FFA Rookie Projection Analysis 2025

Before evaluating individual rookie performances, it is important to understand how the FFA projections performed at the positional level. The table above provides a critical baseline, showing that projection accuracy is shaped far more by positional volatility and usage stability than by talent evaluation alone. QB projections were the least accurate overall, producing both the largest total error and per game deviation. This reflects on the difficulty models face when accounting for midseason changes that immediately spark fantasy production through opportunity and playing time. In contrast, RBs generated the lowest total seasonal error across the largest sample of players, suggesting workload expectations were generally reasonable across projection sources.

WRs and TEs fall between these extremes. WRs were overprojected with 164.71 FPTS before finishing at 123.43, while TEs were underprojected at 86.88 and produced 117.35. The positive difference shown by the TE position was driven by a small number of outliers, notably Harold Fannin and Orande Gadsden. These results illustrate a key theme that will carry throughout this analysis that rookie projection success is driven less by identifying talent, and more by correctly anticipating opportunity, role stability, and how quickly usage assumptions can change once the season begins.

Pre-season Rookie Articles:
Running Backs
Wide Receivers
Quarterbacks and Tight Ends

The points difference visualization above illustrates the full range of rookie outcomes. A small group of players dramatically exceeded expectations, while another group fell far short of their projected totals. This distribution helps with understanding that projection errors are not evenly spread across the rookie class. Instead, a limited number of extreme outcomes account for a large amount of the total error and showing that opportunity changes are a primary driver of projection misses.

The largest positive deviations are led by Jaxson Dart (+196), Orande Gadsden (+131), Harold Fannin (+118), and Tyler Shough (+84). Representing cases where opportunity arrived suddenly and decisively through starting roles, rapid trust from coaching staffs, or scheme fits that were overlooked this offseason. On the negative side, names like Kaleb Johnson (−167), Travis Hunter (−149), Matthew Golden (−122), and Omarion Hampton (−95) fell well short. These cases highlight the opposite reality of highly touted prospects failing to secure fantasy value on a weekly basis due to depth chart logjams, injuries, or poor quarterback play.

Steady Rookie RBs

Among the rookie positions, RBs delivered the most consistent per game production relative to preseason expectations. The PPG scatter plot below shows the majority of players clustering reasonably close to the perfect projection line. This relative stability is why RBs posted the lowest total mean absolute error among all positions, despite having the largest sample size.

Total seasonal point projections still produced meaningful misses, driven by volume rather than efficiency. Players who earned consistent touches generally finished near or above their projected per game output, while those who failed to secure a defined role fell well short. Cam Skattebo (+8.46 Actual PPG – Projected PPG), Quinshon Judkins (+4.56), Kyle Monangai (+4.35), and Woody Marks (+4.73) stand out as notable per game overperformers. On the opposite extreme, Kaleb Johnson (-9.47) represents the clearest miss of the year, and helps show just how impressive Cam Skattebo’s +8.46 PPG overperformance truly was.

The scatter plot also highlights how games played shapes total and per game outcomes. High volume backs such as Ashton Jeanty, RJ Harvey and TreVeyon Henderson appeared in all 17 games and remained close to the projection line. In contrast, players like Omarion Hampton, Cam Skattebo, and Quinshon Judkins demonstrate that seasonal numbers alone can be misleading. While injuries derailed their rookie season totals, when available, they consistently produced above expectations. Per game efficiency remains valuable even when total output is suppressed, making opportunity and availability key drivers of a rookie’s performance.

Rookie WR Expectations Met?

The WR projections produced more mixed results than RBs, reflecting the position’s dependence on external factors beyond the player’s control. The PPG scatter plot shows a wider spread around the perfect projection line than seen at RB, with several rookies landing in meaningful spots around their preseason expectations. While a few WRs aligned closely with projections, the overall dispersion highlights how quarterback play, offensive scheme, target share, and weekly roles can dictate fantasy production.

Emeka Egbuka stands out as one of the clearest successes, delivering a hot start in a competitive receiving room. He provided strong per game efficiency at 11.47 PPG (+.76), and finished 13 points above his seasonal projection. Luther Burden quietly exceeded his modest projection by 1.6 PPG, making the most of every target to come his way. He will look to build upon his 47/652/2 season statline and WR48 finish, though the depth chart in Chicago remains crowded.

Tetairoa McMillan performed near expectations at 12.41 PPG (-.7). He finished as WR16, holding a dominant role in a playoff run as the Bryce Young’s top target with 70 receptions, 122 targets, 1,014 yards and 7 TDs. Jayden Higgins also landed right below his projection by -11 points (-.64). He showed consistent production, continuing that in two playoff games with 9 receptions, 14 targets and 98 yards. These cases show that when opportunity, role, and talent align, conservative preseason models are met or exceeded.

On the other end, misses were glaring. Matthew Golden fell far short of his high preseason projection after being hampered by injury and limited target opportunities, finishing with just 70 points over 14 games (−6.3). Travis Hunter provides an example of availability crushing value, despite a few boom weeks lifting his PPG to 9, playing in only seven games capped his total output at 63 points (−3.47). Tre Harris also underperformed relative to projections, posting a −1.12 PPG deviation despite being active for all 17 games. These outcomes reinforce a key lesson that some highly regarded prospects cannot overcome unstable roles, inconsistent targets, or environmental limitations.

Underprojected Rookie TEs

The TE projections showed the highest volatility among the rookie skill positions and created the only net positive gap. The PPG scatter plot below captures this dynamic. Several TEs finished reasonably close to the perfect projection line, with a handful of overperformers once again driving most of the deviation from preseason expectations.

Harold Fannin and Orande Gadsden stand out as the largest overperformers. Fannin turned a projection of 4 PPG into elite weekly production with 11.7 PPG (+7.7). Amidst the down year for David Njoku, Fannin led the Browns in receptions, targets, yards and TDs. This level of usage and dominance was good for a TE6 finish, significant enough to earn Browns TE coach Christian Jones a promotion to WR coach. Gadsden’s emergence was arguably more unexpected, posting a season statline of 49/664/3. He jumped from near zero expectations, to over 8.7 PPG and finishing TE20 in limited opportunity.

First round picks Tyler Warren (+2.35) and Colston Loveland (+1.6) also exceeded expectations by meaningful margins. Warren started off hot and Loveland came on late, with both benefiting from early playing time, consistent roles, and favorable offensive environments. Together, these four players account for the majority of the position’s positive surprise and explain why TE projections lagged behind actual outcomes.

Conclusion

Overall, rookie projection accuracy is less about raw talent evaluation and more about anticipating opportunity, role stability, and situational factors once the season begins. RBs demonstrated the most consistent alignment with preseason expectations, driven by defined backfields and steady touches. WRs and TEs showed greater volatility due to environmental factors, injuries, and unpredictable usage. Extreme deviations, both positive and negative, are the small number of outliers that account for much of the total error. These findings demonstrate that when projecting NFL success, understanding context, opportunity, and scheme is just as critical as evaluating skill.