Intro
During the recent rapid expansion of eSports, professional teams have adopted many best practices from conventional sports and data analytics is no exception
I am fortunate to be a part of this process through various projects in the field including some public, private and academic work
In this article I will share my thoughts on how and most importantly why Counter-Strike programming is continuously evolving and based on this formulate a message to conventional sport federations and computer game developers.
I am fortunate to be a part of this process through various projects in the field including some public, private and academic work
In this article I will share my thoughts on how and most importantly why Counter-Strike programming is continuously evolving and based on this formulate a message to conventional sport federations and computer game developers.
Counter-strike rapid development
The most important difference of eSports data analytics comes from its nature - the playground is virtual
Counter-Strike has one the most advanced programming communities in the whole eSports.
We have multiple commercial post/prematch statistical analysis services, most notable are scope.gg and leetify.com each automatically processing about 3kk games monthly
Also many huge open-source communities with hundreds of people using them for various tasks daily. This gives broad opportunities to the programming enthusiasts. More and more teams, broadcasts and websites start to apply statistics in what they are doing
Interesting to point out that academic work volume is also increasing over the years
The most important difference of eSports data analytics comes from its nature - the playground is virtual
Counter-Strike has one the most advanced programming communities in the whole eSports.
We have multiple commercial post/prematch statistical analysis services, most notable are scope.gg and leetify.com each automatically processing about 3kk games monthly
Also many huge open-source communities with hundreds of people using them for various tasks daily. This gives broad opportunities to the programming enthusiasts. More and more teams, broadcasts and websites start to apply statistics in what they are doing
Interesting to point out that academic work volume is also increasing over the years
Google Scholar publications per year w/ keywords CS data replay
However accessing detailed CS:GO game replay data became possible in 2016 literally because of 2 enthusiasts who met game developers at an official tournament and convinced them to publish the format of the replay file
Literally the same year the market began to expand with various private and public tools for the professional teams and very soon for the regular players’ games too. And now we have services processing millions of public matches and all professional games. Same applies to Dota2 too
Important lesson here is that the market began to rise once the data became available to the broad public
However accessing detailed CS:GO game replay data became possible in 2016 literally because of 2 enthusiasts who met game developers at an official tournament and convinced them to publish the format of the replay file
Literally the same year the market began to expand with various private and public tools for the professional teams and very soon for the regular players’ games too. And now we have services processing millions of public matches and all professional games. Same applies to Dota2 too
Important lesson here is that the market began to rise once the data became available to the broad public
It's not that easy
One might think this means easy access to all the ingame data without as much effort as in real-life sports. Well, I have to admit that in practice accessing game data is often not that trivial
Sometimes despite professional players' needs not providing access to the game replays could be even a company strategy! Riot with their Valorant is a well known example here. All they provide is a very limited API, so there are almost no notable programming communities out there
On the other hand, some people will always see this as a business opportunity - I know a number of groups making private video recognition tools for clients in various competitive games without replays. For instance, a straightforward application is parsing player coordinates from the in-game radar
One might think this means easy access to all the ingame data without as much effort as in real-life sports. Well, I have to admit that in practice accessing game data is often not that trivial
Sometimes despite professional players' needs not providing access to the game replays could be even a company strategy! Riot with their Valorant is a well known example here. All they provide is a very limited API, so there are almost no notable programming communities out there
On the other hand, some people will always see this as a business opportunity - I know a number of groups making private video recognition tools for clients in various competitive games without replays. For instance, a straightforward application is parsing player coordinates from the in-game radar
Message to the sport federations and computer game developers
Here is one takeaway from the urban planning: people tend to sit where there are places to sit. The same applies directly to the data analytics
Just the fact there are some commercial or private tools in your sports doesn’t mean you fully use the data analytics potential, the same as people sitting on a dusty pavement edges might signal about urban environment issues rather than a good place
I just illustrated how a quite small action made by a Counter-Strike developer led to the emergence of a whole data analytics field. In my opinion key factors here are:
Single data source with all match replays (same in Dota2)
Easy to use open source replay analysis programs providing raw data:
trajectories, player actions, game events and full environment description (say ball curveness in table tennis or wind direction in outside shooting)
The more data you provide in an easy to access way, the more data analysis tools your sports will have. It is that simple. Look at Counter-Strike and Dota2 where you can download and parse 100 recent matches for any player in 10 minutes and get detailed match statistics. What stops your sports from making it possible?
Here is one takeaway from the urban planning: people tend to sit where there are places to sit. The same applies directly to the data analytics
Just the fact there are some commercial or private tools in your sports doesn’t mean you fully use the data analytics potential, the same as people sitting on a dusty pavement edges might signal about urban environment issues rather than a good place
I just illustrated how a quite small action made by a Counter-Strike developer led to the emergence of a whole data analytics field. In my opinion key factors here are:
Single data source with all match replays (same in Dota2)
Easy to use open source replay analysis programs providing raw data:
trajectories, player actions, game events and full environment description (say ball curveness in table tennis or wind direction in outside shooting)
The more data you provide in an easy to access way, the more data analysis tools your sports will have. It is that simple. Look at Counter-Strike and Dota2 where you can download and parse 100 recent matches for any player in 10 minutes and get detailed match statistics. What stops your sports from making it possible?
Prediction
Counter-Strike is an established cultural phenomenon and it will be with us for a long time. The growth of data analytics in the field is driven by its accessibility, which is also constantly improving itself
In the nearest future we will see more professional Counter-Strike teams start using data analytics or various automation, both in-house and by outsourcing that task. Microsoft and SAP already have long-term data analysis eSports partnerships but I expect more such collaborations in the future
In the next 5-10 years we will see the appearance of a new level of data analytics services for the game. This will happen naturally as new generations of data analysts will come and start working based on the findings of the previous ones
Counter-Strike is an established cultural phenomenon and it will be with us for a long time. The growth of data analytics in the field is driven by its accessibility, which is also constantly improving itself
In the nearest future we will see more professional Counter-Strike teams start using data analytics or various automation, both in-house and by outsourcing that task. Microsoft and SAP already have long-term data analysis eSports partnerships but I expect more such collaborations in the future
In the next 5-10 years we will see the appearance of a new level of data analytics services for the game. This will happen naturally as new generations of data analysts will come and start working based on the findings of the previous ones
Conclusion
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The European sports technology sector is rapidly evolving, and our new report highlights how the region is emerging as a major player in the global SportsTech market. Our comprehensive and multifaceted research provides valuable insights into the venture ecosystem, shedding light on the latest trends and innovations driving the industry forward.
Download your copy now at the link below!
🔗 https://sport.pulsar.vc/report