U of T trio capture Big Data Bowl championship thanks to novel metrics they created to measure pass\u002Drush pressure at every moment after the snap
Currently, the only generally accepted statistical means of measuring pressure applied by a defence’s pass rushers in pursuit of the other team’s quarterback are sacks, hurries or QB hits. Similarly, pass-blocking effectiveness is measured only by those same measures, but in the reverse. That is, via those allowed, not forced.
As background, thanks to tracking devices worn by every NFL player, the precise position, direction and speed of every player at any moment, on any given play, is recorded. The league partners with Amazon Web Services to collate this data for its Next Gen Stats operation, which for six years has offered far broader and more analytically meaningful stats, which we often see represented graphically on NFL telecasts.
On Thursday, Inayatali said a big part of being present at the combine this week was all the flattering and helpful feedback the trio received, in person, from data scientists and NFL sports-analytics experts alike, even some coaches. Last year’s charge to sports-data scientists was to dive deep into special teams, and the SFU team addressed punt returners’ decision-making, by devising a novel metric to predict expected return yards remaining on a play after various decisions.Article content
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