Twenty-two candidates · one pecking order
Who will be
the Big Ten’s
best QB?
Ohio State has Julian Sayin. Oregon has Dante Moore. Indiana paired Josh Hoover with Curt Cignetti. Washington and USC have stars of their own. Every fan has a case for his guy. The numbers get a vote, too.
11,762games studied
1,137QB seasons studied
6 / 6metrics beat last year
20–80likely-outcome range
The question behind the question
Can data tell us who is actually best?
Not by itself. A spreadsheet cannot see a receiver win a fifty-fifty ball in November, hear a quarterback command a huddle or know which sophomore is about to make the leap. But it can make the argument play fair.
Start with what each quarterback did last season. Then put him back on the field a year older, in his 2026 offense, against his actual twelve opponents. That is the wager here. The model learned from 11,762 quarterback-games from 2017–2025, weighing recruiting pedigree, experience, coach, school, location and the quality of the defense across the line. It also asks what production returns around him, what arrived through the portal and whether the receiver room and offensive line have enough recruited talent to replace what left.
The result is not one magic number. It is a stat line, a range of plausible outcomes and a ranking that can be argued with. Before we reveal that order, one more question matters: does this beat simply copying last year’s numbers into 2026?
Can the numbers help?
Better than saying “same as last year”
Yes. We gave the model old seasons, asked it to forecast what came next and compared its answers with the easiest guess in sports: just use last year’s numbers again. The model won in all six categories. Shorter bars mean fewer misses.
Completion %
14.1%less errorPassing yards / game
26.7%less errorTouchdowns / game
23%less errorInterceptions / game
26.8%less errorPasser rating
19.1%less errorFive forces
What moves a projection
A quarterback’s old stat line is only the opening bid. Five forces can move it before the ball is snapped in 2026—including one that every fan feels in his bones but almost nobody wants applied to his own quarterback. The colors follow each force into the player cards.
01 / Statistical gravity
Great seasons rarely become permanent floors.
Every fan knows a heater when he sees one. If a guard hits six of eight threes tonight, nobody pencils him in for 75% tomorrow. The shooting was real; so were a few friendly bounces that will be hard to repeat. Quarterback seasons work the same way. Huge years usually cool off a little, while ugly years often recover when the tipped picks and third-and-long disasters stop piling up. It is part of why the Sports Illustrated cover jinx always felt believable: the cover caught an athlete at the summit, then everyone treated the summit like his new normal.
Ohio State has lived this. C.J. Stroud went from 186.6 in 2021 to 177.7 in 2022, and Justin Fields went from 181.4 in 2019 to 175.6 in 2020. Both remained brilliant; neither repeated the career year exactly. Ohio State documents those seasons for Stroud and Fields. Washington’s Demond Williams Jr. offers an even fresher example: 176.9 in limited 2024 work became 161.0 over a full 2025 season, per his official UW bio.
The second real season is where quarterbacks jump.
“Experience” used to mean roster class here. That blurred a fourth-year backup with a fourth-year starter. The corrected model counts only seasons with at least 100 attempts. Among quarterbacks who kept the same school and coach, the first-to-second full season produced the largest average rating gain. Sayin, Underwood and Malik Washington now sit in that bucket; Moore and Demond Williams are entering their third meaningful passing seasons.
- 1 → 2
- +3.3 266 QB seasons
- 2 → 3
- +1.6 137 QB seasons
- 3 → 4
- +0.4 59 QB seasons
By the fourth full season, too few quarterbacks remain for us to promise another leap. On each player card, maturation follows how much the quarterback has actually played—not the year printed next to his name on a roster.
The quarterback never throws alone.
A receiver who wins after the catch and a line that keeps the pocket clean both land in the quarterback’s stat line. The model gives each QB credit for the part we can see coming before the season: proven production that stayed or arrived through the portal, the recruiting strength of the receiver room and the line, and how much OL continuity survived the offseason.
That is only the forecastable slice of the supporting cast—not its full value. No preseason spreadsheet knows which freshman is about to pop, whether five linemen will become one unit or how much healthier a veteran will look by October. If you believe your quarterback has more help than the green number recognizes, that is a perfectly good football reason to take the over on this forecast.
The surprise is what this talent signal does not help predict: interceptions. Adding it made the historical INT forecasts worse, so returning talent stays at 0.0 in every player’s INT breakdown. Better teammates can create completions, yards and touchdowns; picks were forecast more reliably without asking the supporting cast to explain them.
The right environment can change the quarterback.
Fernando Mendoza jumped from a 144.6 passer rating at Cal to 182.9 in Curt Cignetti’s Indiana offense. The model does not hand all of that gain to the coach, but it cannot ignore the pattern either. After separating out returning talent, Indiana’s broader system still adds about eight rating points to Josh Hoover’s forecast. Ohio State and Oregon also receive meaningful boosts.
“System” is bigger than play calling. It can mean scheme, teaching, development, protection rules, personnel deployment and the program habits that follow a coach. It is also an imperfect label: some of what looks like system fit—or statistical gravity—may be surrounding talent the available data cannot measure cleanly.
One wrinkle: this is a fit, not a fixed grade stamped on the school. A playbook is not a coupon worth the same number of points to every quarterback. The same Iowa offense can help—or hurt—Tradon Bessinger, Hank Brown and Jeremy Hecklinski by slightly different amounts because each arrives with different strengths and a different starting line. Read the number as “what this offense does for this player,” not “how good is Iowa’s offense?”
The schedule does not care about your spring hype.
A September tune-up and a November trip into a top defense do not belong in the same bucket. Every opponent carries its final 2025 defensive FEI into the forecast. Michigan’s slate costs Bryce Underwood roughly three rating points versus an average schedule; Indiana’s costs Hoover less than one. That difference can separate a good season from a conference-leading one.
The reveal · ranked by passer rating
The model’s pecking order
Enough scene-setting. Here is who comes out on top. Point estimate first; the smaller line is the 20th–80th percentile range. Competition entries are alternative twelve-game scenarios, not split-playing-time forecasts.
Statistical gravityMaturationReturning talentSystem fitScheduleThe five colored rows show how a returning QB travels from last season to this forecast. Freshmen begin with a recruiting-and-school estimate because they have no college season to start from.
#01 · Ohio State
Julian Sayin
2nd full passing seasonReturningRyan Day
YPA9.108.25–9.77
Comp %72.168.4–75.8
Rating167.5154.5–179.7
Pass YPG290.6224.3–320.6
TD27.320.2–31.1
INT8.76.0–11.0
Statistical gravity-29.2rating
- YPA
- -1.21
- CMP
- -12.3
- YPG
- -27.0
- TD
- -5.8
- INT
- +0.9
Maturation+3.3rating
- YPA
- +0.15
- CMP
- +1.4
- YPG
- +25.1
- TD
- +2.6
- INT
- +0.8
Returning talent+5.0rating
- YPA
- +0.19
- CMP
- +1.4
- YPG
- +6.0
- TD
- +2.5
- INT
- 0.0
System fit+12.8rating
- YPA
- +0.81
- CMP
- +4.8
- YPG
- +31.1
- TD
- +1.7
- INT
- -0.1
Schedule-1.9rating
- YPA
- -0.07
- CMP
- -0.3
- YPG
- -2.4
- TD
- -1.0
- INT
- +0.2
#02 · Oregon
Dante Moore
3rd full passing seasonReturningDan Lanning
YPA8.878.02–9.54
Comp %70.867.1–74.5
Rating164.3151.2–176.4
Pass YPG272.5206.2–302.4
TD25.618.5–29.4
INT7.24.5–9.5
Statistical gravity-13.1rating
- YPA
- -0.43
- CMP
- -6.3
- YPG
- -7.2
- TD
- -2.9
- INT
- +0.2
Maturation+1.6rating
- YPA
- +0.01
- CMP
- +1.3
- YPG
- +6.0
- TD
- +0.4
- INT
- -0.6
Returning talent+2.5rating
- YPA
- +0.09
- CMP
- 0.0
- YPG
- +2.7
- TD
- +1.9
- INT
- 0.0
System fit+10.6rating
- YPA
- +0.59
- CMP
- +4.2
- YPG
- +34.5
- TD
- +2.6
- INT
- -0.5
Schedule-0.9rating
- YPA
- -0.04
- CMP
- -0.1
- YPG
- -1.2
- TD
- -0.5
- INT
- +0.1
#03 · Indiana
Josh Hoover
4th full passing seasonFrom TCUCurt Cignetti
YPA8.417.56–9.08
Comp %66.562.8–70.2
Rating155.7142.7–167.8
Pass YPG253.3187.0–283.2
TD25.318.2–29.1
INT8.25.5–10.5
Statistical gravity-6.5rating
- YPA
- -0.48
- CMP
- -0.8
- YPG
- -13.6
- TD
- -3.8
- INT
- -2.2
Maturation+0.4rating
- YPA
- +0.11
- CMP
- -0.1
- YPG
- -6.9
- TD
- -1.0
- INT
- -0.7
Returning talent+0.6rating
- YPA
- -0.03
- CMP
- +0.3
- YPG
- -0.9
- TD
- +0.7
- INT
- 0.0
System fit+8.4rating
- YPA
- +0.43
- CMP
- +1.3
- YPG
- +8.4
- TD
- +2.9
- INT
- -1.0
Schedule-0.6rating
- YPA
- -0.02
- CMP
- -0.1
- YPG
- -0.7
- TD
- -0.3
- INT
- +0.1
#04 · Washington
Demond Williams Jr.
3rd full passing seasonReturningJedd Fisch
YPA8.567.71–9.23
Comp %68.064.3–71.8
Rating154.8141.8–167.0
Pass YPG251.0184.7–280.9
TD21.013.9–24.8
INT8.45.7–10.7
Statistical gravity-15.1rating
- YPA
- -0.59
- CMP
- -7.2
- YPG
- -19.2
- TD
- -3.0
- INT
- +0.6
Maturation+1.6rating
- YPA
- +0.01
- CMP
- +1.3
- YPG
- +6.0
- TD
- +0.4
- INT
- -0.6
Returning talent-0.7rating
- YPA
- -0.07
- CMP
- +0.3
- YPG
- -3.1
- TD
- -0.4
- INT
- 0.0
System fit+9.7rating
- YPA
- +0.62
- CMP
- +4.4
- YPG
- +33.6
- TD
- +1.6
- INT
- +0.8
Schedule-1.7rating
- YPA
- -0.07
- CMP
- -0.3
- YPG
- -2.1
- TD
- -0.8
- INT
- +0.2
#05 · USC
Jayden Maiava
4th full passing seasonReturningLincoln Riley
YPA8.727.87–9.39
Comp %64.460.7–68.2
Rating151.3138.3–163.5
Pass YPG275.0208.7–305.0
TD21.013.9–24.8
INT8.76.0–11.0
Statistical gravity-8.0rating
- YPA
- -1.00
- CMP
- +0.5
- YPG
- -36.6
- TD
- -1.0
- INT
- -0.0
Maturation+0.4rating
- YPA
- +0.11
- CMP
- -0.1
- YPG
- -6.9
- TD
- -1.0
- INT
- -0.7
Returning talent-2.8rating
- YPA
- -0.10
- CMP
- -2.3
- YPG
- -3.1
- TD
- +0.3
- INT
- 0.0
System fit+5.9rating
- YPA
- +0.58
- CMP
- +0.7
- YPG
- +38.5
- TD
- +1.5
- INT
- -0.1
Schedule-1.9rating
- YPA
- -0.08
- CMP
- -0.3
- YPG
- -2.4
- TD
- -1.0
- INT
- +0.2
#06 · Penn State
Rocco Becht
4th full passing seasonFrom Iowa StateMatt Campbell
YPA7.967.11–8.64
Comp %65.561.8–69.2
Rating145.4132.4–157.6
Pass YPG245.9179.6–275.9
TD19.712.6–23.5
INT8.35.6–10.5
Statistical gravity+0.8rating
- YPA
- -0.13
- CMP
- -0.8
- YPG
- +19.2
- TD
- +3.8
- INT
- +0.1
Maturation+0.4rating
- YPA
- +0.11
- CMP
- -0.1
- YPG
- -6.9
- TD
- -1.0
- INT
- -0.7
Returning talent+4.3rating
- YPA
- +0.16
- CMP
- +2.4
- YPG
- +5.1
- TD
- +0.7
- INT
- 0.0
System fit+5.5rating
- YPA
- +0.23
- CMP
- +3.5
- YPG
- +15.3
- TD
- +0.3
- INT
- -0.2
Schedule-0.4rating
- YPA
- -0.02
- CMP
- -0.1
- YPG
- -0.5
- TD
- -0.2
- INT
- +0.0
#07 · Illinois
Katin Houser
4th full passing seasonFrom East CarolinaBret Bielema
YPA7.826.97–8.49
Comp %65.361.6–69.0
Rating145.1132.0–157.2
Pass YPG226.9160.5–256.8
TD19.112.0–22.9
INT6.94.2–9.2
Statistical gravity-8.7rating
- YPA
- -0.52
- CMP
- -5.3
- YPG
- -9.7
- TD
- +3.4
- INT
- +4.0
Maturation+0.4rating
- YPA
- +0.11
- CMP
- -0.1
- YPG
- -6.9
- TD
- -1.0
- INT
- -0.7
Returning talent+3.2rating
- YPA
- +0.04
- CMP
- +2.3
- YPG
- +2.2
- TD
- +0.6
- INT
- 0.0
System fit+4.8rating
- YPA
- +0.14
- CMP
- +2.6
- YPG
- -11.5
- TD
- -1.1
- INT
- -2.1
Schedule-0.9rating
- YPA
- -0.04
- CMP
- -0.1
- YPG
- -1.1
- TD
- -0.4
- INT
- +0.1
#08 · Nebraska
TJ Lateef
2nd full passing seasonReturningMatt Rhule
YPA7.947.09–8.62
Comp %65.361.6–69.0
Rating144.5131.5–156.7
Pass YPG189.4123.1–219.4
TD14.97.8–18.6
INT6.73.9–8.9
Statistical gravity-4.9rating
- YPA
- -0.18
- CMP
- -3.2
- YPG
- +76.4
- TD
- +7.4
- INT
- +5.1
Maturation+3.3rating
- YPA
- +0.15
- CMP
- +1.4
- YPG
- +25.1
- TD
- +2.6
- INT
- +0.8
Returning talent+4.6rating
- YPA
- +0.15
- CMP
- +2.8
- YPG
- +4.9
- TD
- +0.5
- INT
- 0.0
System fit+10.0rating
- YPA
- +0.58
- CMP
- +4.4
- YPG
- +15.8
- TD
- +0.5
- INT
- -0.3
Schedule-2.2rating
- YPA
- -0.10
- CMP
- -0.4
- YPG
- -2.4
- TD
- -0.7
- INT
- +0.1
#09 · Nebraska
Anthony Colandrea
4th full passing seasonFrom UNLVMatt Rhule
YPA7.867.00–8.53
Comp %61.958.2–65.6
Rating141.7128.7–153.8
Pass YPG231.7165.4–261.7
TD19.712.6–23.5
INT8.05.3–10.3
Statistical gravity-6.7rating
- YPA
- -0.64
- CMP
- -1.9
- YPG
- -16.6
- TD
- +0.9
- INT
- +1.3
Maturation+0.4rating
- YPA
- +0.11
- CMP
- -0.1
- YPG
- -6.9
- TD
- -1.0
- INT
- -0.7
Returning talent-1.3rating
- YPA
- -0.03
- CMP
- -1.6
- YPG
- +4.7
- TD
- +1.0
- INT
- 0.0
System fit+1.7rating
- YPA
- +0.20
- CMP
- -0.2
- YPG
- +5.8
- TD
- -0.1
- INT
- -0.5
Schedule-1.9rating
- YPA
- -0.08
- CMP
- -0.3
- YPG
- -2.4
- TD
- -0.9
- INT
- +0.2
#10 · Michigan
Bryce Underwood
2nd full passing seasonReturningKyle Whittingham
YPA7.867.01–8.53
Comp %62.058.3–65.7
Rating140.5127.5–152.6
Pass YPG203.1136.8–233.1
TD15.48.3–19.2
INT6.13.3–8.3
Statistical gravity+9.3rating
- YPA
- +0.36
- CMP
- -0.6
- YPG
- +8.5
- TD
- +4.8
- INT
- -2.1
Maturation+3.3rating
- YPA
- +0.15
- CMP
- +1.4
- YPG
- +25.1
- TD
- +2.6
- INT
- +0.8
Returning talent-0.7rating
- YPA
- +0.03
- CMP
- -1.1
- YPG
- -5.0
- TD
- -0.2
- INT
- 0.0
System fit+4.6rating
- YPA
- +0.19
- CMP
- +2.3
- YPG
- -9.2
- TD
- -0.8
- INT
- -1.1
Schedule-2.7rating
- YPA
- -0.12
- CMP
- -0.5
- YPG
- -3.1
- TD
- -1.0
- INT
- +0.2
#11 · UCLA
Nico Iamaleava
3rd full passing seasonReturningBob Chesney
YPA7.406.55–8.07
Comp %65.161.5–68.9
Rating139.0126.0–151.2
Pass YPG218.1151.8–248.1
TD17.210.1–21.0
INT7.64.9–9.9
Statistical gravity+11.4rating
- YPA
- +1.35
- CMP
- -3.0
- YPG
- +42.3
- TD
- +3.7
- INT
- +0.9
Maturation+1.6rating
- YPA
- +0.01
- CMP
- +1.3
- YPG
- +6.0
- TD
- +0.4
- INT
- -0.6
Returning talent+3.0rating
- YPA
- +0.06
- CMP
- +1.9
- YPG
- +4.1
- TD
- +0.7
- INT
- 0.0
System fit+0.9rating
- YPA
- +0.06
- CMP
- +0.8
- YPG
- +6.7
- TD
- -0.0
- INT
- +0.2
Schedule-1.3rating
- YPA
- -0.05
- CMP
- -0.2
- YPG
- -1.6
- TD
- -0.6
- INT
- +0.1
#12 · Wisconsin
Colton Joseph
3rd full passing seasonFrom Old DominionLuke Fickell
YPA7.546.69–8.21
Comp %60.556.8–64.2
Rating137.9124.9–150.0
Pass YPG197.6131.3–227.5
TD17.910.8–21.7
INT7.44.7–9.7
Statistical gravity-10.3rating
- YPA
- -0.90
- CMP
- -1.1
- YPG
- +1.4
- TD
- -0.7
- INT
- -1.1
Maturation+1.6rating
- YPA
- +0.01
- CMP
- +1.3
- YPG
- +6.0
- TD
- +0.4
- INT
- -0.6
Returning talent-3.8rating
- YPA
- -0.30
- CMP
- 0.0
- YPG
- -7.9
- TD
- -1.2
- INT
- 0.0
System fit-3.0rating
- YPA
- -0.35
- CMP
- +0.2
- YPG
- -3.1
- TD
- +0.1
- INT
- -0.2
Schedule-0.3rating
- YPA
- -0.01
- CMP
- -0.0
- YPG
- -0.3
- TD
- -0.1
- INT
- +0.0
#13 · Rutgers
Dylan Lonergan
2nd full passing seasonFrom Boston CollegeGreg Schiano
YPA7.106.25–7.77
Comp %61.958.2–65.6
Rating133.5120.5–145.6
Pass YPG199.3133.0–229.2
TD16.89.7–20.6
INT7.64.9–9.9
Statistical gravity-3.7rating
- YPA
- +0.01
- CMP
- -4.2
- YPG
- +10.8
- TD
- +2.3
- INT
- +3.0
Maturation+3.3rating
- YPA
- +0.15
- CMP
- +1.4
- YPG
- +25.1
- TD
- +2.6
- INT
- +0.8
Returning talent-0.1rating
- YPA
- +0.02
- CMP
- -0.8
- YPG
- -5.3
- TD
- +0.2
- INT
- 0.0
System fit-2.7rating
- YPA
- -0.19
- CMP
- -1.4
- YPG
- +0.5
- TD
- -0.1
- INT
- -1.2
Schedule-0.5rating
- YPA
- -0.02
- CMP
- -0.1
- YPG
- -0.6
- TD
- -0.2
- INT
- +0.1
#14 · Northwestern
Aidan Chiles
3rd full passing seasonFrom Michigan StateDavid Braun
YPA7.086.23–7.75
Comp %61.958.2–65.7
Rating133.2120.2–145.4
Pass YPG172.7106.4–202.6
TD14.77.6–18.5
INT7.04.3–9.3
Statistical gravity+0.8rating
- YPA
- +0.58
- CMP
- -3.3
- YPG
- +65.9
- TD
- +5.5
- INT
- +4.8
Maturation+1.6rating
- YPA
- +0.01
- CMP
- +1.3
- YPG
- +6.0
- TD
- +0.4
- INT
- -0.6
Returning talent+3.6rating
- YPA
- +0.15
- CMP
- +1.9
- YPG
- -1.7
- TD
- +0.1
- INT
- 0.0
System fit-4.8rating
- YPA
- -0.43
- CMP
- -0.7
- YPG
- -11.5
- TD
- -0.7
- INT
- -0.3
Schedule-1.8rating
- YPA
- -0.08
- CMP
- -0.3
- YPG
- -2.0
- TD
- -0.7
- INT
- +0.1
#15 · Purdue
Ryan Browne
2nd full passing seasonReturningBarry Odom
YPA7.166.31–7.83
Comp %62.158.4–65.8
Rating132.5119.5–144.7
Pass YPG228.7162.4–258.7
TD17.410.3–21.2
INT9.06.3–11.3
Statistical gravity+14.3rating
- YPA
- +0.57
- CMP
- +2.8
- YPG
- +1.8
- TD
- +4.5
- INT
- -2.1
Maturation+3.3rating
- YPA
- +0.15
- CMP
- +1.4
- YPG
- +25.1
- TD
- +2.6
- INT
- +0.8
Returning talent+1.1rating
- YPA
- +0.07
- CMP
- 0.0
- YPG
- +2.3
- TD
- +0.5
- INT
- 0.0
System fit+1.0rating
- YPA
- +0.10
- CMP
- -0.7
- YPG
- +23.2
- TD
- +2.0
- INT
- +0.1
Schedule-2.4rating
- YPA
- -0.10
- CMP
- -0.4
- YPG
- -3.1
- TD
- -1.3
- INT
- +0.3
#16 · Maryland
Malik Washington
2nd full passing seasonReturningMike Locksley
YPA6.845.99–7.51
Comp %62.458.7–66.2
Rating131.8118.8–143.9
Pass YPG247.2180.9–277.2
TD20.513.4–24.3
INT7.95.2–10.2
Statistical gravity+6.2rating
- YPA
- +0.37
- CMP
- -0.3
- YPG
- -50.0
- TD
- -0.1
- INT
- -1.2
Maturation+3.3rating
- YPA
- +0.15
- CMP
- +1.4
- YPG
- +25.1
- TD
- +2.6
- INT
- +0.8
Returning talent-0.8rating
- YPA
- -0.10
- CMP
- +0.8
- YPG
- +1.6
- TD
- -0.7
- INT
- 0.0
System fit+4.8rating
- YPA
- +0.15
- CMP
- +2.8
- YPG
- +23.6
- TD
- +1.7
- INT
- -0.7
Schedule-0.1rating
- YPA
- 0.00
- CMP
- -0.0
- YPG
- -0.1
- TD
- -0.1
- INT
- +0.0
#17 · Northwestern
Nicco Marchiol
First full passing seasonFrom West VirginiaDavid Braun
YPA7.116.35–7.86
Comp %60.156.7–64.2
Rating128.3113.0–141.1
Pass YPG163.091.3–204.7
TD11.34.3–15.7
INT7.03.9–9.1
Statistical gravity+6.0rating
- YPA
- +0.33
- CMP
- -3.3
- YPG
- +117.8
- TD
- +10.2
- INT
- +5.3
Maturation0.0rating
- YPA
- 0.00
- CMP
- 0.0
- YPG
- 0.0
- TD
- 0.0
- INT
- 0.0
Returning talent-0.2rating
- YPA
- +0.10
- CMP
- -1.6
- YPG
- -2.1
- TD
- +0.2
- INT
- 0.0
System fit-7.3rating
- YPA
- -0.58
- CMP
- -2.1
- YPG
- -10.7
- TD
- -0.4
- INT
- -0.4
Schedule-1.9rating
- YPA
- -0.09
- CMP
- -0.3
- YPG
- -2.0
- TD
- -0.6
- INT
- +0.1
#18 · Minnesota
Drake Lindsey
2nd full passing seasonReturningP.J. Fleck
YPA6.575.72–7.25
Comp %61.858.2–65.6
Rating128.0115.0–140.1
Pass YPG198.2131.9–228.2
TD17.210.1–21.0
INT8.75.9–10.9
Statistical gravity+0.7rating
- YPA
- +0.62
- CMP
- -3.0
- YPG
- -4.6
- TD
- -1.4
- INT
- +3.0
Maturation+3.3rating
- YPA
- +0.15
- CMP
- +1.4
- YPG
- +25.1
- TD
- +2.6
- INT
- +0.8
Returning talent-2.2rating
- YPA
- -0.20
- CMP
- 0.0
- YPG
- -5.9
- TD
- -0.6
- INT
- 0.0
System fit+0.8rating
- YPA
- -0.06
- CMP
- +0.4
- YPG
- +2.3
- TD
- +0.8
- INT
- -0.8
Schedule-1.4rating
- YPA
- -0.06
- CMP
- -0.2
- YPG
- -1.8
- TD
- -0.7
- INT
- +0.1
#19 · Michigan State
Alessio Milivojevic
2nd full passing seasonReturningPat Fitzgerald
YPA6.695.84–7.36
Comp %59.055.3–62.8
Rating125.3112.2–137.4
Pass YPG165.899.5–195.7
TD14.27.1–18.0
INT8.55.8–10.8
Statistical gravity-6.7rating
- YPA
- 0.00
- CMP
- -3.1
- YPG
- +58.2
- TD
- +3.9
- INT
- +4.1
Maturation+3.3rating
- YPA
- +0.15
- CMP
- +1.4
- YPG
- +25.1
- TD
- +2.6
- INT
- +0.8
Returning talent-4.3rating
- YPA
- -0.25
- CMP
- -0.6
- YPG
- -21.9
- TD
- -2.3
- INT
- 0.0
System fit-7.0rating
- YPA
- -0.47
- CMP
- -2.7
- YPG
- +0.3
- TD
- +0.6
- INT
- +0.5
Schedule-1.3rating
- YPA
- -0.06
- CMP
- -0.2
- YPG
- -1.5
- TD
- -0.5
- INT
- +0.1
#20 · Iowa
Tradon Bessinger
First full passing seasonReturningKirk Ferentz
YPA6.485.72–7.24
Comp %57.454.1–61.6
Rating123.2107.9–136.1
Pass YPG155.083.3–196.7
TD14.67.5–18.9
INT7.64.5–9.8
Freshman baseline+126.9rating
- YPA
- +7.01
- CMP
- +56.2
- YPG
- +165.7
- TD
- +14.8
- INT
- +7.7
Maturation0.0rating
- YPA
- 0.00
- CMP
- 0.0
- YPG
- 0.0
- TD
- 0.0
- INT
- 0.0
Returning talent+2.4rating
- YPA
- +0.04
- CMP
- +1.0
- YPG
- -5.5
- TD
- +0.6
- INT
- 0.0
System fit-4.1rating
- YPA
- -0.48
- CMP
- +0.5
- YPG
- -3.0
- TD
- -0.1
- INT
- -0.1
Schedule-1.9rating
- YPA
- -0.09
- CMP
- -0.3
- YPG
- -2.1
- TD
- -0.7
- INT
- +0.1
#21 · Iowa
Hank Brown
First full passing seasonReturningKirk Ferentz
YPA6.325.56–7.07
Comp %58.054.6–62.2
Rating121.7106.4–134.5
Pass YPG114.843.1–156.5
TD10.73.6–15.1
INT6.02.9–8.2
Statistical gravity+22.8rating
- YPA
- +1.57
- CMP
- +5.0
- YPG
- +116.0
- TD
- +10.3
- INT
- +5.5
Maturation0.0rating
- YPA
- 0.00
- CMP
- 0.0
- YPG
- 0.0
- TD
- 0.0
- INT
- 0.0
Returning talent+3.1rating
- YPA
- +0.02
- CMP
- +2.2
- YPG
- -4.1
- TD
- +0.2
- INT
- 0.0
System fit-3.9rating
- YPA
- -0.28
- CMP
- -1.3
- YPG
- -3.7
- TD
- -0.3
- INT
- -0.4
Schedule-1.7rating
- YPA
- -0.09
- CMP
- -0.3
- YPG
- -1.6
- TD
- -0.4
- INT
- +0.1
#22 · Iowa
Jeremy Hecklinski
First full passing seasonReturningKirk Ferentz
YPA6.035.27–6.79
Comp %61.558.2–65.7
Rating117.8102.5–130.6
Pass YPG112.640.9–154.3
TD7.50.4–11.8
INT6.13.0–8.3
Statistical gravity-16.1rating
- YPA
- +2.51
- CMP
- -43.0
- YPG
- +119.2
- TD
- +7.7
- INT
- +6.3
Maturation0.0rating
- YPA
- 0.00
- CMP
- 0.0
- YPG
- 0.0
- TD
- 0.0
- INT
- 0.0
Returning talent+3.1rating
- YPA
- +0.01
- CMP
- +2.4
- YPG
- -4.3
- TD
- +0.3
- INT
- 0.0
System fit-1.1rating
- YPA
- -0.40
- CMP
- +2.4
- YPG
- -1.4
- TD
- -0.1
- INT
- -0.3
Schedule-1.7rating
- YPA
- -0.09
- CMP
- -0.3
- YPG
- -1.6
- TD
- -0.4
- INT
- +0.1
How much should you trust it?
Read the range, not just the ranking
The ranking is the cleanest way to start an argument, but the range is the more honest forecast. Players separated by two or three rating points are not living in different universes. Their probable outcomes overlap, sometimes by a lot.
Think of the band as the forecast’s honest shrug. It covers the middle 60% of outcomes the model considers plausible. It is not a fence: football remains perfectly capable of producing an outlier.
Freshmen are the hardest call because there is no college stat line to anchor them. Their forecasts lean on recruiting, school, coach and schedule. Every number also assumes a meaningful twelve-game role. Iowa, Nebraska and Northwestern appear more than once because those are competing scenarios—not an attempt to divide one season among multiple starters.
The forecast works game by game, asking how often a quarterback will throw, what those throws will produce and how each defense changes the challenge. Experience means seasons in which he actually played, not years spent holding a clipboard. Returning talent blends retained and imported production with receiver and OL recruiting plus OL roster continuity. It is deliberately excluded from the INT forecast because it made those predictions worse. Game statistics come from CollegeFootballData; opponent strength comes from BCF Toys defensive FEI. The model is one voice in the argument—not the final word.
Now it’s your turn
How could this be wrong?
Every preseason model has a blind spot. The fun is figuring out where this one lives before September exposes it.
The leap nobody has seen yet
Maybe Bryce Underwood’s second year is not an average step forward but a launch. Maybe a freshman wins a job and makes the lack of college data look silly by October.
The supporting cast changes the answer
The green number is not the supporting cast’s full worth; it is the part we can reasonably see before kickoff. The model can count returning production and recruiting stars. It cannot know whether a rebuilt line gels, a receiver gets healthy or a freshman becomes a star by October. If you think your guy has better help than the model can see, that is your strongest case for being more optimistic than the projection—and some of that unseen help may still be hiding inside “system” or “statistical gravity.”
The system breaks its own pattern
Cignetti may squeeze even more from Hoover than the model allows. Another coach may change his offense completely. History is useful right up until somebody decides to stop repeating it.
So make your call.