Time Series Analysis of National League Slugging Percentage (Major League Baseball)
Bibliographic record
Abstract
Time Series Analysis of National League Slugging Percentage (Major League Baseball) Major League Baseball records yearly National League slugging percentage values, and for this project the data was examined using a time series with 103 data points, from 1901-2003. The analyses performed include exploratory data analysis such as the plot of the time series, a histogram, a normal QQ-plot, and the Autocorrelation Function and Partial Autocorrelation function of the series. Numerous general choices for which model to select for the data such as Autoregressive (AR) and Moving Average (MA) models were considered, as well as possible transformations or differencing of the data in order to obtain stationarity and constant variance of the series. Once these possible models were chosen, the next step was fitting the models to the series and analyzing the coefficients through parameter estimation, as well as performing a diagnostic check of the residuals for each model. Information Criterion values were also used to interpret the results of the model fitting. To confirm any hypotheses made to that point, predictions were done to evaluate the success of the model in terms of forecasting future values. In this case, the time series including the years from 1901-2003 was used to predict the slugging percentage values for the next ten years, from 2004-2013. Clearly, these results have already been obtained, so the effectiveness of the forecasting was determined decisively by comparing the predicted values and actual values for each model being tested. A convincing choice of the optimal model for this time series was attained. Discipline: Statistics Faculty Mentor: Dr. Cristina Anton
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".