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Replays are log files that enable the user to watch and analyze games.īefore Replays were introduced with Patch 1.08 there have been no options for players to review a match. These can be saved manually after a game or being recorded automatically with the help of third party software. Replays are files generated by StarCraft Brood War's engine with the extension.

7.2 The following tools only work for StarCraft version 1.18 or lower:.

If you ever find yourself using this tool a lot, consider donating to the project at. Demediuk, S., York, P., Drachen, A., Walker, J. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment.

Role identification for accurate analysis in DotA 2.

#Dota replay player how to

How to do statistics on Dota 2 with Python by /u/jeromehage.

Python and DotA2: Analyzing Team Liquid’s Io success and failure by Bill Prin.

Where To Place Wards in DOTA2 by Nadim Kawwa.

Some interesting links using OpenDota and statistical analysis include: I personally find myself staring at the post-game screen stats and replaying the match to see what were some things that should or should not have happened. The OpenDota project is a powerful tool to use if you play DotA and want to get better at it. The usual theory is that the Radiant side has easier access to Roshan while having easier camps to farm. There's a ton of variables to consider why Radiant has a higher win rate than Dire by around 5% that it's hard to pinpoint why this happens.Ī popular theory that is considered is that Radiant plays in the direction of the camera faces as opposed to the Dire, such that mechanical skills take on point here. The figure above is a pretty popular analysis in the DotA 2 community. import requestsĪx = sns.countplot(x="radiant_win",data=df) We'll be using the Public Matches endpoint on the API documentation since that returns only a hundred ranked or unranked matches. Let's say we want to analyze which side is more likely to win, regardless of the draft. What if we want something that isn't normally offered through the OpenDota website and we have to get multiple games to get our result? import matplotlib.pyplot as pltĭf = df] # not necesary, but we only need 'x' and 'y' here.Īx.imshow(img, extent=) # log limits are 64 to 192.Īx = sns.scatterplot(x='x', y='y', data=df) In this case, we'll do a Seaborn scatter plot to identify the placements on the map and we'll also provide a background image. We now have our DataFrame object from the Pandas library to work with.

Player_2_obs_log = data # first five players are Radiant.ĭf = pd.om_dict(player_2_obs_log) # transform it into a pandas DataFrame. To work with this object, we'll first need to read it through Python with requests: import requests In the player's object, you'll see the nested objects from within like this: "players": [ To better view this, you'll need some sort of JSON viewer tool. To replicate the figure above, we'll need to go to the API documentation where you'll end up using the Matches endpoint where you'll see a response schema that returns a JSON object.

#Dota replay player code

We can replicate this setting (minus the filters) with some Python code and using the API. If you go through the vision tab on the website, you'll find the total ward placement of the map, with the option to filter by the minutes, by which the hero placed it, as well as sentries. When you provide a match through the website interface, you can get some interesting match statistics (if they are parsed), for example, ward placement. We'll be looking at API documentation to pull some statistics using Python and libraries typically found in Anaconda as well as comparing it to the web version. From having a replay parser to an API provider. That's where the OpenDota Project comes in, a Community-maintained open-source Dota 2 data platform. Some interesting statistics can pop up by reviewing the game, like ward placement over time, or how much damage each hero's spells do over the entire match? However, some statistics need to be viewed by watching the replay of the match and some people don't have time for that. This includes a variety of data that help players recognize what they might have done right or wrong in a typical pub of DotA 2.

If you ever played Dota2, you might have tinkered with statistics in the post-game screen.

Image from Liquipedia article " Observer Ward".Īn introduction to OpenDota and how to extract data from parsed replays from its API using Python. Brandon Lizarraga | Tuesday, October 19th, 2021 |