USA Today Sports. Pictured: Alvin Kamara, JuJu Smith-Schuster, George Kittle
- See our experts' fantasy football projections, featuring the top five players at each position.
Preparation for the 2019 season is in full swing and our experts Sean Koerner, Matthew Freedman and Chris Raybon are keeping their fantasy football rankings and projections up to date so you can draft confidently.
Their consensus projections for the top five players at each position are outlined below, but you can find their full and up-to-date projections for more than 150 players in our Fantasy Football Draft Kit. Curious how they create their projections? They each outline their methodology later in this story.
2019 Fantasy Football Projections
Top 5 QB Projections
Patrick Mahomes: 329.9 fantasy points
- Passing: 360 completions | 552 attempts | 4,503 yards | 36 TDs | 12 INTs
- Rushing: 58 carries | 247 yrds | 2 TDs
Deshaun Watson: 326.1 fantasy points
- Passing: 340 completions | 514 attempts | 4,127 yards | 28 TDs | 12 INTs
- Rushing: 92 carries | 536 yrds | 4 TDs
Aaron Rodgers: 303.6 fantasy points
- Passing: 360 completions | 565 attempts | 4,176 yards | 30 TDs | 7 INTs
- Rushing: 47 carries | 244 yrds | 2 TDs
Andrew Luck: 301.9 fantasy points
- Passing: 391 completions | 595 attempts | 4,364 yards | 33 TDs | 14 INTs
- Rushing: 49 carries | 198 yrds | 1 TD
Kyler Murray: 299.5 fantasy points
- Passing: 339 completions | 545 attempts | 3,996 yards | 24 TDs | 14 INTs
- Rushing: 87 carries | 526 yrds | 4 TDs
Top 5 RB Projections
Saquon Barkley: 264 fantasy points
- Rushing: 266 carries | 1,237 yards | 10 TDs
- Receiving: 79 receptions | 640 yrds | 3 TDs
Ezekiel Elliott: 257.2 fantasy points
- Rushing: 301 carries | 1,362 yards | 9 TDs
- Receiving: 66 receptions | 541 yrds | 3 TDs
Alvin Kamara: 242.6 fantasy points
- Rushing: 194 carries | 911 yards | 9 TDs
- Receiving: 78 receptions | 704 yrds | 4 TDs
David Johnson: 231.7 fantasy points
- Rushing: 245 carries | 990 yards | 9 TDs
- Receiving: 64 receptions | 624 yrds | 4 TDs
Christian McCaffrey: 229.5 fantasy points
- Rushing: 203 carries | 917 yards | 6 TDs
- Receiving: 86 receptions | 917 yrds | 5 TDs
Top 5 WR Projections
DeAndre Hopkins: 207.7 fantasy points
- Receiving: 100 receptions | 1,449 yrds | 11 TDs
JuJu Smith-Schuster: 194.7 fantasy points
- Receiving: 104 receptions | 1,384 yrds | 9 TDs
Davante Adams: 189.4 fantasy points
- Receiving: 98 receptions | 1,229 yrds | 11 TDs
Odell Beckham Jr.: 185.2 fantasy points
- Receiving: 86 receptions | 1,239 yrds | 10 TDs
Julio Jones: 183.5 fantasy points
- Receiving: 91 receptions | 1,419 yrds | 7 TDs
Top 5 TE Projections
Travis Kelce: 178.9 fantasy points
- Receiving: 93 receptions | 1,230 yrds | 10 TDs
George Kittle: 144.3 fantasy points
- Receiving: 80 receptions | 1,090 yrds | 6 TDs
Zach Ertz: 141.5 fantasy points
- Receiving: 90 receptions | 977 yrds | 7 TDs
O.J. Howard: 118 fantasy points
- Receiving: 55 receptions | 811 yrds | 6 TDs
Hunter Henry: 106.2 fantasy points
- Receiving: 56 receptions | 711 yrds | 6 TDs
Our Experts’ Methodology
When making projections for anything, I do an intense statistical breakdown of how predictive certain stats are and how much you can take away from a given sample size.
Given the small sample size the NFL provides (16-game regular season) and the volatile nature of it (a violent sport with a ton of week-to-week turnover), I attempt to balance the art vs. science that’s required to create accurate player projections. This requires a ton of analysis “outside the lines,” which means I’m constantly using new information — i.e. injuries, depth charts, scheme change, coach speak, etc. — to refine and reflect those factors in my projections.
I start by creating overall team projections with my Power Ratings, which I also use to generate my projected spreads/totals, to map out exactly how many points I expect each team to score. I then extrapolate the number of projected touchdowns, how many will go toward the pass vs. run distribution, and gradually end up arriving at an estimate for every player and every stat.
It’s important to note that these are strictly median projections, which means I would give it a 50/50 chance that a player hits the over or under on that projected stat. It would be a bit messy if I decided to project a player to be closer to their ceiling while others closer to their floor. I try to make it uniform so that it’s easy to digest.
Again, it’s not the job of a projection to get it “exactly right.” Roughly speaking, being accurate is simply being more accurate than your peers. By finding discrepancies between my projections and the market, I consider those +EV opportunities that will give you a huge edge in the long run if you exploit them.
To create player projections, I start with the team and create top-down projections based on a number of factors, including projected win totals, expected pace of play and play-caller run/pass tendencies.
Once I have a sense of how many pass plays and rush attempts a team is likely to have, I start to distribute those opportunities to various players based on how many games I expect them to play.
For quarterbacks, I’m focused on how efficiently they’re likely to be at turning dropbacks into pass attempts, yards and touchdowns. For running backs, wide receivers and tight ends, I focus primarily on market share as well as yards and touchdowns per opportunity.
I start any projection process by running correlations to find what its main drivers are. NFL team performance is largely a function of the starting QB, so although I use a top-down process later on for assigning usage and TD shares to RBs, WRs and TEs, I start my entire process by projecting QBs, which then power my team-level forecasts.
The basis for my team-level offensive projections are pass attempts, passing TD rate, yards, sacks and turnovers. QBs are the main drivers of these to different degrees, and my adjustments later on are based on correlations I’ve run on how the players around them factor in.
I also incorporate route and Air Yard data into my passing model, so the receiver will drive some of the adjustments to a QB’s projections more than others. For example, I weigh QB accuracy more heavily than receiver catch rate, but there’s also a certain component of a QB’s yardage that’s controlled solely by the yards after catch expectation of his receivers.
Once I have projected baselines, I use regression formulas with those baselines as variables.
Projections are both an art and a science, though. So while I have a by-the-book formula for literally every stat I forecast, I am a lot more likely to manually adjust volume than other stats, because a philosophical shift can easily render past volume data useless no matter how recent or large the past sample of data is. In fact, I always manually adjust usage shares such as target share and carry share. (Since I’m projecting in a top-down manner, target share and carry share are more important than simple targets or carries per game.)
In regard to usage, algorithms can help in specific instances, such as the degree of jump in usage for a WR going from Year 1 to Year 2 — generally their breakout season — and also in situations that remain largely unchanged, but that’s almost never the case.
When in doubt, my baseline in the former situations is usually league-average positional target shares overall (WR ~60%, TE ~20%, RB ~20%), as well as within each position (WR1 ~25%, WR2~15%, etc.). I also account for correlations between each position: A team bereft of WR talent will usually throw short passes at a higher rate (i.e. more to RBs, TEs, slot WRs) and vice versa.
For up-to-date rankings and projections, check out our Fantasy Football Draft Kit.