That’s not how a gaussian works and they are talking about percentiles anyways. An 80% player is within 1 sigma as the ± 1 sigma band contains approximately 68% of the population (0 sigma would be the median player, i.e. a 50% player). So a player at exactly +1 sigma would be an 84% player. Top 0.1% is about 4.5 sigma.
Updated the post since you guys were asking:
UPDATE: Okay, some people have brought up what percentage of people we’re talking about in each of the categories above. We can crunch the sample we have (N=208) through a standard deviation calculator, assuming the distribution is normal for these purposes and the sample representative, and see what percent of players are better or worse than a given average relative position on their service record. The standard deviation on the sample is 12.502, on a mean of 51.827, if you want to follow along at home. What it comes to is:
50% player (by average relative position) in the sample: Better than 44.2% of all players.
67% player: Better than 88.8% of all players
75% player: Better than 96.8% of all players
80% player: Better than 98.8% of all players.
You can run the same math in reverse, too. Assuming all the many many assumptions above, for an average score of 1500 in ground RB, which is equivalent to the 61% percentile of game results, you can infer that 76.7% of ground RB players will not be able to achieve that score.
2 Likes
For future reference, here’s a breakdown of average relative position values (%ARP) and population of active gamers who can equal or beat them (%POP), assuming representative sample, normal distribution, and sigma and mean as given above:
| %ARP | %POP |
|---|---|
| 50 | 55.81 |
| 55 | 39.98 |
| 60 | 25.66 |
| 65 | 14.60 |
| 70 | 7.30 |
| 75 | 3.19 |
| 80 | 1.21 |
| 85 | 0.40 |
| 90 | 0.11 |
| %POP | %ARP |
|---|---|
| 50 | 51.8 |
| 45 | 53.4 |
| 40 | 55.0 |
| 35 | 56.6 |
| 30 | 58.4 |
| 25 | 60.3 |
| 20 | 62.3 |
| 15 | 64.8 |
| 10 | 67.8 |
| 5 | 72.4 |
| 1 | 80.9 |
| 0.1 | 90.5 |
3 Likes