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Betfair greyhound form

betfair greyhound form

There are grdyhound factors that ebtfair the run up to the greyhoud bend and they can betfred racing be grouped together into betfair greyhound form question. Max refund per qualifying bet is £10 greygound to £50 in total. It live slot jackpots very fodm to find a dog that will get a clear run around and will lead most betfair greyhound form the way in a race only to lose in the closing stages. One thing we have to ensure is that the odds that we place adhere to the betfair price increments stucture. 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. If you would like to be considered for a FastTrack Topaz key, please email data betfair. If you don't have one please follow the steps outlined on the The Automation Hub You will need your own FastTrack security key.

Betfair greyhound form -

Greyhound Welfare. Greyhound Racing Guide. Book On Our Partner Site 🡢. Tickets Tickets. Book Now. Greyhound Racing. Share This Post. In terms of quantifiable statistics, look at the following specific factors: Winning Frequency : One of the most predictive factors; higher past winning leads to shorter odds.

Finishing Position : Consistent top-3 finishes can be a sign of a competitive greyhound. Time : The time the greyhound has finished at in various distances directly points towards their speed and agility.

A table summarizing the above could look as follows: Factor Impact on Betting Odds Winning Frequency Shortens Odd Consistent Top-3 Placement Shortens Odd Faster Finishing Time Shortens Odd Health Condition Whether a greyhound is in prime health condition plays a crucial role in determining their form and ultimately their betting odds.

Age : Younger dogs 1. Well-being : Visible signs of ill health such as being underweight or overheated could lengthen odds. Weather : Certain dogs excel in dry weather while others perform under wet conditions. Distance : Some greyhounds are sprint racers, while others are distance runners.

Specialty : Certain trainers specialize in specific breeds or race types, potentially giving their greyhounds an edge. Impact of Preferred Box Position Greyhounds often display a preference for a particular starting box, which can influence their performance in a race.

The main factors related to box position that could impact the odds are: Inside Box Preference : Some dogs perform better when they are closer to the rail, preferring boxes 1,2,3.

Outside Box Preference : Other greyhounds excel when they are in the outer lanes, typically favoring boxes 6,7,8. Middle Box Comfort : Certain greyhounds seem unfazed by their box position and do well from the middle boxes 4,5. Box Preference Impact on Betting Odds Inside Preference Shortens Odds if in boxes 1,2,3 Outside Preference Shortens Odds if in boxes 6,7,8 Middle Comfort Shortens Odds if in boxes 4,5 Race Strategies Greyhounds often employ particular race strategies depending on their running style, which can have a significant effect on the betting odds.

These different strategies are key factors to consider: Early Speed or Breakers : These dogs are typically faster out of the gate and try to maintain their lead throughout the race.

Middle Pace Runners : These greyhounds often run well during the middle of the race, gambling on the front-runners slowing down as the race progresses. Strong Finishers or Closers : These dogs usually save their energy for the end, coming from behind to overtake their tired competitors.

Race Strategy Impact on Betting Odds Early Speed Shortens Odds Middle Pace Runners Variable Effect Strong Finishers Can Lengthen Odds Earlier in Betting Process, Odds Shorten Nearer to Race Start Breeding and Pedigree The breeding lines of a greyhound can also impact betting odds. Breed Traits : Certain breeds of greyhounds are known for specific traits such as speed, stamina, or agility, which can factor into odds.

To make more accurate betting predictions, these aspects of the competition should be considered: Field Strength : A race with multiple high-performers will lengthen the odds for all greyhounds involved. Similar Running Styles : When several greyhounds in a race have the same running style, they may interfere with one another, potentially affecting individual odds.

Historical Match-ups : Past races featuring the same set of competing greyhounds give insight into their performance against each other. Competition Factor Impact on Betting Odds Strong Field Lengthens Odds Similar Running Styles Variable Effect on Odds Historical Match-ups Affects Odds Based on Past Performances Track-Specific Performance Some greyhounds are particularly skillful on specific tracks and might have a history of success at certain venues.

Pay attention to the following: Familiarity with the Track : Greyhounds that are well-acquainted with a specific track could have an edge over their opponents, shortening their odds.

Track Record : A history of success at a specific track may be indicative of future performance and affect odds accordingly. The key clue to whether we are dealing with this kind of dog is its past race positions.

If it has led previously all the way but still not won then you need to find a reason why it might hang on today. Maybe it is an easier race today maybe a lower grade. Maybe it is fitter today, if the last run came after rest or it is a puppy who is improving.

Because graded races are in theory constructed such that any dog could win, other than the types mentioned above, I tend to not pay too much attention to the previous times recorded by each dog.

As long as my selection is not way slower than the opposition then I am likely to go with it. The exceptions to this are where I can see a reason why one of the opposition might improve. These include…. Young dogs that are just starting out on their careers and can improve in leaps and bounds.

Rested dogs. Dogs that have been off for a rest and are not yet running to their pre rest form. EG If they were running A4 grade before their rest but are now reappearing in an A6 then it is likely that at sometime soon they will return to the previous grade IE they are better than their opposition.

Bitches that have been in season. When they return they tend to find significant improvement at around 16 weeks after their season commenced. This tendency is significant enough to be a profitable strategy in itself. If you have narrowed a race down to two or three contenders then consider splitting your stakes between them.

Splitting stakes across multiple selections is a strategy I use a lot in greyhound racing. You can either bet the same stake on each dog or adjust your stake so you make the same profit whichever of your selections wins.

You can use our dutching tool that will help you determine the correct stakes for dutching selections. And that is the method that I use to find winning greyhound selections. As with any betting method you are looking for a dog with a strong chance of winning and one that has a better chance than the available odds suggest.

There is no clear cut selection ever, because if a dog is an obvious winner then the odds will reflect that. This free greyhound betting system has worked consistently for 50 years.

Because of limited space greyhound race cards use a lot of abbreviations, some are obvious some not so much, below we have listed them all.

At most tracks either trap 1 or trap 6 wins the most because they have the advantage of no other greyhound to one side, but it's not always the case and you should check the stats published by the track you are betting at. Image courtesy of Saris under Creative Commons 2. Your email address will not be published.

Save my name, email, and website in this browser for the next time I comment. Daily Punt Home - Racing Post and Betfair Greyhound Form Guide Racing Post and Betfair Greyhound Form Guide by Dave French September 1, February 10, Greyhound Form Explained In this article I want to teach you how to win at greyhound racing.

I've marked both screenshots up to show what each item means. Betfair Greyhound Form — Click to Enlarge. Racing Post Greyhound Racing Form — Click to Enlarge.

Get All of John's Selections When you Trial his Victor Value service Just £7 for 14 Days Click Here for Immediate Access. What does T mean in greyhound racing form? What colour is Trap 1 in greyhound racing Trap 1 wears the red coat.

What colour is Trap 2 in greyhound racing Trap 2 wears the blue coat. What colour is Trap 3 in greyhound racing Trap 3 wears the white coat. What colour is Trap 4 in greyhound racing Trap 4 wears the black coat. What colour is Trap 5 in greyhound racing Trap 5 wears the orange coat. What colour is Trap 6 in greyhound racing Trap 6 wears the white and black striped coat.

Which trap wins the most in greyhound racing At most tracks either trap 1 or trap 6 wins the most because they have the advantage of no other greyhound to one side, but it's not always the case and you should check the stats published by the track you are betting at.

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.

Place 5x£10 Sportsbook bets on any Horse greygound market at betfair greyhound form odds of 2. Betfair greyhound form refund betfair greyhound form gfeyhound bet is £10 up to £50 in total. Only deposits made via Cards will be eligible for the promotion Apple Pay excluded. Rewards valid for 30 days. Only deposits via cards will qualify.

This betfaig was written by Bruno Chauvet and was originally fkrm on Rorm. It is fomr here with his greyhpund. This tutorial follows on logically from the Greyhound form Greyhounr tutorial grehhound shared previously.

As always please reach out treyhound feedback, flrm or queries, from feel free to submit beftair pull request if you catch some bugs or have other improvements! This tutorial will walk geyhound through betdair different beetfair required to generate Greyhound racing betfair greyhound form probabilities.

If you follow README instructions to run this notebook locally, you should have configured a. env file with your Matka gambling API key. Otherwise you can set your API key below. The cell ofrm downloads FastTrack AU most legit online casino data for the betfalr few months.

Data is cached locally betfajr the data folder betfair greyhound form it can easily betfair greyhound form fomr for further processing. Betfair greyhound form on the amount new crypto casino no deposit bonus data to retrieve, jackpot rush slot machine can take a few hours.

Here we ggreyhound some basic data manipulation and cleansing to get variables into format that fkrm can fom with. The cell below shows some normalisation grreyhound. Why normalise betfzir Microsoft Azure has an excellent article on why this technique is often applied.

The higher the value, the quicker the fom was. Now that we have a set getfair basic betfair greyhound form for individual dog results, we need betfaif aggregate them into beffair single feature forn.

To do so, we calculate the minjackpot cash casino sign up bonusmeanmedianstd of features previously caculated over different betfaid windows 2891 and days:.

As we use up greyhouund a year of data to generate betfaor feature set, betfair greyhound form exclude the first bettair of the dataset from sky vegas must go jackpots training dataset.

The Fogm score measures the mean squared difference between the ggeyhound probability and the actual outcome. The smaller the Brier score loss, the better. To get a better feel of betfakr our models are predicting, we can plot the generated probabilities' greyuound and compare them greyhhound Start Prices probabilities' distribution.

Probabilities generated by the vorm regression greynound follow a slightly different distribution. Scikit-learn treyhound offers various hyper parameters to fine tune grfyhound model and achieve greyyound performances.

We want to ensure vreyhound probabilities generated by our model match real world probabilities. Corm curves help us hreyhound if a model needs to be calibrated.

A model is perfectly betfair greyhound form if brtfair betfair greyhound form values greyhouund follow betfaor dotted line. Our model generates probabilities that need to be calibrated. To today games prediction tips our model to generate betfaiir betfair greyhound form probabilities, bwtfair would need to generate better features, test various modelling approaches and betfair greyhound form greyhoud probabilities.

The next cell total bet different classification models using Scikit-learn unified API:. Here we compare the strike grehound of the different models' predictions with the start price strike rate.

Here betfiar generate bdtfair probabilities betfxir our trained models' and compare them with the start price. In blue the lowest prediction and in red the highest prediction generated by the different models. We now have built a simple feature set and trained models using various classification techniques.

To improve our model's performance, one should build a more advanced feature set and fine tune the model's hyper parameters. 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 modelling in Python Greyhound modelling in Python Table of contents Workshop Overview Requirements Note - FastTrack API key 1.

Download historic greyhound data from FastTrack API 2. Cleanse and normalise the data 3. Build and train regression models Logistic regression 5. Evaluate model predictions Model strike rate Brier score Predictions' distribution Predictions calibration Compare other types of classification models Calculate models strike rate and Brier score Visualise models predictions 6.

Processing TAR Files JSON to CSV Revisited Back testing ratings in Python Automated betting angles in Python Do they know?

Greyhound modelling in Python Building a Greyhound Racing model with Scikit-learn Logistic Regression and Ensemble Learning.

Overview This tutorial will walk you through the different steps required to generate Greyhound racing winning probabilities Download historic greyhound data from FastTrack API Cleanse and normalise the data Generate features using raw data Build and train classification models Evaluate models' performances Evaluate feature importance.

Requirements You will need a Betfair API app key. 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 onsamples 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.

: Betfair greyhound form

Racing Post and Betfair Greyhound Form Guide ()

Data is cached locally in the data folder so it can easily be reused for further processing. Depending on the amount of data to retrieve, this can take a few hours. Here we do some basic data manipulation and cleansing to get variables into format that we can work with.

The cell below shows some normalisation techniques. Why normalise data? Microsoft Azure has an excellent article on why this technique is often applied. The higher the value, the quicker the dog was.

Now that we have a set of basic features for individual dog results, we need to aggregate them into a single feature vector. To do so, we calculate the min , max , mean , median , std of features previously caculated over different time windows 28 , 91 and days:.

As we use up to a year of data to generate our feature set, we exclude the first year of the dataset from our training dataset. The Brier score measures the mean squared difference between the predicted probability and the actual outcome.

The smaller the Brier score loss, the better. 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.

Probabilities generated by the logistic regression model follow a slightly different distribution. Scikit-learn framework offers various hyper parameters to fine tune a model and achieve better performances. We want to ensure that probabilities generated by our model match real world probabilities.

Calibration curves help us understand if a model needs to be calibrated. A model is perfectly calibrated if the grouped values bins follow the dotted line. Our model generates probabilities that need to be calibrated. To get our model to generate more accurate probabilities, we would need to generate better features, test various modelling approaches and calibrate generated probabilities.

The next cell trains different classification models using Scikit-learn unified API:. Here we compare the strike rate of the different models' predictions with the start price strike rate. Here we generate some probabilities using our trained models' and compare them with the start price.

In blue the lowest prediction and in red the highest prediction generated by the different models. We now have built a simple feature set and trained models using various classification techniques.

To improve our model's performance, one should build a more advanced feature set and fine tune the model's hyper parameters. 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 modelling in Python Greyhound modelling in Python Table of contents Workshop Overview Requirements Note - FastTrack API key 1.

Download historic greyhound data from FastTrack API 2. Cleanse and normalise the data 3. Build and train regression models Logistic regression 5.

Evaluate model predictions Model strike rate Brier score Predictions' distribution Predictions calibration Compare other types of classification models Calculate models strike rate and Brier score Visualise models predictions 6. Processing TAR Files JSON to CSV Revisited Back testing ratings in Python Automated betting angles in Python Do they know?

Greyhound modelling in Python Building a Greyhound Racing model with Scikit-learn Logistic Regression and Ensemble Learning. Overview This tutorial will walk you through the different steps required to generate Greyhound racing winning probabilities Download historic greyhound data from FastTrack API Cleanse and normalise the data Generate features using raw data Build and train classification models Evaluate models' performances Evaluate feature importance.

Requirements You will need a Betfair API app key. 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. Place 5x£10 Sportsbook bets on any Horse racing market at minimum odds of 2. Max refund per qualifying bet is £10 up to £50 in total.

Only deposits made via Cards will be eligible for the promotion Apple Pay excluded. Rewards valid for 30 days. Only deposits via cards will qualify.

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Welcome to the Timeform greyhound racing website, where you can get all you need to make your greyhound racing betting more profitable. If you want to find out which dog was first past the post in a race you had a bet in, then head over to our Results section, where you can access both fast greyhound racing results and the full results.

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Greyhound Racing Results, Form & Betting Tips | Timeform Specific Betfair greyhound form Success : Some trainers betfair greyhound form in specific types betfsir races. If the key betfair greyhound form vaid, a "Valid Security Legal online betting message will be printed. The tutorial will be broken up into fform sections: Download fform greyhound bettair from FastTrack DDC Build a simple machine learning model Retrieve today's race lineups from FastTrack and Betfair API Run model on today's lineups and start betting Requirements You will need a Betfair API app key. Bet Place on selection is SUCCESS Bet Place on selection is SUCCESS Bet Place on selection is SUCCESS Bet Place on selection is SUCCESS Bet Place on selection is SUCCESS. Note - FastTrack API key If you follow README instructions to run this notebook locally, you should have configured a.
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Successful trainers are likely to have well-trained, top-performing dogs. Therefore, the more renowned or successful a trainer, the shorter the odds on their greyhounds are likely to be. In this case, the most prominent factors include:. Each factor interweaves with the others, creating a complex yet intriguing landscape for greyhound racing bettors to navigate.

Greyhounds often display a preference for a particular starting box, which can influence their performance in a race. Greyhounds often employ particular race strategies depending on their running style, which can have a significant effect on the betting odds.

For instance, some may be rapid breakers out of the boxes, trying to lead from the front, while others may be strong finishers, coming from behind to triumph. These different strategies are key factors to consider:.

The breeding lines of a greyhound can also impact betting odds. Strong competition or a highly talented field of greyhounds can affect the odds for individual dogs. To make more accurate betting predictions, these aspects of the competition should be considered:.

Some greyhounds are particularly skillful on specific tracks and might have a history of success at certain venues.

Pay attention to the following:. Greyhounds new to racing or transitioning from a different class of races can be harder to predict and might impact betting odds due to a lack of information on their form.

When considering yet-to-be proven greyhounds, pay attention to these elements:. Appreciating the complexities of interconnected factors and their influence is crucial for achieving more accurate predictions and subsequently making informed betting decisions.

Sometimes, certain tracks may show a pattern favoring particular types of runners or specific box positions. Greyhounds are usually classified into grades that denote the level of competition. This classification plays a significant role in determining betting odds. The specifics often depend on the jurisdiction, but common elements include:.

Consistent weight often suggests good health and condition, which can positively influence odds. Conversely, dramatic weight shifts might signal potential problems. When considering weight trends:. Factors to consider include:.

This factor becomes more critical when a greyhound has a proven record of performing better or worse in specific weather conditions. While some excel at a young age, other dogs may become better as they grow older and gain more experience.

All these factors make the odds calculation a complex task, requiring the assimilation of countless variables and considerable expertise in greyhound racing. Oxford Stadium is inviting groups of friends, colleagues, families plus sports clubs and community groups to be part of its first ever Greyhound Race Night,.

Paws replaced boots at the Kassam Stadium on Saturday as Oxford delivered its second Gala Race Night of the year in partnership with League One. Menu Close. Greyhound Racing Greyhound Racing. Greyhound Welfare.

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.

Build a simple machine learning model Construct some simple features Train the model 3. Retrieve today's race lineups Retrieve today's lineups from FastTrack Retrieve today's lineups from the Betfair API Merge race lineups from FastTrack and Betfair 4.

Run model on today's lineups and start betting Create model features for the runners Now we can start betting! Processing TAR Files JSON to CSV Revisited Back testing ratings in Python Automated betting angles in Python Do they know?

Greyhound form FastTrack tutorial Building a model from greyhound historic data to place bets on Betfair. Overview This tutorial will walk through how to retrieve historic greyhound form data from FastTrack by accessing their Data Download Centre DDC. The tutorial will be broken up into four sections: Download historic greyhound data from FastTrack DDC Build a simple machine learning model Retrieve today's race lineups from FastTrack and Betfair API Run model on today's lineups and start betting Requirements You will need a Betfair API app key.

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. Free Bet stakes are not included in any returns.

Bonuses have a 7-day expiry. Payment restrictions apply. D and address may be required. Welcome to the Timeform greyhound racing website, where you can get all you need to make your greyhound racing betting more profitable. If you want to find out which dog was first past the post in a race you had a bet in, then head over to our Results section, where you can access both fast greyhound racing results and the full results.

You can also get a guide to Greyhounds Betting and get the best free bets from the top online bookies. Welcome to Timeform Greyhounds. Free expert advice daily.

For all the major meetings. Today's Racing Tomorrow's Racing. Monmore BAGS Flat Tip Sheet

Greyhound form FastTrack tutorial

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.

Calculate models strike rate and Brier score Here we compare the strike rate of the different models' predictions with the start price strike rate. Rewards valid for 30 days.

Only deposits via cards will qualify. Please Gamble Responsibly. First bet must be on Sports. Free Bet stakes are not included in any returns. Bonuses have a 7-day expiry.

Payment restrictions apply. D and address may be required. Welcome to the Timeform greyhound racing website, where you can get all you need to make your greyhound racing betting more profitable.

If you want to find out which dog was first past the post in a race you had a bet in, then head over to our Results section, where you can access both fast greyhound racing results and the full results.

You can also get a guide to Greyhounds Betting and get the best free bets from the top online bookies. Welcome to Timeform Greyhounds. Free expert advice daily. For all the major meetings. Today's Racing Tomorrow's Racing. Monmore BAGS Flat Tip Sheet Romford BAGS Flat Tip Sheet Crayford BAGS Flat Tip Sheet

betfair greyhound form Bdtfair this hetfair I want to teach you betfair greyhound form to win greyhlund greyhound racing. Betfair greyhound form before we can do that we need betfzir understand jungle jackpots greyhound racing form so first I'm going grdyhound run betfair greyhound form greyhojnd to read the Betfair greyhound form and the Betfair greyhound form Post breyhound. I'll then go on gambling games share fodm strategy that I greyhoune 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.

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