Teammate Suggestion for League of Legends

Teammate suggestion for ÂŤLeague Of LegendsÂť Web information retrievial

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Di Matteo Valerio - Siliberto Fabio

Context We are talking about an online videogame: League of Legends.

In this game players can fight each other in an arena using a character (a.k.a. Champion). A match is composed by two 5-player teams that can choice the champion they want among around 120 available. The choosing time is peculiar: each player, alternating the team, pick a champion; both teams can see all the choices and one champion cannot be picked more than once. The strategy is the core part at this time.

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Di Matteo Valerio - Siliberto Fabio

What is our objective?

We want to give League of Legends’ players a suggestion about what Champion they should pick in order to have better chances to win their games.

We want to give a rank of the possible Champions that are best suited for the current teams’ composition.

We don’t want just simple statistics.

We want something more.

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Di Matteo Valerio - Siliberto Fabio

How can we achieve it?

We could think of champions as vertices of a graph, with edges representing some kind of relation among them.

We could then run some Link Analysis algorithm to extract useful data and compute the rank that we need.

What algorithm suits for us? Ă ďƒ Page Rank

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Di Matteo Valerio - Siliberto Fabio

Who should we pick?

The Champion that we want must have two important characteristics: }

A lot of victories with the Champions already in our team

}

A lot of victories against the enemies’ Champions.

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Di Matteo Valerio - Siliberto Fabio

Relations between champions So, we have two types of relations, and we represented them with these two graphs: 1.  A partnership graph, where we have a node for each Champion and we connect them with a non-directed edge if they ever won a game together, with an edge weight equal to how many times this happened (so weight >=1).

2.  An adversary graph, with the same set of nodes, this time with directed edges going from a Champion to another if the first has ever been defeated by the second in some game (with weights assigned in similar fashion).

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Di Matteo Valerio - Siliberto Fabio

Example 3 A

B

Partnership graph: Champion A and Champion B won 3 games together.

C

2

D

Adversary graph: Champion C was defeated by Champion D 2 times.

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Di Matteo Valerio - Siliberto Fabio

Why these graphs?

Partnership graph à Create a rank of the best possible Champion to pick from wrt the characters selected by the teammates («I have A and B in my team... Who do they usually win the most with?»).

But our teammates are not always the same! àPersonalized Page Rank.

We precompute a PPR for each Champion, so that at execution time we only have to compute the linear combination of the characters selected by the teammates.

Personalization on what? àFixed teleporting (ex: PPR(A) = teleport only to A).

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Di Matteo Valerio - Siliberto Fabio

Why these graphs?

Adversary graph à Create a rank of best possible Champions wrt the adversarial team («I play against B and C... Who do they usually lose the most with?»).

But the adversaries are not always the same! àPersonalized Page Rank (again).

Like before, we precompute a PPR for each Champion, this time based on the adverasry graph.

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Di Matteo Valerio - Siliberto Fabio

What about weights?

As we said each edge has a weight that represents the number of times a Champion won with or was defeated by another Champion.

But for the PPR weights should be probabilities (i.d. sum of weights of out-edges = 1). How can we get this ? à Weight-proportional

probabilities.

We modified the weights in the following way: for each vertex we computed the sum of weight of all out-edges, then we divided the weight of each of them by that sum.

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Di Matteo Valerio - Siliberto Fabio

So what?

The player will give in input a selection of Champions he has in his team and a selection of adversaries he knows.

The software will compute the two personalized page ranks and will output three lists.

1.

2.

3.

Partnership rank Adversary rank Aggregated rank

Note: in case of empty inputs, the output will correspond to a simple non-personalized Page Rank.

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Di Matteo Valerio - Siliberto Fabio

Aggregated Page Rank The last list (Aggregated Rank) is the aggregated rank of the first two. It is built with the Borda Count Voting System: we give each Champion two scores based on their position, one for each rank, with 0 as the max possible score (the 1st gets 0, the 2nd gets -1 and so on); then we sum the scores and order the Champions by decreasing total score.

With this rank we give a ÂŤresumeÂť of the two previous ranks. The user will thus have three lists of suggestions, and he will be able to base his decision on the one he prefers.

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Di Matteo Valerio - Siliberto Fabio

Notes We analysed top 200 players of Europe West server.

About 5000 matches. All players of these matches had the same skill level, this is ensured by developers that used adhoc matching algorithm.

We implemented an easy command-line interface to let the program be used by real players.

Data we got were confirmed by expert players.

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Di Matteo Valerio - Siliberto Fabio

Example of results

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Di Matteo Valerio - Siliberto Fabio