Hello everyone! My name is Junho,
I’m going to present “Action2Score : An Embedding Approach to Score Player Action”
MOBA is a game genre representing team competition strategy games
where two teams face off to destroy each other's base
Most MOBA games have a Rank system that groups players at a similar proficiency level
A player's rank is determined by Match Making Rating, MMR
MMR increases when the player's team wins in a match and decreases when it loses
It is reasonable that a player's MMR depends on the game results in the team-playing game
However, sometimes it can be unfair
For example, some free riders piggyback on teammates without their effort
On the other hand,
some players show heroic performances even their team is losing
Therefore, game communities use some metrics such as KDA to evaluate players' individual performance
However, those metrics use only end-state statistics,
so we cannot assess respective actions' contributions
Also, with those metrics, some players can be over/underestimated
So, we wanted to design a model that converts a player's action into a score
representing how much the action contributed to the team's achievement
Also, we focused on the causal relationships between the player's actions
Imagine a situation where a player has bought an item and then killed an enemy champion
Killing an enemy is usually considered beneficial for a winning a match
Then the act of buying can be assumed to be likely beneficial
Therefore, we used GRU, one of the RNN structures, to reflect the causal relationships
Moreover, we implemented our model to take player actions in a time-reversed sequence
to estimate the cause action through the result action
RNN structure is combined with single-layered perceptrons,
and it takes a sequence of a player's action and then outputs scores for corresponding actions
Also, our model consists of two main parts
The left part is the GRU-SLP part, which comprises GRU-SLP models that I explained previously,
taking each player's action sequence of a match
The right part is the Discernment and Evaluation part
DEP discerns the winner by comparing the two team's total score,
and calculates the loss to execute backpropagation
We designed two discernment method-loss function pairs for DEP
In the confidence-cross entropy loss pair,
DEP calculates how confident a team given a higher score is the actual winner
The equations below depict how DEP computes the confidence and loss values
On the other hands, DEP does not compute confidence in the other pair
Also, we used the ReLU function as the loss function,
so the loss to be zero unless the discernment is incorrect, regardless of the score gap
To conduct experiment,
we collected more than 240,000 League of Legends match data using Riot API
We used 200,000 matches for training and validation
And the rest of the data was used to build testset
From the match data, we set up six features
Timestamp is the time point at which the event occurred
Champion is a character chosen by a player
Lane is a chosen lane of the player
Position is the player's location on the map when the event occurs
Event is the type of events such as champion kill, and its weight
Distance depicts how much the player is isolated from their teammates
For the experiment, we created seven models to compare performance
Because of the time limitation, I will focus on the performance of the main model
This table displays the discernment accuracy of the model comparing three baselines
Our model discerns the winner much more accurately than using common metrics
Meanwhile, our models have to give similar scores to the actions in a similar context
regardless the player of the action is the winner or not
These figure shows the score distribution according to the action feature vector
The dimension depth of the feature vector was reduced to 1 using PCA
The actions with the similar feature vector can be considered
to have similar worthiness in a match context
We can see that our model gives similar scores
to the actions with similar feature vector
These figure display the relationship between players' rankings
according to model scores and common metrics
It shows some positive correlations between the model score and the common metrics
That depicts that our models properly reflect individual performance evaluation by common metrics
These figures display the number of under/overestimated players by lane
according to common metrics
The graph shows many players can be under/overestimated when using common metrics
Lastly, I will show the visions of our model
The experiment results show that our model has more advantages
than common metrics such as KDA
in evaluating the individual performance of a player
We expect that MOBA games can use our model
to adjust the changing amount of MMR
according to the match outcome
to overcome the limitations of the current MMR systems
Or, they can simply display scores on the learderboard to praise players
Furthermore, although we designed our model to fit League of Legends,
the fundamental structure of our model has enough generality
So the model can be used for other games,
including MOBA games like Dota 2,
and different genres such as First-Person Shooter games,
with only minor adjustment