Using Beta-Bernoulli Conjugacy
Perfect — with an 8–4 record here's the math Tennessee’s posterior win probability under Josh Heupel.
Bayesian Win‑Probability Model (Using 8–4 Data)
We observe:
• \(n = 12\) games
• \(w = 8\) wins
• \(\ell = 4\) losses
We place a Beta prior on Tennessee’s true win probability \(\theta_H\):
\theta_H \sim \text{Beta}(\alpha_0,\ \beta_0)
To stay neutral and let the data speak, use a uniform prior:
\alpha_0 = 1,\quad \beta_0 = 1
Posterior Distribution
By Beta–Bernoulli conjugacy:
\theta_H \mid \text{data} \sim \text{Beta}(\alpha_0 + w,\ \beta_0 + \ell)
Plug in the numbers:
\theta_H \mid \text{data} \sim \text{Beta}(1 + 8,\ 1 + 4)
{\theta_H \mid \text{data} \sim \text{Beta}(9,\ 5)}
This is the full Bayesian belief about Tennessee’s true win probability under Heupel after an 8–4 season.
Posterior Mean (Expected Win Probability)
\mathbb{E}[\theta_H \mid \text{data}]
= \frac{\alpha_0 + w}{\alpha_0 + \beta_0 + n}
= \frac{9}{14}
{\mathbb{E}[\theta_H] = 0.643}
So the Bayesian estimate of Tennessee’s true win probability is 64.3%.
Posterior Predictive Probability of Winning the Next Game
For a new game \(W_{n+1}\):
\mathbb{P}(W_{n+1} = 1 \mid \text{data})
= \mathbb{E}[\theta_H \mid \text{data}]
{\mathbb{P}(\text{Tennessee wins next game}) = 0.643}
This is the Bayesian “next‑game” prediction.
95% Credible Interval
The 95% credible interval for a Beta(9,5) distribution is approximately:
\theta_H^{95\%} \in [0.39,\ 0.86]
Interpretation:
With 95% probability, Tennessee’s true win rate under Heupel lies between 39% and 86%, given an 8–4 season and a neutral prior.
Maverick also predicted a 100% probability that if Tennessee scores more points, and holds opponents to fewer points than they score, they win!