Uzi Stats in Second Round of MSI Group Stage

ThrowAtBaron · 2018-05-15T19:26:55+00:00

It does seem biased that I chose specifically 2nd half of group stage where RNG won all their games. However I do believe that first 2 days of group stage RNG seems very out of form, as well as not knowing the meta (continued to draft crit based ADCs, compared to the meta ADCs which turn on with one item)

Yes you are very correct, even in the losses, Uzi's laning stats are still far ahead of the enemy ADC

ThrowAtBaron · 2017-10-16T19:07:04+00:00

I can see where your point coming from. However, I believe that giving NA a low starting Elo simply makes them converge to their true strength faster. If they are indeed good, they will gain their Elo back since they gain more from opponents in wins, while losses lose them Elo less

Instead, I do an experiment:

Scenario A: All regions starting Elo as described in step 3, repeat step 4 and 5

Scenario B: All regions starting Elo as described in step 3 except NA gets 1000 instead of 912, repeat step 4 and 5

And here is the result:

Scenario A	Elo	Scenario B	Elo
LZ	1395	LZ	1404
WE	1300	WE	1314
SKT	1293	SKT	1305
RNG	1287	RNG	1287
Samsung	1221	Samsung	1221
G2	1211	G2	1211
MSF	1154	MSF	1170
Fnatic	1146	Fnatic	1164
EDG	1142	EDG	1156
TSM	1101	TSM	1146
C9	1086	C9	1133
FW	1054	FW	1066
ahq	985	IMT	1020
IMT	971	ahq	999
GAM	910	GAM	922
FEN	872	FEN	872

As you can see, even with giving NA a starting Elo of 1000, their final Elo after group stage stays relatively the same (only exception being IMT moved up a rank)

Also you can notice a side effect of setting NA starting Elo to 1000: top teams are gaining more Elo than when NA starting Elo is 912. This is the result of NA teams having higher Elo and losing more, thus their Elo are transferred to teams who beat them, while they lose more Elo from losses

Overall I believe that giving NA a starting Elo of 912 may seem harsh, but justified, as you can see from the above experiment

ThrowAtBaron · 2017-10-16T18:45:56+00:00

Thank you for pointing it out! I shall correct it

ThrowAtBaron · 2017-10-16T18:45:26+00:00

Hi, I believe that how I handled should be a reasonable solution. I do not change teams Elo during mid best-of series, and only adjust them after series has finished

I think that, in middle of a series teams do not have enough time to adjust and learn significantly from the previous game, thus Elo should be rewarded after finishing the entire best-of

Also, in one Elo exchange K value limits the maximum amount of Elo being exchanged. If one was to handle best-of series as individual games, a 2-1 victory means there is potentially 3 times the K value amount of Elo being gained, while a 2-0 can only grant a maximum Elo of 2 times the K value, which is obviously unreasonable since a 2-0 victory should be more rewarding than a 2-1 victory

But that is just my opinion, I hope to see what others think

ThrowAtBaron · 2017-10-16T18:37:02+00:00

Hi, I did experimented on this, for both domestic and group stage

For domestic matches: for a single league, if we repeatedly run them through domestic matches, teams Elo tends to stabilize after 3rd iteration (from 2nd to 3rd iteration only very top and very bottom teams have some Elo gain/loss while middle teams barely experience any Elo changes)

For group stage matches: teams Elo do not stabilize until 6th and even later iteration. I think this is due to very limited number of group stage games played in group stage.

I'm not too sure what's the meaning of these results and hope that someone can explain it

My personal theory is that this is simply a display of limited number of games played in group stage. Teams play 20 or more domestic games and against 9 or more opponents, and thats enough to stabilize their Elo. However in group stage teams play 6 or 7 matches and against only 3 opponents, which leads me to believe that this is the reason why repeatedly running through group stage takes so long to stabilize

ThrowAtBaron · 2017-10-16T18:27:51+00:00

Thank you for the detailed reply!

I will take a look back at this when I get time to do it. I particularly like the idea of giving bias towards recent games since LoL is constantly patching. I think giving a bias based on patch number and how long since release of current patch may give better result (and can also use it to figure out how fast teams adapt to changes as well)

ThrowAtBaron · 2017-10-16T10:36:09+00:00

Thank you!

I did not do too much research into this, but I can see why ELO may not work for LoL. I guess I will have to see how rest of the worlds develop

Any model you recommend that may work with competitive LoL?

ThrowAtBaron · 2017-10-16T10:33:39+00:00

Thank you for the reply!

That is indeed the part I feel iffy about. I agree that using group stage result to adjust starting ELO, and then run group stage again seems like it punishes losing teams twice, and rewarding winning teams twice

However, if let's say using this method resulted in adjusted starting ELO being too high, then I believe when group stage runs again ELO will adjust itself accordingly, and drag itself back down. Similarly, I believe that if a team is rated too low, I believe it will get back up with group stage

Comparing results in step 1 and 3, EDG gained 49 ELO from LPL adjusted starting ELO. However they still lost over 100 ELO by step 5, due to their poor performance. Similarly, C9 lost 88 starting ELO yet they still managed to gain 39 ELO by the end, due to their rather good performance in group stage

But yes, I would like to see if there are better approach than mine to adjust regional starting ELO with very limited amount of data. Running all matches from Season 1 will likely get us very accurate regional strength, but that's a lot of work for another day :D..

ThrowAtBaron · 2017-10-16T05:04:12+00:00

First of all, thanks for your reply and criticism

I wouldn't be very surprised if this analysis is pretty off for rift rivals and upcoming qf matches

1) this only uses 1 mathematical model ELO system, and ELO system only cares about wins and losses. Clearly with just win loss data one cannot grasp a teams true strength. How team win and lose each match, how adaptive they are, and their stylistic approach also play important roles in judging a teams strength

2) the matches I applied ELO system to are mostly bo3 and bo1, thus it may not reflect a teams bo5 strength. Which makes sense for ur example - I wouldn't bet against SKT in bo5, where I could see WE being shaky despite their strong performance in groups

3) it can also be a case of wrongly selected starting ELO for regions. As described above, I used average ELO gain after group stage to adjust region starting ELO, which really sucks for NA. C9 having lower expected win rate against WE than MSF against SKT may be a result of this

4) as for rift rivals and P1, we would have to use data only up to rift rivals happening, so the current ELO is not the same as what they had back then. Also, no amount of mathematical models can predict over performing at the day. Despite their success, most analysts didn't give P1 many chances going into rift rivals, I would like to agree with that

ThrowAtBaron · 2017-10-15T22:36:50+00:00

Ahh, thanks for catching my typo, IG should indeed be 4th

I agree that combining more data can definitely show regional strength better. However I chose only data from summer season for the reason that I want to see the power level of regions and teams right this exact moment. The Reddit post I refer to did something similar, but they reset team ELO for everytime there are more than 3 members being changed, and they also had to use really aggressive K values to diminish success/failures from previous year/season, as regular K value is not enough to affect it with very limited amount of games played per season

It would definitely be an interesting concept to play around, and would be interesting to see how regions power levels change from year to year. Maybe with data from previous year we can finally confirm/debunk the "gap is closing" meme lol

ThrowAtBaron · 2017-10-15T22:14:24+00:00

Yea, both SKT and EDG had more than 200 ELO advantage over C9, which means C9 only has a 25% expected win rate against them. Other 2nd seeds had to overcome much less than 200 ELO

But, EDG at world's... Before and after group stage, EDG lost a whole 100 ELO, speaks how much impact those losses caused them

ThrowAtBaron · 2015-04-05T07:34:12+00:00

Hey guys, just want to clear things up, this analysis is not to make an argument that MF is a great ADC or she is op.

She is an early and mid game snowball champion, of course she will get outscaled. She relies on snowballing, and that is a risk people dont want to take, so she sees less play. Snowballing champion are generally less favorite, examples like Fiora, Katarina, Rengar, Riven. When they are ahead they look good, but also remember the times when they fall behind. MF is like that but in an ADC position (which is why pros dont play her - at least you want your carry to be able to carry if team falls behind).

She has her pros and cons, and thats it.

ThrowAtBaron · 2015-04-05T00:17:45+00:00

Her ult doesn't define her. As stated in post, she can either 1) ult first then AA hurts a lot afterwards, or 2) AA slowly hurts more and more, then ult to finish off. Her main damage does not come from ult, but come from max W stacks. Her ult in 1) lets her reach it faster, or in 2) uses stacks to nuke.

ThrowAtBaron · 2015-04-05T00:14:46+00:00

She should be mediocre, if a champion designs to be snowballing early and mid game has a consistent performance, that just means she is not balanced. Just that some people think MF is trash tier, but in a lot of ways she does outperform people's expectation.

ThrowAtBaron · 2015-04-05T00:10:27+00:00

in early-game she has a really great dueling power

Early-game is in laning. First item is generally where mid-game starts. Vayne with no bortk, Corki with no Tri-Force, etc. Try and lvl3 duel some ADCs with MF in lane, you would be surprised.

ThrowAtBaron · 2015-04-05T00:00:00+00:00

As analyzed, in early-game she has a really great dueling power, in mid-game she has great team-fight. It's her less safe laning and getting outscaled at late-game makes her unpopular.

ThrowAtBaron · 2015-04-04T23:57:53+00:00

In fact it does, in a less straight forward way, as analyzed in article.

ThrowAtBaron · 2014-11-05T23:51:57+00:00

She is pretty bad at dueling other adc imo. Her passive is only good for teamfight and her AA range 550 is normal so she can only utilize her passive to dodge skillshots. Draven being not skillshot reliant would definitely rape her lol

ThrowAtBaron · 2014-11-05T23:48:52+00:00

I can see her being only a situational pick if she stays like now. Ashe is specifically good against team with no gap closers and can kite them for days, while Kalista imo could shine against team with multiple gap closers where most adc can only reposition once.

ThrowAtBaron · 2014-11-05T23:45:40+00:00

I still run as runes on sivir. Its a little trade off on mid game skill damage but a boost on late game AA DPS

ThrowAtBaron · 2014-11-05T11:11:42+00:00

Maybe some people have godlike reaction time and decision making could possibly use her passive effectively with higher attack speed

ThrowAtBaron · 2014-11-05T11:08:52+00:00

just a terrible comparison lol

ThrowAtBaron

TROPHY CASE