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Reddit league of legends11/7/2023 Intuitively, these runes tend to be dependent upon matchup due to their nature, and we can test this by checking the rune win rate distributions of specific matchups. Nearly all Darius players take Conqueror and Triumph, but pick rates are split between Legend: Alacrity and Legend: Tenacity. However, Darius is much deadlier than he appears if you take the correct secondary runes. Despite the nerf to Nimbus Cloak in patch 10.16, his win rate barely flinched, settling at 51.45%. Note that RelDiff, holding the win rate difference constant, exhibits hyperbolic growth with respect to overall win rate (i.e., 99% to 100% win rate is a bigger increase than 50% to 51%), much like how cooldown reduction scales per additional point.Ī solo queue menace, Darius hasn't been changed directly for many patches. If RelDiff is less than 2%, we say that a champion is solved, since if any optimization exists, it would only marginally benefit their expected win rate. If Yarner had a 48% win rate overall but the optimal keystone had a 54% win rate, RelDiff would be (54-48)/(100-48) = 11.54%. For example, the expected win rate of a random Yarner is 50%, but the expected win rate of a random Yarner given that they took Phase Rush is 100%, so RelDiff = (100-50)/(100-50) = 100%. It represents the expected proportion of previously lost games that a player would now win after optimization. Relative difference (RelDiff) is a measure of how effective optimization is for a given champion when compared to the average player. WRD can also apply to other facets of meta like itemization and skill order, where building the wrong item or maxing the wrong ability will reduce a player's expected chance of winning. For example, since there is a difference in power between the two keystones, Yarner's win rate is depressed by players taking the wrong keystone. Win rate depression (WRD) is when a champion's win rate is lowered because of players making suboptimal meta decisions. An optimal rune page can also be thought of as the rune page a player would take to maximize their win rate if they had to use it forever. Predator is also called detrimental because its win rate is less than Yarner's overall win rate, meaning players who take Predator are expected to lose more games than the average Yarner. For Yarner, Phase Rush is called optimal since it wins the most, and Predator is suboptimal because it does not. How is optimal defined in terms of runes?Ī rune page is defined to be optimal relative to others if it wins the highest proportion of games assuming everything else is held constant. We measure this effect by computing a number called relative difference. This is because Yarner suffers from win rate depression-many players are not taking the optimal runes, lowering their win rate. Though both Xasuo and Yarner have the same overall win rate, Yarner is clearly the stronger champion. Upon closer inspection, 50% of Yarner players take Predator and lose all their games, and the other 50% of players take Phase Rush and win all their games. Now, consider the champion Yarner that initially appears balanced with a 50% win rate. Suppose the champion Xasuo has a 50% win rate, and all Xasuo players take Conqueror. With just a rudimentary understanding of numbers, you too can use win rate statistics to uncover "hidden OP" champions and get ahead of the curve to climb before Riot eventually nerfs them a year later. In fact, Guardian was Lux's best-performing keystone even at the peak of Aftershock Lux. However, someone who has been paying attention to win rate statistics would know that Guardian Lux support has been overpowered for the past year, even after she was nerfed back in patch 9.14. League of Legends' lead gameplay designer Riot Scruffy recently tweeted that initially, " thought Sona Lux were only OP as a combo." He then implies that Sona-Lux have just recently become individually overpowered when built optimally. In this post, we justify and discuss the optimization of both keystone and minor runes using recent data from patch 10.16, sourced from Lolalytics. Meta decisions are generally the easiest to optimize and improve because they don't require the nuances learned from playing hundreds of games. There are three generalized categories of decisions that a player can optimize: (1) macro, which includes rotations, vision control, and wave management, (2) micro, which includes reaction speed, matchup knowledge, and mechanics, and (3) meta, which includes more theory-related skills such as runes, skill order, and itemization.
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