MMR for ranked matches require approximately 10 games to calibrate.
OpenDota - Dota 2 Statistics
Players who rank in the regional top for solo ranked matchmaking will appear on the Leaderboards. In December of , Valve gave the following MMR distribution for solo unranked matchmaking across the entire player base.
Percentile indicates the percentage of players who are lower than the corresponding MMR. Subsequent analysis of public player profiles collected by OpenDota shows a higher average MMR of approximately This figure is assumed to be skewed upwards, as it is generally believed that newer and less skilled players are less likely to display their MMR publicly, and therefore are not considered in the data analysis.
In general, parties receive bonus MMR when searching for matches. This is meant to compensate for superior party coordination, as well as partying with highly skilled members.
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From Dota 2 Wiki. Retrieved from " https: Navigation menu Namespaces Page Discussion. A surprising match outcome will tend to cause an increase in uncertainty. Each of the two ranked MMRs has its own calibration period. Under certain circumstances, we may need to reactivate calibration, if we think the MMR is inaccurate.
Note that this distribution is from normal matchmaking. The players who participate in ranked matchmaking will be more skilled, more experienced players. We anticipate that any given player will have different expectations and play the game differently in ranked matchmaking compared to normal matchmaking. The ultimate goal of automated matchmaking in Dota 2 is for players to enjoy the game.
The matchmaker seeks matches with the following properties listed in no particular order:. The matchmaker seldom achieves all of those goals perfectly. For any potential match, the matchmaker assigns a quality score for each of the criteria above and then takes a weighted average.
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The matchmaker does not directly try to achieve any particular win rate for players. This is criteria 1 in the listed above.
However, if you are on a winning streak, in general your MMR is probably rising, which will tend to cause you to be matched with higher skilled opponents and teammates. Win rate is not a meaningful measure of player skill. Win count is also not useful as indicator of skill, and the matchmaker does not use it for that purpose. We do try to group players by their level of experience criteria 3 in the list above , primarily because we have found that players at the same skill level but different experience level differ in their expectations of how the game is to be played.
The difference in experience between 40 games and games is considered to be about the same as the difference between games and You can visualize the impact of goals 2 and 3 with a chart where number of games played is the horizontal axis and MMR is the vertical axis. If two players are close together in the diagram, they are considered good candidates to put into a match together. Players who are far apart are considered a poor match. The typical career trajectory of a player new to Dota 2 as he gains experience and moves towards the right is to gradually move upwards as their skill increases.
Dota 2 Matchmaking Stats
When skilled players create new accounts, they follow a bit different trajectory. Their MMR rises relatively quickly, placing them into the top lefthand corner of the diagram, where they will be matched with other players whose skill is high relative to their experience level.
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When parties are involved, things get a bit more complicated. Parties often contain players with a wide discrepancy in skill and experience.
For the purposes of measuring the goodness-of-fit criteria listed as 2 and 3 above, the matchmaker assigns each party aggregate skill and experience numbers. It is these party numbers that are used rather than the individual. In general, when a party with a wide skill range is matched with a solo player, the solo player will have skill and experience near the average of the party. If you notice that one player seems to be significantly less skilled than the other players in the match, it is very likely that they are partied with a high skilled player.
We account for this in two ways.
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First, we track your skill when queuing alone separately from when queuing in a party. Second, we adjust the effective MMRs based on the number of players in the party and the distribution of skill within the party. Observe that the average adjusted MMR for all of the parties is around When the players on a team are sorted by adjusted rank, as they are above, the solo players tend to be bracketed above and below by players playing in parties; furthermore, a party with a smaller MMR spread party F tends to get bracketed by a party with a larger MMR spread party D.
These patterns are typical. Party F, which is formed of players of more equal skill, received a lower bonus. These adjustments were determined using statistical tools more on this below , but an intuitive explanation is that your performance improves more when partying with a higher skilled player than it does when playing with another player of your same skill. Measuring success in matchmaking is difficult. This includes the members of the Dota 2 team!
Fortunately, we gather a lot of it. For example, you might wonder how we determined how to adjust effective MMRs to account for the fact that players in a party tend to perform better than players of equivalent skill queuing solo. We used a statistical tool known as logistic regression, which essentially works by trying to create a function that predicts the odds of victory.
This function contains several coefficients which determine the MMR bonus given to players in a party. Then we use numerical techniques to solve for the coefficients that produce the function which is most accurately able to predict the match outcome. To help tune this threshold, we start with a measure of match quality. The ultimate goal of matchmaking is fun, and we have several metrics which we use to measure match quality.
One such metric measures balance, based on the difference in gold farmed. This is easily visualized on the gold difference graph.
Find the last time when the graph crosses zero, and then measure the area between the horizontal axis and the graph. In general, the smaller this area is, the closer the game was.
Although at one point in this match the Dire had a 10K gold advantage, the Radiant came back and then pulled ahead, only to have their gold lead reversed again. Despite the fact that at one point in time one team appeared to have a significant lead, our balance calculation judges this match a close game.
Armed with this metric among others we have an experimental way to tune the wait time thresholds.