UCLA

Profile vs. FBS, 2019

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+0.0936th
  Defense+0.273rd
• 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.
  Offense44.8%60th
  Defense48.2%9th
• Explosiveness

  Average EPA on the successful snaps: how big the good plays were.
  Offense+1.2050th
  Defense+1.2819th
• Rushing EPA

  EPA per designed run.
  Offense+0.0335th
  Defense+0.0943rd
• Passing EPA

  EPA per pass play.
  Offense+0.1536th
  Defense+0.432nd
• Standard downs

  Success rate on downs where either a run or a pass makes sense, like 1st and 10 or 3rd and 2.
  Offense48.4%55th
  Defense51.3%17th
• Passing downs

  Success rate on downs that all but force a pass, like 2nd and 8 or longer, or 3rd and 5 or longer.
  Offense37.3%79th
  Defense41.2%3rd
• Line yards

  Rushing yards per carry credited to the offensive line, weighting the first few yards most.
  Offense2.7529th
  Defense2.9346th
• Stuff rate

  Share of runs stopped at or behind the line of scrimmage.
  Offense20.4%40th
  Defense22.6%78th
• Havoc

  Share of snaps with a tackle for loss, sack, forced fumble, interception or pass breakup.
  Offense24.1%12th
  Defense17.1%17th

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, 2019

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
   CIN
2. L
   SDSU
3. L
   OU
4. W
   WSU
5. L
   ARIZ
6. L
   ORST
7. W
   STAN
8. W
   ASU
9. W
   COLO
10. L
    UTAH
11. L
    USC
12. L
    CAL

Bars above the line: better than expected for UCLA.

Players, 2019

1. Dorian Thompson-RobinsonQB389 plays · +0.158 pts/play · 68th of 228 QBs
   1.15WAR0.50 to 1.84
2. Joshua KelleyRB197 plays · +0.234 pts/play · 9th of 349 RBs
   0.86WAR0.69 to 1.02
3. Demetric FeltonRB140 plays · +0.123 pts/play · 137th of 349 RBs
   0.32WAR0.20 to 0.45
4. Austin BurtonQB67 plays · +0.233 pts/play · 164th of 228 QBs
   0.29WAR0.19 to 0.40
5. Kyle PhilipsWR66 plays · +0.143 pts/play · 90th of 439 WRs
   0.18WAR0.11 to 0.26
6. Devin AsiasiTE48 plays · +0.134 pts/play · 7th of 207 TEs
   0.12WAR0.05 to 0.20
7. JJ MolsonPK12 plays · +0.457 pts/play · 72nd of 114 kickers
   0.10WAR0.07 to 0.14
8. Wade LeesP39 plays · +0.105 pts/play · 35th of 100 punters
   0.08WAR0.03 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 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, 2019

• Roster talent

  247Sports' talent composite: the recruiting ratings of every player on the roster, transfers included.
  74174122nd of 130Roster talent: 741, 22nd of 130 in FBS
• Recruiting

  Average 247Sports class score over this season's class and the three before it.
  23123123rd of 130Recruiting: 231, 23rd of 130 in FBS
• Returning production

  Share of last season's production (expected points added) by players still on the roster.
  55%55%76th of 130Returning production: 55%, 76th of 130 in FBS
• SP+ rating

  The result: Bill Connelly's opponent-adjusted rating, points per game better than an average FBS team.
  −3.9−3.978th of 130SP+ rating: −3.9, 78th 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, 2019

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 3 clear-cut ones account for 0.13 of the 0.16 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 WSUQ4 2:38 4th & 5 at WSU 17, down 3Went for it; the numbers favored a field goal−10.8 pts
2. at ARIZQ4 7:20 4th & 6 at ARIZ 48, down 3Punted; the numbers favored going for it−1.1 pts
3. at USCQ4 13:01 4th & 17 at USC 33, down 17Went for it; the numbers favored a field goal−1.0 pts
4. vs CALQ3 13:15 4th & 3 at UCLA 37, down 4Punted; the numbers favored going for it (close call)−0.6 pts
5. vs SDSUQ2 3:47 4th & 4 at SDSU 27, down 3Kicked a field goal; the numbers favored going for it (close call)−0.5 pts
6. at USCQ4 5:09 4th & 7 at USC 23, down 17Went for it; the numbers favored a field goal (close call)−0.3 pts
7. at STANQ4 11:21 4th & 10 at STAN 33, up 17Went for it; the numbers favored a field goal (close call)−0.2 pts
8. at CINQ4 2:47 4th & 10 at UCLA 20, down 10Went for it; the numbers favored punting (close call)−0.2 pts

Record vs. expectation, 2019

83rd of 130 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

   CIN
2. L

   SDSU
3. L

   OU
4. W

   WSU
5. L

   ARIZ
6. L

   ORST
7. W

   STAN
8. W

   ASU
9. W

   COLO
10. L

    UTAH
11. L

    USC
12. L

    CAL

What moved it, 2019

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−0.84
• Possession & field position+0.04
• Clock−0.01
• Timeouts−0.03
• Less the pregame edge, counted before kickoff+0.45

Game by game

• CINL 14-24 at CIN42% → 3%Before kickoff −8 · On the field −40: Scoreboard −35, Timeouts −2
• SDSUL 14-23 vs SDSU69% → 6%Before kickoff +19 · On the field −63: Scoreboard −54, Clock −2
• OUL 14-48 vs OU6% → 0%Before kickoff −44 · On the field −6: Scoreboard −10
• WSUW 67-63 at WSU12% → 96%Before kickoff −38 · On the field +84: Scoreboard +56, Clock +3
• ARIZL 17-20 at ARIZ38% → 5%Before kickoff −12 · On the field −33: Scoreboard −32, Timeouts −2
• ORSTL 31-48 vs ORST60% → 1%Before kickoff +10 · On the field −59: Scoreboard −58, Clock −1
• STANW 34-16 at STAN35% → 99%Before kickoff −15 · On the field +64: Scoreboard +60, Clock +3
• ASUW 42-32 vs ASU43% → 97%Before kickoff −7 · On the field +54: Scoreboard +40, Timeouts +1
• COLOW 31-14 vs COLO65% → 99%Before kickoff +15 · On the field +34: Scoreboard +33, Timeouts +2
• UTAHL 3-49 at UTAH7% → 0%Before kickoff −43 · On the field −7: Scoreboard −10
• USCL 35-52 at USC16% → 0%Before kickoff −34 · On the field −15: Scoreboard −19, Timeouts −1
• CALL 18-28 vs CAL55% → 4%Before kickoff +5 · On the field −51: Scoreboard −54, Clock −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.