NateLatshaw

NateLatshaw's Stars

• fchollet/deep-learning-with-python-notebooks

  Jupyter notebooks for the code samples of the book "Deep Learning with Python"

  Language:Jupyter Notebook18.9k6531888.7k

• rmcelreath/stat_rethinking_2022

  Statistical Rethinking course winter 2022

  Language:R4.1k22422444

• erikgahner/awesome-ggplot2

  A curated list of awesome ggplot2 tutorials, packages etc.

  1.6k4616170

• BlakeRMills/MetBrewer

  Color palette package in R inspired by works at the Metropolitan Museum of Art in New York

  Language:R1.1k71783

• thebioengineer/camcorder

  Record plots generated during an R session and replay as a gif!

  Language:R2055257

• arnabbiswas1/kaggle_pipeline_tps_aug_22

  Kaggle Pipeline for tabular data competitions

  Language:Jupyter Notebook2042022

• jfjelstul/worldcup

  A Comprehensive Database on the FIFA World Cup (Men's and Women's)

  Language:HTML1788981

• rd11490/NBA_Tutorials

  Tutorials for processing nba data

  Language:Python162121141

• coolbutuseless/ggsvg

  Use SVG images as ggplot points

  Language:R1406115

• zbinney/Stats_in_Sports_2021

  Materials for the Statistics in Sports class for first-year undergrads

  Language:HTML1235015

• HudsonJamie/tidy_tuesday

  Scripts from Tidy Tuesday fun

  Language:R11412117

• Biogen-Inc/tidyCDISC

  Demo the app here: https://bit.ly/tidyCDISC_app

  Language:R1081014238

• RamiKrispin/USelectricity

  Forecast the US demand for electricity

  Language:R10018115

• AbdoulMa/TidyTuesday

  Contribution to Tidytuesday Data Visualization Challenge

  Language:R713017

• fakyras/ncaa_women_2018

  Kaggle NCAA women challenge winning solution

  Language:R503027

• mtoto/ufc.stats

  UFC Statistics conveniently packaged as a tidy dataframe.

  Language:R487910

• wch/shiny-wordle

  Wordle in Shiny

  Language:R484135

• devOpifex/litter

  :no_entry_sign: Lit components for shiny

  Language:HTML31340

• nflverse/nflseedR

  Functions to Efficiently Simulate and Evaluate NFL Seasons

  Language:R224204

• harshkrishna17/PlayerFinishingOverviewShiny

  A Shiny app that creates xG based finishing reports for soccer players using data from Understat.

  Language:R16200

• JanVanHaaren/how-to-expected-goals

  Repository for a how-to on training an expected-goals model for football.

  Language:Jupyter Notebook16200

• beanumber/ctv-sportsanalytics

  CRAN Task View: Sports Analytics

  Language:R146304

• adisujithkumar/csgo

  A Counter-Strike: Global Offensive (CSGO) parser in Python

  Language:Python10003

• ryuji2357/soccer

  GitHub の最初のリポジトリ

  1020

• Christianlee19/hockey-stats

  Language:R6311

• aviskarkc10/nba-expected-bucket

  Predicting the outcome of shots based on the events and tracking data available for the 2015/16 season.

  Language:Jupyter Notebook5501

• oscarbaruffa/bigbookofr_bot

  A bot to automatically post Big Book of R content

  Language:R52103

• kpelechrinis/NFL-OT

  Language:Python230

• spelk24/Data-Viz

  Weekly Data Viz event

  Language:R20

• tuckerboynton22/nflwinbot

  Bot that tweets NFL win probabilities

  Language:R21