4.4 KiB
https://rpubs.com/patrickoster/481524
https://rpubs.com/patrickoster/481544
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
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.