No kidding… jeeze. It reads like a card counting scheme only the system wants generally equal outcomes (an assumption that 50% winning is fun or presents a challenge. Woke equity in competition isn’t the right way to go about things since fairness in competition is only judged by equal starting points and following the same rules, skill, proximity to similarly skilled athletes, and athleticism determine outcomes, not an algorithm pulling strings to get equitable average win loss chances.)
Pro sports wouldn’t be entertaining if the leagues handicapped the exceptional players by pairing them on squads and teams where all teams perform roughly at a 50% win rate. We want to experience elation from crushing opponents, the nail-biting of a close game, the outrage at perceived injustice, the grief of losing, bending rules, breaking rules and getting away with it, creating new ways to push the envelope.
By predicating MMR and CSR on the assumption that predicting a 50% chance of winning or losing is fair undoes the notion of outcomes being judged on your abilities only by comparison to others instead of by accomplishments or class of skill.
I experienced machine/algorithmic bias in real time where I let my wife play on my account for a few matches. She kept getting paired with people who were increasingly better than her as allies and then playing against opponents that greatly outclassed her, it wasn’t fun for her. The average odds of each team winning… wasn’t really 50% and the algorithm kept paring her with better people only to make it harder for her to have fun.
In contrast, when I picked up the controller, I was paired with people who I outclassed, but the team was being paired up against 3-4 people who were much better than my allies but mathematically it seemed a good match. I kept getting paired with worse and worse allies (I’m not that good…) and opponents who were, in the end, more likely to win since I can’t carry any team on my best day.
The only fair matchups are allies and opponents of similar measurable performance/class, not a mathematician’s convoluted guess (programmers who attempted to write an unbiased algorithm) attempting to get equal outcomes (which is itself done by being bias).
The matches evened out eventually, I’m back to the usual intentional 50% chance win/loss cadence.
The algorithmic bias is what’s frustrating. The equality of class/skill of players competing with rules at the onset is what is fair in real life and at the professional level, winner takes all and winners build momentum in their class/skill brackets and become the best in class. An algorithm’s efforts to force a 50% chance of winning or losing frustrates really good players since to bring down their average chance to win, they’d have to be paired with people who aren’t as good as THEY ARE.
Matching this year’s champion lightweight boxer against last year’s heavyweight boxer isn’t a fair fight despite if mathematically it should be fair based on recent performance. the algorithm can’t take into account factors the programmers couldn’t predict would be factors that affect the guesswork mathematics like if the heavyweight lands one hit, the game is over.
(I don’t know much about boxing, so forgive the analogy if it doesn’t fit.)
What would be fair are matchups between boxers of similar class that, though their own agency, prepared or trained or had better opposition research to gain advantages for the competition of boxing.
If I could, I would play against better opponents than me every time. I learn a lot from really good opponents and learning from what they do.
I think yes. For example, if online ads were honest, they’d tell you which cookie or metric signaled the ad so you knew the stalking was, at least, something that made sense.
“We’re serving you this milk ad on Google because of a post you liked on Facebook.”
An honest explanation for a matchup wouldn’t have to get into all the details, rather, simple statements or notices or infographics to convey:
“You’re paired up against combatants with a 45% chance of your team winning (show MMR/CSR averages?). Due to the low pool of available players and to favor playing on a server closer to you, you’re playing against more experienced players.”
“You’re paired up against combatants with a 51% chance of winning based on average MMR scores and a high likelihood of good communication as a fireteam grouped in a party against a similar fireteam in a party. High availability in your region selected the server closest to you in Virginia with the best connection speed for 7/8 of combatants.”
Post match explanations could follow similar formats:
“In a match where you were expected to lose with a 45% chance of winning, your team won. Your performance was better than expected with 10 more kills than your average for the past season. Combined with your improved performance over the past 5 matches, you’re awarded 18% more rank advancement than had you won against a team with a 50/50 chance of winning.”
“In a match where you team’s chances of winning were 51%, your team lost. Over the past 7 matches with 3/4 of the same fireteam participants for each match, your individual performance improved overall but decreased for objective-based games. Given this information, your team overall loses CSR value, but yours increased slightly.”
If I remember reach, didn’t it have a rudimentary calculation of credits earned after each match?
Obviously, no one wants to read that nonsense above, but this information, if presented in a pleasing way, is what frustrated players lack, I think: an explanation for the change in CSR/MMR, how it was deemed fair to be paired up for the match against opponents or with allies.