more correlation

data_preparation/cleaned/expSmoothWorkAndFatigueData.csv

@@ -1,4 +1,4 @@
-"","day","totalWork","averageWorkLoad","smoothedWork","smoothedFatigueData"
+"","TimeSinceAugFirst","totalWork","averageWorkLoad","smoothedWork","smoothedFatigueData"
 "1",0,4855,255.526315789474,4855,0.734457489003587
 "2",1,1380,138,5921.8864123285,1.17226558337237
 "3",2,6030,231.923076923077,11569.9661032971,1.37713723445689

findings/r2correlation.txt

@@ -1,4 +1,5 @@
 Document Linear Relationship
 7 day moving average team workload - normalized team fatigue: 0.0006
 21 day moving average team workload - normalized team fatigue: 0.0024
-
+normalized team fatigue - game day performance: 0.0696
+normalized team fatigue - paper smoothed workload fatigue: 0.0324

hypotheses_modeling/team_regressions.py

@@ -17,7 +17,11 @@ def k_days_into_future_regression(X, y, k, n0):
   out = []
   for day in y[col][n0 - 1:]:
     prev = day - k
-    xprev = X[X[col] == prev].drop(columns=[col]).to_numpy()[0, :]
+    xprev = X[X[col] == prev].drop(columns=[col]).to_numpy()
+    if xprev.shape[0] != 1:
+      continue
+    else:
+      xprev = xprev[0, :]
     yt = y[y[col] == day].drop(columns=[col]).to_numpy()[0, :]
     inp.append(xprev)
     out.append(yt)
@@ -31,8 +35,8 @@ def k_days_into_future_regression(X, y, k, n0):
 
 def main():
   fatigueSums = pd.read_csv("fatigue_total_sum.csv")
-  workMovingAverage21 = pd.read_csv("21DaySlidingWorkAverage.csv", index_col=0)
-  print(k_days_into_future_regression(workMovingAverage21, fatigueSums, 0, 21))
+  performance = pd.read_csv("../data_preparation/cleaned/expSmoothWorkAndFatigueData.csv", index_col=0).drop(columns=["totalWork", "averageWorkLoad", "smoothedFatigueData"])
+  print(k_days_into_future_regression(fatigueSums, performance, 0, 1))
 
 
 if __name__ == "__main__":

hypotheses_modeling/time_series_days_ranked.csv

@@ -1,4 +1,4 @@
-,Date,DailyElo
+,TimeSinceAugFirst,DailyElo
 0,121,0.0
 1,122,-3.714599999999998
 2,178,0.04346000000000028