Normalized Score Sum
Rescales each match's scores so the top scorer is 100 and the lowest is 0, then adds those up across matches.
sum((score - min) / (max - min) × 100)
Best at
Combining games whose scores live on wildly different scales, when you want a cumulative total.
Where it misleads
Needs real scores. A 100–99 squeaker rescales identically to a 100–10 rout, and playing more always helps.
How it works
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.