mirror of
https://github.com/titanscouting/tra-analysis.git
synced 2024-09-20 08:57:56 +00:00
99 lines
3.0 KiB
Python
99 lines
3.0 KiB
Python
import math
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class Glicko2:
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_tau = 0.5
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def getRating(self):
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return (self.__rating * 173.7178) + 1500
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def setRating(self, rating):
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self.__rating = (rating - 1500) / 173.7178
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rating = property(getRating, setRating)
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def getRd(self):
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return self.__rd * 173.7178
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def setRd(self, rd):
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self.__rd = rd / 173.7178
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rd = property(getRd, setRd)
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def __init__(self, rating = 1500, rd = 350, vol = 0.06):
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self.setRating(rating)
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self.setRd(rd)
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self.vol = vol
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def _preRatingRD(self):
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self.__rd = math.sqrt(math.pow(self.__rd, 2) + math.pow(self.vol, 2))
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def update_player(self, rating_list, RD_list, outcome_list):
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rating_list = [(x - 1500) / 173.7178 for x in rating_list]
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RD_list = [x / 173.7178 for x in RD_list]
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v = self._v(rating_list, RD_list)
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self.vol = self._newVol(rating_list, RD_list, outcome_list, v)
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self._preRatingRD()
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self.__rd = 1 / math.sqrt((1 / math.pow(self.__rd, 2)) + (1 / v))
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tempSum = 0
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for i in range(len(rating_list)):
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tempSum += self._g(RD_list[i]) * \
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(outcome_list[i] - self._E(rating_list[i], RD_list[i]))
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self.__rating += math.pow(self.__rd, 2) * tempSum
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def _newVol(self, rating_list, RD_list, outcome_list, v):
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i = 0
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delta = self._delta(rating_list, RD_list, outcome_list, v)
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a = math.log(math.pow(self.vol, 2))
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tau = self._tau
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x0 = a
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x1 = 0
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while x0 != x1:
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# New iteration, so x(i) becomes x(i-1)
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x0 = x1
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d = math.pow(self.__rating, 2) + v + math.exp(x0)
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h1 = -(x0 - a) / math.pow(tau, 2) - 0.5 * math.exp(x0) \
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/ d + 0.5 * math.exp(x0) * math.pow(delta / d, 2)
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h2 = -1 / math.pow(tau, 2) - 0.5 * math.exp(x0) * \
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(math.pow(self.__rating, 2) + v) \
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/ math.pow(d, 2) + 0.5 * math.pow(delta, 2) * math.exp(x0) \
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* (math.pow(self.__rating, 2) + v - math.exp(x0)) / math.pow(d, 3)
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x1 = x0 - (h1 / h2)
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return math.exp(x1 / 2)
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def _delta(self, rating_list, RD_list, outcome_list, v):
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tempSum = 0
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for i in range(len(rating_list)):
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tempSum += self._g(RD_list[i]) * (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
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return v * tempSum
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def _v(self, rating_list, RD_list):
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tempSum = 0
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for i in range(len(rating_list)):
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tempE = self._E(rating_list[i], RD_list[i])
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tempSum += math.pow(self._g(RD_list[i]), 2) * tempE * (1 - tempE)
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return 1 / tempSum
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def _E(self, p2rating, p2RD):
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return 1 / (1 + math.exp(-1 * self._g(p2RD) * \
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(self.__rating - p2rating)))
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def _g(self, RD):
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return 1 / math.sqrt(1 + 3 * math.pow(RD, 2) / math.pow(math.pi, 2))
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def did_not_compete(self):
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self._preRatingRD() |