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change rating system map prediction accuracy cal

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The new calculation better takes into account low-confidence wrong predictions which have previously inflated the postive results.
This commit is contained in:
Yannik Schmidt
2020-10-18 05:56:03 +02:00
parent 69efa55efa
commit eb8a5ebb5d
1 changed files with 7 additions and 3 deletions
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10
MapSummary.py
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@@ -35,11 +35,15 @@ class MapSummary:
try: try:
self.insurgentWinPercent = self.insurgentWins / self.totalGames*100 self.insurgentWinPercent = self.insurgentWins / self.totalGames*100
self.securityWinPercent = self.securityWins / self.totalGames*100 self.securityWinPercent = self.securityWins / self.totalGames*100
predictionPercision = 1 - sum(self.predictions)/len(self.predictions)
confidenceAverage = sum(self.confidence) / len(self.confidence)
averageSeconds = sum([t.total_seconds() for t in self.times]) / len(self.times) averageSeconds = sum([t.total_seconds() for t in self.times]) / len(self.times)
self.averageTime = datetime.timedelta(seconds=int(averageSeconds)) self.averageTime = datetime.timedelta(seconds=int(averageSeconds))
self.ratingSystemDeviation = predictionPercision*100 - confidenceAverage
mapper = [ 1 if x == 0 else -1 for x in self.predictions ]
self.ratingSystemDeviation = 0
for i in range(0, len(self.confidence)):
self.ratingSystemDeviation += mapper[i] * self.confidence[i]
self.ratingSystemDeviation /= self.totalGames
except ZeroDivisionError: except ZeroDivisionError:
pass pass