Greetings from the secret, WiFi-enabled Razzball bunker! It has taken a while but we finally got our robots to broaden their heretofore MLB-limited expertise and start cranking out NFL in-season projections for both season-long (Standard, 1/2 PPR, PPR) and DFS.
It has been a mad sprint to being ready opening day so I don’t have all the documentation to share but hoping the below summary will suffice for now.
Before I lose those of you who have no interest in how the sausage is made, we’re making both the season-long and DFS projections free for the first 4 weeks of the NFL Season. Below are the links (also can be found under Tools in the top menu). Player pages should be up shortly (ETA end of next week).
Pigskinonator – In-Season Projections
DFSBot – DFS Projections
Still here? Here’s a quick bulleted overview of how we created the Pigskinonator:
- Start with a massive file of play-by-play NFL data (over 10 years’ worth)
- Use Vegas over/under and spreads as the foundation for the team projections.
- Use past game data for players to estimate both their opportunities (e.g., snaps, rushes, targets) and skills as measured by ratios (yards per rush/reception, completions per target). Test biasing factors (e.g., domes, weather, home/road) and adjust the data to be as ‘neutral’ as possible. Regress player data to league-average for the position to deal with the inherent sample size issues.
- Adjust the player data to fit the team projection – e.g., if I’m projecting 42 pass plays for Pittsburgh, I need to make sure the sum of all receiver targets is 42.
There is much more to it but it’s 4AM. Let’s switch to a Q&A format – if you have questions, ping me in the comments of either this post or one of the projection pages.
If I have learned anything from building and maintaining our MLB projections, it is that the first release is just a milestone. The projection methodology and the usefulness of the tools will be better a month from now and it’ll be even better for 2017. How much better depends on all of your feedback.
So, please, dig into the projections. Find the anomalies. Tell me what stats/reference data to add. Help me help you!
