= Solution
Let $Y_i$ be the first team's goal count in match $i$, let $x_i^T$ be row $i$ of the <design matrix>, and let $\beta$ be the <regression coefficient> vector. The fitted <Poisson regression> assumes that the $Y_i$ are <independent random variables> with
$$
Y_i\sim\operatorname{Pois}(\mu_i),
\qquad \log\mu_i=x_i^T\beta.
$$
The <likelihood function> maximized by `glm` is therefore
$$
\boxed{L(\beta;y)
=\prod_{i=1}^{64}
\frac{\mu_i^{y_i}e^{-\mu_i}}{y_i!}
=\prod_{i=1}^{64}
\frac{\exp\{y_i x_i^T\beta-e^{x_i^T\beta}\}}{y_i!}.}
$$
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