NFL games predominantly come down to two simple matchups: each team’s offense against the other team’s defense. If you can accurately predict how effective each team will be, you can be successful in the long run.
To answer these questions I developed the SharpClarke Rating, an analytical framework that evaluates only the predictive elements of a team’s performance and filters out the outsized impact of random or lucky events on the final score and individual statistics. To determine a team’s SharpClarke Rating (or Rating), I analyze every snap of every game and score each team’s offensive and defensive performance from 1 to 10. The Rating gives me a data point that depicts a team’s actual offensive or defensive performance more accurately than any metric based only on results.
From there I calculate the Adjusted SharpClarke Rating (or Adjusted Rating), which represents the difference between a team’s Rating in a given game and the opponent’s season average Rating allowed on that side of the ball. This allows me to gauge performance relative to the opponent, which is more predictive and powerful. A positive Adjusted Rating indicates an above-average performance in that game. The Adjusted Rating creates a baseline performance level for each team, and from there I apply matchup metrics and other factors to make my predictions.
In 2020 this analytical model helped me win 1st place out of 97,000 entrants in a season-long Against the Spread contest on DraftKings, picking 5 games a week at a 70% win rate. My picks were consistently successful throughout the season as I turned a $400 deposit into over $12,000. And I’m not satisfied. I aim to improve my process every week on a journey to establish myself as the best NFL bettor in the world. I hope you’ll join me.
In-depth analysis of every NFL team, detailing success factors, vulnerabilities, and team rankings. The foundation of my NFL picks.
Weekly Spotlight Games, NFL Picks, and Recaps. This is where we make our money, but also where I evaluate every pick and learn from my mistakes.
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