Z-Score Sum
Turns each score into standard deviations above or below that match's average, then sums them. Positive means consistently above the room.
sum((score - mean) / std_dev per match)
Best at
Mature scored datasets where how far above average you were is the interesting part.
Where it misleads
Needs several scored players per match and enough history to be stable; the numbers mean nothing to a casual reader.
How it works
Z-Score Sum converts each match score into standard deviations above or below the match mean, then sums them. It's the statistician's choice for measuring relative performance.
How it works: For each match: z = (your_score - match_mean) / match_std_dev. Sum all z-scores. A positive total means you're consistently above average; a highly positive total means you dominate.
When to use it: When you want a rigorous statistical measure that accounts for both the average and the spread of scores in each match. It rewards performing well above the competition, not just finishing above the minimum score.
Watch out for: Requires at least 2 players with scores per match and enough repeated data for stable interpretation. The numbers aren't intuitive for casual players, so normalized score methods are usually better defaults until a group has a mature scored dataset.