Mississippi State Bulldogs vs Texas Longhorns

League: NCAAF | Date: 2025-10-25 04:20 PM EDT | Last Updated: 2025-10-25 05:48 PM EDT

🧠 Top 3 Overall Best Bets

💰 Best Bet #1 [Mississippi State Bulldogs / Bet Type = Spread +7 / -110 / 58% / Simulation shows 58.5% cover probability vs. implied 52.4%; line stable at -7 with sharp money on home dog per recent movement and X sentiment favoring MSU cover.]

💰 Best Bet #2 [Under / Bet Type = Total 55.5 / -120 / 58% / Avg simulated total 54.1 with 58.2% under probability; Texas defense limits explosive plays (havoc rate top-20), MSU tempo slow vs. elite defenses, under favored in odds.]

💰 Best Bet #3 [Texas Longhorns / Bet Type = Moneyline / -330 / 77% / 77.3% simulated win rate aligns with implied 76.7%; no major injuries, superior SP+ rating (Texas #8 vs. MSU #65), home crowd minimal edge for MSU.]

“`python
import random
import math

def poisson_sample(lmbda):
if lmbda L:
k += 1
p *= random.random()
return k – 1

num_sim = 10000

expected_texas = 30.0
expected_msu = 24.0

spread = 7.0
total_line = 55.5

texas_wins = 0
msu_wins = 0
totals = []
margins = []

random.seed(42)

for _ in range(num_sim):
texas_score = poisson_sample(expected_texas)
msu_score = poisson_sample(expected_msu)
total = texas_score + msu_score
totals.append(total)
margin = texas_score – msu_score
margins.append(margin)

if texas_score &gt; msu_score:<br />
    texas_wins += 1<br />
else:<br />
    msu_wins += 1

msu_win_pct = (msu_wins / num_sim) * 100
texas_win_pct = (texas_wins / num_sim) * 100

Texas covers if margin >7

texas_cover_pct = (sum(1 for m in margins if m > 7) / num_sim) * 100
msu_cover_pct = 100 – texas_cover_pct

avg_total = sum(totals) / num_sim

over_pct = (sum(1 for t in totals if t > total_line) / num_sim) * 100
under_pct = 100 – over_pct

avg_margin = sum(margins) / num_sim

n = num_sim
variance_total = sum((t – avg_total)**2 for t in totals) / n
std_total = math.sqrt(variance_total)
sem_total = std_total / math.sqrt(n)
ci_total_low = avg_total – 1.96 * sem_total
ci_total_high = avg_total + 1.96 * sem_total

variance_margin = sum((m – avg_margin)**2 for m in margins) / n
std_margin = math.sqrt(variance_margin)
sem_margin = std_margin / math.sqrt(n)
ci_margin_low = avg_margin – 1.96 * sem_margin
ci_margin_high = avg_margin + 1.96 * sem_margin

For MSU +7 cover: margin

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