California

Profile vs. FBS, 2017

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+0.0843rd
  Defense+0.1242nd
• 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.
  Offense43.0%49th
  Defense47.1%14th
• Explosiveness

  Average EPA on the successful snaps: how big the good plays were.
  Offense+1.2050th
  Defense+1.0691st
• Rushing EPA

  EPA per designed run.
  Offense+0.0651st
  Defense+0.0840th
• Passing EPA

  EPA per pass play.
  Offense+0.1037th
  Defense+0.1745th
• Standard downs

  Success rate on downs where either a run or a pass makes sense, like 1st and 10 or 3rd and 2.
  Offense45.2%29th
  Defense52.4%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.
  Offense38.9%88th
  Defense35.1%32nd
• Line yards

  Rushing yards per carry credited to the offensive line, weighting the first few yards most.
  Offense2.8140th
  Defense3.0626th
• Stuff rate

  Share of runs stopped at or behind the line of scrimmage.
  Offense20.2%45th
  Defense17.6%32nd
• Havoc

  Share of snaps with a tackle for loss, sack, forced fumble, interception or pass breakup.
  Offense17.2%35th
  Defense15.3%41st

Percentiles among the 130 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, 2017

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
   UNC
2. W
   WEB
3. W
   MISS
4. L
   USC
5. L
   ORE
6. L
   WASH
7. W
   WSU
8. L
   ARIZ
9. L
   COLO
10. W
    ORST
11. L
    STAN
12. L
    UCLA

Bars above the line: better than expected for California.

Players, 2017

1. Ross BowersQB411 plays · +0.174 pts/play · 52nd of 226 QBs
   1.34WAR0.62 to 2.03
2. Patrick LairdRB214 plays · +0.121 pts/play · 34th of 352 RBs
   0.49WAR0.33 to 0.63
3. Vic Wharton IIIWR89 plays · +0.137 pts/play · 22nd of 426 WRs
   0.23WAR0.14 to 0.33
4. Kanawai NoaWR73 plays · +0.153 pts/play · 32nd of 426 WRs
   0.21WAR0.14 to 0.27
5. Matt AndersonPK23 plays · +0.466 pts/play · 7th of 110 kickers
   0.20WAR0.12 to 0.28
6. Vic EnwereRB82 plays · +0.128 pts/play · 167th of 352 RBs
   0.20WAR0.14 to 0.26
7. Jordan VeasyWR47 plays · +0.079 pts/play · 286th of 426 WRs
   0.07WAR0.02 to 0.12
8. Tre WatsonRB16 plays · +0.136 pts/play · 270th of 352 RBs
   0.04WAR0.03 to 0.05

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, 2017

• Roster talent

  247Sports' talent composite: the recruiting ratings of every player on the roster, transfers included.
  60360348th of 130Roster talent: 603, 48th of 130 in FBS
• Recruiting

  Average 247Sports class score over this season's class and the three before it.
  18518545th of 130Recruiting: 185, 45th of 130 in FBS
• Returning production

  Share of last season's production (expected points added) by players still on the roster.
  39%39%101st of 128Returning production: 39%, 101st of 128 in FBS
• SP+ rating

  The result: Bill Connelly's opponent-adjusted rating, points per game better than an average FBS team.
  −1.0−1.074th of 128SP+ rating: −1.0, 74th of 128 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, 2017

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 6 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 MISSQ3 2:47 4th & 1 at MISS 2, down 2Kicked a field goal; the numbers favored going for it−6.2 pts
2. at UCLAQ4 8:35 4th & 2 at UCLA 2, down 10Kicked a field goal; the numbers favored going for it−3.7 pts
3. vs USCQ2 0:00 4th & 1 at USC 4, down 3Kicked a field goal; the numbers favored going for it−2.8 pts
4. vs MISSQ2 0:00 4th & 14 at CAL 45, down 9Went for it; the numbers favored punting−2.2 pts
5. vs WSUQ3 5:13 4th & 3 at WSU 31, up 14Kicked a field goal; the numbers favored going for it−1.9 pts
6. vs WEBQ3 14:53 4th & 1 at CAL 36, down 3Punted; the numbers favored going for it−1.6 pts
7. at COLOQ3 5:13 4th & 1 at CAL 23, down 13Punted; the numbers favored going for it (close call)−0.8 pts
8. vs WSUQ3 12:50 4th & 5 at WSU 43, up 14Punted; the numbers favored going for it (close call)−0.6 pts

Record vs. expectation, 2017

16th of 130 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

   UNC
2. W

   WEB
3. W

   MISS
4. L

   USC
5. L

   ORE
6. L

   WASH
7. W

   WSU
8. L

   ARIZ
9. L

   COLO
10. W

    ORST
11. L

    STAN
12. L

    UCLA

What moved it, 2017

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+1.43
• Possession & field position−0.04
• Clock+0.08
• Timeouts−0.23
• Less the pregame edge, counted before kickoff+1.27

Game by game

• UNCW 35-30 at UNC21% → 81%Before kickoff −29 · On the field +60: Scoreboard +42, Possession & field position −5
• WEBW 33-20 vs WEB95% → 99%Before kickoff +45 · On the field +4: Scoreboard +10
• MISSW 27-16 vs MISS28% → 98%Before kickoff −22 · On the field +69: Scoreboard +47, Timeouts +2
• USCL 20-30 vs USC9% → 3%Before kickoff −41 · On the field −6: Scoreboard −19, Clock −1
• OREL 24-45 at ORE10% → 0%Before kickoff −40 · On the field −10: Scoreboard −14
• WASHL 7-38 at WASH3% → 0%Before kickoff −47 · On the field −3: Scoreboard −5
• WSUW 37-3 vs WSU10% → 100%Before kickoff −40 · On the field +90: Scoreboard +66, Timeouts −1
• ARIZL 44-45 vs ARIZ33% → 88%Before kickoff −17 · On the field +55: Scoreboard +43, Timeouts −24
• COLOL 28-44 at COLO34% → 1%Before kickoff −16 · On the field −33: Scoreboard −34, Timeouts −1
• ORSTW 37-23 vs ORST65% → 98%Before kickoff +15 · On the field +34: Scoreboard +34, Timeouts +1
• STANL 14-17 at STAN14% → 10%Before kickoff −36 · On the field −4: Scoreboard −16, Timeouts −1
• UCLAL 27-30 at UCLA25% → 21%Before kickoff −25 · On the field −4: Scoreboard −12, Possession & field position −2

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.