guga31bb

THIS IS OUTDATED. PLEASE FOLLOW THE FOLLOWING LINK

--> A beginner's guide to nflfastR <--

Basic nflscrapR tutorial

I get a lot of questions about how to get nflscrapR up and running. This guide is intended to help new users build interesting tables or charts from the ground up, taking the raw nflscrapR data.

Quick word if you're new to programming: all of this is happening in R. Obviously, you need to install R on your computer to do any of this. Make sure you save what you're doing in a script (in R, File --> New script) so you can save your work and run multiple lines of code at once. To run code from a script, highlight what you want, right click, and select Run line. As you go through your R journey, you might get stuck and have to google a bunch of things, but that's totally okay and normal. That's how I wrote this thing!

Deryck97

nflscrapR Python Guide

This is an introduction to working with nflscrapR data in Python. This is inspired by this guide by Ben Baldwin.

Using Jupyter Notebooks which come pre-installed with Anaconda is typically the best way to work with data in Python. This guide assumes you are using the Ananconda distribution and therefore already have the required packages installed. If you are not using the Anaconda distribution, install numpy, pandas, and matplotlib.

Once Anaconda has been downloaded and installed, open the Anaconda Navigator. Click launch on the Jupyter Notebook section which will open in your browser.

Collecting and Cleaning Data

There are a couple ways to get nflscrapR data. While you don't necessarily need R for historical data, it is necessary for getting data that has not been uploaded to github. My preferred process is to get data u

aparrish

aparrish

wessonmo

An Alternate Approach to Measuring the Effect of Accumulated QB Hits on Passing Performance

This analysis is a follow-up to @benbbaldwin's post The accumulation of QB hits vs passing efficiency where his conclusion was that:

[For] a given level of hits, there is no evidence that the accumulation of hits makes any difference throughout the course of a game.

As the title suggests, this analysis will take a different approach to measuring the hypothesized effect.

1. Data

Here is the code to get the passing data we'll be using from nflscrapR-data:

wessonmo

Team Strength Exclusion Bias in Expected Points Models

@nflscrapR's Expected Points (EP) is a popular metric among analysts doing public research of play in the NFL. Detailed in the creators' research paper, the metric is derived from a model that was built as a part of a larger system designed to calculate individual wins above replacement values for offensive skill players.

The authors very graciously made public all of their data (nflscrapR-data) and code (nflWAR, nflscrapR-models, nflscrapR) for this project, including the code used to build the EP model. In the init_ep_fg_models.R file of the nflscrapR-models repository, we can see that the following variables are used

Robotto

import pytz
import datetime
import time
import urllib2
import json
import os
import elementtree.ElementTree as ET

url = 'http://scores.nbcsports.com/ticker/data/gamesNEW.js.asp?jsonp=true&sport=%s&period=%d&random=%d'

Mrpatekful

paultopia

import requests
import json
import os
from copy import deepcopy
APIKEY = os.environ["ZOTEROKEY"]
ACCOUNT = os.environ["ZOTEROACCOUNT"]
TESTITEM = '10.2307/4486062'  # just the same example item used on https://github.com/zotero/translation-server 

def get_item_from_identifier(identifier):
    endpoint = "http://zotglitch.glitch.me/search"