LoL Prediction S10 🏹

LOL s10 high elo ranked games prediction.

Introduction

Let’s predict who won the match given team composition and how long game played out

Get dataset

The dataset is a collection of League of Legends High Elo(Challenger, GM, Master, High Diamonds) Ranked games in Season 10, Korea(WWW), North America(NA), Eastern Europe(EUNE), and Western Europe(EUW) servers. These datas were collected from op.gg by web scrapping with python spyder. The latest game was played on Oct.16th on the dataset. In total there are 4028 unique games. Note that I’ve used one-hot encoding hence [99,54,101,73,57,96,52,102,68,52] this list represents number of all unique champions used in each lanes [BlueTop, BlueJG, BlueMid, BlueAdc, BlueSup, RedTop, RedJg, RedMid, RedAdc, RedSup] respectivley. Note that there are in total 151 unique champions with ‘Samira’ as the latest addition.

Some Setups

The tensorboard extension is already loaded. To reload it, use:
  %reload_ext tensorboard

	game_length	mmr	result	server	team_1	team_2	timestamp
0	25m 38s	NaN	Victory	na	Riven,Nidalee,Galio,Jhin,Pantheon	Camille,Olaf,Cassiopeia,Ezreal,Alistar	2020-10-13 09:31:42
1	25m 38s	NaN	Defeat	na	Teemo,Nidalee,Lucian,Caitlyn,Senna	Irelia,Hecarim,Cassiopeia,Jinx,Lulu	2020-10-13 06:00:17
2	25m 38s	NaN	Defeat	na	Malphite,Olaf,Taliyah,Ezreal,Alistar	Sylas,Lillia,Lucian,Senna,Pantheon	2020-10-13 05:06:45
3	25m 38s	NaN	Defeat	na	Neeko,Shen,Orianna,Kai'Sa,Nautilus	Riven,Hecarim,Cassiopeia,Samira,Morgana	2020-10-13 04:28:00
4	25m 38s	NaN	Defeat	na	Fiora,Nunu & Willump,Irelia,Jhin,Karma	Renekton,Elise,Kled,Jinx,Morgana	2020-10-13 04:00:51

	game_length	result	top1	jg1	mid1	adc1	sup1	top2	jg2	mid2	adc2	sup2
0	25m 38s	Victory	Riven	Nidalee	Galio	Jhin	Pantheon	Camille	Olaf	Cassiopeia	Ezreal	Alistar
1	25m 38s	Defeat	Teemo	Nidalee	Lucian	Caitlyn	Senna	Irelia	Hecarim	Cassiopeia	Jinx	Lulu
2	25m 38s	Defeat	Malphite	Olaf	Taliyah	Ezreal	Alistar	Sylas	Lillia	Lucian	Senna	Pantheon
3	25m 38s	Defeat	Neeko	Shen	Orianna	Kai'Sa	Nautilus	Riven	Hecarim	Cassiopeia	Samira	Morgana
4	25m 38s	Defeat	Fiora	Nunu & Willump	Irelia	Jhin	Karma	Renekton	Elise	Kled	Jinx	Morgana
5	25m 38s	Defeat	Irelia	Karthus	Sylas	Samira	Nautilus	Riven	Kayn	Akali	Miss Fortune	Galio
6	25m 38s	Defeat	Galio	Kindred	Syndra	Ezreal	Blitzcrank	Camille	Fiddlesticks	Twisted Fate	Jhin	Morgana
7	25m 38s	Defeat	Poppy	Ekko	Sylas	Samira	Blitzcrank	Lucian	Lillia	Lulu	Caitlyn	Alistar
8	25m 38s	Defeat	Shen	Lillia	Samira	Lucian	Soraka	Taric	Master Yi	Riven	Ezreal	Lulu
9	25m 38s	Defeat	Ornn	Graves	Sylas	Lucian	Alistar	Irelia	Hecarim	Akali	Senna	Leona

{'categories': 'auto',
 'drop': None,
 'dtype': numpy.float64,
 'handle_unknown': 'error',
 'sparse': True}

(4028, 754)
(4028,)

Datas are one hot encoded and cleaned up. Let’s train test split

(2577,)
(2577, 755)

Let’s try Neural Network with dropouts

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten (Flatten)            (None, 755)               0         
_________________________________________________________________
layer_1 (Dense)              (None, 30)                22680     
_________________________________________________________________
dropout (Dropout)            (None, 30)                0         
_________________________________________________________________
layer_2 (Dense)              (None, 16)                496       
_________________________________________________________________
dropout_1 (Dropout)          (None, 16)                0         
_________________________________________________________________
layer_3 (Dense)              (None, 16)                272       
_________________________________________________________________
dropout_2 (Dropout)          (None, 16)                0         
_________________________________________________________________
layer_4 (Dense)              (None, 1)                 17        
=================================================================
Total params: 23,465
Trainable params: 23,465
Non-trainable params: 0
_________________________________________________________________

26/26 [==============================] - 0s 806us/step - loss: 3.8032 - accuracy: 0.6613
accuracy 0.6612903475761414

We got about 0.661 accuracy with just raw neural network with dropouts.

Let’s try random forests

RandomForestClassifier(max_leaf_nodes=32, n_estimators=2000, n_jobs=-1)

validation accuracy: 0.7710516103996896

test accuracy: 0.7704714640198511

Immediate improvement by almost 10% with random forest classifier!

Model Explanability

Let’s look at what we were mostly interested. What are some best team compositions!

Let’s try SHAP summary

We see that feature 0 (game length) tells us that the game favors blue team winning more when game is shorter which is unexpected. Note that it it not significant at all since SHAP value is -0.02 ~ 0.4 at most.
Generally, since all the values are 0 are 1, we can see clear 1-red and 0-blue (When it’s 0 it has no impact on the prediction)
We can see feature 156(blue Mid Akali) helped RED team win more
Whereas Feature 462(red Top Tryndamere) helps the BLUE team win significantly more haha
From this chart, we can clearly see that each champion has very consistent and predictable contribution to their team’s chance of winning

Note that - 119 Kindred blue jg - 638 Caitlyn red adc - 60 Renekton blue top - 162 Cassiopeia blue mid - 535 Akali red mid - 376 Thresh blue support - 471 Volibear red top - 31 Jax blue top - 654 Kalista red adc - 290 Miss Fortune blue adc - 259 Ashe blue adc - 360 Rakan blue support - 210 Orianna blue mid - 462 Tryndamere red top - 445 Riven red top - 425 Lucian red top - 715 Janna red support - 156 Akali blue mid - 72 Sylas blue top

Therefore our best teamp comp impacting positively on winning is … - (Top)Renekton/Jax/Sylas (Jg)Kindred (Mid) Cassiopeia/Orianna (Adc)MF/Ashe (Sup)Thresh/Rakan

Meanwhile worst team comp impacting negatively on winning is … - (Top)Volibear/Trynd/Riven/Lucian (Mid)Akali (Adc)Caitlyn/Kalista (Sup)Janna

We can also note that Jg role seem to not matter much.. : )

What if we didn’t have game length, just champion compositions only?

(2577,)
(2577, 754)

RandomForestClassifier(max_leaf_nodes=32, n_estimators=2000, n_jobs=-1)

validation accuracy: 0.7691113698098564

test accuracy: 0.7692307692307693

Surprisingly, accuracy only drops less than 0.01. We can conclude that planning out a team comp based on champion’s strength on early vs late game does not help win more. This can be explained by an example. Let’s say I picked kayle which is the best late game champion. We may win games with longer duration more but will lose more short games due to her weakness early. So the overall win rate balances out.

Conclusion

best: (Top)Renekton/Jax/Sylas (Jg)Kindred (Mid) Cassiopeia/Orianna (Adc)MF/Ashe (Sup)Thresh/Rakan
worst: (Top)Volibear/Trynd/Riven/Lucian (Mid)Akali (Adc)Caitlyn/Kalista (Sup)Janna

We know that in the world of solo queue, picking the above champions will not gurantee a win. Sometimes people are autofilled, meaning they aren’t playing on their best role. People may disconnect, resulting in games favoring the opposite team. There are too many unknown factors like this, making it impossible to predict 100% of the game outcomes correctly.

As a former high elo NA player myself, I can say that generally, the ‘best team’ above have champions that doesn’t get countered too often and is a good pick into anything. (This may not be the case for top because I’ve never really cared about top lanes as a support player :). But for ‘worst team’ champions, they are often easily countered. (Especially bottom lane)

The biggest surprise was blue team wins more early and red team wins more late (Very slightly but certainly) for some reason. Also jg mattering the least was a surprise as well.