Lec Odds

The LEC (League of Legends European Championship, or previously known as the EU LCS – European League of Legends Championship Series) is a major LoL esports competition in Europe. It was created by Riot Games in 2013 with the intention of becoming a battlefield for some of the best teams in the region. The league is known for its unorthodox strategies and mechanically gifted players, making it a consistent title contender at international tournaments.

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LEC Odds: best LoL Esports European odds

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LEC Live Betting

LEC Regular Season

Each LEC season is divided into Spring Split and Summer Splits. Every split has 10 teams competing with each other over the course of 9 weeks. The competition is conducted in a Double Round Robin format, meaning that every team plays two best-of-ones (or single-game matches) against every opponent.

LEC Playoffs

When the regular season comes to an end, six teams with the best record qualify for the LEC playoffs. There, they clash in nail-biting best-of-five series, and the first team to win three games advances to the next playoffs round. The change in format is particularly important since longer series are perfect for eliminating the variance and ensuring that the strongest lineups climb to the top.

The top-2 teams from the regular season secure byes to the Semifinals. Meanwhile, the other four have to play through the Quarterfinals to challenge them. All playoff contenders are awarded Championship Points that are necessary for qualifying for the League of Legends World Championship. The league’s winner gets the most points, and the fifth/sixth-place teams receive the least.

The Spring Split winner gets a seed in the Mid-Season Invitational (MSI), a massive international competition that takes place during the downtime period between the splits. In 2020 because of the global pandemic, the MSI was cancelled and instead, EU LCS teams competed in a string of online matches to transition between the Spring and Summer Splits.

The Summer Split winner and two teams with the most Championship Points receive an invitation to the prestigious League of Legends World Championship (aka Worlds). Most lineups use the Spring Split to hone their teamwork and develop their playstyle. However, the competition really reaches its peak in summer since every LoL player dreams of qualifying for the World Championship and raising the Worlds trophy. This is the time with punters are presented with the best LEC betting opportunities and it all culminates with the Summer Spring Playoff matches.

LEC Champions: Fnatic, Alliance, and G2

A quick glance at the LEC History will highlight the fact that the early days of the LEC belonged to Fnatic. Holding the most trophies awarded to LEC winners out of all European organizations, this team established itself as a powerful force in the region. And while Fnatic’s winning streak was interrupted by Alliance in the 2014 Summer Split, no one actually managed to overshadow it.

But one team came close.

In 2016, G2 Esports qualified for the LEC and proceeded to win four splits in a row. The feat itself and the one-sided manner in which they accomplished it earned G2 the nickname of “The Kings of Europe”, and they still tower above most European teams. With that, it’s not exactly surprising that Fnatic and G2 Esports are constantly clashing with each other for the right of standing at the top of their region.

LEC at Worlds and MSI

The LEC teams enjoy varying levels of success at international tournaments. In fact, the first League of Legends World Championship was won by Fnatic after they defeated—Against All Authority—in the finals. That was a different time, though, as the overwhelmingly powerful Korean teams haven’t entered the scene yet.

Still, even as competition grew stiffer, Europe kept showing up. The 2013 World Championship had two European teams—Moscow Five and CLG EU—going all the way to the Semifinals. In 2014, all three LEC teams failed to make it out of the group stage, but Europe managed to redeem itself next year when Fnatic and Origen made it to the Semifinals.

The 2016 World Championship once again saw a LEC team – H2K Gaming – reaching the semis. And while Europe couldn’t quite live up to its own standards in 2017, both Misfits and Fnatic made it out of groups to display a high level of play in the Quarterfinals. In 2018, Europe was inches away from winning claiming the World Championship trophy, as Fnatic breezed to the finals before falling prey to the Chinese Invictus Gaming.

A similar trend can be observed at the Mid-Season Invitational. The 2015 MSI had Fnatic passing the group stage before falling short in a close series against the Korean powerhouse SK Telecom T1 (SKT T1). And while the 2016 MSI ended with a disappointing showing by G2 Esports, the Kings of Europe struck back in 2017, going all the way to the finals before losing to SKT T1.

2018 didn’t go Europe’s way either, as it saw Fnatic barely making the Semifinals only to crumble in face of the Chinese Royal Never Give Up. However, 2019 was a huge success story for the region since G2 Esports tore through the competition to lift Europe’s first-ever MSI trophy. The reigning European Champions were once again close of making it to the finals, but they succumbed to Damwon Gaming, the eventual winners of the event.

LEC Homegrown Talent vs Foreign Imports

The LEC has traditionally relied almost exclusively on homegrown talent and player transfers were made mostly between local teams. At the beginning of the season LEC odds are adjusted accordingly based on the changes that affect the rosters. The most accomplished teams held on to their players, with G2 Esports’ success at the highest level being also the result of operating fewer changes.

Exchanges with teams from the Chinese LPL and the South Korean LCK are still few, but in recent years, the North American LCS became more attractive. Team Vitality fans were the first to lose a top player, as Jizuke left the team for Evil Geniuses. LEC betting options changed almost instantly and in 2020 transfers between the North American and European championships increased.

G2 Esports’ Perks left for Cloud9 to fulfill his aspirations as a mid-laner, where he replaced Nisqy who now plays for Fnatic. The latter traded Rekkles to G2 to make the circle complete and the LEC odds were reshuffled once again. Interestingly enough, the reigning European champions got the best of these trades, because in spite of losing their exceptional ADC got a great substitute in Rekkles while weakening Fnatic.

2020 Astralis joined the LEC after taking over Origen’s organization but most of the original players departed for the Immortals. This represents the biggest swap of LoL pro-gamers between the North American and European regions and a new challenge for LEC betting fans. Schalke were strengthened with LCS talent by signing Broken Blade while trading their star top-laner “Odoamne” to Schalke.

The LEC is slowly opening up to players from other regions, but it is still preferred by pro-gamers with European origins. In 2020, there is no single US, Chinese or South Korean player competing for any of the LEC teams.

Why watch the LEC?

At its core, League of Legends is all about the players and LEC History proves that few competitions show player skill better than the League of Legends European Championship. Europe has one of the largest talent pools in the world, meaning that new names are constantly challenging the veterans. And even though the LEC teams aren’t as organized as their counterparts from the LCK or LPL, they make up for it with their willingness to experiment and take risks.

Throughout the regular season and the playoffs, League of Legends fans tune in to watch LEC live stream because of the action-packed nature of the games. As the MSI and Worlds approach, League of Legends European Championship matches provide a glimpse at the creative strategies that might be used in the international competitions. LEC winners performed well at the Worlds in recent years and even though they are yet to actually win the trophy, they always appear close to making that big break.

LEC betting fans also have the opportunity of watching a new franchisee entering the European region on a yearly basis. After Misfits returned to Europe, Astralis is the new franchise to enter the fray after Origen was rebranded in late 2020. None of its core players transitioned to the new organization and the team is made mostly from Academy players. Nukeduck is the exception but in spite of him being a talented and experienced player the team is likely to struggle and Astralis has the highest LEC odds to win the trophy.

1. Background statistics

Variable types

• numeric
• categorical

What do we know:

• Confidence intervals (numeric variable)
• Fisher test (categorical by categorical)
• Simple linear regression (numeric by one numeric variable)
• Linear regression with dummy variables (numeric by any variable)

Today:

• Multiple linear regression (numeric by several numeric variables)
• Multiple linear regression with dummy variables (numeric by any variable)
• Logistic (logit) regression (binary dependent variable by any number of variables of any type)

2.1 How does it work

Logistic or logit regression was developed in [Cox 1958]. It is a regression model wich predicts binary dependent variable using any number of variables of any type.

What do we need?

[underbrace{y_i}_{[-infty, +infty]}=underbrace{beta_0+beta_1cdot x_1+beta_2cdot x_2 + dots +beta_zcdot x_z +epsilon_i}_{[-infty, +infty]}]

But in our case (y) is a binary variable.

• Probability?

[P(y) = frac{mbox{# successes}}{mbox{# failures} + mbox{# successes}}; P(y) in [0, 1]]

• Odds?

[odds(y) = frac{P(y)}{1-P(y)} = frac{mbox{P(successes)}}{mbox{P(failures)}} = frac{mbox{# successes}}{mbox{# failures}}; odds(y) in [0, +infty]]

• Natural logarithm of odds

[log(odds(y)) in [-infty, +infty]]

2.2 Reminder about logarithms

• if log(odds) are greater then 0, it means that we have more successes then failures;
• if log(odds) is equal to 0, it means that we have the same number of successes and failures;
• if log(odds) are less then 0, it means that we have less successes then failures;

2.3 Probability and log(odds)

[log(odds(s)) = logleft(frac{#s}{#f}right)][P(s) = frac{exp(log(odds(s)))}{1+exp(log(odds(s)))}]

Results of the logistic regression can be easily converted to probabilities.

2.4 Sigmoid

Formula for this sigmoid is the following:

[y = frac{1}{1+e^{-x}}]

Feeting our logistic regression we should be able to reverse our sigmoid:

Formula for this sigmoid is the following:

[y = frac{1}{1+e^{-(-x)}} = frac{1}{1+e^{x}}]

Feeting our logistic regression we should be able to move center of our sigmoid to the left/right side:

Formula for this sigmoid is the following:

[y = frac{1}{1+e^{-(x-2)}}]

Feeting our logistic regression we should be able to squeeze/stretch center of our sigmoid:

[y = frac{1}{1+e^{-4x}}]

So the more general formula will be: [y = frac{1}{1+e^{-k(x-z)}}]

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where

• depending on (x) values sigmoid can be reversed
• (k) is squeeze/stretch coefficient
• (z) is coefficient that indicates movement of the sigmoid center to the left or right side

3. Numeric example

It is interesting to know whether the languages with ejective sounds have in average more consonants. So we collected data from phonological database LAPSyD: http://goo.gl/0btfKa.

• Model without predictors

How we get this estimate value?

What does this model say? This model says that if we have no predictors and take some language it has (frac{0.5306283}{(1+e^{-0.5306283})} = 0.3340993) probability to have ejectives.

• Model with numeric predictor

What does this model say? This model says:

[log(odds(ej)) = beta_o + beta_1 times n.cons.lapsyd = -9.9204 + 0.3797 times n.cons.lapsyd]

Lets visualize our model:

So probability for a language that have 30 consonants will be [log(odds(ej)) = -9.9204 + 0.3797 times 30 = 1.4706] Thus, the output YES (the langiage has ejectives) has approximately 1.47 times more chances to occure if the language has 30 consonants than the output NO.

[P(ej) = frac{1.47061}{1+1.4706}=0.8131486]

4. predict(): Evaluating the model’s performance

So we actually can create a plot with confidense intervals.

5. More variables in the model

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[underbrace{log(odds(y))}_{[-infty, +infty]}=underbrace{beta_0+beta_1cdot x_1+beta_2cdot x_2 + dots +beta_zcdot x_z +epsilon}_{[-infty, +infty]}]

The significance of each variable (predictor) is not the same in models with different number of variables. In other words, it depends on the combination of predictors in a specific model.

6. Model selection

AIC (Akaike Information Criterion) is a goodness-of-fit measure to compare the models with different number of predictors. It penalizes a model for having too many predictors. The smaller AIC, the better.
While comparing models, we are looking for the minimal optimal model:
* optimal, as it helps to predict the output in the best way
* minimal optimal, as it uses the minimal number of predictors

Other measures to evaluate the model includes:
* accuracy
* concordance index C (the area under the ROC-curve)
* Nagelkerke pseudo-(R^{2})

7. Interaction of the variables

Interaction happens when the effect of one predictor on the outcome depends on the value of another predictor. Interaction of two predictors can be positive (their joint role increases the effect) or negative (their joint role decreases the effect).
Example: animacy and semantic class; animacy and the choice of syntactic construction; effect of verb transitivity in different language varieties.

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8. Conclusion: Generalized linear models (GLM)

GLM is a broad class of models that include linear regression, logistic regression, log linear regression, Poisson regression, ANOVA, ANCOVA, etc. In order to call a particular method to be GLM, that method should have the following three components:

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• Random Component: It refers a response variable (y), which need to satisfy some assumptions. Examples: Linear regression of y (dependent variable) follows normal distribution. Logistic regression response variable follows binomial distribution.

• Systematic Component: It is nothing but explanatory variables in the model. Systematic components helps to explain the random component.

• Link Function: It is link between systematic and random component. Link function tells how the expected value of response variable relates to explanatory variable. Link function of linear regression is E[y] and link function of logistic regression is logit(??).

What was important today?

• classifiers: binary, multi-class (multinomial)
  
• odds
  
• sigmoid
  
• significance of the variables (predictors)
  
• interactions