Problem & Constraints
Grading, applying complex tiebreaker matrices, and ranking 4,000+ high school student competitors across regional cluster eliminations within tight 2-hour event turnaround windows.
How It's Built
1. Automated Matrix Scoring Pipeline
Vectorized NumPy and Pandas matrix operations evaluating regional cluster answer keys, applying subject-weighted penalties, and computing tiebreakers in seconds.
Trade-off:Vectorized in-memory matrices replaced manual spreadsheet formula recalculations that previously hung for 45+ minutes.
// Vectorized score computation with blueprint-weighted penalties
def compute_scores(raw_matrix: np.ndarray, answer_key: np.ndarray, weights: np.ndarray) -> np.ndarray:
correct_mask = (raw_matrix == answer_key)
return np.dot(correct_mask.astype(float), weights)Hurdles & Solutions
Multi-Way Tiebreaker Deadlocks
Problem:Top national qualifiers frequently tied on total score, requiring recursive evaluation of difficulty-weighted question tiers and timestamp priority.
Resolution:Implemented a deterministic multi-key sorting algorithm evaluating total score, tier-3 problem counts, and verification check marks in sequence.
Results & Numbers
Processed scores and verified rankings for 4,000+ competitors with 100% accuracy and zero tabulation delays.