The last time competitive was mentioned in the League of legends setting was to talk about Clash tournaments. Now, the talk is centered on the gameplay itself, and what Riot has done to improve it.
Last preseason, developers implemented a suite of improvements to Ranked that brought individual skill identification very close to where they want it to be. Across the board, significant improvement in match quality in ranked play can be seen, where 99% of ranked matches pair opposing teams that average within half a division of each other, and teammates themselves are within 1 division of each other. This only improves the closer you get to peak player hours during the day.
With these results, Riot is ready to jump into more targeted, smaller percentile problems to polish down the rough edges. This includes making some existing Matchmaking functions more responsive to changing conditions, modifying the decay policy, and even some long-overdue review of social experiences.
Ranked Decay: Apex tier (Master+) and high Diamond players that take a few weeks off from League are decaying further than optimal, down to visible ranks that are extremely off from their skill level. This creates an odd mismatch-type purgatory that can be demotivating to play in. Though decay intends to ensure that only the current best players are represented at the top of the ladder, it isn’t the intent to send those who have decided to take a few weeks off down to a place they don’t belong. Developers are looking into better options for decay rates that can be rolled out in a low-disruption way before the end of the season.
Dynamic Map Side Advantage: In a perfectly even skill matchup, the blue side has a very slight advantage due to the game's layout, which matchmaking corrects by fielding a red side team with a slightly higher average MMR. The impact of this map advantage can shift from patch to patch, so Riot are looking to make matchmaking's side correction dynamic as well.
Dynamic Position Popularity & Autofill: Position popularity fluctuates throughout the day, but the current algorithm isn’t performing to standard. Early testing indicates that by improving the way position popularity is calculated, Riot can potentially reduce autofill rates as low as ~0.6% of all games, down from the current ~2-5%. Not only are they expecting to see large autofill gains, but they have also seen strong signals that this will reduce queue times by up to 10% across all MMRs.
That would be a huge win across the board, especially for players that specialize in one role. The main thing that needs to be validated before they can ship these improvements is that the secondary role rate doesn’t increase substantially, but, apparently, they are committed to getting these out to players as soon as testing completes.