A starter idea since U.K. is about to join
Here’s a practical backfit framework you can use in Python. It will fit past Powerball draws from 1 Jul 2020 onward, score candidate tickets, and backtest on holdout data.
python
import pandas as pd
import numpy as np
from collections import Counter
from sklearn.model_selection import TimeSeriesSplit
from sklearn.linear_model import LogisticRegression
# ----------------------------
# 1) Load data
# ----------------------------
# Use the official NY dataset CSV export:
# https://data.ny.gov/api/v3/views/d6yy-54nr/export.csv?accessType=DOWNLOAD
df = pd.read_csv("powerball.csv")
# Expected columns vary by export; inspect and adapt:
print(df.columns)
# ----------------------------
# 2) Normalize columns
# ----------------------------
# Common layout:
# winning_numbers, draw_date, multiplier
# Or separate columns for numbers.
# Adjust this section to your file.
# Example parser for a single string column like "09 14 45 56 03 16"
def parse_numbers(s):
nums = [int(x) for x in str(s).replace(",", " ").split()]
white = nums[:5]
pb = nums[5]
return white, pb
# Example:
# df[["white", "pb"]] = df["winning_numbers"].apply(lambda s: pd.Series(parse_numbers(s)))
# df["draw_date"] = pd.to_datetime(df["draw_date"])
# ----------------------------
# 3) Filter date range
# ----------------------------
# df = df[df["draw_date"] >= "2020-07-01"].copy()
# ----------------------------
# 4) Feature engineering
# ----------------------------
def ticket_features(white, pb, hist_white_counts, hist_pb_counts):
white = sorted(white)
feats = {}
feats["sum"] = sum(white)
feats["odd_count"] = sum(n % 2 for n in white)
feats["low_count"] = sum(n <= 34 for n in white)
feats["repeat_hist_score"] = sum(hist_white_counts[n] for n in white) + hist_pb_counts[pb]
feats["freq_score"] = sum(hist_white_counts[n] for n in white) + hist_pb_counts[pb]
return feats
def build_hist_counts(draws):
white_counts = Counter()
pb_counts = Counter()
for white, pb in draws:
white_counts.update(white)
pb_counts[pb] += 1
return white_counts, pb_counts
# ----------------------------
# 5) Scoring model
# ----------------------------
def score_ticket(white, pb, white_counts, pb_counts):
white = sorted(white)
score = 0.0
score += sum(np.log1p(white_counts[n]) for n in white)
score += np.log1p(pb_counts[pb])
score += -abs(sum(white) - 160) / 100.0
score += -abs(sum(n % 2 for n in white) - 3) * 0.1
score += -abs(sum(n <= 34 for n in white) - 2.5) * 0.1
return score
# ----------------------------
# 6) Candidate generator
# ----------------------------
def generate_candidates(n=10000, seed=42):
rng = np.random.default_rng(seed)
cands = []
for _ in range(n):
white = sorted(rng.choice(np.arange(1, 70), size=5, replace=False).tolist())
pb = int(rng.integers(1, 27))
cands.append((white, pb))
return cands
# ----------------------------
# 7) Backtest
# ----------------------------
def backtest(draws, train_window=100, test_window=20):
results = []
for i in range(train_window, len(draws) - test_window, test_window):
train = draws[i-train_window:i]
test = draws[i:i+test_window]
white_counts, pb_counts = build_hist_counts(train)
cands = generate_candidates(5000, seed=i)
ranked = sorted(
cands,
key=lambda t: score_ticket(t[0], t[1], white_counts, pb_counts),
reverse=True
)
top = ranked[0]
hit = any(set(top[0]) == set(actual[0]) and top[1] == actual[1] for actual in test)
results.append({"idx": i, "top_ticket": top, "hit_any_exact": hit})
return pd.DataFrame(results)
# ----------------------------
# 8) Example usage
# ----------------------------
# draws = [(white_list, pb_int), ...] in chronological order
# bt = backtest(draws)
# print(bt)
