Sept. 9, 2020

Using nflfastR play-by-play data to measure kicker performance.

Sept. 8, 2020

This article shows how to use tidyModels to predict QB dropbacks and uses a multilevel model to show which teams are run/pass heavy after accounting for game script

Sept. 6, 2020

Exonerating punters for long returns

Aug. 31, 2020

Does incorporating actual rest time help us predict how a defense will do?

Aug. 31, 2020

Use the nflfastR xYAC & CP models to calculate how many fantasy points an average receiver would expect to earn on each target.

Aug. 29, 2020

Here, we'll look at how to scrape ESPN's and 538's pregame predictions and merge them into nflfastR data

Aug. 29, 2020

Combining lessons from multiple posts to create faceted or animated heatmaps.

Aug. 29, 2020

Methods for modeling density estimates and expected completion percentages across the football field for individual players.

Aug. 28, 2020

Loading your nfl data at 10x speed!

Aug. 27, 2020

Case study how to leverage Generalized Additive Mixed Models (GAMM) to estimate the individual probability of completion per Quarterback as a random effect.

Aug. 26, 2020

Using nflfastR data to visualize where on the field running backs get their carries and how that translates to the Trivial Rush Attempt Percentage (TRAP) model.

Aug. 25, 2020

Building expected interceptions and expected fumbles models to find QBs likely to increase or decrease their interceptions and/or turnovers per dropback from 2019 to 2020.

Aug. 24, 2020

Collection of short answers to common questions.

Aug. 24, 2020

How to get ESPN data and create interactive plots using the plotly ggplot2 library.

Aug. 23, 2020

Looking at what metrics are important for predicting wins. Creating expected season win totals and comparing to reality.

Aug. 22, 2020

Use 538's QB Elo value, a highly predictive measurement of QB impact, to compare QB careers across era

Aug. 21, 2020

Game excitement calculation and a win probability figure.

Aug. 21, 2020

Methods for visualizing NFL passing location data.

Aug. 21, 2020

A look into Rodgers Efficiency Decline. Also some functions for plotting EPA/CPOE moving averages.

Aug. 20, 2020

Using nflfastR data to show how much more efficient passing is than rushing at the team level

Aug. 20, 2020

This article shows how to adjust a team's EPA per play for the strength of their opponent. The benefits of adjusted EPA will be demonstrated as well!

Aug. 20, 2020

Showing how to contribute using Python code

Aug. 19, 2020

Rebuilding player graphs when ID keys go missing or are corrupted.

Aug. 19, 2020

Using Keras in R to build neural networks.

Aug. 19, 2020

Do quarterbacks who get hit see their performance decline throughout the game?

Aug. 19, 2020

This article looks at the percentage of snaps with win probability over an arbitralily chosen critical value and compares it with the true win percentage.

Aug. 18, 2020

This article shows how to scrape football data from Pro Football Reference and how to plot the bad throw percentage data for quarterbacks.

- Animation (1)
- Articles (1)
- Completion Probability Intercept (1)
- CPOE / EPA functions (1)
- Efficiency (1)
- Elo (1)
- espnscrapeR (1)
- Fantasy Football (2)
- field goal (1)
- Figures (12)
- Game Predicitions (1)
- Getting started (1)
- Interactive plots (1)
- Keras (1)
- Logistic Generalized Additive Mixed Models (1)
- Mixed Effects (1)
- nflfastR (21)
- nflfastR xYAC Model (1)
- Opponent adjusted EPA (1)
- Packers (1)
- PFR (1)
- placekicker (1)
- python (5)
- quarterbacks (1)
- Roster (1)
- Scraping (3)
- stan (1)
- Tensorflow (1)
- tidyModels (1)
- Tidymodels (1)
- Total QBR (1)
- turnovers (1)

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Text and figures are licensed under Creative Commons Attribution CC BY-NC 4.0. Source code is available at https://github.com/mrcaseb/open-source-football, unless otherwise noted. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

©2020 - Website creator Sebastian Carl (Twitter: @mrcaseb)