https://matteocampagna.com/en/registry/competition/predicting-archery-scores Changes to this claim in Matteo Campagna's evidence registry: its status, its year-by-year history, revisions of the page. en-gb https://matteocampagna.com/en/registry/competition/predicting-archery-scoresThe two studies that set the predictors against each other do not give a clear answer, and they are few and indirect. In an indoor competition among adolescents, shooting time and heart rate variability before each arrow together explain 11.8 per cent of the score of the single arrow, while the difference between novices and experienced archers on its own weighs about twice as much. In thirty-two Indian archers a machine learning model, trained on one session and tested on the next, is off by about six points out of three hundred, and at the top of the predictors are years of practice, first, and scores in previous competitions, third; in second place, though, is resting systolic blood pressure, taken seated before the warm-up, a measure from the day itself that ranks above past results, while heart rate around the release comes sixth and tenth. Two small Indonesian groups then show that the internal ranking changes little over time: a correlation of 0.898 between two very different training periods and of 0.985 between the start and the end of six weeks. These are associations, the comparisons between predictors come from different analyses, and the best model explains little more than half of the variability: nobody has yet compared a model with the simplest forecast, the average of the last few competitions. With a measure from the day in second place, the status is contested.claim:punteggio-prevedibile-dati:revision:2026-05-28:2Thu, 28 May 2026 08:00:00 GMT https://matteocampagna.com/en/registry/competition/predicting-archery-scoresclaim:punteggio-prevedibile-dati:new_claim:2026-04-08:contestatoWed, 08 Apr 2026 08:00:00 GMT