Benjamin Arancibia bcarancibia

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

THIS IS OUTDATED.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!

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Purpose

This is a list inspired by some of our current or potential lines of work at the World Bank Innovation Labs. The “Innovations in Big Data Analytics” program helps to strengthen the World Bank capabilities to effectively use big data in its operational and strategic work.

We are always looking for great Data Scientists. If you can solve any of these [using open software], you'll be heads down helping us from day one. Email us to brunosanchez@worldbank.org

(This list is updated frequently).

1. Nightlights from Satellite

We are building an open stack to process nightly data from satellite and query light output from all known villages. Currently we are doing 20 years of nightly data for 600,000 villages in India.

General Background and Overview

Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
Models and Issues in Data Stream Systems
Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
[Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t