Python Bitcoin Betting API Trust Dice February 8, 2023 – Posted in: Featured Articles – Tags: Betting Exchange, Sports Betting
How to Predict Winners Using Python and BitOdds Data
For sports bettors wishing to employ data-driven betting techniques, the BitOdds archive offers a gold mine of data. With odds and outcomes for games going all the way back to 2018, we wanted to make it simple for anyone to use this information to advance their betting. We now offer the option to export archive data in two simple formats: CSV and Google Sheets, in response to this.
In this post, we’ll show you how to import and analyze a CSV export using Python and the Pandas data analysis tool, as well as how to use machine learning to create a model that predicts NBA game wins using this data.
Taking the info out of BitOdds
We will export NBA data for the 2020–21 season for this example. Data exporting:
- Activate the BitOdds archive.
- In the competition dropdown, choose NBA.
- Select a time frame, then click Apply.
- Click CSV Download.
We must ensure that the date range includes this time frame because the 2020–21 NBA season went from December 2020 to July 2021. We must execute two exports because the archive only permits exporting 1,000 events at a time. After that, we must aggregate the data in Pandas. You will find two files in your Downloads folder after downloading the CSV files for these two dates spans.
The code in your browser to run
We have made a git repository with the CSV files and a Jupyter notebook that you can use to perform the analysis and explore the data for yourself in order to make it simple to run the code in this article. The following link, which may take a minute to launch, is the simplest way to start the notebook in Binder.
As a result, you won’t need to install anything on your computer to use the notebook in your browser. Although it’s a terrific way to test out the notebook, any changes you make will be gone when your browser is closed.
Executing the program on a computer
Python installation
You can download the Python installer if you use Windows.
Python 3 might already be installed if you are using Linux or macOS. Start python3 at the terminal to find out. Python 3 can be installed using your system’s package manager if it isn’t already. Using Homebrew is advised if you are running macOS.
Going through the dataset
The first thing we do in the notebook is import the necessary Python libraries and set up the matplotlib package to improve the legibility of the plots we will create:
The pd. read csv() method of the Pandas package is then used to load the CSV files. A DataFrame, an object for representing and interacting with tabular data, is the result of this function. Our data is separated into two CSV files, therefore we must execute read csv twice and use pd. concat() to join the two DataFrames into one:
Now that we have choices, we can examine the facts. Using head(), the top five rows are displayed:
| home | away | date_utc | result | market | selection | market_qualifier | is_winner | sportsbet_odds | cloudbet_odds | stake_odds | nitrogen_odds | betbtc_odds | betcoin_odds | average | trustdice_odds | |
| 0 | Suns | Hawks | 2021-03-31 02:00:00 | Suns won 117 – 110 | event_winner | Suns | NaN | yes | 1.44 | 1.44 | 1.44 | 1.43 | 1.436 | 1.44 | 1.438 | NaN |
| 1 | Suns | Hawks | 2021-03-31 02:00:00 | Suns won 117 – 110 | event_winner | Hawks | NaN | no | 2.90 | 2.99 | 2.90 | 2.94 | 2.972 | 2.90 | 2.934 | NaN |
| 2 | Suns | Hawks | 2021-03-31 02:00:00 | Suns won 117 – 110 | spread | Suns | -5.5 | yes | 1.90 | 1.94 | 1.90 | 1.92 | 1.929 | 1.90 | 1.915 | NaN |
| 3 | Suns | Hawks | 2021-03-31 02:00:00 | Suns won 117 – 110 | spread | Hawks | 5.5 | no | 1.95 | 1.96 | 1.95 | 1.94 | 1.947 | 1.95 | 1.950 | NaN |
| 4 | Suns | Hawks | 2021-03-31 02:00:00 | Suns won 117 – 110 | total | Under | 222.5 | no |
From this sample of rows, we may infer the following things about the dataset:
- Every possible wager has its own row. For instance, in the Suns vs. Hawks matchup, there is a row for wagers on the Suns to win and a row for wagers on the Hawks to win.
- The variables event winner, spread, and total are used to identify the market type in the market column.
- The market qualifier column for spread and total markets displays the spread value or total value related to the choice. For instance, the market qualifier in the row with index 2 indicates that the row is for Suns -5.5 points.
- The columns named sportsbet odds, cloud bet odds, etc. contain the market odds for six sportsbooks, and the average of those odds may be found in the average column.
- In the winner column, we can see whether the selection was a winning wager or not, and in the result column, we can see the final result.
The data frame has a total of 7,070 rows and 17 columns, which we can see using selections. shape. We then use selections to get some statistics for the number columns. describe():
This output reveals the following:
- Most sportsbooks offer about 7,000 odds, but Trust Dice only offers 210. The data frame displays the missing odds as NaN.
- The odds are between 1.03 and 17.33, with an average of about 2.1.
Do the odds adequately reflect the home team’s advantage?
We can contrast the rate at which home teams win with the rate at which away teams win to determine if there was a home team advantage in the NBA 2020–21 season. We may use a clever method to get the win rates: when you instruct Pandas to use the mean() function to determine the average of a column of booleans, it will treat False as 0 and True as 1. As a result, the victory rate would be 0.8 or 80% if we took the mean of the is winner column, which had 8 True values and 2 False values.
Next actions
In this article, we’ve demonstrated how to use BitOdds data with sci-kit-learn to create a straightforward prediction model. The figures for the 2020–21 season suggested that it produced a very acceptable 5% profit. The model’s robustness would then be evaluated as the next phase. This might be accomplished by applying the model to data from a different NBA season that was exported from BitOdds or by using the sci-kit-cross-validation learn’s feature.
You might include other data, such as the teams competing, their victory percentages, etc. to further enhance the model. The game’s score could also be predicted using a model, which you could then use to wager on the total points and spread markets.
Python Bitcoin Betting API Trust Dice FAQs
1) Are transactions for bitcoin bets and rewards stored on the blockchain?
The blockchain records all transactions made to and from a bitcoin sportsbook, including deposits and withdrawals. Your bets and payouts are only being recorded by the sportsbook in between; they are not being stored on the blockchain. As a result, until you remove funds back to your personal blockchain wallet, control over your balance is limited.
2) What happens to a wager on a team to win if the game is a tie and that wagering option was not available?
The dead heat rule is the most frequently applied rule by sportsbooks when a draw or a tie was not offered as a betting option. Bets on the equal winners are paid out at the full value of the ticket, split by the number of equal winners as if it were a winner.
3) How legal is cryptocurrency gambling?
Sure, it is.
4) Do cryptocurrency sportsbooks have a winning account cap?
No, this has never been reported to us. The key reason is that since you are anonymous, it would be simple for you to open a new account. As a result, it would be hard for a cryptocurrency sportsbook to restrict your account or block you.
Read Other Articles