Date post: | 22-Jan-2017 |
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Step to Complete the Balancing Analysis
1. Get the battle data (win / loss) for every character against all other character2. Turn the raw data into a matchup chart data3. Turn the matchup data into a payoff chart data4. Perform the linear optimisation5. Plot the result and make recommendation
Get the raw dataWeb scraping on a fan database page http://www.eventhubs.com/tiers/sf5/
For every character web page, I have extracted the match avg. score against all the other character.
Raw Data into a Matchup Chart
divid the result by 10 to make the scale to 1
Example with Alex and ken
Assumption Of the Linear Optimisation
Assumption
The assumption of our model is that at time t, a player community Ct consists of t-1 players, and a new player joins the game, choosing a character who maximizes her win probability in a game against a randomly chosen player from C.
We can make game balancing adjustment by knowing which character should play if player want to maximizes theirwin probability
The Linear Optimization Model
We also add additional constraint related to frequency of play rate. We are going to solve this linear optimisation for every possibility of xi play rate frequency probability. We will then plot the minimum probability and the maximum probability that a player play character i
we only consider probability from 0 to 1 by 0.1 (0, 0,10. 0,20 … to 1) we could have considered more
xi = play rate frequency of xivij = matchup data of fighter i vs fighter j
Here the graph shows the minimum and maximum probability the play rate of eachcharacter base on the assumption of our model.
Recommandation - Player should basically play chun_li in
30% to 100% of their match. Let’s lower her power.
- Player should never play zangief. Let’s increase his power.
Result & Recommandation