fantasyfootball's Introduction
Files included, in chronological order: DATA RETRIEVAL Play-by-Play data nfl_pbp_game.py — retrieves data from NFL API pbp_data_download.png — screenshot of retrieval of API data Weather data scrapeWeatherData.R - retrieves weather data from internet Injury data nfl_injury_extact.py - retrieves injury data from internet FanDuel data salarylist.csv - downloaded from FanDuel.com DATA PROFILING Play-by-Play data pbp_profile_map.py - map class for profiling, uses data from API pbp_profile_reduce.py - reduce class for profiling pbp_profile_log.txt - logs from running map and reduce on API data Weather data ProfileDriver.java - MapReduce driver, uses data scraped by ‘scrapeWeatherData.r’ ProfileMapper.java - Mapper class ProfileReducer.java - Reducer class Data_Profile_Screenshot_1.png and Data_Profile_Screenshot_2.png - screenshots of the above Injury data NFL_Injury.java - MapReduce driver, uses data scraped by ‘nfl_injury_extact.py’ NFL_Injury_Mapper - Mapper class NFL_Injury_Reducer - Reducer class Profiling1.png, Profiling3.png, Profiling5.png - screenshots of the above FanDuel data ProfileData.java - MapReduce driver, uses ‘salarylist.csv’ as input ProfileMapper.java - Mapper class ProfileReducer.java - Reducer class output.txt - the output of the profiling code above DATA CLEANING AND MERGING Play-by-Play data impala_clean_merge.txt - cleans play by play and merges with weather, injury data. Takes as input the API data and cleaned data from weather and injury datasets. pbp_clean_merge.png - screenshot of the cleaning and merging process Weather data NFLDriver.java - MapReduce driver, uses output of ’scrapeWeather.r’ as input NFLMapper.java - Mapper class NFLReducer.java - Reducer class Data_Cleaning_Screenshot_1.png and Data_Cleaning_Screenshot_2.png - screenshots of the above Injury data Cleaning_Data.txt - Hive code for data cleaning FanDuel data newDriver.java - MapReduce driver, uses ‘salarylist.csv’ as input newMapper.java - Mapper class newReducer.java - Reducer class cleaning_screenshot.png - screenshot of the above ANALYTIC Point prediction rdd_regression4.1.py - takes as input the output of ‘impala_clean_merge.py’ and returns predicted point values for each player score_prediction.png - screenshot of ‘rdd_regression4.1.py’ running scores.txt — output from ‘rdd_regression4.1.py’ readPreds.java - takes ‘scores.txt’ as input, along with salary data and the list of eligible for a particular contest and returns input for the team selection model as well as ‘scores.txt’ reformatted to a .csv Position.java and Player.java are Object classes upon which ‘readPreds.java’ relies playerlist.csv - output from ‘readPreds.java’, input to team selection model goodnessinput.csv - output from ‘readPreds.java’, used offline for goodness-of-fit tests in R Team selection Team_Selection.py - takes ‘playlist.csv’ as input and prints the chosen team Team_Selection_Model.jpg - a screenshot of the above running and the output it produced Goodness testing goodnesstesting.R - takes in our predictions and the actual scores from a given week and displays various statistics about the goodness of fit goodnessinput.csv - the output of the prediction model reformatted to a .csv, input to ‘goodness testing.R’ week14.csv - the actual scores for players last week
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