California

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+0.0736th
  Defense+0.1434th
• 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.
  Offense40.7%36th
  Defense45.1%17th
• Explosiveness

  Average EPA on the successful snaps: how big the good plays were.
  Offense+1.2333rd
  Defense+1.2460th
• Rushing EPA

  EPA per designed run.
  Offense+0.0948th
  Defense+0.1139th
• Passing EPA

  EPA per pass play.
  Offense+0.1123rd
  Defense+0.2539th
• Standard downs

  Success rate on downs where either a run or a pass makes sense, like 1st and 10 or 3rd and 2.
  Offense47.4%51st
  Defense48.8%35th
• Passing downs

  Success rate on downs that all but force a pass, like 2nd and 8 or longer, or 3rd and 5 or longer.
  Offense27.0%24th
  Defense35.1%9th
• Line yards

  Rushing yards per carry credited to the offensive line, weighting the first few yards most.
  Offense3.3588th
  Defense3.1046th
• Stuff rate

  Share of runs stopped at or behind the line of scrimmage.
  Offense14.1%77th
  Defense14.9%28th
• Havoc

  Share of snaps with a tackle for loss, sack, forced fumble, interception or pass breakup.
  Offense17.3%33rd
  Defense15.9%45th

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 California 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
   UNT
2. L
   AUB
3. W
   IDHO
4. L
   WASH
5. W
   ASU
6. L
   ORST
7. L
   UTAH
8. L
   USC
9. L
   ORE
10. W
    WSU
11. W
    STAN
12. W
    UCLA
13. L
    TTU

Bars above the line: better than expected for California.

Players, 2023

1. Fernando MendozaQB234 plays · +0.242 pts/play · 85th of 250 QBs
   1.06WAR0.44 to 1.63
2. Jaydn OttRB238 plays · +0.101 pts/play · 24th of 383 RBs
   0.45WAR0.21 to 0.68
3. Ben FinleyQB65 plays · +0.175 pts/play · 182nd of 250 QBs
   0.21WAR0.06 to 0.37
4. Jeremiah HunterWR65 plays · +0.167 pts/play · 75th of 457 WRs
   0.20WAR0.04 to 0.37
5. Jack EndriesTE36 plays · +0.235 pts/play · 24th of 212 TEs
   0.16WAR0.07 to 0.23
6. Trond GrizzellWR39 plays · +0.086 pts/play · 260th of 457 WRs
   0.06WAR−0.01 to 0.13
7. Taj DavisWR35 plays · +0.063 pts/play · 302nd of 457 WRs
   0.04WAR−0.03 to 0.12
8. Lachlan WilsonP43 plays · +0.051 pts/play · 90th of 103 punters
   0.04WAR−0.04 to 0.12

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 California'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.
  68168145th of 130Roster talent: 681, 45th of 130 in FBS
• Recruiting

  Average 247Sports class score over this season's class and the three before it.
  18618656th of 130Recruiting: 186, 56th of 130 in FBS
• Returning production

  Share of last season's production (expected points added) by players still on the roster.
  44%44%78th of 128Returning production: 44%, 78th of 128 in FBS
• SP+ rating

  The result: Bill Connelly's opponent-adjusted rating, points per game better than an average FBS team.
  −0.1−0.166th of 130SP+ rating: −0.1, 66th 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 California's own. A call that differs gives up the gap to the best option; gaps under one point are close calls, and the 5 clear-cut ones account for 0.18 of the 0.22 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. vs AUBQ4 1:44 4th & 13 at AUB 18, down 4Went for it; the numbers favored a field goal−9.7 pts
2. vs AUBQ2 0:00 4th & 20 at AUB 44, up 3Went for it; the numbers favored punting−3.1 pts
3. vs AUBQ4 5:24 4th & 3 at CAL 32, down 4Punted; the numbers favored going for it−2.1 pts
4. vs ASUQ4 12:17 4th & 2 at CAL 43, up 4Punted; the numbers favored going for it−1.6 pts
5. vs WSUQ3 2:43 4th & 2 at CAL 37, up 4Punted; the numbers favored going for it−1.1 pts
6. at TTUQ2 8:09 4th & 2 at CAL 46, down 7Punted; the numbers favored going for it (close call)−0.9 pts
7. vs ASUQ4 3:20 4th & 7 at ASU 25, up 3Went for it; the numbers favored a field goal (close call)−0.8 pts
8. vs ASUQ4 3:20 4th & 7 at ASU 25, up 3Went for it; the numbers favored a field goal (close call)−0.8 pts

Record vs. expectation, 2023

34th of 133 FBS teams at beating the model's pregame expectations

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

1. W

   UNT
2. L

   AUB
3. W

   IDHO
4. L

   WASH
5. W

   ASU
6. L

   ORST
7. L

   UTAH
8. L

   USC
9. L

   ORE
10. W

    WSU
11. W

    STAN
12. W

    UCLA
13. L

    TTU

What moved it, 2023

Every game starts at California'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+0.19
• Possession & field position+0.03
• Clock−0.03
• Timeouts+0.04
• Other+0.02
• Less the pregame edge, counted before kickoff+0.41

Game by game

• UNTW 58-21 at UNT73% → 100%Before kickoff +23 · On the field +27: Scoreboard +33
• AUBL 10-14 vs AUB33% → 6%Before kickoff −17 · On the field −27: Scoreboard −42, Clock −3
• IDHOW 31-17 vs IDHO86% → 99%Before kickoff +36 · On the field +13: Scoreboard +18, Timeouts +1
• WASHL 32-59 at WASH7% → 0%Before kickoff −43 · On the field −7: Scoreboard −10
• ASUW 24-21 vs ASU78% → 96%Before kickoff +28 · On the field +18: Scoreboard +23, Timeouts +2
• ORSTL 40-52 vs ORST27% → 2%Before kickoff −23 · On the field −25: Scoreboard −34, Clock −2
• UTAHL 14-34 at UTAH20% → 0%Before kickoff −30 · On the field −20: Scoreboard −24, Timeouts −1
• USCL 49-50 vs USC18% → 6%Before kickoff −32 · On the field −12: Scoreboard −30, Clock −2
• OREL 19-63 at ORE4% → 0%Before kickoff −46 · On the field −4: Scoreboard −6
• WSUW 42-39 vs WSU50% → 81%Before kickoff 0 · On the field +31: Scoreboard +14, Timeouts +4
• STANW 27-15 at STAN63% → 98%Before kickoff +13 · On the field +34: Scoreboard +42, Clock +2
• UCLAW 33-7 at UCLA26% → 100%Before kickoff −24 · On the field +74: Scoreboard +69, Clock −1
• TTUL 14-34 at TTU35% → 0%Before kickoff −15 · On the field −35: Scoreboard −35, Timeouts −1

Points of win probability, from California's side.

Closest call

VT 20-25 CAL (2026) - California dropped to a 1.5% win probability before pulling it out.