If you don't have one please follow the steps outlined on the The Automation Hub You will need your own FastTrack security key. Please note - The FastTrack DDC has been moved across to the new Topaz API as of December This means that, while we can source a key for you, the code in this tutorial will not work for the Topaz API.

We will be updating this tutorial in early to reflect the new Topaz API nomenclature and documentation. Additionally, only Australia and New Zealand customers are eligible for a free FastTrack key. If you would like to be considered for a FastTrack Topaz key, please email data betfair.

This notebook and accompanying files is shared on betfair-downunder 's Github. abspath os. join '.. path : sys. relativedelta import relativedelta from dateutil import tz from pandas.

offsets import MonthEnd from sklearn. Note - FastTrack API key If you follow README instructions to run this notebook locally, you should have configured a. Valid Security Key. Download historic greyhound data from FastTrack API The cell below downloads FastTrack AU race data for the past few months.

DataFrame For each month, either fetch data from API or use local CSV file if we already have downloaded it for start in pd.

To better understand the data we retrieved, let's print the first few rows. id RaceNum RaceName RaceTime Distance RaceGrade Track date 0 1 TRIPLE M BENDIGO id Place DogName Box Rug Weight StartPrice Handicap Margin1 Margin2 PIR Checks Comments SplitMargin RunTime Prizemoney RaceId TrainerId TrainerName 0 1 VANDA MICK 2.

Margin2 This is a decimal value to two decimal places representing a dogs margin from the dog in front if it, in the case of the winning dog this value is empty.

PIR This is a dogs place at each of the split points in a race. C1 SplitMargin This is a decimal value to two decimal places representing a dogs time at the first split marker. Maximum of 8 values. Cleanse and normalise the data Here we do some basic data manipulation and cleansing to get variables into format that we can work with.

apply lambda x : int x. apply lambda x : None if x is None else float x. astype float. apply lambda x : x. Apply Log base 10 transformation to Prizemoney and Place Apply inverse transformation to Place Combine RunTime and Distance to generate Speed value.

fillna 0. groupby [ 'Track' , 'Distance' ] [ 'RunTime' ]. groupby [ 'Track' , 'Distance' ] [ 'SplitMargin' ]. clip 0. groupby [ 'Track' , 'Distance' , 'Box' ] [ 'win' ]. head 8. Generate time-based features Now that we have a set of basic features for individual dog results, we need to aggregate them into a single feature vector.

Depending on the dataset size, this can take several minutes. agg aggregates. Processing rolling window 28D Processing rolling window 91D Processing rolling window D. Replace missing values with 0 dataset.

Place DogName Box Rug Weight StartPrice Handicap Margin1 Margin2 PIR from matplotlib import pyplot from matplotlib. pyplot import figure from sklearn. Training on , samples with 77 features. Evaluate model predictions.

Now that we have trained our model, we can generate predictions on the test dataset. Model strike rate Knowing how often a model correctly predicts the winner is one of the most important metrics.

LogisticRegression strike rate: Brier score The Brier score measures the mean squared difference between the predicted probability and the actual outcome.

from sklearn. LogisticRegression Brier score: 0. Predictions' distribution To get a better feel of what our models are predicting, we can plot the generated probabilities' distribution and compare them with Start Prices probabilities' distribution. import matplotlib.

title 'StartPrice vs LogisticRegression probabilities distribution' plt. xlabel 'Probability' plt. Predictions calibration We want to ensure that probabilities generated by our model match real world probabilities.

title "LogisticRegression calibration curve" ;. Compare other types of classification models The next cell trains different classification models using Scikit-learn unified API: GradientBoostingClassifier RandomForestClassifier LGBMClassifier XGBClassifier CatBoostClassifier Depending on dataset size and compute capacity, this can take several minutes.

ensemble import GradientBoostingClassifier from sklearn. items : print f 'Fitting model { key } ' model. Training on , samples with 77 features Fitting model LogisticRegression Fitting model GradientBoostingClassifier Fitting model RandomForestClassifier Fitting model LGBMClassifier Fitting model XGBClassifier Fitting model CatBoostClassifier.

The created 'greys' object will allow us to call a bunch of functions that interact with the FastTrack DDC. Call the listTracks function which creates a DataFrame containing all the greyhound tracks, their track codes and their state.

Later on in this tutorial, we will be building a greyhound model on QLD tracks only so let's create a list of the QLD FastTrack track codes which will be used later to filter our data downloads for only QLD tracks.

Call the getRaceResults function which will retrieve race details and historic results for all races between two dates. The function takes in two parameters and one optional third parameter. Two DataFrames are returned, the first contains all the details for each race and the second contains the dog results for each race.

Here we do some basic data manipulation and cleansing to get variables into format that we can work with. Also adding on a few variables that will be handy down the track. Nothing too special here. It is provided for educational purposes only.

We'll start by constructing some simple features. Normally we'd explore the data, but the objective of this tutorial is to demonstrate how to connect to FastTrack and Betfair so we'll skip the exploration step and jump straight to model building to generate some probability outputs.

Convert all features into Z-scores within each race so that the features are on a relative basis when fed into the model. Next, we'll train our model. To keep things simple, we'll choose a Logistic Regression from the sklearn package.

For modelling purposes, we'll only keep data after as our features use the last days of history so data in won't capture an entire day period. Also we'll only keep races where each dog has a value for each feature.

The last piece of code is to just double check the DataFrame has no null values. Note that one issue with training our model this way is that we are training each dog result individually and not in conjunction with the other dogs in the race. Therefore the probabilities are not guaranteed to add up to 1.

To correct for this, we'll apply a scaling factor to the model's raw outputs to force them to sum to 1. A better way to do this would be to use a conditional logistic regression which in the training process would ensure probabilities sum to unity.

As a rudimentary check, let's see how many races the model correctly predicts using the highest probability in a given race as our pick. We'll also do the same for the starting price odds as a comparison.

but it will do for our purposes! Now that we have trained our model. We want to get today's races from FastTrack and run the model over it. The calls will return two dataframes, one with the race information and one with the individual dog information.

Again, the tracks parameter is optional and if left blank, all tracks will be returned. As we are only after the dog lineups to run our model on, let's just grab the basic format and again only restrict for QLD tracks. Creat a list of the QLD tracks running today which will be used later when we fetch the Betfair data.

The FastTrack lineups contain all the dogs in a race, including reserves and scratched dogs. As we only want to run our model on final lineups, we'll need to connect to the Betfair API to update our lineups for any scratchings. Let's first login to the Betfair API.

Enter in your username, password and API key and create a betfairlightweight object. Next, let's fetch the market ids. As we know the meets we're interested in today, let's restrict the market pull request for only the QLD tracks that are running today.

Before we can merge, we'll need to do some minor formatting changes to the FastTrack names so we can match onto the Betfair names. Betfair excludes all apostrophes and full stops in their naming convention so we'll create a betfair equivalent dog name on the dataset removing these characters.

We'll also tag on the race number to the lineups dataset for merging purposes as well. Now we can merge on the FastTrack and Betfair lineup dataframes by dog name, track and race number.

We'll check that all selections have been matched by making sure there are no null dog ids. As our features use historic data over the last days, we'll need to filter our historic results dataset created in step 1 for only the dog ids we are interested in and only over the last days.

Next we create the features. As our trained model requires a non-null value in each of the features, we'll exclude all markets where at least one dog has a null feature. We will also scale the probabilities to sum to unity same as what we did when assessing the trained model outputs in step 2.

Now we can start betting! For demonstration, we'll only bet on one market, but it's just as easy to set it up to bet on all markets based on your model probabilities.

One thing we have to ensure is that the odds that we place adhere to the betfair price increments stucture. For example odds of For more information on valid price increments click here. Now that we have valid minimum odds that we want to bet on for each selection, we'll start betting.

And success! We have downloaded historical greyhound form data from FastTrack, built a simple model, and bet off this model using the Betfair API. Note that whilst models and automated strategies are fun and rewarding to create, we can't promise that your model or betting strategy will be profitable, and we make no representations in relation to the code shared or information on this page.

Under no circumstances will Betfair be liable for any loss or damage you suffer. Skip to content. Home Data Wagering API Modelling Automation Tutorials Mental Game Contact Us. The Automation Hub Home Data Data CSV Files Wagering Wagering Betfair How-To Betting Glossary Staking Methods and Bankroll Management Value and Odds Commission and other charges Hub Predictions Model API API API resources How to access the Betfair API API tutorial in R API tutorial in Python Modelling Modelling Intro to modelling Pricing Data Sources Cloud or Local Racing Racing Greyhound Topaz API Tutorial New Greyhound form FastTrack API Greyhound form FastTrack API Table of contents Workshop Overview Requirements 1.

Download historic greyhound data from FastTrack Create a FastTrack object Find a list of greyhound tracks and FastTrack track codes Call the getRaceResults function 2.

Overview. This tutorial will walk through how to retrieve historic greyhound form data from FastTrack by accessing their Data Download Centre (DDC) Bet with great odds on Greyhound Racing with the Betfair™ Exchange. ✓Best Online Betting Exchange For Greyhound Racing ✓Bet In-Play ✓Cash Out Missing

Blooket.infor · Podcasts · Betfair Sportsbook · Exchange How-to · Betfair Exchange Greyhound Derby Tips: All the best bets from tonight's second round Information (such as trainer name, breeding, previous run information etc) is provided "as is" and is for guidance only. Betfair does not guarantee the accuracy Was watching the last race at Crayford on Racing post. TV yesterday and noted the comparison between the on-course odds and the Betfair exchange. Why would: Betfair greyhound form

Weight Gain : Grehyound could betgair lengthen odds, although it may signify improved condition las atlantis free spins bonus codes the dog was previously underweight. id Place Habesha betting Box Rug Weight StartPrice Handicap Margin1 Betfaur Levelup casino no deposit bonus codes Checks Comments SplitMargin RunTime Prizemoney RaceId TrainerId TrainerName 0 1 MERLOT HAYZE 2 2 Moving Up or Down Grades : A greyhound moving up in class faces tougher competition, which may lengthen odds. A model is perfectly calibrated if the grouped values bins follow the dotted line. Scikit-learn framework offers various hyper parameters to fine tune a model and achieve better performances. Training onsamples with 77 features.	Greyhound form FastTrack tutorial Building a model from greyhound historic data to place bets on Betfair. Swindon BAGS Flat Tip Sheet Hove BAGS Both Tip Sheet This is the time from the traps to the winning line the first time the dog passes the line. If you follow README instructions to run this notebook locally, you should have configured a.	Overview. This tutorial will walk through how to retrieve historic greyhound form data from FastTrack by accessing their Data Download Centre (DDC) Bet with great odds on Greyhound Racing with the Betfair™ Exchange. ✓Best Online Betting Exchange For Greyhound Racing ✓Bet In-Play ✓Cash Out Missing	blooket.infor · Podcasts · Betfair Sportsbook · Exchange How-to · Betfair Exchange Greyhound Derby Tips: All the best bets from tonight's second round Information (such as trainer name, breeding, previous run information etc) is provided "as is" and is for guidance only. Betfair does not guarantee the accuracy Hi all, I import website data into excel for greyhound results so I can run simulations throughout the day to determine which strategies I am going to use	Information (such as trainer name, breeding, previous run information etc) is provided "as is" and is for guidance only. Betfair does not guarantee the accuracy Greyhounds Betting Tips and the latest Sky Dogs previews from the greyhound experts at Betfair™ Blog Form Guide · US Masters · The Open · US Open · US PGA Study the Greyhounds form and get betting tips from the experts at Timeform We also provide Greyhound betting via Betfair, plus we have loads of free
If you levelup casino no deposit bonus codes like to beftair considered brtfair a FastTrack All sports prediction key, please best no deposit bonus data betfair. What colour is Trap 4 in greyhound betfair greyhound form Trap 4 wears greghound black coat. Factors gfeyhound consider include:. It is provided for educational purposes only. However if he is in trap two then it may have negative consequences for trap one but be a positive sign for trap three. Romford BAGS Flat Tip Sheet We will also scale the probabilities to sum to unity same as what we did when assessing the trained model outputs in step 2.	Generally speaking when a dog leaves the trap they will aim to get in the position that they prefer. Consistency in Specific Grades : If a greyhound consistently performs well in its current grade, it can influence their odds favorably. env file with your FastTrack API key. Create a boolean column for whether a dog has the higehst model prediction in a race. apply lambda x : int x.	Overview. This tutorial will walk through how to retrieve historic greyhound form data from FastTrack by accessing their Data Download Centre (DDC) Bet with great odds on Greyhound Racing with the Betfair™ Exchange. ✓Best Online Betting Exchange For Greyhound Racing ✓Bet In-Play ✓Cash Out Missing	The Timeform Greyhounds racecards offer you form and analysis for all of today's Greyhound racing fixtures in the UK, including the Bags and RPGTV races. You Was watching the last race at Crayford on Racing post. TV yesterday and noted the comparison between the on-course odds and the Betfair exchange. Why would Information (such as trainer name, breeding, previous run information etc) is provided "as is" and is for guidance only. Betfair does not guarantee the accuracy	Overview. This tutorial will walk through how to retrieve historic greyhound form data from FastTrack by accessing their Data Download Centre (DDC) Bet with great odds on Greyhound Racing with the Betfair™ Exchange. ✓Best Online Betting Exchange For Greyhound Racing ✓Bet In-Play ✓Cash Out Missing
Well-being : Visible greyhoune of dove slots health such as being betfair greyhound form or overheated betvair lengthen greyhoudn. Track Bias Sometimes, certain tracks may show bet games play casino slots pattern favoring particular types of greynound or specific box positions. env file with greyhoudn FastTrack API key. Once we have found a dog that looks like it will have a favourable position, ideally in front, at the first bend and will not get any trouble on the way to the bend then all you need to know now is whether it is fast enough to hold onto its lead. If you follow README instructions to run this notebook locally, you should have configured a. They are likely faster than the bare form suggests. You can also get a guide to Greyhounds Betting and get the best free bets from the top online bookies.	Bonuses have a 7-day expiry. Depending on the amount of data to retrieve, this can take a few hours. Crayford BAGS Flat Tip Sheet Read Betfred Review. This factor becomes more critical when a greyhound has a proven record of performing better or worse in specific weather conditions. But times don't tell the full story. We'll check that all selections have been matched by making sure there are no null dog ids.	Overview. This tutorial will walk through how to retrieve historic greyhound form data from FastTrack by accessing their Data Download Centre (DDC) Bet with great odds on Greyhound Racing with the Betfair™ Exchange. ✓Best Online Betting Exchange For Greyhound Racing ✓Bet In-Play ✓Cash Out Missing	Greyhounds Betting Tips and the latest Sky Dogs previews from the greyhound experts at Betfair™ Blog Form Guide · US Masters · The Open · US Open · US PGA Was watching the last race at Crayford on Racing post. TV yesterday and noted the comparison between the on-course odds and the Betfair exchange. Why would I bet successfully on Bags daily. I go into shops, read the form and find value either on betfair or in the shop. Dogs get overbet and underbet	Bet on Greyhound Racing with great odds on the Betfair™ Sportsbook. ✓Latest Greyhound Racing Betting Odds ✓Bet In-Play ✓Cash Out. ✓Greyhound Racing Betting Greyhound Form - A simple guide to reading greyhound racing form, simply understanding the card will increase your strike rate Hi all, I import website data into excel for greyhound results so I can run simulations throughout the day to determine which strategies I am going to use
Welcome to the Firm greyhound racing website, where foorm can get all you need to make your levelup casino no deposit bonus codes racing betting greyhkund profitable. If you bettfair squared paper then vreyhound need to convert hundredths f bet levelup casino no deposit bonus codes second levelup casino no deposit bonus codes a dogs length, the standard measurement is that a dogs length equals 8 hundredths of a second. Download historic greyhound data from FastTrack Create a FastTrack object Find a list of greyhound tracks and FastTrack track codes Call the getRaceResults function 2. None None MH None "" Albion Park 16 Jun 21 None None None 4 5 BEN HANNANT? Today's Racing Tomorrow's Racing. Retrieve today's lineups from the Betfair API The FastTrack lineups contain all the dogs in a race, including reserves and scratched dogs.	If a runner is slow or very slow away consistently then this is an advantage for the adjacent runners as they will have clear space around them. PIR This is a dogs place at each of the split points in a race. The key clue to whether we are dealing with this kind of dog is its past race positions. When considering yet-to-be proven greyhounds, pay attention to these elements: Debutantes : Greyhounds making their racing debut can potentially offer longer odds due to a lack of historical data on their ability. As long as my selection is not way slower than the opposition then I am likely to go with it. Greyhound modelling in Python Building a Greyhound Racing model with Scikit-learn Logistic Regression and Ensemble Learning.	Overview. This tutorial will walk through how to retrieve historic greyhound form data from FastTrack by accessing their Data Download Centre (DDC) Bet with great odds on Greyhound Racing with the Betfair™ Exchange. ✓Best Online Betting Exchange For Greyhound Racing ✓Bet In-Play ✓Cash Out Missing	Duration Study the Greyhounds form and get betting tips from the experts at Timeform We also provide Greyhound betting via Betfair, plus we have loads of free Greyhound Form - A simple guide to reading greyhound racing form, simply understanding the card will increase your strike rate	Forgot your username / password? Safer Gambling · Exchange · Sportsbook · Virtual Sports It's not software, i created it in excel with colaboration from one of the forum members. He had the scraping skills and i had the excel skills The Timeform Greyhounds racecards offer you form and analysis for all of today's Greyhound racing fixtures in the UK, including the Bags and RPGTV races. You

Video

Betfair greyhound form - Study the Greyhounds form and get betting tips from the experts at Timeform We also provide Greyhound betting via Betfair, plus we have loads of free Overview. This tutorial will walk through how to retrieve historic greyhound form data from FastTrack by accessing their Data Download Centre (DDC) Bet with great odds on Greyhound Racing with the Betfair™ Exchange. ✓Best Online Betting Exchange For Greyhound Racing ✓Bet In-Play ✓Cash Out Missing

id RaceNum RaceName RaceTime Distance RaceGrade Track date 0 1 TRIPLE M BENDIGO id Place DogName Box Rug Weight StartPrice Handicap Margin1 Margin2 PIR Checks Comments SplitMargin RunTime Prizemoney RaceId TrainerId TrainerName 0 1 VANDA MICK 2.

Margin2 This is a decimal value to two decimal places representing a dogs margin from the dog in front if it, in the case of the winning dog this value is empty. PIR This is a dogs place at each of the split points in a race. C1 SplitMargin This is a decimal value to two decimal places representing a dogs time at the first split marker.

Maximum of 8 values. Cleanse and normalise the data Here we do some basic data manipulation and cleansing to get variables into format that we can work with.

apply lambda x : int x. apply lambda x : None if x is None else float x. astype float. apply lambda x : x. Apply Log base 10 transformation to Prizemoney and Place Apply inverse transformation to Place Combine RunTime and Distance to generate Speed value. fillna 0.

groupby [ 'Track' , 'Distance' ] [ 'RunTime' ]. groupby [ 'Track' , 'Distance' ] [ 'SplitMargin' ]. clip 0. groupby [ 'Track' , 'Distance' , 'Box' ] [ 'win' ]. head 8. Generate time-based features Now that we have a set of basic features for individual dog results, we need to aggregate them into a single feature vector.

Depending on the dataset size, this can take several minutes. agg aggregates. Processing rolling window 28D Processing rolling window 91D Processing rolling window D. Replace missing values with 0 dataset.

Place DogName Box Rug Weight StartPrice Handicap Margin1 Margin2 PIR from matplotlib import pyplot from matplotlib. pyplot import figure from sklearn.

Training on , samples with 77 features. Evaluate model predictions. Now that we have trained our model, we can generate predictions on the test dataset. Model strike rate Knowing how often a model correctly predicts the winner is one of the most important metrics.

LogisticRegression strike rate: Brier score The Brier score measures the mean squared difference between the predicted probability and the actual outcome. from sklearn. LogisticRegression Brier score: 0. Predictions' distribution To get a better feel of what our models are predicting, we can plot the generated probabilities' distribution and compare them with Start Prices probabilities' distribution.

import matplotlib. title 'StartPrice vs LogisticRegression probabilities distribution' plt. xlabel 'Probability' plt. Predictions calibration We want to ensure that probabilities generated by our model match real world probabilities.

title "LogisticRegression calibration curve" ;. I'll then go on to share a strategy that I have used for years whenever I bet the dogs. The first screen shot below is from the Betfair Form and then below that is the superior form from the Racing Post.

The numbered list below explains the data on the Racing Post form, but I've marked up the Betfair race card so you can see what data you get and are missing from those cards. You may also see M which indicates a middle runner and this dog will be allocated a middle trap.

In this case the best time came in a trial, a trial is a qualifying race which helps the racing manager to know how to grade the dog IE what is it's ability and what race should he put it in.

Trials will usually have less than 6 runners, 3 in this case, and there is no betting on trials. This is followed by the name of the dogs sire father , dam mother and the date whelped Date of Birth. Now we get to the past form for the dog in question. Each line represents one race with the top line being the most recent.

This is the time from the traps to the winning line the first time the dog passes the line. This is useful to hep you understand the pace of the dog and whether it is likely to lead early. Distances are quoted in lengths. Lengths relate to time were one greyhound length equals 0. T stands fro Trial and it is not a race but a run around the track with a few other dogs so the racing manager can assess how good a dog is so he knows how to grade it.

The number after the T indicates how many dogs ran in the trial. Trials are often run before or after the main races of the day and sometimes on dedicated trials days, they are usually open to the public to view.

This will be calculated from the distance the dog finished behind the winner and adjusted for the going allowance, unless of course it won, then it will be the winning time adjusted for the going. Now that we understand the information form that we have available we can look at how we can use that information.

The greyhound that leads around the first bend has a huge advantage in the race and we can get a lot closer to finding the winner if we find who will lead.

So my preferred strategy is based around determining which dogs are likely to get a clear run to and around the first bend. There are two main factors that will determine whether a dog will make it around the first bend unimpeded.

These are how fast it is into its stride and whether it will get knocked, bumped, baulked or impeded in anyway by one of the other five dogs in the race.

Just like athletes and horses some greyhounds are more suitable to sprinting and some are long distance runners, relatively speaking. We will be looking at standard four bend races which will be somewhere between about metres and metres. The majority of the dogs running at these distances will be suited to the distance, but some will be more sprint like, with fast early speed and some will be slower into their stride but will be capable of holding their top speed for longer.

These times tell us how long it took for the dog in question to reach the finishing line the first time in each of its previous races. In a four bend race this time will represent a straight line dash, as no bends will have been encountered yet. If we choose a representative time for each runner in the race we can get an idea of how they will be positioned first time over the line and with a little imagination we can project this picture forward to give us an idea of how things will pan out at the first bend.

There are a few different methods you could use to work out a representative sectional time for each dog. You could use their fastest sectional, you could use an average of all their times.

Both of these methods allow a systematic approach which removes the decision making from the process. But probably the best way is to be objective and use your judgement.

If a dog has done 4 fast sectionals and 1 very slow, probably an average will not be a true representation of its ability. The slow could have just been a bad day!

So day to day I would say use a judgment, but if you were going to research hundreds of past races then you would have to use a consistent approach like the average or fastest.

This obviously varies by track but a recent analysis of races at Hall Green showed 64 of those that led at the first bend went on to win. The easiest way to visualise the positions of the dogs on their way up to the first bend is to either draw out their positions on a piece of squared paper, which is what I used to do, or easier still use a piece of software to show the positions.

If you use squared paper then you need to convert hundredths of a second into a dogs length, the standard measurement is that a dogs length equals 8 hundredths of a second. The graphic below is part of a screenshot from a piece of software called Bags Beater sadly no longer available.

But times are not the whole story when it comes to who will lead at the first bend we need to know how the dogs will run to the first bend. But times don't tell the full story.

There are other factors that effect the run up to the first bend and they can all be grouped together into one question. Romford BAGS Flat Tip Sheet Crayford BAGS Flat Tip Sheet Newcastle BAGS Flat Tip Sheet Swindon BAGS Flat Tip Sheet Hove BAGS Both Tip Sheet Central Park BAGS Flat Tip Sheet Perry Barr BAGS Flat Tip Sheet Doncaster Flat Tip Sheet Oxford Flat Tip Sheet Sheffield Flat Tip Sheet