Jesse Cohen takes a deep dive in the trends around the catchable goal interest rate to discover “masked profiles” – players who underperform but may be ready for a future outbreak.
Part of the statistical analysis in this article was performed with the help of artificial intelligence (youSing a tailor-made data analysis and report model of the GPT “App Store” and GPT 03-mini-high to validate). Far from a turnkey solution, the statistical methodology and results were developed and validated for many hours of work. When it is used carefully in this way, AI can be a powerful tool to test fantasy benefits.
Introduction
The Dynasty market has changed a lot since we started playing. In a good competition, even the worst manager is aware of variance and regression, and the statistential toolbox is full. We have yprr, TPRR And 1Rrretc.; the Always useful efficiency staple fpoe; and through services such as the Rotoviz Advanced States ExplorerBetter access to ‘real-life’ statistics such as IAY ConV % and Catchable Target %.
The toolbox is so full that many players (and analysts) get lost in the smoke. Nevertheless, viewing advanced statistics is a useful way to refine mental buckets for the season. Good players are goodAnd usually show with statistics that cut through the sound of traditional teleclatistics.
This approach emphasizes “Bad Stat” players (ie Advanced Stat UnderPerformers), which is logical; Bad players are bad. But this cohort also contains many of the largest rebounds and breakouts year after year. So they get a second look. We fill in the context, consider changes in staff and schedule.
Before applying those contextual factors: is there a data -driven way, uses the use of the extensive toolbox to mark a sub -cohort of “bad stat” players who may be more likely to change an impactful fantasy values in the N+1 year? Not to one new Fancy Stat, but to sharpen the decision-making in the buy-low bin before we return to the ‘good’ players.
My hypothesis: a low catchable target % (CT %) in year N, especially when combined with others suppressed Features such as Low YPRR or Low YAC/REC, can help players on the surface of whom talent or opportunities are possible masked by Quarterback game, schedule, pressure percentage or simply bad luck on the relatively small monster that is an NFL season.
The results? Surprisingly encouraging. The “masked profiles” cohort generated from the dataset catch
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