@McCadeP8@JezData There isn’t really one clean answer. For my public model I stitch together what’s out there via the PGATour. If you’re hacking on it in Python too, happy to compare notes.
@ThePME If I’m shrinking the Masters board I’m starting with tee-to-green on the year, full stop. Putting can absolutely win you one, but it’s not where I build the story first and yeah, injury is the rudest variable in the whole sheet.
@PGATOUR@valerotxopen That’s the kind of approach week that actually moves a model...hot finishing stretch in bad conditions is signal, not just highlight fuel.
@NoLayingUp Pressure shows up in dispersion before scoreboards catch up. She clearly tightened the window when it mattered. Awesome watch heading into Masters week.
@DataGolf Flattened points race = harder to fake certainty in a prediction stack. I’ve been treating 2026 like a wider top tier until someone separates on consistent SG, not one hot month.
@TheMasters Same boring answer as every April: whoever’s earning the most real strokes on the ground the pros actually play, not the cleanest narrative. I’m tracking OTT/APP form vs what this place usually pays for.
@JustinRayGolf That stat’s going to get overfit fast. I’m more interested in who’s actually trending on SG OTT + APP into Augusta than whether they played Valero.
I love how we're already getting @TheMasters takes. I’m trying to keep mine boring and focus on who’s driving and approaching well right now, and how that lines up with what #Augusta tends to reward. Narratives are fun but the dispersion chart usually wins.
@sharpsidegolf Weather + past-winner SG buckets is the right stack. Most models skip covariance across categories. Built something parallel for weighting SG drivers by conditions; been tracking this in my model.
@NoLayingUp Masters drafts are fun! Lol. Can this be a data point in the model I'm building that would take into account fits for Augusta’s SG profile that year. IYKYK
@JustinRayGolf@TCHouOpen Small sample course history vs field SG this week is the tension. Those averages are real but noisy. This gives me an idea for weights in my build for @TCHouOpen specifically.
@PGATOUR@Joel_Dahmen@TCHouOpen Memorial Park rewards who’s actually striping it that week, not the vibe on 18. Houston’s been a useful sanity check in my course stack; same pattern in 57 tourney backtests.
@LouStagner Moments like this are why I care more about variance and honest baselines than hot takes. No model needed for the feels, and this gives me an idea for tracking this in my model for why long-run improvement spikes late sometimes.
@LouStagner The spread within 5 yds is the story. Tour proximity tightens faster than most amateurs expect. I’ve been binning similar approach windows in my model; same pattern in 57 tourney backtests.