Kevin Cole's 2018 NFL Betting Model: Week 6

Along with working extensively on NFL projections this season, I’m also putting the data to use projecting game outcomes and scores. We can, of course, apply these projections against the going betting spreads and over/under totals to see where there might be value.

Each week this season I’m going to post the model’s picks on with the lines as of Thursday and Sunday.

This week, we’re introducing a new model for side bets only. The first version of the model is what I will call 1.0, and this model is the results of a lot of work during the season to develop a power ranking system based on play-by-play data.

The data I’m using also have advanced metrics, including expected points added (EPA) and success rates based on EPA.

I will continue track total record for the year, along with what the results are of the new model.

Methodology

The betting model is based on a combination of offense and defensive play-by-play stats, including success rate, or how often a play has positive EPA, yards per play and EPA per play. The offensive and defense metrics are calculated for every team on a weekly basis and those trailing results are the basis for projecting team strength in upcoming matchups.

Week 4 Review

Last week, the model had a down week flat at 2-7 based on Thursday lines and 3-5 on Sunday.

Here are the numbers so far this year.

betting_model_week6_results_table

Week 6 Power Grades

The power grades here are the expected point differential for each team against an average team on a neutral field. The offensive and defensive ranks are based on 2018, the total power rank also incorporates offensive and defensive stats going back to 2017.

betting_model_week6_power_ranking

Week 6 Picks

Picks as of Thursday at roughly 2 p.m. ET

These picks are based on the power rankings above, with adjustments for home field advantage, and additional/fewer days of rest.

betting_model_week6_table

About the Author

colekev
Kevin Cole (colekev)

Kevin Cole previously worked on Wall Street as an equity and credit analyst before transitioning to data-related sports analysis. For a number of years, Kevin has specialized in creating predictive sports models that includes published work at Pro Football Focus, Rotoworld, numberFire, and RotoViz. He is now the Director of Data and Analytics at RotoGrinders.