UCLA

Profile vs. FBS, 2015

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+0.2183rd
  Defense+0.0763rd
• 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.
  Offense46.6%80th
  Defense44.3%35th
• Explosiveness

  Average EPA on the successful snaps: how big the good plays were.
  Offense+1.2677th
  Defense+1.0199th
• Rushing EPA

  EPA per designed run.
  Offense+0.1483rd
  Defense+0.0833rd
• Passing EPA

  EPA per pass play.
  Offense+0.3079th
  Defense+0.0867th
• Standard downs

  Success rate on downs where either a run or a pass makes sense, like 1st and 10 or 3rd and 2.
  Offense48.1%56th
  Defense50.3%24th
• Passing downs

  Success rate on downs that all but force a pass, like 2nd and 8 or longer, or 3rd and 5 or longer.
  Offense43.3%99th
  Defense30.2%76th
• Line yards

  Rushing yards per carry credited to the offensive line, weighting the first few yards most.
  Offense2.7328th
  Defense2.9831st
• Stuff rate

  Share of runs stopped at or behind the line of scrimmage.
  Offense21.3%29th
  Defense14.7%6th
• Havoc

  Share of snaps with a tackle for loss, sack, forced fumble, interception or pass breakup.
  Offense3.0%11th
  Defense1.3%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, 2015

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 UCLA 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
   UVA
2. W
   UNLV
3. W
   BYU
4. W
   ARIZ
5. L
   ASU
6. L
   STAN
7. W
   CAL
8. W
   COLO
9. W
   ORST
10. L
    WSU
11. W
    UTAH
12. L
    USC
13. L
    NEB

Bars above the line: better than expected for UCLA.

Players, 2015

1. Josh RosenQB439 plays · +0.260 pts/play · 22nd of 205 QBs
   2.13WAR1.35 to 2.98
2. Paul PerkinsRB249 plays · +0.139 pts/play · 10th of 333 RBs
   0.65WAR0.45 to 0.86
3. Thomas DuarteWR78 plays · +0.230 pts/play · 19th of 411 WRs
   0.34WAR0.20 to 0.46
4. Jordan PaytonWR100 plays · +0.179 pts/play · 20th of 411 WRs
   0.33WAR0.20 to 0.47
5. Ka'imi FairbairnPK17 plays · +0.425 pts/play · 39th of 115 kickers
   0.14WAR0.09 to 0.18
6. Nate StarksRB46 plays · +0.125 pts/play · 197th of 333 RBs
   0.11WAR0.07 to 0.14
7. Darren AndrewsWR52 plays · +0.108 pts/play · 201st of 411 WRs
   0.11WAR0.04 to 0.17
8. Matt MengelP46 plays · +0.083 pts/play · 56th of 92 punters
   0.07WAR0.01 to 0.16

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 UCLA'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, 2015

• Roster talent

  247Sports' talent composite: the recruiting ratings of every player on the roster, transfers included.
  79379314th of 128Roster talent: 793, 14th of 128 in FBS
• Recruiting

  Average 247Sports class score over this season's class and the three before it.
  25525513th of 128Recruiting: 255, 13th of 128 in FBS
• Returning production

  Share of last season's production (expected points added) by players still on the roster.
  53%53%76th of 127Returning production: 53%, 76th of 127 in FBS
• SP+ rating

  The result: Bill Connelly's opponent-adjusted rating, points per game better than an average FBS team.
  +18.6+18.616th of 128SP+ rating: +18.6, 16th 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, 2015

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 UCLA'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 0.08 of the 0.15 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 CALQ2 2:49 4th & 1 at CAL 1, up 17Kicked a field goal; the numbers favored going for it−2.7 pts
2. at NEBQ2 4:23 4th & 1 at UCLA 39, up 7Punted; the numbers favored going for it−1.5 pts
3. at UTAHQ2 6:11 4th & 1 at UCLA 45, up 7Punted; the numbers favored going for it−1.4 pts
4. vs ASUQ2 10:47 4th & 2 at ASU 42, down 9Punted; the numbers favored going for it−1.3 pts
5. vs WSUQ3 1:38 4th & 2 at UCLA 24, down 5Punted; the numbers favored going for it−1.2 pts
6. vs ASUQ3 1:57 4th & 2 at UCLA 26, down 19Punted; the numbers favored going for it (close call)−0.9 pts
7. at ORSTQ2 11:31 4th & 1 at ORST 20, tiedKicked a field goal; the numbers favored going for it (close call)−0.8 pts
8. vs WSUQ3 12:11 4th & 3 at UCLA 47, up 2Punted; the numbers favored going for it (close call)−0.8 pts

Record vs. expectation, 2015

106th of 128 FBS teams at beating the model's pregame expectations

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

1. W

   UVA
2. W

   UNLV
3. W

   BYU
4. W

   ARIZ
5. L

   ASU
6. L

   STAN
7. W

   CAL
8. W

   COLO
9. W

   ORST
10. L

    WSU
11. W

    UTAH
12. L

    USC
13. L

    NEB

What moved it, 2015

Every game starts at UCLA'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−0.29
• Possession & field position−0.07
• Clock+0.03
• Timeouts+0.03
• Less the pregame edge, counted before kickoff−1.04

Game by game

• UVAW 34-16 vs UVA86% → 99%Before kickoff +36 · On the field +13: Scoreboard +19
• UNLVW 37-3 at UNLV97% → 100%Before kickoff +47 · On the field +3: Scoreboard +7
• BYUW 24-23 vs BYU86% → 93%Before kickoff +36 · On the field +7: Scoreboard +19, Timeouts +2
• ARIZW 56-30 at ARIZ63% → 100%Before kickoff +13 · On the field +37: Scoreboard +42, Clock +1
• ASUL 23-38 vs ASU82% → 1%Before kickoff +32 · On the field −81: Scoreboard −65, Clock −2
• STANL 35-56 at STAN36% → 0%Before kickoff −14 · On the field −36: Scoreboard −38, Clock +1
• CALW 40-24 vs CAL66% → 98%Before kickoff +16 · On the field +32: Scoreboard +32, Timeouts +1
• COLOW 35-31 vs COLO89% → 92%Before kickoff +39 · On the field +3: Scoreboard +15, Timeouts +3
• ORSTW 41-0 at ORST88% → 100%Before kickoff +38 · On the field +12: Scoreboard +16, Clock +1
• WSUL 27-31 vs WSU76% → 17%Before kickoff +26 · On the field −59: Scoreboard −40, Possession & field position −3
• UTAHW 17-9 at UTAH58% → 97%Before kickoff +8 · On the field +39: Scoreboard +47, Clock +3
• USCL 21-40 at USC38% → 0%Before kickoff −12 · On the field −37: Scoreboard −38, Timeouts +1
• NEBL 29-37 at NEB69% → 3%Before kickoff +19 · On the field −67: Scoreboard −44, Timeouts −3

Points of win probability, from UCLA's side.

Closest call

UCLA 67-63 WSU (2019) - UCLA dropped to a 0.4% win probability before pulling it out.