public SkillGaussian(SkillGaussian gaussian)
{
ID = gaussian.ID;
IsJammer = gaussian.IsJammer;
Precision = gaussian.Precision;
Pam = gaussian.Pam;
LastUpdated = gaussian.LastUpdated;
}
public SkillGaussian(SkillGaussian gaussian)
{
ID = gaussian.ID;
IsJammer = gaussian.IsJammer;
Precision = gaussian.Precision;
Pam = gaussian.Pam;
LastUpdated = gaussian.LastUpdated;
}
// at some point, we'll need to take the current sigma of the player and increase it, probably based on time since last data
private double CalculateTimeEffect(SkillGaussian player, DateTime newTime)
{
// how much do we want to scale the player's std dev?
// it probably ought to be flat, not proportional
// one way to think of it: how long would a well-understood player have to disappear to be treated as new again?
// let's say two years-ish, and see what happens
TimeSpan timeSpan = newTime - player.LastUpdated;
if(timeSpan > _baseTimeSpan)
{
timeSpan = _baseTimeSpan;
}
double ratio = ((double)timeSpan.Ticks) / _baseTimeSpan.Ticks;
double range = _baseSigma - player.Sigma;
return range * ratio;
}
private SkillGaussian CreatePlayerSkill(int playerID, bool isJammer, DateTime dateOfGame)
{
// variances, not std dev, are sum-able
// a^2 + b^2 != (a+b)^2
var dictionary = isJammer ? _jammerSkillDictionary : _blockerSkillDictionary;
if(!dictionary.ContainsKey(playerID))
{
SkillGaussian newPlayer = new SkillGaussian(playerID, 500, _baseVariance, isJammer, dateOfGame);
dictionary[playerID] = newPlayer;
return newPlayer;
}
var startingPlayer = dictionary[playerID];
double tau = CalculateTimeEffect(startingPlayer, dateOfGame);
startingPlayer.AddVariance(Math.Pow(tau, 2));
startingPlayer.LastUpdated = dateOfGame;
return startingPlayer;
}
private void CalculateNewPlayerRating(double c, double team1Multiplier, double v, double w, SkillGaussian player)
{
double meanMultiplier = player.Variance / c;
double stdDevMultiplier = meanMultiplier / c;
double playerMeanDelta = (team1Multiplier * meanMultiplier * v);
double newMean = player.Mean + playerMeanDelta;
double newVariance = player.Variance * (1 - (w * stdDevMultiplier));
SkillGaussian newValues = new SkillGaussian(player.ID, newMean, newVariance, player.IsJammer, player.LastUpdated);
if(player.IsJammer)
{
_jammerSkillDictionary[player.ID] = newValues;
}
else
{
_blockerSkillDictionary[player.ID] = newValues;
}
}