Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/sedemmler/WagerBrain
A package containing the essential math required for sports betting and gambling.
https://github.com/sedemmler/WagerBrain
betting betting-odds bookmaker bookmakers gambling math odds sports-betting sports-stats sportsbook wager
Last synced: 14 days ago
JSON representation
A package containing the essential math required for sports betting and gambling.
- Host: GitHub
- URL: https://github.com/sedemmler/WagerBrain
- Owner: sedemmler
- License: mit
- Created: 2020-03-26T19:19:51.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2020-05-02T17:35:46.000Z (over 4 years ago)
- Last Synced: 2024-08-01T16:55:12.207Z (3 months ago)
- Topics: betting, betting-odds, bookmaker, bookmakers, gambling, math, odds, sports-betting, sports-stats, sportsbook, wager
- Language: Python
- Size: 1.34 MB
- Stars: 228
- Watchers: 13
- Forks: 33
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# WagerBrain
A package containing the essential math and tools required for sports betting and gambling. Once you've scraped odds from Covers.com, Pinnacle, Betfair, or wherever, import WagerBrain and start hunting for value bets.
**Phase 1 (_complete_):**
- Convert Odds between American, Decimal, Fractional
- Convert Odds to Implied Win Probabilities and back to Odds
- Calculate Profit and Total Payouts
- Calculate Expected Value
- Calculate Kelly Criterion
- Calculate Parlay Odds, Total Payout, Profit
**Phase 2 (_complete_):**
- Evaluate Wager-Arbitrage Opportunities
- Calculate bookmaker spread/cost
- Calculate the Bookmaker's Vig
- Calculate Win Probability from a team's ELO (538-style)
**Phase 3 (_in progress_):**
- Scrapers to gather data (Basketball Reference, KenPom etc.) [_Partially implemented_]
- Value Bets (take in sets of odds, probabilities and output the most effective betting implementation)
- Scan for Arbitrage (search scrape bookmakers to feed into Phase 2's Arbitrage evaluator)
# Examples
Parlay 3 wagers from different sites offering different odds-styles:
```
odds = [1.91, -110, '9/10']
parlay_odds(odds)
>>>> 6.92
```
No clue how to read decimal odds because you're American? (wager * decimals odds, though...super simple), then convert them back to American-style odds:
```
american_odds(6.92)
>>>> +592
```
What's the Vig on the Yankees vs Dodgers?
```
Yankees -115
Dodgers +105
Betting 115 to win 100 on Yankees
Betting 100 to win 205 on Dodgers
vig(115,215,100,205)
>>>> 2.26%
```
Arbitrage Example
```
5Dimes Pinnacle
Djokovic *1.360* 1.189
Nadal 3.170 *5.500*
odds = [1.36, 5.5]
stake = 1000
basic_arbitrage(odds, stake)
>>>> Bet $801.53 on Djokovic
>>>> Bet $198.47 on Nadal
>>>> Win $90.51 regardless of the outcome
```
KenPom NCAAB Scraper
```
ken_pom_scrape()
>>>>
Rk Team Conf ... OppO OppD NCOS AdjEM
0 1.0 Kansas B12 ... 107.4 94.7 9.58
1 2.0 Gonzaga WCC ... 103.5 101.0 -2.09
2 3.0 Baylor B12 ... 106.4 96.2 1.38
3 4.0 Dayton A10 ... 104.1 101.3 -0.74
4 5.0 Duke ACC ... 106.0 98.7 2.60
.. ... ... ... ... ... ... ...
364 349.0 Maryland Eastern Shore MEAC ... 97.6 104.1 7.78
365 350.0 Howard MEAC ... 96.7 105.0 0.96
366 351.0 Mississippi Valley St. SWAC ... 97.8 103.9 5.14
367 352.0 Kennesaw St. ASun ... 102.0 103.7 4.10
368 353.0 Chicago St. WAC ... 100.6 104.3 -0.75
```