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The Buy Leone WR and TE models are machine learning algorithms that use a player’s recent opportunities to predict future performances (PPR points per game).

More specifically, the models look at every target a player has seen over the last 6 weeks. The model provides an expected fantasy point outcome for each individual target based on a variety of factors, most importantly:

-Team Scoring Expectation
-Air Yards
-Field Position

Those expected fantasy points become the primary input into a model thats weights recent performance at a higher weight than past performance, and incorporates aggregate per game totals, such as air yards, targets, and fantasy points.

Our goal is to have an objective process to find the players whose opportunity has out-paced their actual results. Through that, we’re identifying players who have a high likelihood of beating their last 3 game average moving forward.

In order to isolate the most potentially profitable players, we are comparing the Buy Leone model prediction against a player’s rolling 3-game fantasy point average (going back no further than the previous 6 NFL weeks), and filtering out all players who don’t meet the following criterion:

-Buy Leone prediction greater than rolling 3-game fantasy point average
-Minimum 8 fantasy point prediction via Buy Leone model (6 for tight ends)
-Minimum 8 fantasy point projection via ETR’s Weekly Projections (at time of publication, 6 for tight ends)

You may notice some players that have barely underperformed (rounded difference of just 0.0 or 0.1). We’ve still included these players since the model is pretty conservative with its future predictions, making them worthwhile considerations despite the tiny gaps between projected and past performance.

This model will improve as the year goes on. Currently, the model is using only the last 4 NFL weeks, excluding Week 18 of last season, and weighted very heavily to Week 1 of the 2022 season. As we get further into the season, the model will use the last 6 NFL weeks and will weigh most recent weeks a little less heavier (but still at a higher rate than past weeks). The WR and TE results are combined below, but these are two separate, albeit very similar, models.

It’s important to keep in mind that the Buy Leone model by design minimally accounts for player talent and surrounding team talent. This is intentional to keep the model as objective as possible. Still, it’s important to keep in mind that:

-Past player opportunity doesn’t guarantee future player opportunity (this is accounted for in the machine learning process)
-Player, team talent, and upcoming matchup are all important factors that may be undervalued by the model

As a result, Adam Levitan is leaving his context adjusted takeaways and commentary below the model results.

Enough housekeeping, here are the model results:

KEY
Buy Leone Proj – Mike Leone’s machine learning algorithm’s projected future PPR PPG, based primarily on recent player opportunity.
Last 3 FPPG – A player’s average PPR PPG over their last 3 games.
Under Performance – The difference between a player’s Buy Leone Proj and their Last 3 FPPG.
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