u/Dry-Impress8941

Built a walk-forward jockey/trainer/OR/RPR/TS Elo model on UK racing (Betfair archive + Kaggle raceform, verified to 94.9% match), properly avoided lookahead, still can't beat the closing line even with the richer data. What do serious horseplayers use that public ratings data doesn't capture?

I tested favourite-longshot bias split by home/away favourite status in football/basketball/hockey/rugby league — turns out almost nobody's published this specific four-cell test outside the original 2009 football paper. Anyone tried it informally and found it's a dead end in a particular sport before I sink time into it?

What's the right way to compute a confidence interval on mean ROI for a betting/trading strategy where individual trades cluster by event (e.g. multiple correlated trades on the same card/matchday), rather than being i.i.d.? Looking for the right choice between cluster-robust SEs, wild cluster bootstrap, and block bootstrap for this shape of data.

We tested a set of pre-registered hypotheses across ~9 independent samples (different sports) with a 95% CI-excludes-zero bar. Is a flat, stricter significance threshold (e.g. requiring |z|>3 instead of |z|>1.96) a reasonable ad hoc substitute for formal multiple-testing correction (Bonferroni/Benjamini-Hochberg) across the full family of tests, or should proper FDR control be used instead?

Built a walk-forward jockey/trainer/OR/RPR/TS Elo model on UK racing (Betfair archive + Kaggle raceform, verified to 94.9% match), properly avoided lookahead, still can't beat the closing line even with the richer data. What do serious horseplayers use that public ratings data doesn't capture?

Tested favourite-longshot bias split by home/away favourite status in football/basketball/hockey/rugby league — turns out almost nobody's published this specific four-cell test outside the original 2009 football paper. Anyone tried it informally and found it's a dead end in a particular sport before I sink time into it?