• Hi Guest Just in case you were not aware I wanted to highlight that you can now get a free 7 day trial of Horseracebase here.
    We have a lot of members who are existing users of Horseracebase so help is always available if needed, as well as dedicated section of the fourm here.
    Best Wishes
    AR

Simple, Profitable System

runnerjp runnerjp - as alluded to in one of my prior posts, this is EXACTLY how is see this approach being useful. monitor it from 09:00->13:00 and see which candidates have stayed in the top slots and how their %age vs price has varied. use this to look at low odds lay based candidates. I only have 4 days of data (i've saved all the json from each day), so will look in 4-5 weeks time to see if there's anything to be had in this approach.
Hi jimibt jimibt - Sounds like you are going by the exact approach I am. I have seen your name on the BA forum. Are you pulling from the site every 30min? - I was thinking of doing this race by race and monitoring the price movements as a race on the whole but I dont have a BF api key and dont think they will like my scraping their site!
 
Hi jimibt jimibt - Sounds like you are going by the exact approach I am. I have seen your name on the BA forum. Are you pulling from the site every 30min? - I was thinking of doing this race by race and monitoring the price movements as a race on the whole but I dont have a BF api key and dont think they will like my scraping their site!
runnerjp runnerjp - i'm scraping the site (oddschecker), every 15 minutes between 09:00 and 13:00. i save each scrape in a composite json file for the given day. at present, I just use the data to create a graph of the activity (to spark ideas). my goal is to analyse the relationships between % swing and odds over time to see how these steamers (and drifters) perform when brokewn down into *same category* bands. I've not yet arrived at any conclusions due to the small timescale, but using this for lay candidates below 4 odds does look decent so far, as does some of the higher odds steamers (5-7 odds) if looking at back based bets (not really my thing tbh :D)

Yes, you will have seen me on te BA forum, I use BA to prototype ideas but ulitmately use an API rolled version (in .Net) for the final versions (not because BA is lacking, more because of the control i can exert using external data sources etc in realtime -plus the ability to fire off multiple async threads at the same instant and have each thread analyse the moment, looking for the best outcome).

All fun stuff, but profitable when properly cooked.
 
runnerjp runnerjp - the definition of the json file follows the oddschecker datapoints pretty much in parallel, so not much to be gained from the data on its own. however, as mentioned above, post processing all this json gunk after the event as an analytical excercise MAY start to show patterns.

if/when i make inroads (i.e. when i have sufficient data), i will post some observations. will obviously start up a new thread at this point.

FYI, here's what we have thus far today - just to give a flavour of how the json builds on a typical day.... (since i'm *in the data*, i pull out the drifters also - these could also bear fruit, especially if low priced faves appear in this list [such as Sea La Rosa today!!])
 

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  • 2020-12-22.json.txt
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Spot on mate - You have the same thought process as me. Why are you sticking it into a Json file BTW and not directly into a db ?

Have you a BF API then - If so is it worth it just to pull live odds ect?
 
Spot on mate - You have the same thought process as me. Why are you sticking it into a Json file BTW and not directly into a db ?

Have you a BF API then - If so is it worth it just to pull live odds ect?
I'm using a json file at this stage as it was a quick knee jerk project that i pulled together as an experiment after reading this thread. if it bears fruit, then for sure I'll throw it into a DB.

Yes, I have an API key, but I don't use any BF related info linked into this just yet. However, for analysing things further down the line, then I will link into the BF odds. One thing that is useful with oddschecker odds (and more so with OC bookies [when they come back]) is that you can compare OC odds to BF odds and see if there's any balancing of the book going on that you can take advantage of in BF.

As i said, still not 100% certain of this is a goer or not, but it's ultra cheap to throw up a quick scrape and json feed which I can analyse with a pre-existing stucture, so the only pain point is the hour or so up front in looking at the elements required for the scrape. TBH -i had imagined that oddschecker would chuck everything uot in json format (i.e a hidden api) and transform it in the UI via react or jquery - that would have of course made things far simpler... however!! ;)
 
I'm using a json file at this stage as it was a quick knee jerk project that i pulled together as an experiment after reading this thread. if it bears fruit, then for sure I'll throw it into a DB.

Yes, I have an API key, but I don't use any BF related info linked into this just yet. However, for analysing things further down the line, then I will link into the BF odds. One thing that is useful with oddschecker odds (and more so with OC bookies [when they come back]) is that you can compare OC odds to BF odds and see if there's any balancing of the book going on that you can take advantage of in BF.

As i said, still not 100% certain of this is a goer or not, but it's ultra cheap to throw up a quick scrape and json feed which I can analyse with a pre-existing stucture, so the only pain point is the hour or so up front in looking at the elements required for the scrape. TBH -i had imagined that oddschecker would chuck everything uot in json format (i.e a hidden api) and transform it in the UI via react or jquery - that would have of course made things far simpler... however!! ;)
been messing around today and come up with a script like you said. Also doing oddsportal as well - more data the better right!
 
Empty DataFrame
Columns: [Venue, SelectionName, Percent]
Index: [] Venue SelectionName Percent
0 Limerick 13:22 Shadow Rider 16.48
1 Leopardstown 13:40 One Cool Poet 15.17
2 Wetherby 14:40 Mint Condition 15.15
5 Wetherby 14:05 Canelo 13.44
6 Limerick 12:15 Palm Beach 12.54
 
Empty DataFrame
Columns: [Venue, SelectionName, Percent]
Index: [] Venue SelectionName Percent
0 Limerick 13:22 Shadow Rider 16.48 +0.46
1 Leopardstown 13:40 One Cool Poet 15.17 -1
2 Wetherby 14:40 Mint Condition 15.15 +2.10
5 Wetherby 14:05 Canelo 13.44 +5.20 :party:
6 Limerick 12:15 Palm Beach 12.54 -1
+5.76!!!
 
Venue SelectionName Percent
2 Sedgefield 15:25 Good Look Charm 19.21 +4.20
6 Sedgefield 14:55 Crackdeloust 11.92 -1
10 Lingfield 14:45 Sophosc 11.30 -1
11 Huntingdon 14:05 Muse Of Fire 11.03 -1
16 Sedgefield 13:55 Holly James 8.99 -1
+0.20
 
Venue SelectionName Percent
0 Wolverhampton 15:00 Three Platoon 28.24
2 Leopardstown 13:10 Notebook 16.46
5 Wetherby 13:35 Hacker Des Places 14.54
6 Leopardstown 13:10 Put The Kettle On 13.93
7 Wolverhampton 17:10 Rayyan 11.41
 
Venue SelectionName Percent
1 Limerick 15:20 Ujumpthelastuwin 19.12
2 Newcastle 18:35 Lomu 17.42
3 Newcastle 15:30 Duel In The Sun 15.96
4 Catterick 12:20 Barrichello 15.48
5 Catterick 15:07 Toi Storey 12.28
 
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