I combined the names of race horses and jockeys and then examined the letter combinations that produced profanities; the model was that the pair with the largest number of swear words would perform best.
Upon testing I find that I have considerable skill in predicting the winners of steeplechases of less than 3 miles, p=80%, but less skill when predicting loner races or on the flat.
I am seeking Federal funding based on this initial success to expand the model by increasing the size of the profanity index; essentially by three mechanisms;
1) visiting places where unusual profanities are uttered (justification for line item 2 in the Budget; “The Three Year Global Pub Crawl),
2) persuading the compilers of dictionaries that words such as ‘Brity’ and Thomst’ are in fact profanities (justification for line item 3 in the Budget; “The Russian Mafia and Linguist re-education”),
3) rerunning races which have provided the wrong outcome to reduce the statistical variability (justification for line item 3 in the Budget; “The Italian Mafia and the Fix-Is-In”).
I have high hopes.