Reading the article, they don't seem to know what people should do with it. It feels like a recruiting tool more than anything, especially given the non-commercial license.
The player data seems far too low of resolution to be meaningful.
See for example, the Moana Island data set. [1]
I definitely foresee papers on BVH construction using this scene.
For graphics research in academia, there's a dearth of real-world data sets like this, so the ones that do get released are gold. And for graphics research in industry, one may have access to good internal data sets for development and testing, but getting permission to publish anything with them tends to be a giant hassle. It's often easier to just use publicly available data sets. Plus, that makes it easier to compare results across papers.
[1] https://www.disneyanimation.com/resources/moana-island-scene...
Extracting and converting the player data from the .usd files would not be fun, though.
You didn't consider the main factor for CoD - cheating. Which clearly seems to be an inside thing.
Not sure if anything meaningful can be obtained by analyzing anything that has player data on it considering every video game out there is prone to this.
Why is the cheating clearly an insider thing?
Why aren't you sure if anything meaningful can be derived from the movement data?
What do you mean by "prone to this"?
Are you sure they didn't consider "cheating" as a possible use of the movement data?
Could they have considered it but thrown it away as off-topic and implausible?
Player movement data that is too fast for normal players could be seen as cheating. An AI isn't strictly needed for that, just check displacement over time.
If anything though, I could see how player behavior of match winners could be used to both identify varying level of cheaters and players that use various methods for providing an advantage (i.e., keyboard mouse, joystick extensions, etc) and automatically sequester or even handicap their accounts.
It appears to me that so much effort is placed on trying to identify and hamper cheaters in real time, when that both seems extremely resource intensive and unnecessary, considering you have all the digital evidence proof of cheating you need after the fact, you just have to understand what you are looking at.
It's not resource intensive at all compared to the alternative of ahaving humans doing post match reviews. It's all "AI" and automated reviews because it's cheaper. Half of the "anti-cheat" tactic is anyway using your computer resources to run some anti cheat tool.
These games are optimized for revenue so every action is dictated by that. Including catching/banning cheaters. If it costs too much to do it properly, or (and this is actually plausible) cheaters are a significant enough portion of the already small chunk of players who create recurring revenue, then there's no incentive to take real action.
This data is probably useful for actual academic rather than practical purposes today. They're building the knowledge they might want to use in a few years.
It's actually getting increasingly hard to tell. Old cheating use to be snap-to-the head type of cheating.
The newer cheats work really hard to resemble natural players. Soft aim, intentionally missed shots, non-perfect recoil control.
Predicting a winner will be difficult but I would not be surprised if you could loosely predict rank (does Warzone track player rank?) off of movement alone. You may be able to predict more accurately by looking at the associations between two players and their movement. From my prior experience in FPS games, positioning, awareness, and aim are the core pillars of success. Unfortunately as far as I can tell from the data set, only player position is tracked.
I remember an old game where they defined map rules in FPS CTF maps that there should be more than one path to each flag (usually three) and flag areas should be partially visible from one base to another. There were lots of rules like these, some more flexible than others.
The assumption with this type of game is that player will play the same big map over an over for the season (or something like that, changing the map is very rare, might not happen at all over the lifespan of the game), and but they can pick which part of the map they start in and explore from there. So it is, I guess, more similar to having data from all of the maps, for classic first person shooters.