Updated Sept. 15, 2026: This article originally combined an Arizona State projection with an unrelated Fresno State roster note. It has been refocused on one clear question: what happened after ESPN Analytics gave Arizona State an overwhelming Week 1 edge against Morgan State?
The answer was about as decisive as a preseason model can hope for. Arizona State beat Morgan State 70-7 on Sept. 5, scoring touchdowns on each of its first seven possessions and producing 682 yards of offense.
Before the game, ESPN Analytics had placed Arizona State’s win probability at roughly 99%, according to reporting by The Arizona Republic. A probability that high does not predict an exact margin, but the Sun Devils played like a team with an enormous matchup advantage.
Arizona State turned the forecast into immediate separation
The Sun Devils scored 28 points in the first quarter and 21 more in the second, taking a 49-7 lead into halftime. Morgan State never scored again.
Quarterback Cutter Boley produced the biggest individual performance. Arizona State’s official recap credited him with 387 passing yards and six touchdown passes, both career highs. His six touchdowns were the most by an Arizona State quarterback since Andrew Walter threw six in 2004.
Wide receiver Reed Harris added 137 yards and two touchdowns in his Sun Devil debut, and the offense finished with 682 total yards. Arizona State’s 70 points tied the second-highest single-game total in program history.
Those numbers explain why the pregame probability was so lopsided. Models generally react to differences in team strength, roster quality, recent performance and home-field context. On this night, the underlying gap showed up in almost every phase.
A 99% projection still does not mean the game is automatic
Win probability is often misunderstood as certainty. A 99% projection still allows for an upset; it simply says the model sees that upset as extremely unlikely before kickoff.
The useful way to judge the Arizona State forecast is not that the model somehow predicted 70-7. It did not. The value of the projection was identifying a matchup in which the Sun Devils should control the game if they played close to expectation.
Arizona State did more than that. The Sun Devils eliminated suspense almost immediately, which makes this one of the cleaner examples of a heavy pregame favorite validating the broad direction of an analytics model.
Week 2 showed why one blowout cannot define a season
The important follow-up came seven days later. Arizona State went on the road to face No. 10 Texas A&M and lost 48-20, dropping to 1-1.
That result is the necessary context for the Morgan State blowout. A team can be overwhelmingly better than one opponent and still face major questions against a ranked Power Four team the following week.
The contrast between 70-7 and 20-48 is not evidence that the Week 1 projection was wrong. It is evidence that college-football forecasts are matchup-specific. The Sun Devils’ advantage over Morgan State said little about how they would handle a much stronger Texas A&M roster on the road.
What Arizona State can actually take from the opener
The Morgan State game still mattered. It gave Boley a high-confidence start, allowed Arizona State to distribute snaps, and showed that the offense could generate explosive production when it held a clear talent advantage.
The opener also established a ceiling for the passing game. Six touchdown passes and 387 yards from Boley are useful evidence even after accounting for the opponent. The question for Arizona State is how much of that efficiency can carry into games where protection, separation and field position are harder to win.
For FSN’s broader Arizona State coverage, that is the more durable storyline than the original 99% number. The Sun Devils have already experienced both extremes of an early-season schedule: a game they were expected to dominate and a ranked road test that exposed a much smaller margin for error.
Why the original prediction still has value
Preseason analytics are most useful when they are treated as a baseline, not as a substitute for the game. Arizona State’s Week 1 result strongly supported the model’s view of the matchup, while Week 2 immediately reminded everyone that those probabilities reset when the opponent changes.
The Sun Devils did exactly what a 99% favorite is supposed to do against Morgan State: they removed doubt quickly and won comfortably. What comes next is the harder part — proving that the same offensive strengths can matter when the model no longer sees the game as nearly automatic.