Skip to content
View josedv82's full-sized avatar
73 followers · 18 following

Achievements

Achievement: StarstruckAchievement: Pull Shark

Achievements

Achievement: StarstruckAchievement: Pull Shark

Block or report josedv82

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
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 15 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