Big data behind Steam: Game developers end up getting only 38% of the flow

Not long ago, Valve released the Steam platform’s 2019 list, and although many games are classified as platinum, gold, silver and bronze by revenue, it’s often difficult to determine the revenue range of a Steam game because of the opaque data. Recently, two independent game developers dug deep into thousands of Steam game data in their own way and gave their own judgment:

Big data behind Steam: Game developers end up getting only 38% of the flow

Behind the revenue data: Only 38% of developers get to the top

As competition in the gaming market intensifies, how to measure returnons of investment becomes a concern for all peers. For an independent game developer, the risk assessment can be made more accurate if you have some understanding of the game revenue profile. But how much revenue do we see from independent developers behind Steam sales?

Independent developer Danny Weinbaum gives a way to calculate real revenue:

All the data, except dollar prices and platform draw, are approximate, combining all of the above factors to get a multiplier of 0.38, in other words, if the game sells for $1, for every sale, the developer can get $0.38.

For a game that has already been released, the first week’s revenue accounts for 13% of the total revenue in the five years, in addition, the three-month revenue accounts for 33%, the annual income contribution is 58%, the two-year income accounts for 75%, the three-year 87%, the four-year income accounted for 95%.

So, if a game is released seven months later, seven months of revenue in this way is almost 40 percent of total five-year revenue. With the increase of the number of games, and steam user volume and user consumption growth slowly, the success rate of the game decreased year by year.

Big data behind Steam: Game developers end up getting only 38% of the flow

However, another developer, Sergio Garces, said Danny’s data was missing to limit age games and that Early Access’s end statistics were inaccurate. In addition, some prices are wrong due to statistical problems. To do this, he used the Steam API as an excuse to capture data for the last week of 2019, with at least 10 comments and 1,000 owners covering the game, and 7,000 games counted, to draw the following conclusions:

65 is the best “guess” number (i.e. the number of comments multiplied by 65 for forecast revenue); many game owners don’t write comments, and when predicting revenue, we choose to ignore these situations because the larger the multiplier, the higher the revenue result; 30-100 is a better range because it covers most games. The 65 is the average, which covers most low-selling games, and of course, mistakes also underestimate revenue.

After counting the number of owners, we need to translate it into revenue, and obviously it’s not as simple as multiplying the dollar price, and Sergio thinks Danny’s previous revenue calculations are good and useful for predicting long-term revenue because it predicts future revenue based on current game sales. The prediction method in this article is mainly to look at the amount of comments at different times, for which more than 1000 comments were called in the first five years of the game, visualised after the following chart:

Big data behind Steam: Game developers end up getting only 38% of the flow

This chart covers data on more than 400 games, the red line is the daily average, the blue is the median, the green line is what Danny used in his article, and from the trend, most of the income is concentrated in the first year, more than you think, so it’s relatively conservative.

Sergio Garces, who used historical evaluation data to create simpler models of progress to predict future estimates, says it’s not complicated because some games have linear revenue curves and others logaributt. Both approaches are used, and they are used to generate a range that produces as realistic results as possible based on the Best Guess number.

To add, it’s hard to predict what will happen in five years’ time because we don’t have enough data and the game market is going to change a lot, so the current rules don’t apply to the future.

Growing Steam platform: More and more high-income games

It is well known that the number of games on the Steam platform has been exploding in recent years. However, the number of new game releases has reached its peak, and we have even seen a decline in the number of new indie games in 2019.

Big data behind Steam: Game developers end up getting only 38% of the flow

Some people say that indie games are getting harder and harder to do, this is actually supported by data, in recent years released the game, the median continued to decline, we can see from the following data:

Big data behind Steam: Game developers end up getting only 38% of the flow

But Sergio wants to look at the problem from another angle, we don’t look at the percentage of game success rate, but compare it from absolute numbers. After all, more and more people are releasing games, so the number of winners is getting higher:

Big data behind Steam: Game developers end up getting only 38% of the flow

Indeed, even more and more games fail, but it is equally true that more and more people have succeeded, which is encouraging.

Ratings are important.

Danny used the following image to show the comparison between ratings and income, but Sergio did it again:

Big data behind Steam: Game developers end up getting only 38% of the flow

The median, not the average, is used here, so there is no need to calculate the revenue forecast. Interestingly, Sergio’s numbers don’t drop by 70% as Danny shows, so “The End of The Indie Game” is more of a mix of noise.

Obviously, it’s normal to generate high revenue when you consider player buying behavior. Many players read reviews before buying a game, and the lower the rating, the lower their willingness to buy. Conversely, if the quality of the game is high and the player likes it, they will be more willing to recommend it to their friends, word-of-mouth communication is of great importance to the success of the Steam game.

Label

The market as a whole can understand some trends, but in order to get more information, we need to split it up, and the best way to do that is to use the Steam tag.

Chris Zukowski has written a good article about his use of tag data for marketing before making a game. It is the right thing to do to understand a domain and analyze it with data, rather than simply looking at the data to extract some tags to analyze the reasons for the game’s success.

The Steam platform uses the most tags:

Big data behind Steam: Game developers end up getting only 38% of the flow

Screenshot of Steam Platform Game (partial) Forecast Revenue Data:

Big data behind Steam: Game developers end up getting only 38% of the flow