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- # Importing and Cleaning Data
-
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- # Data Visualization
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- # Analysis
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- # Report
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-
-
- ## 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
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- 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:
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- - 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. Since this will be heavily dealing with multivariate time series data leading up to a game, using a Long
- Short-term Network (LSTM) would bring promising results.
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