Download Dota 2 Match Replay

[Beatrix Gerke]

Id like to be able to watch a replay as a broadcaster, allowing me draw on the screen (not just the minimap) as casters do. Is there a way to do this, or can you only do this in a live game where you're sitting in a "broadcaster" slot?

Given this recent suggestion to the Dota 2 dev forums and an older post, I do not think that drawing on the terrain while spectating or watching a replay is possible at all in the current build of Dota 2.

You could probably pick up some support if you were to post this on a site with a larger Dota 2 following, like Reddit's /r/dota2, and possibly get Valve to notice. Response times on dev.dota2 about small issues like this are famously slow.

Aside from that, not having this feature while trying to become an amateur caster should not be a big deal. If you cannot cast without it, your casting abilities lack more than you may think. Always try to work on saying relevant information to the game, but not having dead air; try not to be like Ayesee back when he was new to Dota, "turning a bagel into a breadstick" comes to mind, just don't do it.

Since you mentioned that you are working with a friend, try to figure out each of your strengths. If you have paid some attention to casters like Tobi, they typically pair up with a color commentator who can offer more indepth knowledge about the game, like Synderen for example. If you think you know more about the game than your friend, try to let them handle generating excitement and you explain the pro's actions.

Replays can be downloaded and watched in Spectator mode. Unlike spectator mode, however, replays can be watched at increased or decreased speeds, or just the highlights. After playing a match, it may take a moment for the replay to become available.

No, I just haven't played a match in a while, lol. There is an alternate way that lets you skip the first half of the steps. If you're friends with that player, you can load up dota, and load up their profile from the in-game friends menu, and it won't matter if they haven't played a standard dota match in a while.

People can change names, there's also a number of cases of multiple people sharing the exact same name. Also, if it's the guy with a Wario avatar, that's not a legit replay, we've been wiping his scores due to some console shenanigans on his part. We're gonna try a fix to it soon and hope said fix works to keep him from breaking the game.

I think it's on by default. I never turned mine on, as have my friends and they all show custom games. Thanks, it's nice to be able to turn it off so in the future you can prevent people just stealing the current meta strategy and then spamming it to get a high score.

u won't be able to prevent that everyone is able to see your custom games other than by making your steam and dotabuff profile private and always play single player (its already too late if you've played multiplayer before, since they can just look up the other players games and by that get to your dota2 profile ingame)

Go to Recent Games, and any game listed that says it's a Custom Game with Io as the Hero Played will be it. To match it with the Leaderboard, find the game duration that matches most closely, and wah-lah, that's the replay you want to find!

OpenDota is a volunteer-developed, open source platform providing Dota 2 data. It provides a web interface for casual users to browse through the collected data, as well as an API to allow developers to build their own applications with it.

Data is collected through the Steam WebAPI, as well as replay parsing of .dem files. The replay file contains much more data than the WebAPI, at the cost of additional CPU time spent to process the file. As a result, replay parsing can only be done for a subset of the matches played, while basic data from the API is collected for every public match.

Yes! The code is under the MIT license, a permissive license that allows commercial and personal usage free of charge. We would also appreciate a credit/attribution in your project if you use the code!

The esports industry is expanding continuously in the digital era. Not only the number of esports audiences is rising, but also the market revenue has dramatically increased and is likely to reach billions U.S. dollars in 2022 [25]. Moreover, the COVID-19 pandemic positively impacts the live streaming business, introducing several casual streaming viewers into games and esports [14]. These statistics point out the promising growth potential of the industry.

This paper contains six main sections. The next section presents the summary of the related works including, data collection, data processing, machine learning models, and skill prediction in games. Section 3 shows the methodology of our works. Section 4 presents the result of the experiments, followed by the discussion in Sect. 5 Finally, Sect. 6 concludes our paper and provides the direction of future work.

In this section, the review of the related work of multi-player online battle arena games (MOBA) and their analytics are presented composed of the data collection, feature selection and processing, as well as the player skill prediction.

The features which can obtain from both both WebAPI and OpenData API are in JSON format. The data are numeric, e.g., end-game kills, deaths, or assists, are already in a well-defined format. So, these fundamental data required minimum effort to process. Most work further processed these features into per minute form as duration affects most numerical data [3, 9, 35]. For qualitative features such as item bought, or hero ability upgrades, some works applied one-hot encoding [35].

After decompressing all the replay files, the OpenDota parser [6] is applied to parse the files Even though many works used Clarity for log extraction, the OpenDota parser can compute some relevant features which Clarity does not, such as lane efficiency. However, the parser is designated to run on Java Web Server. Therefore, we modify the parser so that it does not need to start an HTTP server, to lean the pipeline. In our work, the "Jpype" package, a Python package is used.

Once the parser outputs the match logs in JSON format, the data including the logs and other match statistics are transformed from the semi-structured to the structured and loaded into a relational database server, Microsoft SQL Server. Note that the corrupted replay are filtered out. Figure 1 shows the schema of the output database.

As a result, we are able to collect 13,416 matches from the data collection process, which means there are 134,160 observations or players as the dataset. From the observations, most players had a Legend rank medal, i.e., 30.08% of the dataset (Fig. 2a). We consider that players who have a rank medal below or equal to Legend as unskillful players as in [22]. From our criteria, 38.66% of the observations are skillful, and the rest are the opposite (Fig. 2b).

One of the main contribution of our work is feature engineering. Not only the feature engineering method is presented, but we also demonstrate how the Dota 2 tactical decisions is quantified into the numbers. Generally, the features described in the literature [8, 9] is applied, additionally the new features are introduced in this work,e.g., the harassment or survival efficiency.

We identify the core players. The core players are such player with the highest gold received for each lane who do not roam. We consider the players who do not qualify the conditions as the support players.

OpenDota provides many statistics ranged from the most simple number such as kills, deaths, and assists to the very complex attributes. Some complex features may need further derive from the logs in which OpenDota provides the method to calculate such features.

We separate the features from OpenDota into four categories as shown in Table 1. The basic" category is the retrievable statistics via API without calculation. The second category is end-game statistics," the features found from the last interval (or second) of logs. The next category is death related features, including life duration and buyback count. The last one contains action count, town portal score purchased, teamfight participation and lane efficiency, which reflects the tactical decision of player.

Equation 1 shows the calculation method for the resource distribution. The resource distributed to player position-i, \(r_i\) is equal to their gold \(g_i\) divided by their team gold, \(\sum _j=1^5 g_j\).

We also calculate the safelane and offlane gold ratio. This directly exposes the understanding of the support position. We divide the gold earned by core position with support position of each lane, excluding midlane. The lower number indicates that support players takes the resources from the core players more, which can cost the team a match.

We use the damage to enemy heroes in the first fifteen minutes to measure the harassment. The harassment is classified into two features, which are total harassment and in-lane harassment ratio. In-lane harassment ratio is the damage done to the enemy in the same lane respected to their position out of their harassment. The low value of ratio indicates that the player focused less on harassing in-lane.

This feature indicates how much the players can obtain resources while getting harassed. The gained gold is multiplied by the damage received in the first fifteen minutes. Therefore, a small number means the players are not getting much harassed, but they still cannot farm. A very high number indicates that the players are getting harassed; however, they still manage to gather a lot of golds. An average number can be interpreted a few ways, e.g. the players obtain resources because they are not getting harassed, however, they cannot farm due to harassment, or they can reasonably harvest while getting attacked mildly. Our assumption is that a higher number indicates the higher skill of the player.

In our work, the feature engineering process is carried out on SQL Server as the data from the pre-processed phase is stored there. Also, processing the data using other tools, e.g. Python, might be slower as the size of the data, approximately half of a terabyte, exceeds the memory limit.

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