/// <summary>
/// Run through all players within a resultset and calculate their new ratings.
///
/// Players within the resultset who did not compete during the rating period
/// will have see their deviation increase (in line with Prof Glickman's paper).
///
/// Note that this method will clear the results held in the association result set.
/// </summary>
/// <param name="results"></param>
public void UpdateRatings(RatingPeriodResults results)
{
foreach (var player in results.GetParticipants())
{
if (results.GetResults(player).Count > 0)
{
CalculateNewRating(player, results.GetResults(player));
}
else
{
// if a player does not compete during the rating period, then only Step 6 applies.
// the player's rating and volatility parameters remain the same but deviation increases
player.SetWorkingRating(player.GetGlicko2Rating());
player.SetWorkingRatingDeviation(CalculateNewRatingDeviation(player.GetGlicko2RatingDeviation(),
player.GetVolatility()));
player.SetWorkingVolatility(player.GetVolatility());
}
}
// now iterate through the participants and confirm their new ratings
foreach (var player in results.GetParticipants())
{
player.FinaliseRating();
}
// lastly, clear the result set down in anticipation of the next rating period
results.Clear();
}
/// <summary>
/// Run through all players within a resultset and calculate their new ratings.
///
/// Players within the resultset who did not compete during the rating period
/// will have see their deviation increase (in line with Prof Glickman's paper).
///
/// Note that this method will clear the results held in the association result set.
/// </summary>
/// <param name="results"></param>
public void UpdateRatings(RatingPeriodResults results)
{
UpdateWorkingRatings(results);
// now iterate through the participants and confirm their new ratings
foreach (var player in results.GetParticipants())
{
player.FinaliseRating();
}
// lastly, clear the result set down in anticipation of the next rating period
results.Clear();
}
public void UpdateWorkingRatings(RatingPeriodResults results)
{
foreach (var player in results.GetParticipants())
{
if (results.GetResults(player).Count > 0)
{
CalculateNewRating(player, results.GetResults(player));
}
else
{
// if a player does not compete during the rating period, then only Step 6 applies.
// the player's rating and volatility parameters remain the same but deviation increases
player.SetWorkingRating(player.GetGlicko2Rating());
player.SetWorkingRatingDeviation(CalculateNewRatingDeviation(player.GetGlicko2RatingDeviation(),
player.GetVolatility()));
player.SetWorkingVolatility(player.GetVolatility());
}
}
}
/// <summary>
/// Run through all players within a resultset and calculate their new ratings.
///
/// Players within the resultset who did not compete during the rating period
/// will have see their deviation increase (in line with Prof Glickman's paper).
///
/// Note that this method will clear the results held in the association result set.
/// </summary>
/// <param name="results"></param>
public void UpdateRatings(RatingPeriodResults results)
{
foreach (var player in results.GetParticipants())
{
if (results.GetResults(player).Count > 0)
{
CalculateNewRating(player, results.GetResults(player));
}
else
{
// if a player does not compete during the rating period, then only Step 6 applies.
// the player's rating and volatility parameters remain the same but deviation increases
player.SetWorkingRating(player.GetGlicko2Rating());
player.SetWorkingRatingDeviation(CalculateNewRatingDeviation(player.GetGlicko2RatingDeviation(),
player.GetVolatility()));
player.SetWorkingVolatility(player.GetVolatility());
}
}
// now iterate through the participants and confirm their new ratings
foreach (var player in results.GetParticipants())
{
player.FinaliseRating();
}
// lastly, clear the result set down in anticipation of the next rating period
results.Clear();
}
