diff --git a/data_preparation/cleaned/expSmoothWorkAndFatigueData.csv b/data_preparation/cleaned/expSmoothWorkAndFatigueData.csv
new file mode 100644
index 0000000..df1b22e
--- /dev/null
+++ b/data_preparation/cleaned/expSmoothWorkAndFatigueData.csv
@@ -0,0 +1,356 @@
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diff --git a/data_preparation/createWorkSequenceData.R b/data_preparation/createWorkSequenceData.R
index f202b22..21cb979 100644
--- a/data_preparation/createWorkSequenceData.R
+++ b/data_preparation/createWorkSequenceData.R
@@ -27,6 +27,92 @@ plot(dayList, averageWorkLoad, main="Average Work Load")
 plot(dayList, workLoad, main="Daily Total Work Load")
 
 
+
+
+
+fatigueFunction <- function(workLoad, index)
+{
+  if(index == 1)
+  {
+    return(workLoad[1])
+  }
+  else
+  {
+    return(workLoad[index] + (exp(1)^(-1/15))*fatigueFunction(workLoad, index -1))
+  }
+}
+
+smoothedWork <- c()
+for(day in dayList)
+{
+  smoothedWork <- c(smoothedWork, fatigueFunction(workLoad, day + 1))
+}
+
+plot(dayList, smoothedFatigue)
+
+
+
+
+
+fatigueData <- readFatigueSums()
+
+dayNum <- max(fatigueData$TimeSinceAugFirst)
+
+dayList <- 0:dayNum
+
+
+slidingAverage <- c()
+window <- 21 - 1
+for(day in window:dayNum)
+{
+  windowAverage <- mean(fatigueData$fatigueSum[c((day-window):day)], na.rm = T)
+  
+  slidingAverage <- c(slidingAverage, windowAverage)
+}
+
+smoothedFatigueData <- c()
+for(day in dayList)
+{
+  smoothedFatigueData <- c(smoothedFatigueData, fatigueFunction(fatigueData$fatigueSum, day + 1))
+}
+
+plot(dayList, smoothedFatigueData)
+
+
+workTibble <- tibble(day = dayList, totalWork = workLoad, 
+                     averageWorkLoad = averageWorkLoad, 
+                     smoothedWork = smoothedWork,
+                     smoothedFatigueData = smoothedFatigueData)
+
+workGraph <- ggplot(data = workTibble) + 
+  theme(plot.title = element_text(hjust = 0.5)) + 
+  ggtitle("Team's Smoothed Work") + 
+  geom_point(mapping = aes(x=day, y=smoothedWork)) + 
+  labs(x = "Days Since August First 2017", y = "Teams Training Work")+ 
+  theme_bw()
+
+fatGraph <- ggplot(data = workTibble) + 
+  theme(plot.title = element_text(hjust = 0.5)) + 
+  ggtitle("Team's Percieved Fatigue") + 
+  geom_point(mapping = aes(x=day, y=smoothedFatigueData)) + 
+  labs(x = "Days Since August First 2017", y = "Teams Average Normalized Fatigue")+ 
+  theme_bw()
+
+
+for(gameDay in games$Date)
+{
+  fatGraph <- fatGraph + geom_vline(xintercept = gameDay, linetype="dotted", 
+                                      color = "blue", size=1.0)
+  workGraph <- workGraph + geom_vline(xintercept = gameDay, linetype="dotted", 
+                                        color = "blue", size=1.0)
+}
+workGraph
+fatGraph
+
+write.csv(dataTibble, "cleaned/expSmoothWorkAndFatigueData.csv")
+
+
+
 slidingAverage <- c()
 
 window <- 31 - 1
@@ -46,7 +132,7 @@ plot(density(workLoad), main="Total Work Load Average")
 dataTibble <- tibble(TimeSinceAugFirst = window:numDays, slidingWorkAverage = slidingAverage)
 
 
-ggplot(data = dataTibble) + 
+workGraph <- ggplot(data = dataTibble) + 
   theme(plot.title = element_text(hjust = 0.5)) + 
   ggtitle("Team's 7 Day Moving Average") + 
   geom_point(mapping = aes(x=TimeSinceAugFirst, y=slidingWorkAverage)) + 
@@ -54,37 +140,40 @@ ggplot(data = dataTibble) +
   theme_bw()
 
 
+for(gameDay in games$Date)
+{
+  workGraph <- workGraph + geom_vline(xintercept = gameDay, linetype="dotted", 
+                                      color = "blue", size=1.0)
+}
 
-write.csv(dataTibble, "cleaned/slidingWorkAverageSevenDay.csv")
-
+workGraph
 
-################################      Wellness Data      ###################################
 
-fatigueData <- readFatigueSums()
+write.csv(dataTibble, "cleaned/slidingWorkAverageSevenDay.csv")
 
-dayNum <- max(fatigueData$TimeSinceAugFirst)
 
-dayList <- 0:dayNum
+################################      Wellness Data      ###################################
 
 
-slidingAverage <- c()
-window <- 21 - 1
-for(day in window:dayNum)
-{
-  windowAverage <- mean(fatigueData$fatigueSum[c((day-window):day)], na.rm = T)
-  
-  slidingAverage <- c(slidingAverage, windowAverage)
-}
 
 graphingTib <- tibble(slidingAverage = slidingAverage, days = window:dayNum)
 
-ggplot(data = graphingTib) + 
+fGraph <- ggplot(data = graphingTib) + 
   theme(plot.title = element_text(hjust = 0.5)) + 
   ggtitle("Team's Average Normalized Fatigue") + 
   geom_point(mapping = aes(x=days, y=slidingAverage)) + 
   labs(x = "Days Since August Twenty First 2017", y = "Teams Average Normalized Fatigue")+ 
   theme_bw()
 
+
+for(gameDay in games$Date)
+{
+  fGraph <- fGraph + geom_vline(xintercept = gameDay, linetype="dotted", 
+                                      color = "blue", size=1.0)
+}
+
+fGraph
+
 plot(density(slidingAverage))
 plot(window:dayNum, slidingAverage)
 
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diff --git a/findings/SmoothedFatigue.png b/findings/SmoothedFatigue.png
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diff --git a/findings/SmoothedWork.png b/findings/SmoothedWork.png
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