Normalized Score Sum
Read the shape of this family
Family illustration · not a computed standing
Uses the raw game scores, rescaled so different games compare. The formula and explanation below describe Normalized Score Sum.
How it works About 1 min read
Normalized Score Sum scales each match's raw scores to a 0-100 range before summing. The top scorer in each match gets 100, the lowest gets 0, and everyone else falls in between proportionally.
How it works: For each match: normalized = (your_score - min_score) / (max_score - min_score) × 100. Sum all normalized scores across matches.
When to use it: When you have raw score data and want to combine results from matches where scoring scales differ (e.g., one game might have scores of 20-50, another 100-300). It is easier to explain than z-scores and works well as the broad score-based default.
Watch out for: Requires actual score data -- won't work with placement-only results. Min-max normalization is relative to each match's field: a narrow 100-99 win can produce the same top/bottom normalized result as a 100-10 blowout. Use Z-Score Sum when a mature scored dataset needs to preserve how far above average a player was.
Will it fit your results?
Works well with
Games whose results this method reads cleanly
Also in Score-Based
Uses the raw game scores, rescaled so different games compare.