From 66b448c0b7b5ee8969cc802fee40d70c35e9f169 Mon Sep 17 00:00:00 2001 From: jrtechs Date: Sat, 4 May 2019 22:35:35 -0400 Subject: [PATCH] Initial outline of datafest blog post. --- .../posts/data-science/datafest-2019.md | 96 ++++++++++++++++++ .../media/datafest/clusterMess.png | Bin 0 -> 154418 bytes .../media/datafest/hoursOfSleepBoxPlot.png | Bin 0 -> 39782 bytes .../media/datafest/hoursOfSleepDensity.png | Bin 0 -> 41363 bytes .../data-science/media/datafest/network.png | Bin 0 -> 151483 bytes .../data-science/media/datafest/noShit.png | Bin 0 -> 30807 bytes .../media/datafest/teamFatigue.png | Bin 0 -> 65447 bytes .../data-science/media/datafest/teamWork.png | Bin 0 -> 66891 bytes 8 files changed, 96 insertions(+) create mode 100644 blogContent/posts/data-science/datafest-2019.md create mode 100644 blogContent/posts/data-science/media/datafest/clusterMess.png create mode 100644 blogContent/posts/data-science/media/datafest/hoursOfSleepBoxPlot.png create mode 100644 blogContent/posts/data-science/media/datafest/hoursOfSleepDensity.png create mode 100644 blogContent/posts/data-science/media/datafest/network.png create mode 100644 blogContent/posts/data-science/media/datafest/noShit.png create mode 100644 blogContent/posts/data-science/media/datafest/teamFatigue.png create mode 100644 blogContent/posts/data-science/media/datafest/teamWork.png diff --git a/blogContent/posts/data-science/datafest-2019.md b/blogContent/posts/data-science/datafest-2019.md new file mode 100644 index 0000000..1a92765 --- /dev/null +++ b/blogContent/posts/data-science/datafest-2019.md @@ -0,0 +1,96 @@ + + +# Importing and Cleaning Data + + +# Data Visualization + + +# Analysis + + +# Report + + + +## Abstract + +The way in which a team trains is critical in ensuring that everyone performs at their peak performance +during a game. In order to effectively train a team to optimize their gameday performance, it would make +intuitive sense to monitor their training data with respect to their perceived fatigue. Through analyzing +time series data provided by our partnering women’s rugby team, it was observed that this team altered +their training schedule close to games. Although there is some relationship between the two in the long +run, our attempts at modeling fatigue and work load in the short run suggests little to no correlation using +linear regressions. This suggests that modeling fatigue is a more complex problem including a slew of factors +both psychological and physical which spans over a period of time; coaches should pay attention not only to +training but also sleep and mental wellness for happy and competitive teams. To most effectively forecast an +individual’s performance during a game, we propose a system which takes into account physiological factors +such as desire and physical factors such as sleep, soreness and amount of training. + +## Methodology + +We employed a wide range of techniques for establishing our models and hypotheses, including smoothing +of time series Information, testing of hypotheses based on a prior understanding of the domain, plotting +and visually analyzing pairs of variables, and artificial intelligence algorithms that found various linear and +nonlinear patterns in the dataset. Coefficients of determination were calculated to determine fitness of linear +models, and F1 scores were analyzed to validate complex nonlinear classification models. + + +## Modeling Fatigue + +Fatigue can be effectively and linearly modeled using daily records and time series moving +averages of acute chronic ratios, daily workload, sleep quality, and sleep hours. +This means that instead of only lowering training before competitions, coaches +should put focus on preparing the athletes physically and mentally through a +combination of measures with a focus on sleep. + + +| Iterations/100 | Mean Squared Error | +| ----------- | ----------- | +| 1 | 90.4998 | +| 11 | 1.0265 | +| 21 | 0.9604 | +| 31 | 0.8671 | +| 41 | 0.7838 | +|100 | 0.0925 | +Sample Size: 304864 +Final R2: 0.532 + + +## Predicting Performance + +Trivially, performance of an individual cannot be modeled using simple linear regressions +only involving one factors. We therefore developed and optimized a deep neural + network to capture the patterns involving fatigue, sleep, and self-rated performance. + +The structure of the network is a 3-layer (input, output, and a hidden layer) +sigmoid classifier that was trained on batches of 32 samples from players with +respect to features: normalized perceived fatigue, sliding average of +perceived fatigue, sliding average over sleep hours, and the perceived sleep quality of +the players. It is optimized through the Adam optimizer with a learning rate of +.005 and cross entropy to calculate the loss between the logits and labels. + +The logits of the work are a confidence output on which class the network +feels the sample most likely belongs to, the real value of which is the +classification of perceived performance by the player. Through this method, +we can show a correlation between fatigue, sleep, and self-rated performance, +as well as a means to predict this self-rate performance based off of fatigue +and self-perceived sleep quality. + +Results with LR=.01, Batch=32: + +- Accuracy before training: 20.44388% +- Loss after step 49: .531657 +- Accuracy after training: 74.846625% +- F1 Score: .94 + +![](media/datafest/network.png) + +## Future Work + +With more data to to test with we can further improve and validate out models. With historical data from +other teams we can take our analysis one step further. Based on the training, performance, and fatigue +information from other teams we can use that to create a model to make a recommendation for our team’s +training. This model would be able to make recommendations for our training intensity leading up to a +game. 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