The POPP Model

Our POPP Score is based on the most important and correlative aspect of football to fantasy PPR output: Opportunity. The available research is clear and simple, the more opportunity a player has with the ball in their hands, the more fantasy points they will score. The POPP score model hand selects a few of the most relevant and significant metrics that measure a player's opportunity and weighs them based on their correlation to fantasy points per game. Our team of analysts uses historical player data, offensive coordinators tendencies, team trends, offseason and training camp reports, and more in order to project these metrics for the upcoming season and compute a player's POPP Score.  

In order to compute projected PPG, we use a regression analysis with projected POPP Score, a player's 2-year average "efficiency index", and their previous year's PPG. A player's efficiency is measured based on how many fantasy points they score over their carries plus targets, then divided over the league average. We then put that projection through confidence interval testing to find a player's projected ceiling and floor at the 90% confidence level (α=0.1). Our projected PPG model is in its first year of implementation so we encourage users to rely more strongly on the POPP Score when in doubt. 

• PPG — Fantasy points per game
• Floor / Ceiling — the 5th and 95th percentile outcomes. 
• POPP Score — cross-position opportunity score that is highly correlative to PPG. While scores vary by position and should be treated relative to position, generally treat 13+ scores as historic, 12-13 as elite, 10.5-12 as great, 9-10.5 as good, 7-9 as OK, 5-7 as poor, 4-5 as terrible, and below 4 as irrelevant based on opportunity.  
• ADP — Average draft position

We back-tested the POPP model based on our projections made last year with only one analyst and much less time and resources spent researching than this year. Even with that, our model and projections still beat out last year's most accurate fantasy football analyst, Justin Boone (2025 FantasyPros Most Accurate Expert Award winner). With more analyst researchers, time, and resources going into this year's projections, we expect to beat the industry leaders by even larger margins. The time to get on board for an industry leading edge is now. 

See the full POPP Fantasy vs Justin Boone accuracy comparison — including why per-game correlation matters more than season totals.

Corr. = Correlation

The POPP score only covers players eligible at the flex position: RB, WR, and TE. Because quarterback output depends on far different metrics, they are excluded from calculations. While we do not compute a POPP score for quarterbacks, we do offer rankings, statistics, and analysis on all fantasy relevant quarterbacks driven by advanced analytics and metrics with high correlation to fantasy production.

Additionally, the POPP score model is designed to be most correlative to PPR formats. While we offer data, research, and insight that is relevant to all fantasy players, please be aware that we currently focus solely PPR analysis. While they won't be available until the fall, we are currently building a trade analyzer tool and preparing the site for in-season rankings and player cards. To convey interest in future expansions into standard, half-PPR, and other formats, or to express any other requests, suggestions, and concerns, please shoot us an email at poppfantasy@gmail.com!

Quarterback play is fundamentally different from flex-eligible positions. RBs, WRs, and TEs rely heavily on volume-based opportunity, which is exactly what the POPP Score measures. QB fantasy output on the other hand is dependent upon far different factors and primarily depends on various efficiency and scoring metrics. We plan on creating a model in the near future to project quarterback points per game, but the main obstacle right now is how volatile the correlations are between various QB metrics and fantasy output year-to-year. Because we are currently witnessing an evolution at the position, different metrics most greatly correlate to fantasy output every year. 

Because the metrics that correlate most strongly to QB fantasy scoring are entirely separate from the opportunity-based inputs that power the POPP model, including them would require a second, entirely different model. Rather than dilute the POPP Score by forcing QB projections into a framework that was not designed for them, we have chosen to exclude QBs from the model and focus on perfecting flex-position opportunity analytics for now.