  The approaches commonly used to model the number of goals in a football match
are characterised by strong assumptions about the dependence between the number
of goals scored by the two competing teams and about their marginal
distribution. In this work, we argue that the assumptions traditionally made
are not always based on solid arguments and sometimes they can be hardly
justified. In light of this, we propose a modification of the Dixon and Coles
(1997) model by relaxing the assumption of Poisson-distributed marginal
variables and by introducing an innovative dependence structure. Specifically,
we define the joint distribution of the number of goals scored during a match
by means of thoroughly chosen marginal (Mar-) and conditional distributions
(-Co). The resulting Mar-Co model is able to balance flexibility and conceptual
simplicity. A real data application involving five European leagues suggests
that the introduction of the novel dependence structure allows to capture and
interpret fundamental league-specific dynamics. In terms of betting
performance, the newly introduced Mar-Co model does not perform worse than the
Dixon and Coles one in a traditional framework (i.e. 1-X-2 bet) and it
outperforms the competing model when a more comprehensive dependence structure
is needed (i.e. Under/Over 2.5 bet).
