vignettes/tennis.md
In dashee87/betScrapeR: R package to scrape live sports betting odds
title: "betScrapeR example"
author: "David Sheehan"
date: "2016-09-08"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{betScrapeR example}
%\VignetteEngine{knitr::rmarkdown}
\usepackage[utf8]{inputenc}
How to use tennisScraper
This page will describe how to use the tennisScraper function. While I'll go through the process in some detail, if you're not familiar with R and/or how to install this package, then consult the more general introductory tutorial here
Before We start
I'm going to assume you've installed betScraper and the abettor package. If so, we start by loading the betScraper package.
require("betScrapeR")
I'm also going to assume you have a Betfair API key. If not, then read the instructions on the homepage of this package. We'll be calling the Betfair API to find out current back and lay prices for our tennis players. Therefore, we need to ensure we have a valid session token. Let's make sure by logging in.
# logging in
abettor::loginBF("username","password","appkey")
If you're not interested in programming and more interested in finding arbs, then you can go straight to the tennis shiny app. Note that it relies on the great ShinySky package. So if you haven't yet, you'll need to install it before running app.
# installing ShinySky package
# devtools::install_github("AnalytixWare/ShinySky")
launch("tennis")
If run from the console, the recommended way (as it's the most efficient) to use tennisScraper is to pass a tennis match data frame. We can do this constructing a data frame of all the tennis matches offered on the Betfair exchange.
# Tennis = 2;
betfair_tennis=abettor::listMarketCatalogue(eventTypeIds = "2", marketTypeCodes = "MATCH_ODDS",
inPlayOnly = NULL,
marketProjection =c("RUNNER_DESCRIPTION","EVENT"),
fromDate = format(Sys.time()-60*60*24, "%Y-%m-%dT%TZ"),
toDate = format(Sys.time()+60*60*72, "%Y-%m-%dT%TZ"));
betfair_tennis is a dataframe containg information (start time, player names, etc) related to every upcoming tennis match. If this all looks unfamiliar to you so far, then more details about calling the Betfair API from R can be found on the abettor documentation. Let's familiarise ourselves with the structure of betfair_tennis:
head(betfair_tennis)
## marketId marketName totalMatched
## 1 1.126707762 Match Odds 0.00
## 2 1.126724393 Match Odds 281136.82
## 3 1.126723799 Match Odds 65414.06
## 4 1.126729835 Match Odds 189017.17
## 5 1.126729746 Match Odds 1087.49
## 6 1.126730149 Match Odds 1548115.71
## runners
## 1 11731114, 10483998, Mena/Velotti, Romboli/Zampieri, 0, 0, 1, 2
## 2 8461316, 7287672, Pedro Cachin, Arthur De Greef, 0, 0, 1, 2
## 3 8736474, 8826915, Diego Schwartzman, Juan Sebastian Gomez, 0, 0, 1, 2
## 4 2010693, 9128738, Lopez/Lopez, Carreno Bus/Garcia-Lope, 0, 0, 1, 2
## 5 11731108, 7653428, Hernandez-Fernande/Saez, Escobar/Quiroz, 0, 0, 1, 2
## 6 2538236, 6199453, Serena Williams, Karolina Pliskova, 0, 0, 1, 2
## event.id event.name event.countryCode
## 1 27920386 Mena/Velotti v Romboli/Zampieri CO
## 2 27921310 Cachin v De Greef ES
## 3 27921270 Schwartzman v Ju Gomez CO
## 4 27921515 Lopez/Lopez v Carreno Bus/Garcia-Lope US
## 5 27921505 Hernandez-Fernande/Saez v Escobar/Quiroz CO
## 6 27921565 Ser Williams v Ka Pliskova US
## event.timezone event.openDate
## 1 UTC 2016-09-08T00:42:00.000Z
## 2 UTC 2016-09-08T20:27:00.000Z
## 3 UTC 2016-09-08T21:28:00.000Z
## 4 UTC 2016-09-08T21:47:00.000Z
## 5 UTC 2016-09-08T22:42:00.000Z
## 6 UTC 2016-09-08T23:00:00.000Z
This tutorial was written on 8th September 2016, during the US Open. Novak Djokovic faces Gael Monfils in the Semi Finals tomorrow. So, let's see if we can find arbs for Nole. Or maybe just see what odds we can get on him to make make the final.
Okay, so we have a data frame of all tennis matches on the Betfair exchange. One of these will be the Djokovic v Monfils match. Let's find it.
betfair_tennis[grepl("Djokovic",betfair_tennis$event$name),]
## marketId marketName totalMatched
## 55 1.126708112 Match Odds 420091.2
## runners event.id
## 55 2249229, 2257300, Novak Djokovic, Gael Monfils, 0, 0, 1, 2 27920442
## event.name event.countryCode event.timezone
## 55 N Djokovic v Monfils US UTC
## event.openDate
## 55 2016-09-09T19:00:00.000Z
This contains all the information tennisScraper needs to find the corresponding odds offered by bookmakers. We just need to tell it that we've decided to give it a data frame (this will make more sense later).
tennisScraper(type = "df", match = betfair_tennis[grepl("Djokovic",betfair_tennis$event$name),])
## eventId marketId players selectionId Back Price Back Size
## 1 27920442 1.126708112 Novak Djokovic 2249229 1.15 371898.10
## 2 27920442 1.126708112 Gael Monfils 2257300 7.20 675.84
## Lay Price Lay Size Bet 365 Sky Bet Betstars Boylesports Betfred
## 1 1.16 3193.82 1.111111 1.125 1.142857 1.125 1.125
## 2 7.40 895.01 6.500000 6.000 5.750000 6.500 6.000
## Sportingbet Bet Victor Paddy Power Stan James 888sport Ladbrokes
## 1 1.142857 1.125 1.142857 1.142857 1.142857 1.125
## 2 5.750000 7.000 5.500000 6.000000 6.000000 6.000
## Coral William Hill Winner Betfair Sportsbook Betway BetBright
## 1 1.142857 1.142857 1.125 1.142857 1.142857 1.142857
## 2 5.500000 5.500000 6.000 6.500000 5.500000 6.000000
## Netbet UK Unibet Bwin 32Red Bet 10Bet Marathon Bet 188Bet
## 1 1.117647 1.142857 1.117647 1.142857 1.117647 1.157895 1.125
## 2 6.846154 6.000000 6.000000 6.000000 6.846154 6.800000 5.600
## Black Type Betfair Betdaq Matchbook
## 1 1.125 1.142857 1.153846 1.166667
## 2 6.000 6.800000 6.800000 7.000000
So, the bookies and the exchange agree that Djokovic is the clear favourite. But there's other ways to run tennisScraper. Look back at the Djokovic exchange data and you'll see an event Id and market Id (you can also find these ids from exchange urls). Let's pass these ids to tennisScraper, though we need to tell it to expect an id.
#event id
tennisScraper(type = "id", match = "27920442")
## eventId marketId players selectionId Back Price Back Size
## 1 27920442 1.126708112 Novak Djokovic 2249229 1.15 371898.10
## 2 27920442 1.126708112 Gael Monfils 2257300 7.20 675.84
## Lay Price Lay Size Bet 365 Sky Bet Betstars Boylesports Betfred
## 1 1.16 3193.82 1.111111 1.125 1.142857 1.125 1.125
## 2 7.40 895.01 6.500000 6.000 5.750000 6.500 6.000
## Sportingbet Bet Victor Paddy Power Stan James 888sport Ladbrokes
## 1 1.142857 1.125 1.142857 1.142857 1.142857 1.125
## 2 5.750000 7.000 5.500000 6.000000 6.000000 6.000
## Coral William Hill Winner Betfair Sportsbook Betway BetBright
## 1 1.142857 1.142857 1.125 1.142857 1.142857 1.142857
## 2 5.500000 5.500000 6.000 6.500000 5.500000 6.000000
## Netbet UK Unibet Bwin 32Red Bet 10Bet Marathon Bet 188Bet
## 1 1.117647 1.142857 1.117647 1.142857 1.117647 1.157895 1.125
## 2 6.846154 6.000000 6.000000 6.000000 6.846154 6.800000 5.600
## Black Type Betfair Betdaq Matchbook
## 1 1.125 1.142857 1.153846 1.166667
## 2 6.000 6.800000 6.800000 7.000000
#market id
tennisScraper(type = "id", match = "1.126708112")
## eventId marketId players selectionId Back Price Back Size
## 1 27920442 1.126708112 Novak Djokovic 2249229 1.15 371898.10
## 2 27920442 1.126708112 Gael Monfils 2257300 7.20 675.84
## Lay Price Lay Size Bet 365 Sky Bet Betstars Boylesports Betfred
## 1 1.16 3193.82 1.111111 1.125 1.142857 1.125 1.125
## 2 7.40 895.01 6.500000 6.000 5.750000 6.500 6.000
## Sportingbet Bet Victor Paddy Power Stan James 888sport Ladbrokes
## 1 1.142857 1.125 1.142857 1.142857 1.142857 1.125
## 2 5.750000 7.000 5.500000 6.000000 6.000000 6.000
## Coral William Hill Winner Betfair Sportsbook Betway BetBright
## 1 1.142857 1.142857 1.125 1.142857 1.142857 1.142857
## 2 5.500000 5.500000 6.000 6.500000 5.500000 6.000000
## Netbet UK Unibet Bwin 32Red Bet 10Bet Marathon Bet 188Bet
## 1 1.117647 1.142857 1.117647 1.142857 1.117647 1.157895 1.125
## 2 6.846154 6.000000 6.000000 6.000000 6.846154 6.800000 5.600
## Black Type Betfair Betdaq Matchbook
## 1 1.125 1.142857 1.153846 1.166667
## 2 6.000 6.800000 6.800000 7.000000
This might all seem very organised. What if I just want to quickly know the odds for Nole's next match? tennisScrapeR has got that covered. Just pass the function a string of part of your player's name.
tennisScraper(type = "name", match = "djokovic")
## eventId marketId players selectionId Back Price Back Size
## 1 27920442 1.126708112 Novak Djokovic 2249229 1.15 371898.10
## 2 27920442 1.126708112 Gael Monfils 2257300 7.20 675.84
## Lay Price Lay Size Bet 365 Sky Bet Betstars Boylesports Betfred
## 1 1.16 3193.82 1.111111 1.125 1.142857 1.125 1.125
## 2 7.40 895.01 6.500000 6.000 5.750000 6.500 6.000
## Sportingbet Bet Victor Paddy Power Stan James 888sport Ladbrokes
## 1 1.142857 1.125 1.142857 1.142857 1.142857 1.125
## 2 5.750000 7.000 5.500000 6.000000 6.000000 6.000
## Coral William Hill Winner Betfair Sportsbook Betway BetBright
## 1 1.142857 1.142857 1.125 1.142857 1.142857 1.142857
## 2 5.500000 5.500000 6.000 6.500000 5.500000 6.000000
## Netbet UK Unibet Bwin 32Red Bet 10Bet Marathon Bet 188Bet
## 1 1.117647 1.142857 1.117647 1.142857 1.117647 1.157895 1.125
## 2 6.846154 6.000000 6.000000 6.000000 6.846154 6.800000 5.600
## Black Type Betfair Betdaq Matchbook
## 1 1.125 1.142857 1.153846 1.166667
## 2 6.000 6.800000 6.800000 7.000000
Just beware that with this approach. tennisScraper can only handle one match at a time. It will return an error if your name matches more than one event.
tennisScraper(type = "name", match = "novak")
## error
## 1 Name found in multiple Betfair tennis matches. Please be more specific
## matches
## 1 Haase/Westerhof v Dzumhur/Novak & N Djokovic v Monfils
In fact, an error data frame is returned for a variety of reasons. For example, Ireland's #1 James McGee is not playing this week, so running tennisScraper on this occasion will return an error.
tennisScraper(type = "name", match = "James McGee")
## error
## 1 Couldn't find that name in any Betfair tennis match
Summary
So, you've done it. With very little effort, you've combined exchange and bookie data for any tennis match. If you've had any issues/difficulties with this tutorial, then please do let me know here. Oh, and good luck, Nole!
dashee87/betScrapeR documentation built on May 14, 2019, 6:12 p.m.
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Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
title: "betScrapeR example" author: "David Sheehan" date: "2016-09-08" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{betScrapeR example} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc}
How to use tennisScraper
This page will describe how to use the tennisScraper function. While I'll go through the process in some detail, if you're not familiar with R and/or how to install this package, then consult the more general introductory tutorial here
Before We start
I'm going to assume you've installed betScraper and the abettor package. If so, we start by loading the betScraper package.
require("betScrapeR")
I'm also going to assume you have a Betfair API key. If not, then read the instructions on the homepage of this package. We'll be calling the Betfair API to find out current back and lay prices for our tennis players. Therefore, we need to ensure we have a valid session token. Let's make sure by logging in.
# logging in
abettor::loginBF("username","password","appkey")
If you're not interested in programming and more interested in finding arbs, then you can go straight to the tennis shiny app. Note that it relies on the great ShinySky package. So if you haven't yet, you'll need to install it before running app.
# installing ShinySky package
# devtools::install_github("AnalytixWare/ShinySky")
launch("tennis")
If run from the console, the recommended way (as it's the most efficient) to use tennisScraper is to pass a tennis match data frame. We can do this constructing a data frame of all the tennis matches offered on the Betfair exchange.
# Tennis = 2;
betfair_tennis=abettor::listMarketCatalogue(eventTypeIds = "2", marketTypeCodes = "MATCH_ODDS",
inPlayOnly = NULL,
marketProjection =c("RUNNER_DESCRIPTION","EVENT"),
fromDate = format(Sys.time()-60*60*24, "%Y-%m-%dT%TZ"),
toDate = format(Sys.time()+60*60*72, "%Y-%m-%dT%TZ"));
betfair_tennis is a dataframe containg information (start time, player names, etc) related to every upcoming tennis match. If this all looks unfamiliar to you so far, then more details about calling the Betfair API from R can be found on the abettor documentation. Let's familiarise ourselves with the structure of betfair_tennis:
head(betfair_tennis)
## marketId marketName totalMatched
## 1 1.126707762 Match Odds 0.00
## 2 1.126724393 Match Odds 281136.82
## 3 1.126723799 Match Odds 65414.06
## 4 1.126729835 Match Odds 189017.17
## 5 1.126729746 Match Odds 1087.49
## 6 1.126730149 Match Odds 1548115.71
## runners
## 1 11731114, 10483998, Mena/Velotti, Romboli/Zampieri, 0, 0, 1, 2
## 2 8461316, 7287672, Pedro Cachin, Arthur De Greef, 0, 0, 1, 2
## 3 8736474, 8826915, Diego Schwartzman, Juan Sebastian Gomez, 0, 0, 1, 2
## 4 2010693, 9128738, Lopez/Lopez, Carreno Bus/Garcia-Lope, 0, 0, 1, 2
## 5 11731108, 7653428, Hernandez-Fernande/Saez, Escobar/Quiroz, 0, 0, 1, 2
## 6 2538236, 6199453, Serena Williams, Karolina Pliskova, 0, 0, 1, 2
## event.id event.name event.countryCode
## 1 27920386 Mena/Velotti v Romboli/Zampieri CO
## 2 27921310 Cachin v De Greef ES
## 3 27921270 Schwartzman v Ju Gomez CO
## 4 27921515 Lopez/Lopez v Carreno Bus/Garcia-Lope US
## 5 27921505 Hernandez-Fernande/Saez v Escobar/Quiroz CO
## 6 27921565 Ser Williams v Ka Pliskova US
## event.timezone event.openDate
## 1 UTC 2016-09-08T00:42:00.000Z
## 2 UTC 2016-09-08T20:27:00.000Z
## 3 UTC 2016-09-08T21:28:00.000Z
## 4 UTC 2016-09-08T21:47:00.000Z
## 5 UTC 2016-09-08T22:42:00.000Z
## 6 UTC 2016-09-08T23:00:00.000Z
This tutorial was written on 8th September 2016, during the US Open. Novak Djokovic faces Gael Monfils in the Semi Finals tomorrow. So, let's see if we can find arbs for Nole. Or maybe just see what odds we can get on him to make make the final.
Okay, so we have a data frame of all tennis matches on the Betfair exchange. One of these will be the Djokovic v Monfils match. Let's find it.
betfair_tennis[grepl("Djokovic",betfair_tennis$event$name),]
## marketId marketName totalMatched
## 55 1.126708112 Match Odds 420091.2
## runners event.id
## 55 2249229, 2257300, Novak Djokovic, Gael Monfils, 0, 0, 1, 2 27920442
## event.name event.countryCode event.timezone
## 55 N Djokovic v Monfils US UTC
## event.openDate
## 55 2016-09-09T19:00:00.000Z
This contains all the information tennisScraper needs to find the corresponding odds offered by bookmakers. We just need to tell it that we've decided to give it a data frame (this will make more sense later).
tennisScraper(type = "df", match = betfair_tennis[grepl("Djokovic",betfair_tennis$event$name),])
## eventId marketId players selectionId Back Price Back Size
## 1 27920442 1.126708112 Novak Djokovic 2249229 1.15 371898.10
## 2 27920442 1.126708112 Gael Monfils 2257300 7.20 675.84
## Lay Price Lay Size Bet 365 Sky Bet Betstars Boylesports Betfred
## 1 1.16 3193.82 1.111111 1.125 1.142857 1.125 1.125
## 2 7.40 895.01 6.500000 6.000 5.750000 6.500 6.000
## Sportingbet Bet Victor Paddy Power Stan James 888sport Ladbrokes
## 1 1.142857 1.125 1.142857 1.142857 1.142857 1.125
## 2 5.750000 7.000 5.500000 6.000000 6.000000 6.000
## Coral William Hill Winner Betfair Sportsbook Betway BetBright
## 1 1.142857 1.142857 1.125 1.142857 1.142857 1.142857
## 2 5.500000 5.500000 6.000 6.500000 5.500000 6.000000
## Netbet UK Unibet Bwin 32Red Bet 10Bet Marathon Bet 188Bet
## 1 1.117647 1.142857 1.117647 1.142857 1.117647 1.157895 1.125
## 2 6.846154 6.000000 6.000000 6.000000 6.846154 6.800000 5.600
## Black Type Betfair Betdaq Matchbook
## 1 1.125 1.142857 1.153846 1.166667
## 2 6.000 6.800000 6.800000 7.000000
So, the bookies and the exchange agree that Djokovic is the clear favourite. But there's other ways to run tennisScraper. Look back at the Djokovic exchange data and you'll see an event Id and market Id (you can also find these ids from exchange urls). Let's pass these ids to tennisScraper, though we need to tell it to expect an id.
#event id
tennisScraper(type = "id", match = "27920442")
## eventId marketId players selectionId Back Price Back Size
## 1 27920442 1.126708112 Novak Djokovic 2249229 1.15 371898.10
## 2 27920442 1.126708112 Gael Monfils 2257300 7.20 675.84
## Lay Price Lay Size Bet 365 Sky Bet Betstars Boylesports Betfred
## 1 1.16 3193.82 1.111111 1.125 1.142857 1.125 1.125
## 2 7.40 895.01 6.500000 6.000 5.750000 6.500 6.000
## Sportingbet Bet Victor Paddy Power Stan James 888sport Ladbrokes
## 1 1.142857 1.125 1.142857 1.142857 1.142857 1.125
## 2 5.750000 7.000 5.500000 6.000000 6.000000 6.000
## Coral William Hill Winner Betfair Sportsbook Betway BetBright
## 1 1.142857 1.142857 1.125 1.142857 1.142857 1.142857
## 2 5.500000 5.500000 6.000 6.500000 5.500000 6.000000
## Netbet UK Unibet Bwin 32Red Bet 10Bet Marathon Bet 188Bet
## 1 1.117647 1.142857 1.117647 1.142857 1.117647 1.157895 1.125
## 2 6.846154 6.000000 6.000000 6.000000 6.846154 6.800000 5.600
## Black Type Betfair Betdaq Matchbook
## 1 1.125 1.142857 1.153846 1.166667
## 2 6.000 6.800000 6.800000 7.000000
#market id
tennisScraper(type = "id", match = "1.126708112")
## eventId marketId players selectionId Back Price Back Size
## 1 27920442 1.126708112 Novak Djokovic 2249229 1.15 371898.10
## 2 27920442 1.126708112 Gael Monfils 2257300 7.20 675.84
## Lay Price Lay Size Bet 365 Sky Bet Betstars Boylesports Betfred
## 1 1.16 3193.82 1.111111 1.125 1.142857 1.125 1.125
## 2 7.40 895.01 6.500000 6.000 5.750000 6.500 6.000
## Sportingbet Bet Victor Paddy Power Stan James 888sport Ladbrokes
## 1 1.142857 1.125 1.142857 1.142857 1.142857 1.125
## 2 5.750000 7.000 5.500000 6.000000 6.000000 6.000
## Coral William Hill Winner Betfair Sportsbook Betway BetBright
## 1 1.142857 1.142857 1.125 1.142857 1.142857 1.142857
## 2 5.500000 5.500000 6.000 6.500000 5.500000 6.000000
## Netbet UK Unibet Bwin 32Red Bet 10Bet Marathon Bet 188Bet
## 1 1.117647 1.142857 1.117647 1.142857 1.117647 1.157895 1.125
## 2 6.846154 6.000000 6.000000 6.000000 6.846154 6.800000 5.600
## Black Type Betfair Betdaq Matchbook
## 1 1.125 1.142857 1.153846 1.166667
## 2 6.000 6.800000 6.800000 7.000000
This might all seem very organised. What if I just want to quickly know the odds for Nole's next match? tennisScrapeR has got that covered. Just pass the function a string of part of your player's name.
tennisScraper(type = "name", match = "djokovic")
## eventId marketId players selectionId Back Price Back Size
## 1 27920442 1.126708112 Novak Djokovic 2249229 1.15 371898.10
## 2 27920442 1.126708112 Gael Monfils 2257300 7.20 675.84
## Lay Price Lay Size Bet 365 Sky Bet Betstars Boylesports Betfred
## 1 1.16 3193.82 1.111111 1.125 1.142857 1.125 1.125
## 2 7.40 895.01 6.500000 6.000 5.750000 6.500 6.000
## Sportingbet Bet Victor Paddy Power Stan James 888sport Ladbrokes
## 1 1.142857 1.125 1.142857 1.142857 1.142857 1.125
## 2 5.750000 7.000 5.500000 6.000000 6.000000 6.000
## Coral William Hill Winner Betfair Sportsbook Betway BetBright
## 1 1.142857 1.142857 1.125 1.142857 1.142857 1.142857
## 2 5.500000 5.500000 6.000 6.500000 5.500000 6.000000
## Netbet UK Unibet Bwin 32Red Bet 10Bet Marathon Bet 188Bet
## 1 1.117647 1.142857 1.117647 1.142857 1.117647 1.157895 1.125
## 2 6.846154 6.000000 6.000000 6.000000 6.846154 6.800000 5.600
## Black Type Betfair Betdaq Matchbook
## 1 1.125 1.142857 1.153846 1.166667
## 2 6.000 6.800000 6.800000 7.000000
Just beware that with this approach. tennisScraper can only handle one match at a time. It will return an error if your name matches more than one event.
tennisScraper(type = "name", match = "novak")
## error
## 1 Name found in multiple Betfair tennis matches. Please be more specific
## matches
## 1 Haase/Westerhof v Dzumhur/Novak & N Djokovic v Monfils
In fact, an error data frame is returned for a variety of reasons. For example, Ireland's #1 James McGee is not playing this week, so running tennisScraper on this occasion will return an error.
tennisScraper(type = "name", match = "James McGee")
## error
## 1 Couldn't find that name in any Betfair tennis match
Summary
So, you've done it. With very little effort, you've combined exchange and bookie data for any tennis match. If you've had any issues/difficulties with this tutorial, then please do let me know here. Oh, and good luck, Nole!
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