Sources of Data

Some of you may be wondering how I can even find data on these matches. After all, who records random matches from a random video game? Could I analyze my own matches? However, since Counter-Strike is considered a competitive “esport”, there are various third party sites that record official matches between professional teams (hltv.org for example). With this, I can filter through specific games that satisfy certain criteria like map type or skill level and download the associated “demo” file.

DEMO Files

Instead of just watching a tv cast or youtube recording of every tournament match and recording every small detail that occurs, it might be better to automate data collection. I turn the the game’s demo system for this. Demo files (.dem files) are a native feature to the Counter-Strike that basically document every action from every player throughout an entire match and neatly package it into a exportable file. There are a couple things I can do with these files. I can either open the file up from within the Counter-Strike game itself or upload it to a third party website. However, these options are both slightly limiting. I am looking into “aggressive” strategies which I will need to define. Third party websites will certainly have no way to measure that. To fix this, I will have to write my own code to analyze the demos which is a whole other problem…

Classifying Aggressive Strategies

Firstly, I will only be looking at matches on a specific map from the game. Think of this as a unique chessboard in which each match takes place. The rationale behind only looking at a single map is because different maps influence the strategies used on them. It wouldn’t be fair to compare what is or is not aggressive between a chessboard and checkers board using the same criteria.

Secondly, I will only be looking at matches played between D1 professional teams. Apparent skill gaps may influence how aggressive a player is. As such, in professional games every player will ideally have the same mechanical skill. This should mean that more weight is placed on strategy and thus the “aggressiveness” of it.

To start, I will record certain actions from each individual player throughout the game and classify them. Each classification will be assigned an arbitrarily defined value of “aggressiveness”. I will also record the end result of this action as successful or unsuccessful.

Limitations

Only looking at a single map further limits the scope of my implications. Can the measure of aggressiveness be applicable to other areas other than Counter-Strike specifically? Can this measurement even apply to other maps within Counter-Strike itself? However, this does save the amount of work I would need to do.

Also, only looking at professional matches is a double-edged sword. Not all play is limited to professionals. Would similar strategies work in general or only in official matches?

Oral Defense

How generalizable is your research?

Again, this is a considerable obstacle for my project. While the competitive esports scene is already large enough, I believe there should be something more concrete than implications for a video game industry. A few areas which aggressive strategy could potentially generalize to include: game psychology, economics, geopolitics, etc. However, it still seems like a vague relationship. Even then, it could be argued that my study of aggressive strategy are only applicable to a specific map within Counter-Strike.