data VS nflscrapR-data

FiveThirtyEight Datasets: Datasets related to articles and investigations published by FiveThirtyEight. FiveThirtyEight Datasets

You of course already know this answer, but just to put it into more perspective. Here are the SPI ranking equivalents to what he did with these 11 goals in Scotland and Switzerland.

To answer these questions I decided to look at 538’s RAPTOR ratings. RAPTOR uses player tracking data to estimate how much each player contributes on the offensive and defensive ends. The total RAPTOR score should be something like the “number of points a player contributes to his team’s offense and defense per 100 possessions, relative to a league-average player.” Higher is better, best during the regular season has been Nikola Jokic at +14. You can read more about it here or play with an interactive tool on their website here. I don’t really care about the details of why it’s a good statistic, but it seems pretty helpful and most importantly for my purposes you can download the data here for free.

EDIT: I came to this data from this repository which has a nice csv collection for machine training.

Looks like we may actually be collectively underrating our guys now. That's an interesting change. Based on SPI (rating = 72.4) we would be:

Before Disney/ABC get any -ideas-, might be a good chance to get our hands on at least their data[0]!

[0]: https://data.fivethirtyeight.com/

Most young women expect words to be involved when their partner seeks their consent. 43% of young men actually ask for verbal confirmation of consent. Overall, verbal indicators of consent or nonconsent are more common than nonverbal indicators. More open communication also increases the likelihood of orgasm for women.

DHM does not use an equity sample. Of all polling operations they rank 250 out of 517. Id like to see another pollster https://github.com/fivethirtyeight/data/blob/master/pollster-ratings/pollster-ratings.csv

Inspired by the creators nflscrapR and nflfastR I decided to construct nflscraPy, a collection of functions to scrape NFL Data from Pro Football Reference – and hopefully an expanding number of data sources/sets.

https://github.com/ryurko/nflscrapR-data - NFL play by play data - great for winning your fantasy football league

i was curious so i pulled the 2019 data (largest full-season dataset I could find with EPA/WP numbers included).

This interactive graph is created in Observable, using D3 and Vega-Lite javascript libraries. NFL play-by-play data is from Ron Yurko's dataset, filtered, cleaned up and re-organized. Football plays are organized into an array of sequences of possessions/drives and consist of ~50 play-level properties and 7 sequence/possession-level properties.