Believe it or not, Deathmatch isn't as strict as ranked when it comes to fair matchmaking
Believe it or not, Deathmatch isn't as strict as ranked when it comes to fair matchmaking
The matchmaking is slightly different but overall very similiar. We are a little looser in DM but we don't have to worry about team balance so it's easier to make matches. I think DM is just a different beast tho, because some players(like myself) perform differently in one mode compared to the other. I have amazing aim, but I hold W like a COD player.
Highly appreciate it if you answer this, but is there a smurf queue in Valorant? and why do some new accounts have an easier sailing upwards with an insane win ratio >70 & others "also new" get stuck with near or under 50% win ratio, same player, playing the same agents and style, both accounts placed s2 at creation and double promoted to g1, so starting from the same place, one accounts stuck g1/g2 and the other d2 right now?
No smurf queue, and if one account is ranking up faster it's because it's doing better. We don't have a winners or losers queue, some people bring that up alot; there is no benefit to this and just reduces fair match making(which is our goal, to have the fairest matches).
Lower Rank's use more encounter MMR, and higher use more Win/Loss MMR. Encounter cares more about who you beat in duels, win/loss cares about who you win the match against. Players that win duels, and win matches, will climb faster then players that just do one or the other.
The only reason an account would get stuck is that the performance is different on that account, you can look at TenZ and see him take an account to Radiant MMR in 20 games. MMR is just a ladder, beat people and you steal their spot and climb the ladder.
Not sure why the accounts would be a different rank, but usually when people bring this up and we do investigations it's because they group with different people and play differently on both accounts. When we looked into the statement "If you are hardstuck make an Alt" the data showed us that you have less than a 40% chance to get your MMR above your main account, and after enough games, they their MMR got very close to each other.
So it is most likely who you are playing with, the agents you are playing, or how you play on each account. No hidden systems, it would be extremely complicated and make no sense to target specific accounts and our team isn't a fan of smurf queue; we should just be able to find your MMR fast to get you to the right spot instead of quarantine you.
Players that win duels, and win matches, will climb faster then players that just do one or the other.
Does that mean players who lock in duelist have an advantage in climbing since they will have an easier time winning duels?
It's a little more complex, it takes into account assists, ability usage, site taking, etc. At the end of the day it doesn't matter what agent you play, every time you take a positive action against an opponent you are stealing MMR from them in a sense.
I know you try to make it seem fair, but if a Skye fully flashes an enemy and the entry picks up the kill for a first blood, who gets rewarded more? Is it really equal?
Duelists benefit from the fact that all their teammates' utility goes into setting up kills for them. Drone, flash, smoke, everything is set up for them to get that kill, and then they can continue running into CT and getting multifrags since they were first on site. Not to mention the cheesy off angles agents with get out of jail free cards can abuse to win more gun duels and appear to be better than his rank, lol.
Meanwhile Sage plants, gets shock darted to 50 hp and dies to 2 bodyshots in the next gun duel.
I think that even if the MMR system is made as fair as possible, certain agents will always have an advantage due to the nature of their playstyle and the tools available to them (on agents without a flash, some attack halves are just unplayable if your flashers/smokers are bad or get picked off early)
Encounter MMR is definitely good for ranking smurfs up quickly, but for true fairness pure win/loss is unbeatable and reduces the hassle of weighing all these different contributions to winning. I suppose it comes down to whether we prioritise reducing smurfing or fair climbing.
I think it's hard when you just kind of abstract out, "Well, how effective is the killer vs. the assister, and I think that the assister is more/less effective, etc." because it's purely opinion-based. It's the same argument for or against win/loss MMR vs. Encounter. It's just an opinion on how valuable each one is.
That's why when you make an MMR system, you validate these things and find out the most effective way to test and measure data sets like this. If our rating system was terrible, we could see that in data and adjust how the system measures these things(which we've done for years before Valorant was even live!). We also have simulations and other things we run and crazy intelligent data scientists who help us.
Think of it this way. We have many people playing matches, using a system that's trying to pair them up based on how it's rating them. Let's say we got it wrong and valued Kills too heavily. Suddenly, the system would be putting players who were good at getting kills too high for some reason. Those players would then get put into matches, expecting a fair 50:50 outcome, but for some reason, the data would show us these players are losing on average more than they are winning. We would then know something was wrong, and for some reason, these players were being rated incorrectly based on something.
After we see that these players are being placed in matches where the matchmaker was incorrect, we could take those matches that were played and see what adjustments would have made their rating more accurate in predicting the match outcome better. So we could go back and kind of reverse engineer the numbers to say, "Hey, look after running the data kills are weighed too heavily, and when we reversed the math, we found out the correct value to weight kills, and the match outcome now looks correct."
This works because when we set the correct values to measure something like kills, we can go back and run it through that simulation using the matches that were already played where we saw the issue. So we would adjust the MMR to the new kill weight and run the simulator. The simulator would update all the player's MMR based on the new value. We could then look at the data and see, "Hey, in the old system, their MMR said they should have been winning 50:50, but they were winning 46:54. The new MMR has a predicted outcome of 46:54 matching their actual win rate."
So to sum it up, this isn't very easy. We see that our matchmaker is not good at making fair matches; we reverse the math to figure out why MMR is inaccurate. After making adjustments, we run the MMR system through those matches again, and if the new MMR adjustments accurately predict match outcome, we've improved the system.
This is how you solve the issue of "Well, I think X should be weighted more heavily than Y."; because these things are measurable, and if we got it wrong, we would be able to see our matchmaker was not very good at making fair matches. It's not that we walked into work and debated about how effective kills should be because you need a way to measure success and how the system is operating effectively.
Also, all of this is how matchmakers have been built over the years! There are tons of data points, GDC talks, and things like True Skill(developed by Microsoft) that have been building years of data and learnings to figure out how to measure player skill and create suitable matches! While I may have explained a small way we measure/improve the system, it's so complex that I can't possibly cover everything being done to matchmakers and the learnings over the years.
Also, I know I'll probably have some people see me say "predicted match outcome" things like 50% win rate, etc. The matchmaker is never trying to make you have a 50% win rate; it's trying to put you into matches where you have a 50% chance of winning/losing. Adjacently you will be pushed towards a 50% win rate, but this can all change due to your skill changes, good/bad days, etc. The matchmaker wants to make a match where both teams have an equal chance of winning, which leads to it using MMR to make those 50:50 matches. This is why when talking about MMR, you can speak of winrate as an indicator if you should be climbing or falling!
Thanks for coming to my TED talk, I know it isn't straightforward, so there may be questions(I can try and answer).
I had a quick question about the encounter win/loss, you mentioned if you win a duel against an enemy you basically steal their MMR. Does it matter how you win that duel ? For example is there a difference in MMR gained using a judge vs an op vs a vandal/phantom or even winning after flashing them?
It does rate how effective you were in that duel, and a bunch of other factors. Doing damage to them, or flashing them, is better then doing nothing.
I think it's hard when you just kind of abstract out, "Well, how effective is the killer vs. the assister, and I think that the assister is more/less effective, etc." because it's purely opinion-based. It's the same argument for or against win/loss MMR vs. Encounter. It's just an opinion on how valuable each one is.
That's why when you make an MMR system, you validate these things and find out the most effective way to test and measure data sets like this. If our rating system was terrible, we could see that in data and adjust how the system measures these things(which we've done for years before Valorant was even live!). We also have simulations and other things we run and crazy intelligent data scientists who help us.
Think of it this way. We have many people playing matches, using a system that's trying to pair them up based on how it's rating them. Let's say we got it wrong and valued Kills too heavily. Suddenly, the system would be putting players who were good at getting kills too high for some reason. Those players would then get put into matches, expecting a fair 50:50 outcome, but for some reason, the data would show us these players are losing on average more than they are winning. We would then know something was wrong, and for some reason, these players were being rated incorrectly based on something.
After we see that these players are being placed in matches where the matchmaker was incorrect, we could take those matches that were played and see what adjustments would have made their rating more accurate in predicting the match outcome better. So we could go back and kind of reverse engineer the numbers to say, "Hey, look after running the data kills are weighed too heavily, and when we reversed the math, we found out the correct value to weight kills, and the match outcome now looks correct."
This works because when we set the correct values to measure something like kills, we can go back and run it through that simulation using the matches that were already played where we saw the issue. So we would adjust the MMR to the new kill weight and run the simulator. The simulator would update all the player's MMR based on the new value. We could then look at the data and see, "Hey, in the old system, their MMR said they should have been winning 50:50, but they were winning 46:50. The new MMR has a predicted outcome of 46:50 matching their actual win rate."
So to sum it up, this isn't very easy. We see that our matchmaker is not good at making fair matches; we reverse the math to figure out why MMR is inaccurate. After making adjustments, we run the MMR system through those matches again, and if the new MMR adjustments accurately predict match outcome, we've improved the system.
This is how you solve the issue of "Well, I think X should be weighted more heavily than Y."; because these things are measurable, and if we got it wrong, we would be able to see our matchmaker was not very good at making fair matches. It's not that we walked into work and debated about how effective kills should be because you need a way to measure success and how the system is operating effectively.
Also, all of this is how matchmakers have been built over the years! There are tons of data points, GDC talks, and things like True Skill(developed by Microsoft) that have been building years of data and learnings to figure out how to measure player skill and create suitable matches! While I may have explained a small way we measure/improve the system, it's so complex that I can't possibly cover everything being done to matchmakers and the learnings over the years.
Also, I know I'll probably have some people see me say "predicted match outcome" things like 50% win rate, etc. The matchmaker is never trying to make you have a 50% win rate; it's trying to put you into matches where you have a 50% chance of winning/losing. Adjacently you will be pushed towards a 50% win rate, but this can all change due to your skill changes, good/bad days, etc. The matchmaker wants to make a match where both teams have an equal chance of winning, which leads to it using MMR to make those 50:50 matches. This is why when talking about MMR, you can speak of winrate as an indicator if you should be climbing or falling!
Thanks for coming to my TED talk, I know it isn't straightforward, so there may be questions(I can try and answer).
Also as a side note, the higher rank you get the more Win/Loss takes over and Encounter MMR matters less. This is because at the high ranks reaction time and aim start to even out, or become less impactful. We understand that people can win games at higher ranks due to being good IGL's or being extremely effective in using their skills. It doesn't mean you can't do this at lower ranks, you still have win/loss MMR at all ranks, but after measuring players skill and MMR we've found out that most players above a certain point will just outperform lower ranks due to their raw aim/mechanics(then they hit the wall of game knowledge).