USC

Profile vs. FBS, 2023

Farther out is better on every spoke. Dashed ring: the FBS median.

• EPA per play

  Expected points added on an average snap: how much each play moved the scoring odds.
  Offense+0.3095th
  Defense+0.228th
• Success rate

  Share of snaps that stayed on schedule: half the yards needed on 1st down, 70% on 2nd, all of them on 3rd or 4th.
  Offense47.4%90th
  Defense45.9%10th
• Explosiveness

  Average EPA on the successful snaps: how big the good plays were.
  Offense+1.4899th
  Defense+1.3032nd
• Rushing EPA

  EPA per designed run.
  Offense+0.1883rd
  Defense+0.225th
• Passing EPA

  EPA per pass play.
  Offense+0.4492nd
  Defense+0.2727th
• Standard downs

  Success rate on downs where either a run or a pass makes sense, like 1st and 10 or 3rd and 2.
  Offense51.5%83rd
  Defense51.1%12th
• Passing downs

  Success rate on downs that all but force a pass, like 2nd and 8 or longer, or 3rd and 5 or longer.
  Offense37.5%91st
  Defense34.0%12th
• Line yards

  Rushing yards per carry credited to the offensive line, weighting the first few yards most.
  Offense3.1454th
  Defense3.3017th
• Stuff rate

  Share of runs stopped at or behind the line of scrimmage.
  Offense19.1%22nd
  Defense15.7%41st
• Havoc

  Share of snaps with a tackle for loss, sack, forced fumble, interception or pass breakup.
  Offense16.1%51st
  Defense15.0%23rd

Percentiles among the 133 FBS teams this season, where higher is better for the offense and the defense alike (a defense's numbers are what it allowed). The line in each bar is the FBS median. Garbage time excluded. Source: CollegeFootballData.

Drives vs. expectation, 2023

Every snap has an expected-points value: what a drive from that spot, down and distance is worth on average. A drive over expected beat its starting spot; defense counts what USC allowed, so lower is better. The adjusted number fits every team against the opponents it actually played, so it leans toward average early in a season. Ranks are among FBS teams.

Game by game

1. W
   SJSU
2. W
   NEV
3. W
   STAN
4. W
   ASU
5. W
   COLO
6. W
   ARIZ
7. L
   ND
8. L
   UTAH
9. W
   CAL
10. L
    WASH
11. L
    ORE
12. L
    UCLA
13. W
    LOU

Bars above the line: better than expected for USC.

Players, 2023

1. Caleb WilliamsQB351 plays · +0.325 pts/play · 13th of 250 QBs
   2.14WAR1.58 to 2.79
2. MarShawn LloydRB94 plays · +0.163 pts/play · 73rd of 383 RBs
   0.29WAR0.20 to 0.37
3. Miller MossQB41 plays · +0.356 pts/play · 168th of 250 QBs
   0.27WAR0.13 to 0.43
4. Brenden RiceWR33 plays · +0.296 pts/play · 85th of 457 WRs
   0.18WAR0.10 to 0.26
5. Tahj WashingtonWR49 plays · +0.192 pts/play · 91st of 457 WRs
   0.18WAR0.05 to 0.30
6. Eddie CzaplickiP34 plays · +0.189 pts/play · 29th of 103 punters
   0.12WAR0.03 to 0.20
7. Mario WilliamsWR16 plays · +0.227 pts/play · 237th of 457 WRs
   0.07WAR0.04 to 0.10
8. Denis LynchPK13 plays · +0.254 pts/play · 86th of 113 kickers
   0.06WAR0.03 to 0.10

By position group, against FBS

Linemen and defenders are valued as units, in points per game against the median FBS unit, because play-by-play doesn't say who was on the field. Players are valued against a replacement-level backup, so the two aren't added together.

Wins above replacement credits each play's expected points to the players involved, adjusted for the opponent, and compares USC's players with a typical backup; 53 points make a win. Only snaps while the game was in doubt count, so a starter on a team that blows opponents out plays fewer of them: read value per play beside WAR. The line under each player (or the range on a phone) is the 90% interval. Ranks are among FBS teams and players. How this works

What it had and what it did, 2023

• Roster talent

  247Sports' talent composite: the recruiting ratings of every player on the roster, transfers included.
  8968968th of 130Roster talent: 896, 8th of 130 in FBS
• Recruiting

  Average 247Sports class score over this season's class and the three before it.
  22722719th of 130Recruiting: 227, 19th of 130 in FBS
• Returning production

  Share of last season's production (expected points added) by players still on the roster.
  74%74%24th of 128Returning production: 74%, 24th of 128 in FBS
• SP+ rating

  The result: Bill Connelly's opponent-adjusted rating, points per game better than an average FBS team.
  +9.4+9.429th of 130SP+ rating: +9.4, 29th of 130 in FBS

Ranks and bars among the 130 FBS teams with a talent rating this season, best first; the line in each bar is the FBS median.

Talent conversion

Conversion by season

Fourth-down calls vs. the numbers, 2023

Each real fourth down is valued three ways (go for it, kick, punt) from the score, clock and field position before the snap, using league-wide odds rather than USC's own. A call that differs gives up the gap to the best option; gaps under one point are close calls, and the 1 clear-cut ones account for 0.01 of the 0.05 expected wins. The model sees the situation, not the injury report, so a call it disagrees with can still be right. Ranks are among FBS teams; 1st gave up the least.

Costliest calls against the numbers

1. at CALQ2 0:03 4th & 11 at CAL 45, down 11Went for it; the numbers favored punting−1.2 pts
2. at ASUQ4 13:27 4th & 3 at USC 29, up 6Punted; the numbers favored going for it (close call)−0.7 pts
3. vs WASHQ4 7:42 4th & 25 at WASH 42, down 3Punted; the numbers favored a field goal (close call)−0.7 pts
4. vs UTAHQ2 4:29 4th & 6 at UTAH 44, tiedPunted; the numbers favored going for it (close call)−0.5 pts
5. vs UTAHQ2 15:00 4th & 1 at USC 34, tiedPunted; the numbers favored going for it (close call)−0.4 pts
6. vs UCLAQ4 4:27 4th & 14 at UCLA 30, down 25Went for it; the numbers favored a field goal (close call)−0.3 pts
7. vs LOUQ4 1:32 4th & 2 at LOU 44, up 14Punted; the numbers favored going for it (close call)−0.3 pts
8. at OREQ4 11:27 4th & 9 at ORE 9, down 22Went for it; the numbers favored a field goal (close call)−0.2 pts

Record vs. expectation, 2023

113th of 133 FBS teams at beating the model's pregame expectations

Expected wins adds up USC's chance of winning each game as the model saw it at kickoff - a 70% favorite counts 0.7.

1. W

   SJSU
2. W

   NEV
3. W

   STAN
4. W

   ASU
5. W

   COLO
6. W

   ARIZ
7. L

   ND
8. L

   UTAH
9. W

   CAL
10. L

    WASH
11. L

    ORE
12. L

    UCLA
13. W

    LOU

What moved it, 2023

Every game starts at USC's pregame chance; everything after that is the field. The model's own breakdown of each game's final play splits the on-field part by what drove it, and the parts add up exactly. How it works →

• Scoreboard−1.36
• Possession & field position−0.04
• Clock−0.02
• Timeouts−0.12
• Other+0.01
• Less the pregame edge, counted before kickoff−1.03

Game by game

• SJSUW 56-28 vs SJSU97% → 13%Before kickoff +47 · On the field −84: Scoreboard −33, Possession & field position −2
• NEVW 66-14 vs NEV99% → 100%Before kickoff +49 · On the field +1: Scoreboard +2
• STANW 56-10 vs STAN97% → 100%Before kickoff +47 · On the field +3: Scoreboard +5
• ASUW 42-28 at ASU98% → 99%Before kickoff +48 · On the field +1: Scoreboard +7
• COLOW 48-41 at COLO94% → 95%Before kickoff +44 · On the field 0: Scoreboard +17, Clock +1
• ARIZW 43-41 vs ARIZ92% → 85%Before kickoff +42 · On the field −6: Scoreboard +19, Timeouts −11
• NDL 20-48 at ND47% → 0%Before kickoff −3 · On the field −47: Scoreboard −44, Timeouts −1
• UTAHL 32-34 vs UTAH71% → 5%Before kickoff +21 · On the field −66: Scoreboard −53, Clock −3
• CALW 50-49 at CAL82% → 94%Before kickoff +32 · On the field +12: Scoreboard +30, Clock +2
• WASHL 42-52 vs WASH43% → 4%Before kickoff −7 · On the field −39: Scoreboard −46, Clock −3
• OREL 27-36 at ORE15% → 3%Before kickoff −35 · On the field −12: Scoreboard −19, Timeouts −1
• UCLAL 20-38 vs UCLA73% → 1%Before kickoff +23 · On the field −72: Scoreboard −63, Clock −3
• LOUW 42-28 vs LOU47% → 99%Before kickoff −3 · On the field +53: Scoreboard +44, Timeouts +2

Points of win probability, from USC's side.

Closest call

SJSU 28-56 USC (2023) - USC dropped to a 2.6% win probability before pulling it out.