California

Profile vs. FBS, 2016

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+0.1980th
  Defense+0.263rd
• 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.
  Offense47.1%81st
  Defense50.2%2nd
• Explosiveness

  Average EPA on the successful snaps: how big the good plays were.
  Offense+1.1946th
  Defense+1.1946th
• Rushing EPA

  EPA per designed run.
  Offense+0.1260th
  Defense+0.281st
• Passing EPA

  EPA per pass play.
  Offense+0.2574th
  Defense+0.2523rd
• Standard downs

  Success rate on downs where either a run or a pass makes sense, like 1st and 10 or 3rd and 2.
  Offense50.7%73rd
  Defense53.5%7th
• Passing downs

  Success rate on downs that all but force a pass, like 2nd and 8 or longer, or 3rd and 5 or longer.
  Offense39.2%89th
  Defense40.9%2nd
• Line yards

  Rushing yards per carry credited to the offensive line, weighting the first few yards most.
  Offense3.1169th
  Defense3.474th
• Stuff rate

  Share of runs stopped at or behind the line of scrimmage.
  Offense17.3%73rd
  Defense13.2%1st
• Havoc

  Share of snaps with a tackle for loss, sack, forced fumble, interception or pass breakup.
  Offense13.4%88th
  Defense12.5%6th

Percentiles among the 128 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, 2016

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
   HAW
2. L
   SDSU
3. W
   TEX
4. L
   ASU
5. W
   UTAH
6. L
   ORST
7. W
   ORE
8. L
   USC
9. L
   WASH
10. L
    WSU
11. L
    STAN
12. W
    UCLA

Bars above the line: better than expected for California.

Players, 2016

1. Davis WebbQB535 plays · +0.211 pts/play · 15th of 218 QBs
   2.12WAR1.20 to 3.04
2. Tre WatsonRB151 plays · +0.202 pts/play · 36th of 347 RBs
   0.57WAR0.41 to 0.71
3. Khalfani MuhammadRB142 plays · +0.159 pts/play · 74th of 347 RBs
   0.42WAR0.27 to 0.57
4. Chad HansenWR126 plays · +0.156 pts/play · 15th of 417 WRs
   0.37WAR0.16 to 0.55
5. Matt AndersonPK22 plays · +0.425 pts/play · 16th of 116 kickers
   0.17WAR0.11 to 0.24
6. Demetris RobertsonWR63 plays · +0.141 pts/play · 112th of 417 WRs
   0.17WAR0.09 to 0.25
7. Vic EnwereRB61 plays · +0.141 pts/play · 208th of 347 RBs
   0.16WAR0.08 to 0.24
8. Bug RiveraWR39 plays · +0.124 pts/play · 241st of 417 WRs
   0.09WAR0.06 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, 2016

• Roster talent

  247Sports' talent composite: the recruiting ratings of every player on the roster, transfers included.
  60160147th of 128Roster talent: 601, 47th of 128 in FBS
• Recruiting

  Average 247Sports class score over this season's class and the three before it.
  19419439th of 128Recruiting: 194, 39th of 128 in FBS
• Returning production

  Share of last season's production (expected points added) by players still on the roster.
  15%15%127th of 128Returning production: 15%, 127th of 128 in FBS
• SP+ rating

  The result: Bill Connelly's opponent-adjusted rating, points per game better than an average FBS team.
  +2.4+2.462nd of 128SP+ rating: +2.4, 62nd of 128 in FBS

Ranks and bars among the 128 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, 2016

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 4 clear-cut ones account for 0.08 of the 0.13 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 ORSTQ4 1:49 4th & 11 at ORST 42, down 3Went for it; the numbers favored a field goal−2.8 pts
2. at SDSUQ4 7:14 4th & 2 at CAL 24, down 4Punted; the numbers favored going for it−2.4 pts
3. at SDSUQ2 6:22 4th & 1 at CAL 49, down 7Punted; the numbers favored going for it−1.7 pts
4. vs UCLAQ4 12:26 4th & 2 at UCLA 2, up 16Kicked a field goal; the numbers favored going for it−1.1 pts
5. vs OREQ3 7:22 4th & 1 at CAL 30, up 20Punted; the numbers favored going for it (close call)−0.9 pts
6. at ASUQ2 3:19 4th & 3 at ASU 3, up 11Kicked a field goal; the numbers favored going for it (close call)−0.8 pts
7. at USCQ2 14:17 4th & 1 at CAL 17, down 14Punted; the numbers favored going for it (close call)−0.7 pts
8. vs UCLAQ2 9:42 4th & 3 at UCLA 27, up 6Kicked a field goal; the numbers favored going for it (close call)−0.7 pts

Record vs. expectation, 2016

48th of 128 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

   HAW
2. L

   SDSU
3. W

   TEX
4. L

   ASU
5. W

   UTAH
6. L

   ORST
7. W

   ORE
8. L

   USC
9. L

   WASH
10. L

    WSU
11. L

    STAN
12. W

    UCLA

What moved it, 2016

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−0.22
• Possession & field position−0.03
• Clock+0.09
• Timeouts+0.05
• Less the pregame edge, counted before kickoff+0.50

Game by game

• HAWW 51-31 vs HAW91% → 100%Before kickoff +41 · On the field +9: Scoreboard +13
• SDSUL 40-45 at SDSU29% → 6%Before kickoff −21 · On the field −24: Scoreboard −28, Timeouts −2
• TEXW 50-43 vs TEX25% → 97%Before kickoff −25 · On the field +72: Scoreboard +43, Timeouts +4
• ASUL 41-51 at ASU31% → 4%Before kickoff −19 · On the field −27: Scoreboard −30
• UTAHW 28-23 vs UTAH55% → 83%Before kickoff +5 · On the field +29: Scoreboard +23, Possession & field position −3
• ORSTL 44-47 at ORST71% → 18%Before kickoff +21 · On the field −53: Scoreboard −40, Timeouts +25
• OREW 52-49 vs ORE55% → 71%Before kickoff +5 · On the field +16: Timeouts −23, Scoreboard +19
• USCL 24-45 at USC14% → 1%Before kickoff −36 · On the field −13: Scoreboard −19
• WASHL 27-66 vs WASH11% → 0%Before kickoff −39 · On the field −11: Scoreboard −16, Clock −1
• WSUL 21-56 at WSU11% → 0%Before kickoff −39 · On the field −11: Scoreboard −14
• STANL 31-45 vs STAN21% → 2%Before kickoff −29 · On the field −19: Scoreboard −30, Clock −2
• UCLAW 36-10 vs UCLA29% → 100%Before kickoff −21 · On the field +71: Scoreboard +57, Possession & field position +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.