Bet with an edge â built on process
Picks Office isnât a tool, an app, or an aggregator. Itâs my sharp betting process, run every day like a wagering desk: screen markets, run models, add research, shop lines, choose timingâthen bet.
Statistics
Numbers Don't Lie
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Every pick includes contextâwhat it is, why itâs on the card, and what would invalidate it.
The Process
Value betting, executed like a desk
The core is simple: find prices that imply more probability than reality, and execute with discipline. No parlays. No hype plays. No guarantees â just picks plus context.
- Market screening
- Every day starts with scanning markets for mispriced numbers. Iâm not looking for âbest betsâ â Iâm looking for prices.
- Models + research
- Models run, then I layer in research (injuries, usage, matchups, context) to pressure-test the edge and the assumptions.
- Line shopping + timing
- Execution matters. I shop lines across books, track movement, and choose timing. The bet is the last step â not the first.
import numpy as np
from scipy.special import expit as sigmoid
teams = {t: 1500 + np.random.randn()*25 for t in ['LAL','BOS','MIL','PHI','MIA','GSW','NYK','DAL','DEN','CHI']}
schedule = [(np.random.choice(list(teams)), np.random.choice(list(teams))) for _ in range(500) if _ % 11 != 0]
def elo_prob(r1, r2): return 1 / (1 + 10 ** ((r2 - r1) / 400))
def simulate_result(a, b): return int(elo_prob(teams[a], teams[b]) + np.random.normal(0, 1) > 0.5)
def update_rating(r, s, expected, k=20): return r + k * (s - expected)
results = []
for a, b in schedule:
   res = simulate_result(a, b)
   ea, eb = elo_prob(teams[a], teams[b]), elo_prob(teams[b], teams[a])
   teams[a], teams[b] = update_rating(teams[a], res, ea), update_rating(teams[b], 1 - res, eb)
   results.append(res)
X = np.array([[teams[a], teams[b], elo_prob(teams[a], teams[b])] for a, b in schedule])
y = np.array(results)
w = np.linalg.pinv(X.T @ X) @ X.T @ y
predict = lambda a, b: sigmoid(np.dot([teams[a], teams[b], elo_prob(teams[a], teams[b])], w))
bets = [(a, b, predict(a, b)) for a, b in schedule if abs(predict(a, b) - 0.5) > 0.2]Follow us on X for real-time updates
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