6695 Sam Johnstone Keeper 32 1993-03-25
## 8 6696 Daley Blind Centre Back 17 1990-03-09
## 9 6697 Chris Smalling Centre Back 12 1989-11-22
## 10 6698 Marcos Rojo Centre Back 5 1990-03-20
## 11 6699 Phil Jones Centre Back 4 1992-02-21
## 12 6700 Luke Shaw Left-Back 23 1995-07-12
## 13 6702 Matteo Darmian Right-Back 36 1989-12-02
## 14 6703 Antonio Valencia Right-Back 25 1985-08-04
## 15 6704 Morgan Schneiderlin Defensive Midfield 28 1989-11-08
## 16 6705 Michael Carrick Defensive Midfield 16 1981-07-28
## 17 6706 Timothy Fosu-Mensah Defensive Midfield 24 1998-01-02
## 18 6707 Ander Herrera Central Midfield 21 1989-08-14
## 19 6708 Marouane Fellaini Central Midfield 27 1987-11-22
## 20 6709 Bastian Schweinsteiger Central Midfield 31 1984-08-01
## 21 6710 Juan Mata Attacking Midfield 8 1988-04-28
## 22 6712 Memphis Depay Left Wing 7 1994-02-13
## 23 6713 Ashley Young Left Wing 18 1985-07-09
## 24 6714 Jesse Lingard Left Wing 14 1992-12-15
## 25 6715 Wayne Rooney Secondary Striker 10 1985-10-24
## 26 6716 Anthony Martial Centre Forward 11 1995-12-05
## 27 6717 Marcus Rashford Centre Forward 19 1997-10-31
## nationality contractUntil marketValue
## 1 France 2021-06-30 70,000,000 <U+0080>
## 2 Sweden 2017-06-30 12,000,000 <U+0080>
## 3 Cote d'Ivoire 2020-06-30 20,000,000 <U+0080>
## 4 Armenia 2020-06-30 37,000,000 <U+0080>
## 5 Spain 2019-06-30 40,000,000 <U+0080>
## 6 Argentina 2018-06-30 5,000,000 <U+0080>
## 7 England 2017-06-30 250,000 <U+0080>
## 8 Netherlands 2018-06-30 22,000,000 <U+0080>
## 9 England 2019-06-30 20,000,000 <U+0080>
## 10 Argentina 2019-06-30 19,000,000 <U+0080>
## 11 England 2019-06-30 14,000,000 <U+0080>
## 12 England 2018-06-30 21,000,000 <U+0080>
## 13 Italy 2019-06-30 15,000,000 <U+0080>
## 14 Ecuador 2017-06-30 8,000,000 <U+0080>
## 15 France 2019-06-30 28,000,000 <U+0080>
## 16 England 2017-06-30 3,000,000 <U+0080>
## 17 Netherlands 2017-06-30 1,000,000 <U+0080>
## 18 Spain 2018-06-30 28,000,000 <U+0080>
## 19 Belgium 2018-06-30 15,000,000 <U+0080>
## 20 Germany 2018-06-30 10,000,000 <U+0080>
## 21 Spain 2018-06-30 31,000,000 <U+0080>
## 22 Netherlands 2019-06-30 25,000,000 <U+0080>
## 23 England 2018-06-30 10,000,000 <U+0080>
## 24 England 2018-06-30 6,000,000 <U+0080>
## 25 England 2019-06-30 35,000,000 <U+0080>
## 26 France 2019-06-30 32,000,000 <U+0080>
## 27 England 2020-06-30 7,000,000 <U+0080>
So, let's have a look at Man Utd's next league fixture and their past 5 meetings with those opponents.
next_fixture_id <- fdo_listTeamFixtures("66", timeFrame = "n100", response = "minified")$fixtures$id[1]
fdo_getFixture(next_fixture_id,past=5,response="compressed")
## $fixture
## $fixture$id
## [1] 150779
##
## $fixture$cId
## [1] 426
##
## $fixture$date
## [1] "2016-10-02T11:00:00Z"
##
## $fixture$stat
## [1] "TIMED"
##
## $fixture$mday
## [1] 7
##
## $fixture$htId
## [1] 66
##
## $fixture$atId
## [1] 70
##
## $fixture$res
## $fixture$res$ght
## NULL
##
## $fixture$res$gat
## NULL
##
##
##
## $head2head
## $head2head$count
## [1] 5
##
## $head2head$timeFrameStart
## [1] "2014-02-01"
##
## $head2head$timeFrameEnd
## [1] "2016-02-02"
##
## $head2head$homeTeamWins
## [1] 2
##
## $head2head$awayTeamWins
## [1] 2
##
## $head2head$draws
## [1] 1
##
## $head2head$lastHomeWinHomeTeam
## $head2head$lastHomeWinHomeTeam$id
## [1] 146865
##
## $head2head$lastHomeWinHomeTeam$cId
## [1] 398
##
## $head2head$lastHomeWinHomeTeam$date
## [1] "2016-02-02T20:00:00Z"
##
## $head2head$lastHomeWinHomeTeam$stat
## [1] "FINISHED"
##
## $head2head$lastHomeWinHomeTeam$mday
## [1] 24
##
## $head2head$lastHomeWinHomeTeam$htId
## [1] 66
##
## $head2head$lastHomeWinHomeTeam$atId
## [1] 70
##
## $head2head$lastHomeWinHomeTeam$res
## $head2head$lastHomeWinHomeTeam$res$ght
## [1] 3
##
## $head2head$lastHomeWinHomeTeam$res$gat
## [1] 0
##
##
##
## $head2head$lastWinHomeTeam
## $head2head$lastWinHomeTeam$id
## [1] 146865
##
## $head2head$lastWinHomeTeam$cId
## [1] 398
##
## $head2head$lastWinHomeTeam$date
## [1] "2016-02-02T20:00:00Z"
##
## $head2head$lastWinHomeTeam$stat
## [1] "FINISHED"
##
## $head2head$lastWinHomeTeam$mday
## [1] 24
##
## $head2head$lastWinHomeTeam$htId
## [1] 66
##
## $head2head$lastWinHomeTeam$atId
## [1] 70
##
## $head2head$lastWinHomeTeam$res
## $head2head$lastWinHomeTeam$res$ght
## [1] 3
##
## $head2head$lastWinHomeTeam$res$gat
## [1] 0
##
##
##
## $head2head$lastAwayWinAwayTeam
## NULL
##
## $head2head$lastWinAwayTeam
## $head2head$lastWinAwayTeam$id
## [1] 146924
##
## $head2head$lastWinAwayTeam$cId
## [1] 398
##
## $head2head$lastWinAwayTeam$date
## [1] "2015-12-26T12:45:00Z"
##
## $head2head$lastWinAwayTeam$stat
## [1] "FINISHED"
##
## $head2head$lastWinAwayTeam$mday
## [1] 18
##
## $head2head$lastWinAwayTeam$htId
## [1] 70
##
## $head2head$lastWinAwayTeam$atId
## [1] 66
##
## $head2head$lastWinAwayTeam$res
## $head2head$lastWinAwayTeam$res$ght
## [1] 2
##
## $head2head$lastWinAwayTeam$res$gat
## [1] 0
##
## $head2head$lastWinAwayTeam$res$HT
## $head2head$lastWinAwayTeam$res$HT$ght
## [1] 2
##
## $head2head$lastWinAwayTeam$res$HT$gat
## [1] 0
##
##
##
##
## $head2head$fixtures
## id cId date stat mday htId atId res.ght res.gat
## 1 146865 398 2016-02-02T20:00:00Z FINISHED 24 66 70 3 0
## 2 146924 398 2015-12-26T12:45:00Z FINISHED 18 70 66 2 0
## 3 136858 354 2015-01-01T12:45:00Z FINISHED 20 70 66 1 1
## 4 136913 354 2014-12-02T19:45:00Z FINISHED 14 66 70 2 1
## 5 131988 341 2014-02-01T15:00:00Z <NA> 24 70 66 2 1
## res.HT.ght res.HT.gat
## 1 NA NA
## 2 2 0
## 3 NA NA
## 4 NA NA
## 5 NA NA
Summary
As you can see, there's lots of useful information that can be retrieved from the football-data.org API with just a few lines of R code.
As always, if any parts were unclear or if you have any difficulties, please do get in touch here.
dashee87/footballR documentation built on May 14, 2019, 6:12 p.m.
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title: "fdo_tutorial" author: "David Sheehan" date: "25 September 2016" output: html_document
How to use the football-data.org API
This page will describe how to use the footballR package to perform calls to the football-data.org API. The data obtainable from these functions includes past/previous fixtures, league tables, team information for Europe's top leagues and major international competitions.
Before We start
The football-data.org API is free to use and (as of September 2016) appears to be regularly updated. An app key is not necessary, but it's recommeneded, as it's free and allows the API owner to manage and control API usage. It's a simple process; just enter your name and email address here.
The API is extensively documented, so please refer to the online documentation if you require any further information.
Code
Let's load the package (assuming you've already installed it).
# load package
require("footballR")
Let's start off by checking all the competitions that can be accessed via this API. Note: An app key is not necessary (though recommended), this tutorial will perform API calls without an app key. An app key can be incorporated quite easily, by appending the token argument to the function.
# without app key
fdo_listComps()
## _links.href
## 1 http://api.football-data.org/v1/competitions/424
## 2 http://api.football-data.org/v1/competitions/426
## 3 http://api.football-data.org/v1/competitions/427
## 4 http://api.football-data.org/v1/competitions/428
## 5 http://api.football-data.org/v1/competitions/430
## 6 http://api.football-data.org/v1/competitions/431
## 7 http://api.football-data.org/v1/competitions/432
## 8 http://api.football-data.org/v1/competitions/433
## 9 http://api.football-data.org/v1/competitions/434
## 10 http://api.football-data.org/v1/competitions/435
## 11 http://api.football-data.org/v1/competitions/436
## 12 http://api.football-data.org/v1/competitions/437
## 13 http://api.football-data.org/v1/competitions/438
## 14 http://api.football-data.org/v1/competitions/439
## 15 http://api.football-data.org/v1/competitions/440
## _links.href
## 1 http://api.football-data.org/v1/competitions/424/teams
## 2 http://api.football-data.org/v1/competitions/426/teams
## 3 http://api.football-data.org/v1/competitions/427/teams
## 4 http://api.football-data.org/v1/competitions/428/teams
## 5 http://api.football-data.org/v1/competitions/430/teams
## 6 http://api.football-data.org/v1/competitions/431/teams
## 7 http://api.football-data.org/v1/competitions/432/teams
## 8 http://api.football-data.org/v1/competitions/433/teams
## 9 http://api.football-data.org/v1/competitions/434/teams
## 10 http://api.football-data.org/v1/competitions/435/teams
## 11 http://api.football-data.org/v1/competitions/436/teams
## 12 http://api.football-data.org/v1/competitions/437/teams
## 13 http://api.football-data.org/v1/competitions/438/teams
## 14 http://api.football-data.org/v1/competitions/439/teams
## 15 http://api.football-data.org/v1/competitions/440/teams
## _links.href
## 1 http://api.football-data.org/v1/competitions/424/fixtures
## 2 http://api.football-data.org/v1/competitions/426/fixtures
## 3 http://api.football-data.org/v1/competitions/427/fixtures
## 4 http://api.football-data.org/v1/competitions/428/fixtures
## 5 http://api.football-data.org/v1/competitions/430/fixtures
## 6 http://api.football-data.org/v1/competitions/431/fixtures
## 7 http://api.football-data.org/v1/competitions/432/fixtures
## 8 http://api.football-data.org/v1/competitions/433/fixtures
## 9 http://api.football-data.org/v1/competitions/434/fixtures
## 10 http://api.football-data.org/v1/competitions/435/fixtures
## 11 http://api.football-data.org/v1/competitions/436/fixtures
## 12 http://api.football-data.org/v1/competitions/437/fixtures
## 13 http://api.football-data.org/v1/competitions/438/fixtures
## 14 http://api.football-data.org/v1/competitions/439/fixtures
## 15 http://api.football-data.org/v1/competitions/440/fixtures
## _links.href id
## 1 http://api.football-data.org/v1/competitions/424/leagueTable 424
## 2 http://api.football-data.org/v1/competitions/426/leagueTable 426
## 3 http://api.football-data.org/v1/competitions/427/leagueTable 427
## 4 http://api.football-data.org/v1/competitions/428/leagueTable 428
## 5 http://api.football-data.org/v1/competitions/430/leagueTable 430
## 6 http://api.football-data.org/v1/competitions/431/leagueTable 431
## 7 http://api.football-data.org/v1/competitions/432/leagueTable 432
## 8 http://api.football-data.org/v1/competitions/433/leagueTable 433
## 9 http://api.football-data.org/v1/competitions/434/leagueTable 434
## 10 http://api.football-data.org/v1/competitions/435/leagueTable 435
## 11 http://api.football-data.org/v1/competitions/436/leagueTable 436
## 12 http://api.football-data.org/v1/competitions/437/leagueTable 437
## 13 http://api.football-data.org/v1/competitions/438/leagueTable 438
## 14 http://api.football-data.org/v1/competitions/439/leagueTable 439
## 15 http://api.football-data.org/v1/competitions/440/leagueTable 440
## caption league year currentMatchday
## 1 European Championships France 2016 EC 2016 7
## 2 Premier League 2016/17 PL 2016 6
## 3 Championship 2016/17 ELC 2016 9
## 4 League One 2016/17 EL1 2016 9
## 5 1. Bundesliga 2016/17 BL1 2016 5
## 6 2. Bundesliga 2016/17 BL2 2016 7
## 7 DFB-Pokal 2016/17 DFB 2016 2
## 8 Eredivisie 2016/17 DED 2016 7
## 9 Ligue 1 2016/17 FL1 2016 7
## 10 Ligue 2 2016/17 FL2 2016 9
## 11 Primera Division 2016/17 PD 2016 6
## 12 Liga Adelante 2016/17 SD 2016 7
## 13 Serie A 2016/17 SA 2016 6
## 14 Primeira Liga 2016/17 PPL 2016 6
## 15 Champions League 2016/17 CL 2016 2
## numberOfMatchdays numberOfTeams numberOfGames lastUpdated
## 1 7 24 51 2016-07-10T21:32:20Z
## 2 38 20 380 2016-09-25T09:59:54Z
## 3 46 24 552 2016-09-25T10:05:15Z
## 4 46 24 552 2016-09-25T10:05:41Z
## 5 34 18 306 2016-09-25T00:02:13Z
## 6 34 18 306 2016-09-25T00:02:43Z
## 7 5 64 48 2016-08-28T07:00:07Z
## 8 34 18 306 2016-09-25T10:00:23Z
## 9 38 20 380 2016-09-25T10:00:44Z
## 10 38 20 380 2016-09-25T10:01:07Z
## 11 38 20 380 2016-09-25T10:01:41Z
## 12 42 22 462 2016-09-25T10:07:55Z
## 13 38 20 380 2016-09-25T10:08:20Z
## 14 34 18 306 2016-09-25T10:08:33Z
## 15 10 32 96 2016-09-17T10:00:15Z
# with app key
# appkey = "myappkey"
# fdo_listComps(token=appkey)
There's quite a bit of information in there, some of which we may not be interested in. We can streamline the output by including an appropriate response argument.
# default is reponse = "full"
fdo_listComps(response = "minified")
## id caption league year currentMatchday
## 1 424 European Championships France 2016 EC 2016 7
## 2 426 Premier League 2016/17 PL 2016 6
## 3 427 Championship 2016/17 ELC 2016 9
## 4 428 League One 2016/17 EL1 2016 9
## 5 430 1. Bundesliga 2016/17 BL1 2016 5
## 6 431 2. Bundesliga 2016/17 BL2 2016 7
## 7 432 DFB-Pokal 2016/17 DFB 2016 2
## 8 433 Eredivisie 2016/17 DED 2016 7
## 9 434 Ligue 1 2016/17 FL1 2016 7
## 10 435 Ligue 2 2016/17 FL2 2016 9
## 11 436 Primera Division 2016/17 PD 2016 6
## 12 437 Liga Adelante 2016/17 SD 2016 7
## 13 438 Serie A 2016/17 SA 2016 6
## 14 439 Primeira Liga 2016/17 PPL 2016 6
## 15 440 Champions League 2016/17 CL 2016 2
## numberOfMatchdays numberOfTeams numberOfGames lastUpdated
## 1 7 24 51 2016-07-10T21:32:20Z
## 2 38 20 380 2016-09-25T09:59:54Z
## 3 46 24 552 2016-09-25T10:05:15Z
## 4 46 24 552 2016-09-25T10:05:41Z
## 5 34 18 306 2016-09-25T00:02:13Z
## 6 34 18 306 2016-09-25T00:02:43Z
## 7 5 64 48 2016-08-28T07:00:07Z
## 8 34 18 306 2016-09-25T10:00:23Z
## 9 38 20 380 2016-09-25T10:00:44Z
## 10 38 20 380 2016-09-25T10:01:07Z
## 11 38 20 380 2016-09-25T10:01:41Z
## 12 42 22 462 2016-09-25T10:07:55Z
## 13 38 20 380 2016-09-25T10:08:20Z
## 14 34 18 306 2016-09-25T10:08:33Z
## 15 10 32 96 2016-09-17T10:00:15Z
And we can change the season that we're interested in (the package generally defaults to the current season).
fdo_listComps(season = "2015",response = "minified")
## id caption league year currentMatchday
## 1 394 1. Bundesliga 2015/16 BL1 2015 34
## 2 395 2. Bundesliga 2015/16 BL2 2015 34
## 3 396 Ligue 1 2015/16 FL1 2015 38
## 4 397 Ligue 2 2015/16 FL2 2015 38
## 5 398 Premier League 2015/16 PL 2015 38
## 6 399 Primera Division 2015/16 PD 2015 38
## 7 400 Segunda Division 2015/16 SD 2015 42
## 8 401 Serie A 2015/16 SA 2015 38
## 9 402 Primeira Liga 2015/16 PPL 2015 34
## 10 403 3. Bundesliga 2015/16 BL3 2015 38
## 11 404 Eredivisie 2015/16 DED 2015 34
## 12 405 Champions League 2015/16 CL 2015 10
## 13 425 League One 2015/16 EL1 2015 16
## numberOfMatchdays numberOfTeams numberOfGames lastUpdated
## 1 34 18 306 2016-06-15T08:09:51Z
## 2 34 18 306 2016-05-15T16:00:18Z
## 3 38 20 380 2016-05-13T07:00:02Z
## 4 38 20 380 2016-05-14T05:57:07Z
## 5 38 20 380 2016-05-19T15:12:55Z
## 6 38 20 380 2016-05-16T07:16:11Z
## 7 42 22 462 2016-07-04T21:35:12Z
## 8 38 20 380 2016-05-16T07:15:29Z
## 9 34 18 306 2016-05-15T19:23:33Z
## 10 38 20 380 2016-05-14T14:15:13Z
## 11 34 18 306 2016-05-08T18:39:50Z
## 12 10 32 125 2016-06-06T10:11:53Z
## 13 46 24 552 2016-05-19T19:00:12Z
If you require more information on a function, then run the function with a question mark e.g. ?fdo_listComps.
You'll notice from the above data frame that the league id for the 2016/17 season of the Premier League is 426. Let's have a look at all the teams in the that EPL season.
fdo_listCompTeams(id = "426",response = "minified")
## $count
## [1] 20
##
## $teams
## id name shortName squadMarketValue
## 1 322 Hull City FC Hull 122,250,000 <U+0080>
## 2 338 Leicester City FC Foxes 210,500,000 <U+0080>
## 3 340 Southampton FC Southampton 199,000,000 <U+0080>
## 4 346 Watford FC Watford 128,500,000 <U+0080>
## 5 343 Middlesbrough FC Middlesbrough 104,750,000 <U+0080>
## 6 70 Stoke City FC Stoke 172,250,000 <U+0080>
## 7 62 Everton FC Everton 239,250,000 <U+0080>
## 8 73 Tottenham Hotspur FC Spurs 365,500,000 <U+0080>
## 9 354 Crystal Palace FC Crystal 157,750,000 <U+0080>
## 10 74 West Bromwich Albion FC West Bromwich 107,600,000 <U+0080>
## 11 328 Burnley FC Burnley 66,500,000 <U+0080>
## 12 72 Swansea City FC Swans 106,100,000 <U+0080>
## 13 65 Manchester City FC ManCity 518,000,000 <U+0080>
## 14 71 Sunderland AFC Sunderland 92,500,000 <U+0080>
## 15 1044 AFC Bournemouth Bournemouth 121,750,000 <U+0080>
## 16 66 Manchester United FC ManU 534,250,000 <U+0080>
## 17 57 Arsenal FC Arsenal 468,500,000 <U+0080>
## 18 64 Liverpool FC Liverpool 387,200,000 <U+0080>
## 19 61 Chelsea FC Chelsea 514,800,000 <U+0080>
## 20 563 West Ham United FC West Ham 241,500,000 <U+0080>
## crestUrl
## 1 http://upload.wikimedia.org/wikipedia/de/a/a9/Hull_City_AFC.svg
## 2 http://upload.wikimedia.org/wikipedia/en/6/63/Leicester02.png
## 3 http://upload.wikimedia.org/wikipedia/de/c/c9/FC_Southampton.svg
## 4 https://upload.wikimedia.org/wikipedia/en/e/e2/Watford.svg
## 5 https://upload.wikimedia.org/wikipedia/en/2/2c/Middlesbrough_FC_crest.svg
## 6 http://upload.wikimedia.org/wikipedia/de/a/a3/Stoke_City.svg
## 7 http://upload.wikimedia.org/wikipedia/de/f/f9/Everton_FC.svg
## 8 http://upload.wikimedia.org/wikipedia/de/b/b4/Tottenham_Hotspur.svg
## 9 http://upload.wikimedia.org/wikipedia/de/b/bf/Crystal_Palace_F.C._logo_(2013).png
## 10 http://upload.wikimedia.org/wikipedia/de/8/8b/West_Bromwich_Albion.svg
## 11 https://upload.wikimedia.org/wikipedia/en/0/02/Burnley_FC_badge.png
## 12 http://upload.wikimedia.org/wikipedia/de/a/ab/Swansea_City_Logo.svg
## 13 https://upload.wikimedia.org/wikipedia/en/e/eb/Manchester_City_FC_badge.svg
## 14 http://upload.wikimedia.org/wikipedia/de/6/60/AFC_Sunderland.svg
## 15 https://upload.wikimedia.org/wikipedia/de/4/41/Afc_bournemouth.svg
## 16 http://upload.wikimedia.org/wikipedia/de/d/da/Manchester_United_FC.svg
## 17 http://upload.wikimedia.org/wikipedia/en/5/53/Arsenal_FC.svg
## 18 http://upload.wikimedia.org/wikipedia/de/0/0a/FC_Liverpool.svg
## 19 http://upload.wikimedia.org/wikipedia/de/5/5c/Chelsea_crest.svg
## 20 http://upload.wikimedia.org/wikipedia/de/e/e0/West_Ham_United_FC.svg
We can see the current league table for this competition.
fdo_leagueTable(id = "426",response = "minified")
## $leagueCaption
## [1] "Premier League 2016/17"
##
## $matchday
## [1] 6
##
## $standing
## rank team teamId playedGames
## 1 1 ManCity 65 6
## 2 2 Spurs 73 6
## 3 3 Arsenal 57 6
## 4 4 Liverpool 64 6
## 5 5 Everton 62 6
## 6 6 ManU 66 6
## 7 7 Crystal 354 6
## 8 8 Chelsea 61 6
## 9 9 West Bromwich 74 6
## 10 10 Watford 346 5
## 11 11 Foxes 338 6
## 12 12 Hull 322 6
## 13 13 Bournemouth 1044 6
## 14 14 Southampton 340 5
## 15 15 Middlesbrough 343 6
## 16 16 Swans 72 6
## 17 17 Burnley 328 5
## 18 18 West Ham 563 5
## 19 19 Stoke 70 6
## 20 20 Sunderland 71 6
## crestURI
## 1 https://upload.wikimedia.org/wikipedia/en/e/eb/Manchester_City_FC_badge.svg
## 2 http://upload.wikimedia.org/wikipedia/de/b/b4/Tottenham_Hotspur.svg
## 3 http://upload.wikimedia.org/wikipedia/en/5/53/Arsenal_FC.svg
## 4 http://upload.wikimedia.org/wikipedia/de/0/0a/FC_Liverpool.svg
## 5 http://upload.wikimedia.org/wikipedia/de/f/f9/Everton_FC.svg
## 6 http://upload.wikimedia.org/wikipedia/de/d/da/Manchester_United_FC.svg
## 7 http://upload.wikimedia.org/wikipedia/de/b/bf/Crystal_Palace_F.C._logo_%282013%29.png
## 8 http://upload.wikimedia.org/wikipedia/de/5/5c/Chelsea_crest.svg
## 9 http://upload.wikimedia.org/wikipedia/de/8/8b/West_Bromwich_Albion.svg
## 10 https://upload.wikimedia.org/wikipedia/en/e/e2/Watford.svg
## 11 http://upload.wikimedia.org/wikipedia/en/6/63/Leicester02.png
## 12 http://upload.wikimedia.org/wikipedia/de/a/a9/Hull_City_AFC.svg
## 13 https://upload.wikimedia.org/wikipedia/de/4/41/Afc_bournemouth.svg
## 14 http://upload.wikimedia.org/wikipedia/de/c/c9/FC_Southampton.svg
## 15 https://upload.wikimedia.org/wikipedia/en/2/2c/Middlesbrough_FC_crest.svg
## 16 http://upload.wikimedia.org/wikipedia/de/a/ab/Swansea_City_Logo.svg
## 17 https://upload.wikimedia.org/wikipedia/en/0/02/Burnley_FC_badge.png
## 18 http://upload.wikimedia.org/wikipedia/de/e/e0/West_Ham_United_FC.svg
## 19 http://upload.wikimedia.org/wikipedia/de/a/a3/Stoke_City.svg
## 20 http://upload.wikimedia.org/wikipedia/de/6/60/AFC_Sunderland.svg
## points goals goalsAgainst goalDifference
## 1 18 18 5 13
## 2 14 10 3 7
## 3 13 15 7 8
## 4 13 16 9 7
## 5 13 10 4 6
## 6 12 12 7 5
## 7 10 10 7 3
## 8 10 10 9 1
## 9 8 7 6 1
## 10 7 10 9 1
## 11 7 8 11 -3
## 12 7 7 12 -5
## 13 7 4 9 -5
## 14 5 4 6 -2
## 15 5 6 9 -3
## 16 4 5 10 -5
## 17 4 3 8 -5
## 18 3 7 13 -6
## 19 2 4 15 -11
## 20 1 5 12 -7
By default, it returns the current league table. Through the matchDay argument, we can also look at past league tables.
fdo_leagueTable(id = "426", matchDay = 3, response = "minified")
## $leagueCaption
## [1] "Premier League 2016/17"
##
## $matchday
## [1] 3
##
## $standing
## rank team teamId playedGames
## 1 1 ManCity 65 3
## 2 2 Chelsea 61 3
## 3 3 ManU 66 3
## 4 4 Everton 62 3
## 5 5 Hull 322 3
## 6 6 Middlesbrough 343 3
## 7 6 Spurs 73 3
## 8 8 Arsenal 57 3
## 9 9 Foxes 338 3
## 10 10 West Bromwich 74 3
## 11 11 Liverpool 64 3
## 12 12 West Ham 563 3
## 13 13 Burnley 328 3
## 14 13 Swans 72 3
## 15 15 Southampton 340 3
## 16 16 Sunderland 71 3
## 17 17 Crystal 354 3
## 18 18 Watford 346 3
## 19 19 Bournemouth 1044 3
## 20 20 Stoke 70 3
## crestURI
## 1 https://upload.wikimedia.org/wikipedia/en/e/eb/Manchester_City_FC_badge.svg
## 2 http://upload.wikimedia.org/wikipedia/de/5/5c/Chelsea_crest.svg
## 3 http://upload.wikimedia.org/wikipedia/de/d/da/Manchester_United_FC.svg
## 4 http://upload.wikimedia.org/wikipedia/de/f/f9/Everton_FC.svg
## 5 http://upload.wikimedia.org/wikipedia/de/a/a9/Hull_City_AFC.svg
## 6 https://upload.wikimedia.org/wikipedia/en/2/2c/Middlesbrough_FC_crest.svg
## 7 http://upload.wikimedia.org/wikipedia/de/b/b4/Tottenham_Hotspur.svg
## 8 http://upload.wikimedia.org/wikipedia/en/5/53/Arsenal_FC.svg
## 9 http://upload.wikimedia.org/wikipedia/en/6/63/Leicester02.png
## 10 http://upload.wikimedia.org/wikipedia/de/8/8b/West_Bromwich_Albion.svg
## 11 http://upload.wikimedia.org/wikipedia/de/0/0a/FC_Liverpool.svg
## 12 http://upload.wikimedia.org/wikipedia/de/e/e0/West_Ham_United_FC.svg
## 13 https://upload.wikimedia.org/wikipedia/en/0/02/Burnley_FC_badge.png
## 14 http://upload.wikimedia.org/wikipedia/de/a/ab/Swansea_City_Logo.svg
## 15 http://upload.wikimedia.org/wikipedia/de/c/c9/FC_Southampton.svg
## 16 http://upload.wikimedia.org/wikipedia/de/6/60/AFC_Sunderland.svg
## 17 http://upload.wikimedia.org/wikipedia/de/b/bf/Crystal_Palace_F.C._logo_%282013%29.png
## 18 https://upload.wikimedia.org/wikipedia/en/e/e2/Watford.svg
## 19 https://upload.wikimedia.org/wikipedia/de/4/41/Afc_bournemouth.svg
## 20 http://upload.wikimedia.org/wikipedia/de/a/a3/Stoke_City.svg
## points goals goalsAgainst goalDifference
## 1 9 9 3 6
## 2 9 7 2 5
## 3 9 6 1 5
## 4 7 4 2 2
## 5 6 4 2 2
## 6 5 3 2 1
## 7 5 3 2 1
## 8 4 6 5 1
## 9 4 3 3 0
## 10 4 2 2 0
## 11 4 5 6 -1
## 12 3 3 5 -2
## 13 3 2 4 -2
## 14 3 2 4 -2
## 15 2 2 4 -2
## 16 1 3 5 -2
## 17 1 1 3 -2
## 18 1 3 6 -3
## 19 1 2 5 -3
## 20 1 2 6 -4
And we can find out the upcoming/previous fixtures for this competition. By default, it returns all (380 in this case) fixtures for this competition. Like before, we can filter it by match day.
fdo_listCompFixtures(id = "426", matchDay = 3, response = "minified")
## $count
## [1] 10
##
## $fixtures
## id competitionId date status matchday
## 1 150822 426 2016-08-27T11:30:00Z FINISHED 3
## 2 150823 426 2016-08-27T14:00:00Z FINISHED 3
## 3 150815 426 2016-08-27T14:00:00Z FINISHED 3
## 4 150821 426 2016-08-27T14:00:00Z FINISHED 3
## 5 150819 426 2016-08-27T14:00:00Z FINISHED 3
## 6 150817 426 2016-08-27T14:00:00Z FINISHED 3
## 7 150816 426 2016-08-27T14:00:00Z FINISHED 3
## 8 150818 426 2016-08-27T16:30:00Z FINISHED 3
## 9 150824 426 2016-08-28T12:30:00Z FINISHED 3
## 10 150820 426 2016-08-28T15:00:00Z FINISHED 3
## homeTeamName homeTeamId awayTeamName awayTeamId
## 1 Tottenham Hotspur FC 73 Liverpool FC 64
## 2 Watford FC 346 Arsenal FC 57
## 3 Chelsea FC 61 Burnley FC 328
## 4 Southampton FC 340 Sunderland AFC 71
## 5 Leicester City FC 338 Swansea City FC 72
## 6 Everton FC 62 Stoke City FC 70
## 7 Crystal Palace FC 354 AFC Bournemouth 1044
## 8 Hull City FC 322 Manchester United FC 66
## 9 West Bromwich Albion FC 74 Middlesbrough FC 343
## 10 Manchester City FC 65 West Ham United FC 563
## result.goalsHomeTeam result.goalsAwayTeam odds.homeWin odds.draw
## 1 1 1 2.37 3.25
## 2 1 3 4.80 3.70
## 3 3 0 1.25 5.50
## 4 1 1 1.60 3.90
## 5 2 1 1.72 3.70
## 6 1 0 1.72 3.50
## 7 1 1 2.20 3.20
## 8 0 1 7.50 4.00
## 9 0 0 2.50 3.00
## 10 3 1 1.28 5.50
## odds.awayWin
## 1 3.00
## 2 1.72
## 3 15.00
## 4 5.80
## 5 4.75
## 6 5.50
## 7 3.40
## 8 1.50
## 9 3.00
## 10 11.00
Or if you're interested in what fixtures are taking place in the next 10 days
fdo_listCompFixtures(id = "426", timeFrame = "n10", response = "minified")
## $count
## [1] 12
##
## $fixtures
## id competitionId date status matchday
## 1 150794 426 2016-09-25T15:00:00Z TIMED 6
## 2 150787 426 2016-09-26T19:00:00Z TIMED 6
## 3 150776 426 2016-09-30T19:00:00Z TIMED 7
## 4 150781 426 2016-10-01T11:30:00Z TIMED 7
## 5 150780 426 2016-10-01T14:00:00Z TIMED 7
## 6 150783 426 2016-10-01T14:00:00Z TIMED 7
## 7 150784 426 2016-10-01T14:00:00Z TIMED 7
## 8 150777 426 2016-10-01T14:00:00Z TIMED 7
## 9 150779 426 2016-10-02T11:00:00Z TIMED 7
## 10 150778 426 2016-10-02T13:15:00Z TIMED 7
## 11 150782 426 2016-10-02T13:15:00Z TIMED 7
## 12 150775 426 2016-10-02T15:30:00Z TIMED 7
## homeTeamName homeTeamId awayTeamName awayTeamId
## 1 West Ham United FC 563 Southampton FC 340
## 2 Burnley FC 328 Watford FC 346
## 3 Everton FC 62 Crystal Palace FC 354
## 4 Swansea City FC 72 Liverpool FC 64
## 5 Sunderland AFC 71 West Bromwich Albion FC 74
## 6 Watford FC 346 AFC Bournemouth 1044
## 7 West Ham United FC 563 Middlesbrough FC 343
## 8 Hull City FC 322 Chelsea FC 61
## 9 Manchester United FC 66 Stoke City FC 70
## 10 Leicester City FC 338 Southampton FC 340
## 11 Tottenham Hotspur FC 73 Manchester City FC 65
## 12 Burnley FC 328 Arsenal FC 57
## result.goalsHomeTeam result.goalsAwayTeam odds.homeWin odds.draw
## 1 NA NA 3.00 3.25
## 2 NA NA 3.00 3.20
## 3 NA NA 1.72 4.00
## 4 NA NA 5.80 4.40
## 5 NA NA 2.45 3.25
## 6 NA NA 2.20 3.50
## 7 NA NA 2.10 3.40
## 8 NA NA 6.50 4.40
## 9 NA NA 1.30 5.00
## 10 NA NA 2.00 3.60
## 11 NA NA 3.60 3.50
## 12 NA NA 7.00 4.50
## odds.awayWin
## 1 2.40
## 2 2.45
## 3 4.33
## 4 1.50
## 5 2.90
## 6 3.10
## 7 3.40
## 8 1.50
## 9 12.00
## 10 3.60
## 11 2.00
## 12 1.44
Apologies to Liverpool fans (well, pretty much all non Man Utd fans), we're going to focus on Man Utd. You'll notice that their team id is 66. Let's use that id to get some team information.
# returns all Man Utd away fixtures in the previous 14 days
fdo_listTeamFixtures("66", timeFrame = "p14", venue="away", response = "minified")
## $timeFrameStart
## [1] "2016-09-11"
##
## $timeFrameEnd
## [1] "2016-09-25"
##
## $count
## [1] 1
##
## $fixtures
## id competitionId date status matchday homeTeamName
## 1 150803 426 2016-09-18T11:00:00Z FINISHED 5 Watford FC
## homeTeamId awayTeamName awayTeamId result.goalsHomeTeam
## 1 346 Manchester United FC 66 3
## result.goalsAwayTeam odds.homeWin odds.draw odds.awayWin
## 1 1 6.5 4 1.5
# general information on Man Utd
fdo_team("66")
## $`_links`
## $`_links`$self
## $`_links`$self$href
## [1] "http://api.football-data.org/v1/teams/66"
##
##
## $`_links`$fixtures
## $`_links`$fixtures$href
## [1] "http://api.football-data.org/v1/teams/66/fixtures"
##
##
## $`_links`$players
## $`_links`$players$href
## [1] "http://api.football-data.org/v1/teams/66/players"
##
##
##
## $name
## [1] "Manchester United FC"
##
## $code
## [1] "MUFC"
##
## $shortName
## [1] "ManU"
##
## $squadMarketValue
## [1] "534,250,000 <U+0080>"
##
## $crestUrl
## [1] "http://upload.wikimedia.org/wikipedia/de/d/da/Manchester_United_FC.svg"
Finally, we can find all the players currently playing with Man Utd. The data includes nationality and market value (not sure where those figures came from but Pogba's only worth €70m)
fdo_teamPlayers("66",response="minified")
## $count
## [1] 27
##
## $players
## id name position jerseyNumber dateOfBirth
## 1 487 Paul Pogba Central Midfield 6 1993-03-15
## 2 2593 Zlatan Ibrahimovic Centre Forward 9 1981-10-03
## 3 3664 Eric Bailly Centre Back 3 1994-04-12
## 4 4795 Henrikh Mkhitaryan Attacking Midfield 22 1989-01-21
## 5 6693 David de Gea Keeper 1 1990-11-07
## 6 6694 Sergio Romero Keeper 20 1987-02-22
## 7 6695 Sam Johnstone Keeper 32 1993-03-25
## 8 6696 Daley Blind Centre Back 17 1990-03-09
## 9 6697 Chris Smalling Centre Back 12 1989-11-22
## 10 6698 Marcos Rojo Centre Back 5 1990-03-20
## 11 6699 Phil Jones Centre Back 4 1992-02-21
## 12 6700 Luke Shaw Left-Back 23 1995-07-12
## 13 6702 Matteo Darmian Right-Back 36 1989-12-02
## 14 6703 Antonio Valencia Right-Back 25 1985-08-04
## 15 6704 Morgan Schneiderlin Defensive Midfield 28 1989-11-08
## 16 6705 Michael Carrick Defensive Midfield 16 1981-07-28
## 17 6706 Timothy Fosu-Mensah Defensive Midfield 24 1998-01-02
## 18 6707 Ander Herrera Central Midfield 21 1989-08-14
## 19 6708 Marouane Fellaini Central Midfield 27 1987-11-22
## 20 6709 Bastian Schweinsteiger Central Midfield 31 1984-08-01
## 21 6710 Juan Mata Attacking Midfield 8 1988-04-28
## 22 6712 Memphis Depay Left Wing 7 1994-02-13
## 23 6713 Ashley Young Left Wing 18 1985-07-09
## 24 6714 Jesse Lingard Left Wing 14 1992-12-15
## 25 6715 Wayne Rooney Secondary Striker 10 1985-10-24
## 26 6716 Anthony Martial Centre Forward 11 1995-12-05
## 27 6717 Marcus Rashford Centre Forward 19 1997-10-31
## nationality contractUntil marketValue
## 1 France 2021-06-30 70,000,000 <U+0080>
## 2 Sweden 2017-06-30 12,000,000 <U+0080>
## 3 Cote d'Ivoire 2020-06-30 20,000,000 <U+0080>
## 4 Armenia 2020-06-30 37,000,000 <U+0080>
## 5 Spain 2019-06-30 40,000,000 <U+0080>
## 6 Argentina 2018-06-30 5,000,000 <U+0080>
## 7 England 2017-06-30 250,000 <U+0080>
## 8 Netherlands 2018-06-30 22,000,000 <U+0080>
## 9 England 2019-06-30 20,000,000 <U+0080>
## 10 Argentina 2019-06-30 19,000,000 <U+0080>
## 11 England 2019-06-30 14,000,000 <U+0080>
## 12 England 2018-06-30 21,000,000 <U+0080>
## 13 Italy 2019-06-30 15,000,000 <U+0080>
## 14 Ecuador 2017-06-30 8,000,000 <U+0080>
## 15 France 2019-06-30 28,000,000 <U+0080>
## 16 England 2017-06-30 3,000,000 <U+0080>
## 17 Netherlands 2017-06-30 1,000,000 <U+0080>
## 18 Spain 2018-06-30 28,000,000 <U+0080>
## 19 Belgium 2018-06-30 15,000,000 <U+0080>
## 20 Germany 2018-06-30 10,000,000 <U+0080>
## 21 Spain 2018-06-30 31,000,000 <U+0080>
## 22 Netherlands 2019-06-30 25,000,000 <U+0080>
## 23 England 2018-06-30 10,000,000 <U+0080>
## 24 England 2018-06-30 6,000,000 <U+0080>
## 25 England 2019-06-30 35,000,000 <U+0080>
## 26 France 2019-06-30 32,000,000 <U+0080>
## 27 England 2020-06-30 7,000,000 <U+0080>
So, let's have a look at Man Utd's next league fixture and their past 5 meetings with those opponents.
next_fixture_id <- fdo_listTeamFixtures("66", timeFrame = "n100", response = "minified")$fixtures$id[1]
fdo_getFixture(next_fixture_id,past=5,response="compressed")
## $fixture
## $fixture$id
## [1] 150779
##
## $fixture$cId
## [1] 426
##
## $fixture$date
## [1] "2016-10-02T11:00:00Z"
##
## $fixture$stat
## [1] "TIMED"
##
## $fixture$mday
## [1] 7
##
## $fixture$htId
## [1] 66
##
## $fixture$atId
## [1] 70
##
## $fixture$res
## $fixture$res$ght
## NULL
##
## $fixture$res$gat
## NULL
##
##
##
## $head2head
## $head2head$count
## [1] 5
##
## $head2head$timeFrameStart
## [1] "2014-02-01"
##
## $head2head$timeFrameEnd
## [1] "2016-02-02"
##
## $head2head$homeTeamWins
## [1] 2
##
## $head2head$awayTeamWins
## [1] 2
##
## $head2head$draws
## [1] 1
##
## $head2head$lastHomeWinHomeTeam
## $head2head$lastHomeWinHomeTeam$id
## [1] 146865
##
## $head2head$lastHomeWinHomeTeam$cId
## [1] 398
##
## $head2head$lastHomeWinHomeTeam$date
## [1] "2016-02-02T20:00:00Z"
##
## $head2head$lastHomeWinHomeTeam$stat
## [1] "FINISHED"
##
## $head2head$lastHomeWinHomeTeam$mday
## [1] 24
##
## $head2head$lastHomeWinHomeTeam$htId
## [1] 66
##
## $head2head$lastHomeWinHomeTeam$atId
## [1] 70
##
## $head2head$lastHomeWinHomeTeam$res
## $head2head$lastHomeWinHomeTeam$res$ght
## [1] 3
##
## $head2head$lastHomeWinHomeTeam$res$gat
## [1] 0
##
##
##
## $head2head$lastWinHomeTeam
## $head2head$lastWinHomeTeam$id
## [1] 146865
##
## $head2head$lastWinHomeTeam$cId
## [1] 398
##
## $head2head$lastWinHomeTeam$date
## [1] "2016-02-02T20:00:00Z"
##
## $head2head$lastWinHomeTeam$stat
## [1] "FINISHED"
##
## $head2head$lastWinHomeTeam$mday
## [1] 24
##
## $head2head$lastWinHomeTeam$htId
## [1] 66
##
## $head2head$lastWinHomeTeam$atId
## [1] 70
##
## $head2head$lastWinHomeTeam$res
## $head2head$lastWinHomeTeam$res$ght
## [1] 3
##
## $head2head$lastWinHomeTeam$res$gat
## [1] 0
##
##
##
## $head2head$lastAwayWinAwayTeam
## NULL
##
## $head2head$lastWinAwayTeam
## $head2head$lastWinAwayTeam$id
## [1] 146924
##
## $head2head$lastWinAwayTeam$cId
## [1] 398
##
## $head2head$lastWinAwayTeam$date
## [1] "2015-12-26T12:45:00Z"
##
## $head2head$lastWinAwayTeam$stat
## [1] "FINISHED"
##
## $head2head$lastWinAwayTeam$mday
## [1] 18
##
## $head2head$lastWinAwayTeam$htId
## [1] 70
##
## $head2head$lastWinAwayTeam$atId
## [1] 66
##
## $head2head$lastWinAwayTeam$res
## $head2head$lastWinAwayTeam$res$ght
## [1] 2
##
## $head2head$lastWinAwayTeam$res$gat
## [1] 0
##
## $head2head$lastWinAwayTeam$res$HT
## $head2head$lastWinAwayTeam$res$HT$ght
## [1] 2
##
## $head2head$lastWinAwayTeam$res$HT$gat
## [1] 0
##
##
##
##
## $head2head$fixtures
## id cId date stat mday htId atId res.ght res.gat
## 1 146865 398 2016-02-02T20:00:00Z FINISHED 24 66 70 3 0
## 2 146924 398 2015-12-26T12:45:00Z FINISHED 18 70 66 2 0
## 3 136858 354 2015-01-01T12:45:00Z FINISHED 20 70 66 1 1
## 4 136913 354 2014-12-02T19:45:00Z FINISHED 14 66 70 2 1
## 5 131988 341 2014-02-01T15:00:00Z <NA> 24 70 66 2 1
## res.HT.ght res.HT.gat
## 1 NA NA
## 2 2 0
## 3 NA NA
## 4 NA NA
## 5 NA NA
Summary
As you can see, there's lots of useful information that can be retrieved from the football-data.org API with just a few lines of R code.
As always, if any parts were unclear or if you have any difficulties, please do get in touch here.
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