Which combination of observed player statistics best explains a declared measure of World Cup impact, and can machine learning find player-tournament performances whose recorded contribution exceeds what traditional production alone predicts?
An end-to-end study of 2,927 player-tournament observations that combines role-relative measurement, reliability adjustment, nested validation, temporal testing, clustering, and explainable machine learning.
Field noteThe declared index combines reliability-adjusted production (45%), availability (35%), discipline (10%), and recorded versatility (10%).
−2.29 pp mean ΔTSClutch versus explicitly non-clutch efficiency
DS-002
Destination 02 · 2026 · 20 min read
Pressure, Production, and Noise
Which player-level offensive characteristics best predict the preservation or improvement of scoring efficiency during NBA clutch situations relative to explicitly non-clutch conditions?
A reproducible study of whether normal-condition offensive traits explain or predict the preservation of scoring efficiency during NBA clutch situations, with explicit non-clutch baselines, repeated validation, shrinkage, simulation, and multi-season stability checks.
Field noteMean clutch-minus-non-clutch true shooting was −2.29 percentage points, with a bootstrap 95% interval from −4.06 to −0.54.
How has geographic concentration evolved from 2015-2026, and how well do concentration, volatility, diversification, reserves, and producer-network structure explain and predict supply-disruption exposure?
A reproducible study of geographic concentration, country-mineral networks, structural vulnerability, and simulated supply disruption across a synthetic panel of 24 critical minerals and 35 producer countries.
Field noteGallium, Terbium, Dysprosium, Tungsten, and Germanium were the five most structurally vulnerable minerals across six ranking families.
I’m a student researcher and builder interested in using data, artificial intelligence, and engineering to understand complex systems and create meaningful tools.
I follow questions across data, research, and technology—then turn the evidence into tools and visual stories people can explore.
