UCLA

Profile vs. FBS, 2025

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+0.0836th
  Defense+0.2112th
• 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.
  Offense41.9%45th
  Defense50.3%1st
• Explosiveness

  Average EPA on the successful snaps: how big the good plays were.
  Offense+1.1716th
  Defense+1.1196th
• Rushing EPA

  EPA per designed run.
  Offense+0.1679th
  Defense+0.195th
• Passing EPA

  EPA per pass play.
  Offense+0.0513th
  Defense+0.2818th
• Standard downs

  Success rate on downs where either a run or a pass makes sense, like 1st and 10 or 3rd and 2.
  Offense46.6%38th
  Defense54.0%3rd
• Passing downs

  Success rate on downs that all but force a pass, like 2nd and 8 or longer, or 3rd and 5 or longer.
  Offense31.8%64th
  Defense39.5%1st
• Line yards

  Rushing yards per carry credited to the offensive line, weighting the first few yards most.
  Offense3.0666th
  Defense3.326th
• Stuff rate

  Share of runs stopped at or behind the line of scrimmage.
  Offense19.2%44th
  Defense13.8%5th
• Havoc

  Share of snaps with a tackle for loss, sack, forced fumble, interception or pass breakup.
  Offense15.9%43rd
  Defense12.0%4th

Percentiles among the 136 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, 2025

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. L
   UTAH
2. L
   UNLV
3. L
   UNM
4. L
   NU
5. W
   PSU
6. W
   MSU
7. W
   MD
8. L
   IU
9. L
   NEB
10. L
    OSU
11. L
    WASH
12. L
    USC

Bars above the line: better than expected for UCLA.

Players, 2025

1. Nico IamaleavaQB361 plays · +0.156 pts/play · 81st of 231 QBs
   1.06WAR0.46 to 1.61
2. Jalen BergerRB72 plays · +0.091 pts/play · 206th of 365 RBs
   0.12WAR0.09 to 0.16
3. Jaivian ThomasRB56 plays · +0.086 pts/play · 241st of 365 RBs
   0.09WAR0.06 to 0.12
4. Anthony WoodsRB23 plays · +0.200 pts/play · 245th of 365 RBs
   0.09WAR0.05 to 0.13
5. Mateen BhaghaniPK16 plays · +0.252 pts/play · 71st of 112 kickers
   0.08WAR0.02 to 0.13
6. Titus Mokiao-AtimalalaWR37 plays · +0.109 pts/play · 242nd of 451 WRs
   0.08WAR0.01 to 0.14
7. Kwazi GilmerWR73 plays · +0.048 pts/play · 268th of 451 WRs
   0.07WAR−0.07 to 0.21
8. Rico Flores Jr.WR31 plays · +0.067 pts/play · 338th of 451 WRs
   0.04WAR−0.01 to 0.09

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, 2025

• Roster talent

  247Sports' talent composite: the recruiting ratings of every player on the roster, transfers included.
  76776726th of 134Roster talent: 767, 26th of 134 in FBS
• Recruiting

  Average 247Sports class score over this season's class and the three before it.
  18518561st of 134Recruiting: 185, 61st of 134 in FBS
• Returning production

  Share of last season's production (expected points added) by players still on the roster.
  22%22%91st of 132Returning production: 22%, 91st of 132 in FBS
• SP+ rating

  The result: Bill Connelly's opponent-adjusted rating, points per game better than an average FBS team.
  −8.7−8.796th of 134SP+ rating: −8.7, 96th of 134 in FBS

Ranks and bars among the 134 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, 2025

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 2 clear-cut ones account for 0.05 of the 0.09 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 MDQ4 7:44 4th & 2 at MD 38, down 3Kicked a field goal; the numbers favored going for it−3.2 pts
2. at UNLVQ2 0:04 4th & 1 at UNLV 15, down 23Kicked a field goal; the numbers favored going for it−1.7 pts
3. vs UNMQ2 5:09 4th & 2 at UCLA 30, down 7Punted; the numbers favored going for it (close call)−0.9 pts
4. vs WASHQ2 1:44 4th & 14 at WASH 28, down 13Went for it; the numbers favored a field goal (close call)−0.7 pts
5. at OSUQ2 0:00 4th & 13 at UCLA 22, down 24Went for it; the numbers favored punting (close call)−0.4 pts
6. vs UTAHQ3 12:38 4th & 4 at UTAH 28, down 19Kicked a field goal; the numbers favored going for it (close call)−0.3 pts
7. at USCQ4 0:46 4th & 5 at USC 6, down 19Went for it; the numbers favored a field goal (close call)−0.2 pts
8. vs PSUQ3 13:16 4th & 10 at PSU 38, up 23Kicked a field goal; the numbers favored going for it (close call)−0.2 pts

Record vs. expectation, 2025

96th of 136 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. L

   UTAH
2. L

   UNLV
3. L

   UNM
4. L

   NU
5. W

   PSU
6. W

   MSU
7. W

   MD
8. L

   IU
9. L

   NEB
10. L

    OSU
11. L

    WASH
12. L

    USC

What moved it, 2025

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−0.64
• Possession & field position+0.03
• Clock−0.02
• Timeouts−0.24
• Less the pregame edge, counted before kickoff+0.59

Game by game

• UTAHL 10-43 vs UTAH33% → 0%Before kickoff −17 · On the field −33: Scoreboard −30, Timeouts −6
• UNLVL 23-30 at UNLV57% → 2%Before kickoff +7 · On the field −55: Scoreboard −44, Possession & field position +1
• UNML 10-35 vs UNM90% → 0%Before kickoff +40 · On the field −90: Scoreboard −72
• NUL 14-17 at NU32% → 81%Before kickoff −18 · On the field +49: Timeouts +39, Clock +3
• PSUW 42-37 vs PSU5% → 99%Before kickoff −45 · On the field +94: Scoreboard +73, Timeouts −22
• MSUW 38-13 at MSU28% → 100%Before kickoff −22 · On the field +72: Scoreboard +52, Timeouts +9
• MDW 20-17 vs MD60% → 69%Before kickoff +10 · On the field +9: Scoreboard +53, Timeouts −44
• IUL 6-56 at IU5% → 0%Before kickoff −45 · On the field −5: Scoreboard −7
• NEBL 21-28 vs NEB48% → 5%Before kickoff −2 · On the field −43: Scoreboard −50, Clock −3
• OSUL 10-48 at OSU2% → 0%Before kickoff −48 · On the field −2: Scoreboard −4
• WASHL 14-48 vs WASH20% → 0%Before kickoff −30 · On the field −20: Scoreboard −25
• USCL 10-29 at USC7% → 1%Before kickoff −43 · On the field −6: Scoreboard −11, Timeouts −1

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.