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Visualizing gaming trends on SteamLaurens Bolle
KU [email protected]
Melvin HulsmansKU Leuven
[email protected]
Dries VerreydtKU Leuven
[email protected]
ABSTRACTGamers and developers alike could be interested in the
growthof the gaming market [10]. Will simulation games becomeoverly
abundant soon, do we see a return of arcade or retrogaming? Which
genres should I develop for if I want to makea game in the most
popular genre? What if I want to makeone in which I have less
chance of competition from othergames? These questions and more can
be answered by usingour visualization of game genres on Steam.
ACM Classification KeywordsH.5.m. Information Interfaces and
Presentation (e.g. HCI):Miscellaneous
Author KeywordsInformation visualization, Gaming, Steam,
Trendvisualization
INTRODUCTIONSteam is one of the largest gaming platforms for PC
[6]. Itprovides a platform where gamers can find, buy and
playgames. Even though it is one of the largest platforms
already,Steam is still rapidly growing. To view and explore the
con-tent of Steam in a new way, we introduce a visualization of
thetrends of game genres. This information visualization
allowsusers to examine the past and current popularity of the
vari-ous genres, enabling them to make informed decisions aboutthe
type of game they want to buy or develop next.
GOAL AND AUDIENCEThe goal of our project is to provide a
visualization that helpsto discover gaming trends on Steam. It
shows how popular-ity of game genres evolves by visualizing the
release dates ofgames, grouped per genre. The visualization helps
game de-velopers choose what kind of games are worth making nowand
in the near future. A new game in a genre that has lost allits
popularity probably will not be worth creating in compar-ison to a
game in the genre of a booming trend. On the otherhand, the
visualization is also useful for developers if theywant to make an
original or unique game. Genres in whichthere have been many games
released recently are probablysaturated and a new game in that
genre will not make that big
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of an impact. The developer is better off choosing a genre
thatis increasing in popularity but has not yet reached its
peak.For gamers themselves, it is always fun to see in which
gen-res most games are created, since they are likely to be
popularand have many other players. This can help the gamer
choosewhich game he wants to play next and as a direct
consequenceimprove his gaming experience.
TECHNOLOGY
The data setOriginInitially, we were going to use the official
Steam WebAPI [15], but unfortunately it couldn’t offer all of the
infor-mation we would need for our visualization, such as
releasedates, total hours played or sales figures. Therefore, we
usean unofficial API1, from which we could also only get
releasedates. While not being the best metric for the popularity of
agame genre, it at least gives a good first indication, since
wepresume developers wouldn’t keep creating games for unpop-ular
genres. The number of requests that can be submitted tothis API in
a short time period is very limited. In order to copewith this
restriction, we wrote a script which periodically re-quests a small
subset of the games and saves the responses ina local file,
eventually resulting in a complete local data set.These requests
contain a lot more information than we need,so we extracted the
relevant fields and combined them intoone compact JSON file.
Before we were able to retrieve data from this unofficial
APIusing this script, we did have some complications to over-come
such as ‘Cross-Origin Requests’ being blocked [1]. Wewere able to
circumvent this issue by hosting a local webserver. We sent the
requests from our JavaScript in a lo-cal browser to this web
server, which forwarded them to theSteam server. The web server
then forwarded the responsesto our JavaScript where we could access
this data.
FormatResponses from the Steam server came in a JSON format.This
was easily transformed into objects in JavaScript to ex-tract the
parts of the information we required, which we thensaved locally
again in JSON format. This made the resultingdata file much smaller
in size, roughly 1.5 MB. Because ofthe great support for JSON in
D3.js, it is fast and easy to loadthe file whenever we start the
visualization. In order to iden-tify a game, we store its Steam
application ID and its name.Apart from that information, every
entry has a list of genres,since it could belong to multiple
genres, and its release date.1Documentation of the unofficial
Storefront API:
https://wiki.teamfortress.com/wiki/User:RJackson/StorefrontAPI
https://wiki.teamfortress.com/wiki/User:RJackson/StorefrontAPIhttps://wiki.teamfortress.com/wiki/User:RJackson/StorefrontAPI
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Distribution of valuesThe data retrieved dated back from 1997 to
now, as shown inFigure 1. Only 26 games dated back before 2005,
this is whywe removed all the data leading up to the year 2005.
Figure 1: Visualization including all the years that
containedany data.
Since 2012 Steam also accepts non-gaming applications [17].This
led to our data containing nine genres that are not gam-ing genres
but rather tags for the type of software. Examplesof these genres
are Web Publishing and Photo Editing. Anold version of the
visualization with all the genres is shown inFigure 2. The removed
genres contained around two percentof all our data points. Removing
them made the visualizationmore clear without losing valuable
information.
Figure 2: Visualization including all the genres found in
thedata.
The eight most common genres have a very similar distribu-tion
from 2005 to 2015. Slowly increasing over the earlieryears with a
larger increase the closer we get to the present.All genres have a
very large increase in 2014 and they allhave their current maximum
in 2014. The data for 2015 isonly partially collected, but for most
genres it already is overhalf the value of 2014. The less common
genres have zerovalues for the earlier years, but there was enough
data to startfrom 2005.
When the data was grouped up in quarters of a year, the
highervalues within a year were almost always the fourth
quarterwith a lot lower values for the first and second quarter of
theyear.
The less common genres show more irregularities from
thesedistributions because there is less data to work with.
D3.jsSince this was our first time programming in D3, we took
alook at the examples page of D32. Here we found a couple
oftechniques that we decided to use for ourselves. It should
benoted that some code is copied and edited to work with
ourvisualization, as detailed below.
We used the example on http://bl.ocks.org/mbostock/3887051 as a
base for our grouped bar chart. The codeon
http://bl.ocks.org/mbostock/3943967 taught us howto work with
radiobuttons, transitions and how to make astacked bar chart. The
method to construct our y-axis lineswas inspired by
http://bl.ocks.org/mbostock/4323929.After combining all of these
components we still needed toimplement a line graph, for which we
took the following twosites as examples:
http://bl.ocks.org/mbostock/3883245,http://bl.ocks.org/mbostock/3884955.
Connecting theseexample visualizations, customizing them to our
liking andmaking them compatible with our data format proved to bea
considerable challenge. For example, our data consisted ofa long
list of games with their respective attributes. But thestacked bar
chart required the data to be organized in a listwith each element
representing a year containing the numberof games per genre.
VISUALIZATION AND INTERACTION
Road to the visualization
Achievement visualizationInitially we wanted to focus on
‘achievements’. An achieve-ment of a game is a badge that you get
on your Steam ac-count showing that you have achieved a certain
feat. Thesefeats can range from completing the tutorial to
performing anearly impossible action. We wanted to visualize how
manyachievements of a certain game were actually achieved by
theentire community. This could enable so-called
‘achievementhunters’ to find the more ‘valuable’ achievements,
since theywould not have been completed by many other users. We
alsowanted to add a login function that would work with the
steamaccount. This would reduce the visualization to only show
thegames that the user owns. The way we wanted to visualizethis was
by showing a cluster of orbs where each orb wouldcontain the cover
art and name of a certain game. When a userwould click on a certain
game’s orb, all the other orbs woulddisappear and the achievements
of this specific game wouldsurround the orb. Every achievement’s
icon would be in aseparate orb and would contain the percentage and
amount ofplayers that obtained it.
However, Steam already provides many of these features in-side
the client itself. While Steam doesn’t provide the infor-mation in
an interactive visualization, recreating these con-cepts seemed
redundant unless we could add a significantnumber of novel
features. We also wanted the visualization to
2The D3.js examples gallery can be found at:
https://github.com/mbostock/d3/wiki/Gallery
http://bl.ocks.org/mbostock/3887051http://bl.ocks.org/mbostock/3887051http://bl.ocks.org/mbostock/3943967http://bl.ocks.org/mbostock/4323929http://bl.ocks.org/mbostock/3883245http://bl.ocks.org/mbostock/3884955https://github.com/mbostock/d3/wiki/Galleryhttps://github.com/mbostock/d3/wiki/Gallery
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allow users to explore the data and gain new insights,
some-thing which would be difficult when only displaying
achieve-ments. Due to these problems, we decided to drop our
origi-nal idea.
Trend Visualization GenresOur next idea involved visualizing the
evolution of the var-ious game genres available in the Steam store.
This subjectallows for users to explore the data and possibly find
storiesor interesting questions such as: “When did Indie games
be-come popular?”
We started by looking for methods to visualize this data.
Thefirst visualization that we found was the stacked area
chart.Figure 13 shows an example of this kind of chart. This
vi-sualization is good to portray a trend throughout the years
ifyou have one or more data sets. Our sets are the genres thathad a
certain number of games released in a certain year. Un-fortunately
stacked area charts have some problems as well,as explained in the
‘Related work’ section, which is why wedidn’t use this
technique.
Figure 3 is another visualization we considered, based on
thework of Rahman [11]. This visualization makes it easy to ei-ther
compare the different genres in a single year, or view theevolution
of a single genre throughout the years. The problemhowever is the
growth of the Steam platform, which is verydifficult to view using
this visualization. For example, the to-tal number of games more
than doubled in 2014 compared to2013. Deducing this information
would be difficult for a userwhen using this visualization.
Figure 3: The number of released games per year, separatedby
genre, visualized using the technique of Rahman [11].
Figure 4 shows a D3.js recreation [4] of the famous Gap-minder
[12] visualization. Where the original dynamicallyplots wealth
versus life expectancy over time in which theorbs represent
countries, we could plot number of releasesversus number of players
with the orbs representing the dif-ferent genres. We opted against
using this visualization sinceSteam only exists since 2003 [14], so
the animation would
be somewhat limited. Furthermore, no data is publicly avail-able
about the number of players, nor could we find any
otherinteresting, and available, metrics to plot.
Figure 4: D3.js recreation [4] of the Gapminder [12]
visual-ization.
Finally we also considered using a stacked area chart, as
il-lustrated in Figure 13. The ‘Related works’ section
discussessome problems with this visualization, such as the
difficultyof viewing the evolution of one of the higher layers. Due
tothe problems with the stacked area chart, we decided to baseour
visualization on bar charts.
Different ViewsOur visualization has different views. We wanted
to be able todisplay all the information that can be obtained from
the data.Trying to make one view that could do this turned out to
makethe visualization unclear. This is also a bad working
methodaccording to Few [7]. This could mean that the user isn’t
ableto see certain information or stories that could be
obtainedfrom the data. Just choosing the most important view is
alsonot a solution. This way stories might, and in our case will
belost. A good example of this is when a story can be found
bylooking at multiple views.
Grouped bar chartThe first view we will discuss is the grouped
bar chart. Ourfirst implementation of this can be found in Figure
5. Thisvisualization enables the user to compare genres within
oneyear. You also still retain a general overview of all the
genresand their growth throughout the years. However the
overalltrend of a genre throughout the years is rather difficult to
see.
Our final visualization can be found in Figure 6. We can seehere
that the legend is expanded with checkboxes. These al-low the user
to choose the genres that are displayed. Selectingor deselecting
genres results in rescaling. By doing this thereis less loss of
screen space. The rescaling also allows the userto closer inspect
smaller genres. When deselecting large gen-res the smaller genres
will be displayed a lot larger as can beseen in Figure 7.
We also added numbers on top of the bars. These will
alsorescale. They were added because the exact amount of thegames
with a certain genre that were released on a year couldnot be
determined from the y-axis. Of course this was only
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Figure 5: First implementation
Figure 6: Grouped bar charts showing the absolute number ofgames
for each genre per year
(a) Large genres selected
(b) Large genres deselected
Figure 7: Visual example of rescaling
(a) Mockup
(b) Final visualization
Figure 8: Stacked bar charts showing the absolute number ofgames
for each genre
the case if the amount of games was sufficiently high enough.The
intended use of the y-axis is to be able to get a roughindication
of the amount.
Stacked bar chart with absolute numbersThe absolute view was
made to show the growth of Steam andits genres. Before implementing
this we first made a mockup,which can be found in Figure 8a. Our
final visualization canbe found in Figure 8b. It can be seen that
again the check-boxes are added to the legend. In the mockup they
were sepa-rate but we combined them to increase the data-ink ratio.
Wechose to put the color of the genre behind its name. This
be-cause when merging the legend and the checkboxes the colorswould
fall behind the checkboxes. Selecting and deselectinggenres will
rescale the genres to use as much screen space aspossible.
Another difference is that we removed the search bar, sincethere
is enough space to display all the genres and still keepthem
readable. We also added the exact amount of genresinside of the
bars. The numbers will scale relative to the totalamount of games.
These numbers were also added becausethe exact amount, of the games
of a certain genre that werereleased in a year, could not be
determined from the y-axis.This again only applies if the amount of
games is sufficientlyhigh.
Another thing we added is an on-click event. This can beseen in
Figure 9. We added this because like in a stacked areachart,
viewing the evolution of one of the higher layers in astacked bar
chart is difficult, this due to the different baselines
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(a) Action on the bottom of the graph
(b) Strategy on the bottom of the graph
Figure 9: Visual example of the move-to-bottom on-clickevent
caused by the bottom layer. The click-event will move thebars of
the genre that was clicked on to the bottom. This wasour way to
remove one of the biggest problems with a stackedbar chart.
Stacked bar chart with relative numbersBecause the platform of
steam has been growing a lot in thelast couple of years the user
needs a view that allows him toclearly see how the share of a genre
has grown or shrunk. Toget this information from the absolute or
relative view wouldalready be difficult if the total amount of
games stayed thesame each year. However with the constant growth of
steamand the birth of new genres it is hard to just see this.
Becauseof this we added a view which made it immediately clear howa
genre has grown in comparison to the other genres.
Our first mockup can be found in Figure 10a and the final
vi-sualization can be seen in Figure 10b. Here again we can seethat
the legend has been changed, checkboxes and numbershave been added
and the search bar has been removed. Thereasons and how the
rescaling works are the same as in theStacked bar chart with
absolute numbers section. Again theon-click sorting of the bar
charts was added to counter theproblems with the stacked
charts.
The data as well as the numbers are now displayed in
per-centages. When deselecting genres the non-selected genreswill
be displayed in gray. This to show that selecting all thegenres
will result in displaying 100% of the games.
(a) Mockup
(b) Final visualization
Figure 10: Stacked bar charts showing the number of
gamesrelative to the total number of games per year
Comparing genres in detailThe last view of our visualization is
intended for the user tocompare different genres with each other in
more detail. Todo this we increased the granularity of the time
axis to quar-ters of a year and display the data using lines
instead of bars.This can be seen in the mock-up in Figure 11a and
the finalvisualization in Figure 11b. Again the checkboxes and
leg-end were added and merged. The search bar was again
foundredundant. The rescaling also works in this view.
We displayed the lines of the unchecked genres in gray.
Onereason we do this is to give the user an indication about
wherethe currently selected lines are positioned in the general
data.The main reason was to indicate that there is still data to
bedisplayed. We did not do this in the absolute bar charts
andgrouped bar charts because the rescaling does not allow this.The
reason for this is that bars of the grouped bar chart wouldoverlap
with the grayed data and the absolute bar chart’s barwould push it
off the screen. In the line charts however therescaling will also
move the lines off the screen but the slopewill indicate the
general trend it will go through. If the userwants more information
he can just select the genre. Becausea lot of lines are close to
each other it is not a good idea toselect too many genres at the
same time. It is better to selecta small amount and make use of the
automatic rescaling tocompare even the small genres.
InteractionAs shown and explained earlier the user can interact
withthe bars or lines by selecting or deselecting genres,
choos-
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(a) Mockup
(b) Final visualization
Figure 11: Line graph showing the evolution of genres
perquarter, instead of per year
ing views through the radio buttons and clicking on the barsin
the stacked bar charts.
An interaction that is not explained yet is hovering over
thelegend, bars or lines, which is integrated in all the
graphs.When a user hovers over a certain genre in the legend its
cor-responding bars are given a frame as in Figure 12a in case
wehave bar chars. In case of the line chart the lines are
high-lighted as seen in Figure 12b, here the purple line is the
high-lighted one.
When the user hovers over a bar or line the genre correspond-ing
to the data that is hovered over will be given a frame. Inthe bar
chart this will look the same as Figure 12a with theexception of
the location of the mouse cursor. In the compareview this will look
the same as Figure 12b with the exceptionof the mouse cursor. Also
the line that is hovered over willnot turn purple.
RELATED WORKAlthough Steam was initially released in 2003 [14]
and iscurrently one of the largest PC gaming platforms [6],
aca-demic research of the various aspects of this community hasbeen
scarce. Blackburn et al. [3] analyzed the behaviourof cheaters in
the Steam gaming community. Meanwhile,Becker et al. [2] claim to be
the first to analyze Steam’s so-cial network in 2012, showing it
behaves very similar to otherpopular social networks. Besides these
examples, Steam has,to the best of our knowledge, seldom been the
focus of aca-demic research, especially in the field of information
visual-ization and has thus been of little influence on our
work.
(a) Hover over legend in relative
(b) Hover over legend in line graph
Figure 12: Example visualizing the hover over interaction
Outside of academic research, there are some visualizationson
the web targeting Steam. An interesting example, that isvery
related to our own work, is Steam Charts [13]. Thisweb page has
been tracking the number of concurrent play-ers on an hourly
interval for every game in the Steam catalogsince July 2012. With
interactive line graphs and numbers,it provides a very good
overview of the popularity of variousgames. However it is not
possible to compare different gamesor view the evolution of game
categories or genres. Our visu-alization specifically targets the
evolution of the popularity ofdifferent game genres and provides
various views to comparethem.
Unrelated to Steam or gaming in general, Wattenberg [16]created
a very popular web-based visualization applet, the, which enables
users to interactively explore name data -specifically, the
evolution of the popularity of specific namesover time. Although
the topic is very different, the goal of thevisualization is very
similar to ours: allowing users to exploreand discover certain
trends in the data. As illustrated in Fig-ure 13, NameVoyager uses
a stacked area chart to visualizeits numbers.
We chose to use stacked bars, instead of a stacked area chartfor
our visualization, because people are generally bad atestimating
areas [8]. Müller and Schumann [9] also men-tion that stacked bar
charts are good representations for time-dependent data and
according to Mackinlay [8], length is amore accurate representation
of quantitative data, which fur-
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Figure 13: NameVoyager visualization of baby names start-ing
with ‘Jo’. Using a stacked area chart the popularity ofthe
different names since the 1900s is shown. (Source im-age: [16])
ther strengthens our decision for using stacked bar chartsrather
than area based representations. The NameVoyager al-lows for
hovering over a layer to highlight it and clicking alayer removes
the other layers, giving you a clear overviewof the evolution of
that specific name. This highlighting andselecting is a very useful
feature when dealing with stackeddata, since a bad ordering of the
data makes it hard to view theevolution of one of the higher layers
[5]. Therefore, we im-plemented a very similar feature where we,
instead of remov-ing all of the other layers, reorder them and put
the selectedone as the bottom one, similar to the baseline
reordering assuggested by Yi et al. [18]. While we have chosen to
use a lotof stacked bar charts, Few [7] argues that line graphs are
evenbetter for showing multiple time-series, which is why we
alsoprovide a line graph with more detailed information.
In Yi et al. [19], four pillars for making people gain in-sights
using information visualization are provided: 1) Pro-vide Overview,
2) Adjust, 3) Detect Pattern, and 4) MatchMental Model. We aim to
provide insights for gamers anddevelopers into the different game
genres on Steam with ourvisualization. Therefore we made sure to
incorporate thesefour pillars. We provide overview (1) by
displaying the evo-lution of the complete data set in various
charts. The user canadjust these charts (2) by reordering the
layers, (de)selectingadditional game genres or by changing the
view. It is then upto the user to detect patterns in the data (3).
The visualizationsupports the user in this step by providing a
visual represen-tation of the data, which decreases the gap between
the dataand user’s mental model (4) of it, thereby reducing
cognitiveload in understanding [19].
OUTRO: WHAT COULD BE DONE BETTER?Because we did not start from
an existing data set we useda lot of our programming and research
time to find, createand use the data we needed. We used the release
dates ofgames as a metric because this was the only metric we
couldfind and retrieve. We would have liked to use better
metricssuch as sales numbers or amount of hours played for eachgame
to better estimate popularity of games and their genres,but
unfortunately they were not publicly available. Perhaps
after discovering this, we should have considered
completelychanging subjects and look for a better data set.
A minor thing we could have changed was made clear to us inthe
end, the colors of the numbers on the visualization couldbe made
dynamic to contrast better to the colors of the visu-alization
elements.
Tool tips could be added to cover information that is notshown
because the size of the visualization elements is toosmall. All the
information could be present without beingunreadable, just hidden
behind some interaction to keep theoverview clean.
CONCLUSION & FUTURE WORKWe made a visualization of the Steam
platform which, to thebest of our knowledge, did not exist yet.
Current developersand Steam users might be interested in our
result. We believethat with a better data set and less problems
along the road,the visualization could have reached its full
potential. Thisof course doesn’t take away that the current
visualization canbe used to find interesting facts about the Steam
platform andteach the users more about the trends of genres.
The experience was very educational both on the language
ofD3.js, which required a different mindset to program in andthe
lessons about visualizations that showed us which ideaswere good
and more importantly which ones were not.
Future workA feature we wanted to implement but decided not to
becauseof time constraints is a search function for a specific
game. Itwould highlight the specific genre and year the data point
ofthe game would be in.
Another more personalized feature we considered was allow-ing
the user to log in with his Steam account to place theplayer into
the global statistics. Making the user able to com-pare himself to
others and situate himself on the Steam plat-form. Because of the
way we used the Steam API howeverwe were not able to implement
this.
Finally, as mentioned in the Outro, adding a tooltip when
hov-ering over the line graphs to show the exact data points,
couldhave been a valuable addition. Due to time constraints
thisfeature was also dropped.
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IntroductionGoal and audienceTechnologyThe data
setOriginFormatDistribution of values
D3.js
Visualization and interactionRoad to the
visualizationAchievement visualizationTrend Visualization
Genres
Different ViewsGrouped bar chartStacked bar chart with absolute
numbersStacked bar chart with relative numbersComparing genres in
detailInteraction
Related workOutro: what could be done better?Conclusion &
Future workFuture work
REFERENCES