1/1/2023 0 Comments Dictionary regress![]() Kyle Pitts (ATL)Ĭlose to Kmet on the above is Falcons tight end Kyle Pitts, who erupted for 68 receptions for 1,026 yards as a rookie but managed just one touchdown.Īn athletic maven at the position, Pitts lived up to his pre-draft billing, finishing fifth in yards per route run (2.02) and ninth in YAC (318). But last year's numbers suggest that ceiling can be tapped into with a similar volume and some positive touchdown regression. Kmet is currently going as the TE12 in full-PPR leagues, which may be close to his ceiling. A coaching change should bode well for Justin Fields and the entire offense as a whole, while Kmet's target competition outside of wide receiver Darnell Mooney remains relatively thin. The Bears moved on from Matt Nagy at the end of 2021, hiring Matt Eberflus as their new head coach. When it comes to this season, not much has changed for Kmet's situation. He finished as the TE21 with 121.2 fantasy points, while the model suggests he should've scored closer to 161.5 points - which ranks him as the TE9 in the expected points model. The regression model also suggests that Kmet fell well short of his fantasy point expectations. According to the RotoViz linear regression model, Kmet's 93 targets should have resulted in 4.95 touchdowns - lining up almost perfectly with my look at the historical average. My quick look at this historical data revealed that tight ends who drew 70+ targets in a season averaged 5.1 touchdowns. ![]() Touchdown production for fantasy tight ends. Any reference to fantasy points is based on full-PPR scoring. NOTE: Advanced stats and metrics courtesy of PFF.com, and RotoViz. Now fully woke to every good player to draft this upcoming season, I thought the next best thing to do was share it with the world. In hopes of getting a better understanding of players who overperformed and underperformed expectations in 2021, I used the RotoViz Screener tool to look at some linear regressions. But memories triggered by emotion can often distort reality - which only adds further value to these exercises. It's easy as fantasy managers to remember the best about a player who led us to a championship or the worst about a player who burned us. At the very least it can serve as a tool for making an educated decision. When considering how a player could perform from one season to the next, looking at regression models can help us determine whether or not a player is worth our precious draft capital. ![]() Often a source of contention in the fantasy football Twitter space, regression can be both positive and negative. ![]()
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