### Table of Contents

# Explaining Devtome Earnings

This article explains the Devtome earnings methodologies. It’s a little simplified because a full calculation would require going through every writer and every statistic to demonstrate final results. We’ll go through each column header on a notional Devtome round file, for example devtome_35.csv located here.

To check the calculation scripts for yourself you need to have Python installed, which can be downloaded free from https://www.python.org/ Then save devtome.py from here and edit with IDLE to view code. For reference * means multiply and the order of operators is brackets, exponents (such as 2 squared, 2^2), divide, multiply, add, subtract.

Note: User statistics are available in an easier to view online format here

## Explanations

**Name**

Devtome username

**Coin Address**

DVC invoice address

**Collated Word Count**

Words submitted in collated articles

**Collated Weighted Word Count**

Collated Word Count * 0.3

It’s 0.3 because that’s the relative weighting for collated articles versus original articles.

**Image Count**

Images submitted in articles

**Original Word Count**

Words submitted in original articles

**Word Count**

Collated Word Count + Original Word Count

**Weighted Word Count**

Collated Word Count + Original Word Count + (Image Count * 10)

Each image counts as 10 words.

**Cumulative Payout**

Total round shares

**Previous Cumulative Payout**

Number of shares paid out in previous rounds

**Payout**

Cumulative Payout - Previous Cumulative Payout

**Unique Page Views**

Number of page views received that round, data obtained from google analytics after a round ends

**Popularity Times Rating**

Unique Page Views * Rating Median/99

It’s 99 because that’s the maximum rating.

**Advertising Portion**

Popularity Times Rating/Total Popularity Times Rating

**Advertising Revenue**

Advertising Portion * Total Advertising Revenue

**Views per Thousand Words**

1000 * Unique Page Views/Weighted Word Count

**Normalized Popularity**

Takes an individual measure of Views per Thousand Words, compares it with the average and deviation from the average Views per Thousand Words to give a comparable measure.

A manual calculation takes several steps and is the same for all normalized measures, the first being to multiply an individual measure by the reciprocal of the average. For anyone who wants to do it for themselves, once you have these numbers:

a. Individual Views per Thousand Words * 1/Average of all Views per Thousand Words (where greater than 0)

b. Calculate 1/Views per Thousand Words for each author (where greater than 0)

c. 0.5/Standard Deviation (all measures of b. above)

Normalized Popularity = a. ^ c.

**Rating Median**

The median (middle) of all writer’s ratings.

**Normalized Rating Median**

Takes an individual measure of Rating Median, compares it with the average and deviation from the average rating median to give a comparable measure. The calculation is the same as for Normalized Popularity, but using Rating Median data instead.

**Categorized Articles**

Total articles that have been categorised

**Articles**

Total Articles

**Categorization**

Categorised Articles/Articles

**Normalized Categorization**

This takes an individual measure of Categorization, compares it with the average and deviation from the average categorization to give a comparable measure. The calculation is the same as for Normalized Popularity, but using Categorization data instead.

**Normalized Worth**

0.1 * Normalized Categorization + 0.3 * Normalized Popularity + 0.6 * Normalized Rating Median

**Earnings Multiplier**

This is the result of an iteration that solves for individual payout and multipliers in the context of maintaining the same Total Devtome payout.

**Earnings**

This multiplies round Payout by Earnings Multiplier, rounded to a whole number.

## Worked Example

Using devtome_35.csv. Any small differences are due to rounding, where the Python script generally rounds to three significant figures.

**Name**

Weisoq

**Coin Address**

1Cy9e1Yuwboj63XRkMkT6W6YsGDtYDrsUp

**Collated Word Count**

2735

**Collated Weighted Word Count**

2735 * 0.3 = 820

**Image Count**

39

**Original Word Count**

54623

**Word Count**

2735 + 54623 = 57358

**Weighted Word Count**

54623 + 820 + 39 * 10 = 55833

**Cumulative Payout**

56

**Previous Cumulative Payout**

55

**Payout**

56 - 55 = 1

**Unique Page Views**

314

**Popularity Times Rating**

314 * 95/99 = 301

**Advertising Portion**

301/331593 = 0.000908

**Advertising Revenue**

0.000908 * Total advertising revenue

**Views per Thousand Words**

1000 * 314/55833 = 5.62

**Normalized Popularity**

a. 5.62 * (1/9.95) = 0.565

b. Calculate

c. 0.5/standard deviation(b.) = 0.09

Normalized Popularity = 0.565 ^ 0.09 = 0.948

**Rating Median**

95

**Normalized Rating Median**

a. 95 * (1/76.1) = 1.248

b. Calculate

c. 0.5/standard deviation(b.) = 2.88

Normalized Popularity = 1.248 ^ 2.88 = 1.9

**Categorized Articles**

18

**Articles**

18

**Categorization**

18/18 = 1

**Normalized Categorization**

a. 1 * (1/0.913) = 1.095

b. Calculate

c. 0.5/standard deviation(b.) = 2.18

Normalized Popularity = 1.095 ^ 2.18 = 1.22

**Normalized Worth**

0.1*1.22 + 0.3*0.948 + 0.6*1.9 = 1.54

**Earnings Multiplier**

MAX(MIN(0.861*1.54,1.4999),0.5001) = 1.33

0.861 is the result of an iteration where individual Normalized Worth measures are applied to all author’s round Payouts, then an overall multiplier is adjusted until the resultant total Earnings is equal to the sum of all Payouts (624 shares). This is therefore applying all respective writer multipliers while keeping the total revenue neutral.

**Earnings**

1.33 * 1 (rounded to a whole number) = 1