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josedv82/README.md

My (publicly available) R packages:

  • matuR: Calculate and plot athlete maturation and biobanding metrics.
  • airball: Extract schedule density, travel related metrics and injury transactions for NBA teams since 1947.
  • bdgramR: Human body diagram visualizations and ggplot2 extensions in R.

Shiny APPs:

  • NBA Schedule Density: An app to visualize and interact with various schedule & travel related factors for the 2016-21 seasons.
  • NBA EPV Dashboard: A conceptual dashboard idea to visualize Expected Possession Value (EPV) in Basketball resembling a stock trading app.
  • Basketball Event Tracker An app to plot basketball events and export the dataset with the corresponding coordinates and other related info.
Research Collaborations:

  • Casals M., Fernández J., Martínez V., Lopez M., Langohr K., & Cortés J. (2022). A systematic review of sport-related packages within the R CRAN repository. International Journal of Sports Science & Coaching, 0(0). Link

  • Torres-Ronda L, Gámez I, Robertson S, Fernández J (2022) Epidemiology and injury trends in the National Basketball Association: Pre- and per-COVID-19 (2017–2021). PLoS ONE 17(2): e0263354. Link

  • Schelling X, Fernández J, Ward P, Fernández J, Robertson S. (2021) Decision Support System Applications for Scheduling in Professional Team Sport. The Team's Perspective. Front Sports Act Living. Jun 4;3:678489. Link

  • Cohan, A, Schuster, J, Fernández J. (2021) A Deep Learning Approach to Injury Forecasting in NBA Basketball. Journal of Sports Analytics. 1 Jan. 277 – 289. Link

  • Schuster, J, Fernández J, Cohan, A. (2021) Hiding in plain sight: Schedule density and travel influence on NBA games outcomes. First ever scientific sports paper released as NFT on Opensea.io. December 2021. Link

Pinned Loading

  1. EPV_NBA_Dashboard EPV_NBA_Dashboard Public

    A conceptual dashboard to visualize Expected Possession Value (EPV) in the NBA.

    R 11

  2. NBA_Schedule_XGBoost_Classifier NBA_Schedule_XGBoost_Classifier Public

    Predicting NBA game outcomes using schedule related information. This is an example of supervised learning where a xgboost model was trained with 20 seasons worth of NBA games and uses SHAP values …

    Jupyter Notebook 3

  3. NBA-Game-Density-App NBA-Game-Density-App Public

    An app to visually explore the density (and other related factors) of the schedule for NBA teams.

    R 7 2

  4. airball airball Public

    An R package to extract common schedule and travel metrics for NBA teams.

    R 16 1

  5. shinyAMS-resources shinyAMS-resources Public

    A compilation of resources for sport scientist building Athlete Management Tools in Shiny

    21 4

  6. public_sport_science_datasets public_sport_science_datasets Public

    An ongoing compilation of publicly available datasets for sport science projects.

    47 4