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TL;DR I trained a machine learning model to figure out which champions are banned more than their performance and popularity deserves. The top 5 most over-banned/hated champions are Samira, Darius, Morgana, Zed, and Yone. The bottom 5 are Jhin, Ezreal, Ashe, Kaisa, and Thresh.
The process:
- Data taken is for Plat+, global player-base, patch 10.21. Data is collected from lolalytics.com.
- For each champion, we record their pick-rate, ban-rate, and win-rate.
- We train a multivariate linear regression model with the data. The model is trained such that it tries to predict the ban-rate as a function of the win-rate and play-rate of a champion.
- Actually, ban-rate and play-rate are log'd first, then put into the regression model.
- For each champion, the model will predict a ban-rate based on their win/play rates. We take the difference between the predict ban-rate and the actual ban-rate. If the actual ban-rate is significantly higher, it indicates that a champion is being banned more than their stats deserve.
- I admit this method has its weaknesses; i.e. it assumes that the relationship between the different variables are in fact linear. Upon inspection, ban-rate does appear to be a linear function of pick-rate. However, it is difficult to say exactly whether ban-rate is a linear function of win-rate, since the relationship between the two is surprisingly weak to begin with.
- Another concern is whether the win-rate and play-rate are correlated with each other, since you ideally want independent features for multivariate regression. Win-rate and play-rate are in fact not correlated with each other (p-value=0.92).
Champions towards the top of the list are widely hated, with ban-rates significantly higher than their play-rate and win-rates would justify. Champions at the bottom are they opposite: They tend to be popular, yet have ban-rates much lower than normal for their pick-rate and win-rate. Champions at the middle aren't really liked or disliked; people are generally ambivalent about them.
Below is the complete list of ([actual ban rate] - [predicted ban rate]) values for each champion.
Samira:63.05
Darius:33.33
Morgana:29.93
Zed:29.23
Yone:23.49
Yasuo:21.41
Evelynn:20.76
Hecarim:19.83
Akali:19.35
Pantheon:16.32
Kassadin:16.17
Yuumi:13.39
Lillia:10.77
Master Yi:9.90
Renekton:8.57
Lulu:8.21
Vladimir:8.01
Irelia:7.41
Lucian:7.26
Blitzcrank:7.19
Fizz:6.98
Fiora:6.08
Karthus:6.04
Nautilus:6.00
Ekko:5.96
Graves:5.83
Caitlyn:5.01
Elise:4.92
Jax:4.79
LeBlanc:4.59
Sylas:3.96
Rammus:3.83
Leona:3.67
Shaco:3.65
Mordekaiser:3.25
Kha'Zix:3.00
Fiddlesticks:2.95
Rengar:2.64
Katarina:2.40
Sett:2.31
Malphite:2.26
Garen:2.25
Illaoi:2.11
Pyke:1.96
Zoe:1.95
Malzahar:1.90
Volibear:1.65
Olaf:1.64
Tryndamere:1.64
Nidalee:1.62
Cassiopeia:1.53
Quinn:1.41
Diana:1.29
Teemo:1.24
Draven:1.04
Nasus:0.97
Talon:0.93
Rek'Sai:0.85
Urgot:0.81
Nocturne:0.75
Zac:0.75
Swain:0.73
Vayne:0.62
Brand:0.61
Kled:0.42
Heimerdinger:0.26
Yorick:0.23
Tahm Kench:0.23
Kayn:0.23
Aatrox:0.21
Dr. Mundo:0.18
Nunu:0.16
Qiyana:0.14
Jayce:0.13
Xerath:0.09
Gangplank:0.08
Trundle:0.07
Poppy:0.03
Anivia:0.03
Taliyah:0.02
Lissandra:0.01
Rumble:0.00
Syndra:-0.01
Ivern:-0.03
Neeko:-0.04
Azir:-0.06
Aurelion Sol:-0.08
Maokai:-0.09
Cho'Gath:-0.09
Vel'Koz:-0.10
Kog'Maw:-0.11
Taric:-0.11
Corki:-0.12
Veigar:-0.12
Zyra:-0.14
Shyvana:-0.14
Varus:-0.15
Kennen:-0.15
Wukong:-0.16
Skarner:-0.17
Gnar:-0.17
Kalista:-0.18
Sejuani:-0.20
Ziggs:-0.20
Udyr:-0.24
Amumu:-0.31
Gragas:-0.34
Viktor:-0.34
Annie:-0.35
Galio:-0.40
Zilean:-0.42
Warwick:-0.43
Aphelios:-0.45
Xin Zhao:-0.45
Sivir:-0.51
Singed:-0.52
Xayah:-0.53
Senna:-0.57
Braum:-0.61
Kayle:-0.64
Ryze:-0.64
Sona:-0.71
Camille:-0.79
Jarvan IV:-0.82
Shen:-0.94
Tristana:-1.00
Kindred:-1.03
Twitch:-1.06
Sion:-1.07
Riven:-1.11
Ornn:-1.36
Vi:-1.38
Twisted Fate:-1.71
Soraka:-2.15
Rakan:-2.29
Ahri:-2.31
Karma:-2.44
Alistar:-2.64
Nami:-2.71
Lux:-2.96
Bard:-3.00
Jinx:-3.35
Janna:-4.85
Lee Sin:-5.79
Miss Fortune:-5.93
Orianna:-6.12
Thresh:-9.45
Kai'Sa:-10.87
Ashe:-17.44
Ezreal:-25.13
Jhin:-35.18