  Football forecasting models traditionally rate teams on past match results,
that is based on the number of goals scored. Goals, however, involve a high
element of chance and thus past results often do not reflect the performances
of the teams. In recent years, it has become increasingly clear that accounting
for other match events such as shots at goal can provide a better indication of
the relative strengths of two teams than the number of goals scored. Forecast
models based on this information have been shown to be successful in
outperforming those based purely on match results. A notable weakness, however,
is that this approach does not take into account differences in the probability
of shot success among teams. A team that is more likely to score from a shot
will need fewer shots to win a match, on average. In this paper, we propose a
simple parametric model to predict the probability of a team scoring, given it
has taken a shot at goal. We show that the resulting forecasts are able to
outperform a model assuming an equal probability of shot success among all
teams. We then show that the model can be combined with predictions of the
number of shots achieved by each team, and can increase the skill of forecasts
of both the match outcome and of whether the total number of goals in a match
will exceed 2.5. We assess the performance of the forecasts alongside two
betting strategies and find mixed evidence for improved performance.
