Updated Righties for season end.

and if you want to know what this all means I asked gpt-5 to explain: Here’s what your analysis has revealed so far, simplified into bullet points: Foals are centred around the midpoint of their parents’ grades. → On average, kids don’t beat or lag their parents — they regress toward the middle. Parity matters (odd vs even parent gaps): Odd gaps → midpoint lands exactly on a grade → foals cluster tightly there. Even gaps → midpoint is between grades → foals split ~50/50 across the two nearest grades. Overshooting is real but conditional: Foals sometimes exceed the better parent. Overshoot is common when parents are close (gap 0–1). Overshoot probability collapses as the gap widens (≈0 beyond gap 3). Variance is fixed: The spread around the midpoint is ~1 grade band regardless of gap. This is not what a “pure normal distribution” would do — instead, it fits better with DNA recombination mechanics. Recombination model fits: Parents contribute discrete “DNA slots.” When parents are similar, foals can stack high slots and overshoot. When parents are far apart, foals average down and rarely beat either. OU (subgrade) metric works: By converting grades to numbers, you can measure hidden fidelity inside each band. Foals can exceed parents’ OU, showing “hidden monsters” even within the same visible grade. Sire effects are measurable: Some sires consistently produce foals better than expected, even after correcting for parent gap. Others underperform. This gives a way to rank sires by true breeding value, not just by visible grade. **not my words, this is from the AI overlords.