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Condition

Impact on the match simulation

In the context of the match simulation, a player's condition affects their in-game performance via a fixed function. A fixed amount of the player's rating is weighted by their condition rating.
Logic:
  • Given an Elo rating and a condition then the player's rating in the game is:
GameRating=(PositionRating1200)+(1200condition)GameRating = (PositionRating-1200)+(1200 * condition)
Example:
  • A player has an Elo rating of 2000 and
  • A condition of 100%:
GameRating(100%)=(20001200)+(1200100%)=2000GameRating(100\%)=(2000-1200)+(1200*100\%)=2000
  • A condition of 80%:
GameRating(80%)=(20001200)+(120080%)=1760GameRating(80\%)=(2000-1200)+(1200*80\%)=1760
  • A condition of 50%:
GameRating(50%)=(20001200)+(120050%)=1400GameRating(50\%)=(2000-1200)+(1200*50\%)=1400

Relationship between fitness and condition

During a match, a player's condition decreases, meaning they get tired and their performance worsens. The rate at which a player loses condition is dependent on their fitness, however, the rate at which they recover is independent of fitness.
When decreasing a player's 0-100 condition, using the below formula we convert the number to an Elo value, a domain that is easier to operate over mathematically.
Elo(x)=ln(1x0.151)+0.001517000.0015Elo(x) = -\frac{ln(\frac{1}{x-0.15}-1)+0.0015*1700}{0.0015}
​We decrease the condition, then convert it back to a condition value (0-100) using the below formula
Condition(x)=11+e0.0015(x1700)+0.15Condition(x) = \frac{1}{1+e^{- 0.0015(x-1700)}}+0.15
Players recover whilst they're not playing in a game.