sportsbet-eda-vanshs29362

Tiny exploratory-data-analysis and seaborn visualisation helpers for sports-betting data. Point it at a log of bets (or the built-in sample) and get back plain summaries, betting metrics, and clean seaborn charts.

• Install name: sportsbet-eda-vanshs29362 (used by pip / PyPI)
• Import name: sportsbet_eda (used in Python)

Install

pip install sportsbet-eda-vanshs29362

Quickstart

import sportsbet_eda as sb

# Use your own bet log, or start with the built-in synthetic one:
df = sb.sample_bets(200)

# --- EDA -----------------------------------------------------------------
sb.summarize(df)          # {'rows': 200, 'columns': 9, 'names': [...], 'dtypes': {...}}
sb.missing(df)            # missing values per column (pandas Series)
sb.numeric_columns(df)    # ['odds', 'stake', 'profit']

# --- Betting metrics -----------------------------------------------------
sb.bet_summary(df)        # {'bets': 200, 'wins': ..., 'win_rate': ..., 'roi': ...}
sb.win_rate(df)           # fraction of decided bets that won
sb.roi(df)                # total profit / total staked

# --- Odds math (scalars or pandas Series) --------------------------------
sb.implied_probability(2.50)                   # 0.40
sb.implied_probability(-150, kind="american")  # 0.60
sb.american_to_decimal(150)                    # 2.5
sb.no_vig_probability([1.91, 1.91])            # [0.5, 0.5]  (margin removed)
sb.bookmaker_margin([1.91, 1.91])              # 0.0471      (the "vig")

Charts (seaborn)

Every plot returns a matplotlib Axes, so you can show or save it:

import matplotlib.pyplot as plt
import sportsbet_eda as sb

df = sb.sample_bets()
sb.set_theme()                              # optional clean seaborn theme

sb.plot_cumulative_profit(df)               # bankroll curve over time
sb.plot_profit_by(df, by="sport")           # total profit per sport
sb.plot_win_rate_by(df, by="market")        # win rate per market
sb.plot_odds_distribution(df)               # histogram of odds
sb.plot_correlation(df)                     # heatmap of numeric columns

plt.show()                                  # or ax.figure.savefig("out.png")

Expected data

The betting functions work on a tidy "one row per settled bet" table. The sample frame uses these columns, and you can point the functions at your own column names with the keyword arguments:

result matching is forgiving: win/won/w/1/true/yes count as wins and push/void/tie/draw are excluded from the win rate; everything else is a loss.

Develop

python -m pip install -e ".[test]"   # editable install with test deps
pytest                               # run the tests
python -m build                      # build wheel + sdist into dist/
python -m twine check dist/*         # validate the artifacts

License

MIT — see LICENSE.