A successful basketball player stands at 6 feet [X] inches (or [Y] cm), with a z-score from the dataset. This z-score directly measures how many standard deviations his height is from the mean height in that dataset.

Z-Score Explanation

The z-score tells us precisely that: a positive value like 1.95 or 2.66 (from similar examples) means the height is above the mean by exactly that many standard deviations.

Without the exact z-score value in your query (it appears blank), the answer is simply the given z-score number. For instance, if it's 1.95, he's 1.95 standard deviations above the mean.

Real-World Context

NBA averages hover around 6'6"–6'7" (198–201 cm), so 6'2" (188 cm) would be unusually short—likely yielding a negative z-score—but success stories like Allen Iverson (6'0") prove height isn't everything.

Variations in these problems (e.g., 6'4" at z=2.66 or 6'7" at z=3.74) highlight how datasets define "above average."

Quick Examples from Stats

Height| cm| Z-Score| Deviations Above Mean
---|---|---|---
6'2"| 188| 1.95| 1.952
6'4"| 193| 2.66| 2.666
6'7"| 201| 3.74| 3.744

TL;DR: His height is the z-score value (e.g., 1.95) standard deviations above the mean. Plug in your exact number for the answer.

Information gathered from public forums or data available on the internet and portrayed here.