This is so cool. I'd love to see it's method's applied to other games. In college we played a lot of Super Smash Bros. 64 (same idea as SF2). At work now, Dr. Mario has shockingly become the game to master.
Kartlytics: Applying Big Data Analytics to Mario Kart
11–20 of 34 posts
Re: Kartlytics: Applying Big Data Analytics to Mario Kart
#12Liquipedia needs this for StarCraft2 VODs.
Re: Kartlytics: Applying Big Data Analytics to Mario Kart
#13I can't be the only person that thinks this would also be a great idea for GoldenEye, right? I have a lot of learning to do to make that happen, but would happily provide moral support to anyone that might want to attempt it!
Absolutely! GoldenEye is a favorite here too. I haven't thought about it too much, but I bet it would be easy to adapt kartlytics to that, since it also uses a pretty straightforward on-screen display.
Re: Kartlytics: Applying Big Data Analytics to Mario Kart
#14Liquipedia needs this for StarCraft2 VODs.
You don't even need the VODs, because a replay encodes all the information about the game already in machine readable form. I wouldn't be surprised if someone somewhere is already doing large scale analysis of available SC2 replays.
Re: Kartlytics: Applying Big Data Analytics to Mario Kart
#15Sounds like a nice proof of concept, but I would have tried to identify the in-game variables as in [0] and identify the state of the game from there. Maybe it would take a bit longer, but in the end one could track if a player is sliding or was using an item and so on. Grabbing video frames and doing some kind of template matching on the still images sounds a bit... inconvenient.
Re: Kartlytics: Applying Big Data Analytics to Mario Kart
#16Earlier quoted context omitted.
How many sprites can SF2 possibly have? :)
Not sure, but any who aren't too familiar with fighters might be surprised to learn of frame data [0][1] dissection. 0: http://nki.combovideos.com/data.html 1: http://nki.combovideos.com/flame.html
For anyone interested in the complexities of Super Street Fighter II Turbo, check out a recent tournament: http://www.youtube.com/watch?v=i2gMAOPIHIo
Re: Kartlytics: Applying Big Data Analytics to Mario Kart
#17Sounds like a nice proof of concept, but I would have tried to identify the in-game variables as in [0] and identify the state of the game from there. Maybe it would take a bit longer, but in the end one could track if a player is sliding or was using an item and so on. Grabbing video frames and doing some kind of template matching on the still images sounds a bit... inconvenient. [0]: http://orbitaldecay.com/N64/Les…
Author here. That's an approach we considered as well, but we didn't want to have to use an emulator to play the game. Still, you could potentially get much richer information with that approach, and I'd love to see a demo based on it!
[Edited for detail]
Re: Kartlytics: Applying Big Data Analytics to Mario Kart
#18This is so cool. I'd love to see it's method's applied to other games. In college we played a lot of Super Smash Bros. 64 (same idea as SF2). At work now, Dr. Mario has shockingly become the game to master.
A friend and I were thinking of doing something similar to this for Smash and League of Legends. We probably should!
Re: Kartlytics: Applying Big Data Analytics to Mario Kart
#19I can't be the only person that thinks this would also be a great idea for GoldenEye, right? I have a lot of learning to do to make that happen, but would happily provide moral support to anyone that might want to attempt it!
The answers to that game are obvious -- Oddjob and the laser.
Re: Kartlytics: Applying Big Data Analytics to Mario Kart
#20This is so cool. I'd love to see it's method's applied to other games. In college we played a lot of Super Smash Bros. 64 (same idea as SF2). At work now, Dr. Mario has shockingly become the game to master.
A friend and I were thinking of doing something similar to this for Smash and League of Legends. We probably should!
From a thing I said two years ago:
"Exporting games down to stats per millisecond. Stats I'd want: all the obvious stuff, like gold, KDAs, minion kills, items: but possibly more interesting would be HP per millisecond, and any secondary stats as well. Ability cooldowns. Ward expenditure.
"Could possibly do something interesting for positioning, but I'm not sure how to express that. X,Y coords? That would be enough; it'd be hard to consume, but that's what data processing is for. Clicks and pings would also be worth dumping."
I think you could get a lot of coarse-grained positioning data from the minimap, and possibly coarse-grained HP snapshots from spectator mode. You could definitely get ultimate/summoner spell expenditures from snapshots, too.
I really do expect interesting data to come out of analyzing the spatial layout of the teams.