We have historical data dating back 100 or so years. Seeing as its the only probability tool we have then it has to be used. Why do you think seasons are referred to after they've happened as having been average or above average or below? It's using these statistics.
As with any situation that requires planning, it is always the right move to plan for the worst. Standard planning.
Originally Posted by: some faraway beach
Well, classifying a season after it's happened against a long-term average is straightforward. But I feel that calculating whether forecasters got their pre-event probabilities correct needs a lot more than even 100 forecasts and outcomes to determine.
To illustrate: Let's say you have the data for all the 20th century's UK winters. You know what constitutes an average UK winter over that period. Starting in 2001 you start making seasonal forecasts against those averages. You could forecast 33% below-average, 33% approx. average and 33% above-average one year and it turns out above-average. Next year you go 40%, 40%, 20%. It's above average again. Are those forecasts right or wrong? They both included the correct outcome among their probabilities. How many forecasts and outcomes do you need before you can make a robust estimate that your forecasting method is offering percentages which are accurate? Personally I'd want 100 goes at the very minimum, and preferably a lot more than that, But we'll all be dead by then.
You might feel you can tell whether forecasters are on the wrong track with their probabilities with fewer results to check than that. But I think most people's experience of life, investments or supporting football teams is that odd results, ill luck or good fortune can persist for way longer than you could plausibly plan for.
Originally Posted by: Matty H