UCLA

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+0.1571st
  Defense+0.1916th
• 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.
  Offense46.0%78th
  Defense46.6%17th
• Explosiveness

  Average EPA on the successful snaps: how big the good plays were.
  Offense+1.2261st
  Defense+1.1375th
• Rushing EPA

  EPA per designed run.
  Offense+0.0233rd
  Defense+0.209th
• Passing EPA

  EPA per pass play.
  Offense+0.2676th
  Defense+0.2129th
• Standard downs

  Success rate on downs where either a run or a pass makes sense, like 1st and 10 or 3rd and 2.
  Offense51.3%84th
  Defense51.8%15th
• Passing downs

  Success rate on downs that all but force a pass, like 2nd and 8 or longer, or 3rd and 5 or longer.
  Offense34.9%64th
  Defense34.2%38th
• Line yards

  Rushing yards per carry credited to the offensive line, weighting the first few yards most.
  Offense2.548th
  Defense3.2311th
• Stuff rate

  Share of runs stopped at or behind the line of scrimmage.
  Offense21.8%26th
  Defense15.8%12th
• Havoc

  Share of snaps with a tackle for loss, sack, forced fumble, interception or pass breakup.
  Offense18.2%24th
  Defense13.4%12th

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 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
   TA&M
2. W
   HAW
3. L
   MEM
4. L
   STAN
5. W
   COLO
6. L
   ARIZ
7. W
   ORE
8. L
   WASH
9. L
   UTAH
10. W
    ASU
11. L
    USC
12. W
    CAL
13. L
    KSU

Bars above the line: better than expected for UCLA.

Players, 2017

1. Josh RosenQB448 plays · +0.230 pts/play · 18th of 226 QBs
   1.93WAR1.12 to 2.78
2. Jordan LasleyWR94 plays · +0.208 pts/play · 3rd of 426 WRs
   0.37WAR0.23 to 0.50
3. Devon ModsterQB63 plays · +0.239 pts/play · 161st of 226 QBs
   0.28WAR0.17 to 0.40
4. Bolu OlorunfunmiRB113 plays · +0.117 pts/play · 132nd of 352 RBs
   0.25WAR0.14 to 0.35
5. Soso JamaboRB105 plays · +0.099 pts/play · 168th of 352 RBs
   0.19WAR0.10 to 0.27
6. Darren AndrewsWR75 plays · +0.125 pts/play · 58th of 426 WRs
   0.17WAR0.10 to 0.26
7. Theo HowardWR73 plays · +0.082 pts/play · 156th of 426 WRs
   0.11WAR0.05 to 0.17
8. JJ MolsonPK19 plays · +0.281 pts/play · 59th of 110 kickers
   0.10WAR0.03 to 0.18

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, 2017

• Roster talent

  247Sports' talent composite: the recruiting ratings of every player on the roster, transfers included.
  83183111th of 130Roster talent: 831, 11th of 130 in FBS
• Recruiting

  Average 247Sports class score over this season's class and the three before it.
  24924916th of 130Recruiting: 249, 16th of 130 in FBS
• Returning production

  Share of last season's production (expected points added) by players still on the roster.
  67%67%51st of 128Returning production: 67%, 51st of 128 in FBS
• SP+ rating

  The result: Bill Connelly's opponent-adjusted rating, points per game better than an average FBS team.
  +4.9+4.951st of 128SP+ rating: +4.9, 51st 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 UCLA'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 0.12 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 ASUQ4 9:38 4th & 2 at ASU 2, up 7Kicked a field goal; the numbers favored going for it−2.8 pts
2. vs CALQ3 4:08 4th & 1 at UCLA 20, up 7Punted; the numbers favored going for it−2.7 pts
3. vs CALQ3 12:17 4th & 1 at UCLA 23, up 8Punted; the numbers favored going for it−1.2 pts
4. vs CALQ4 12:10 4th & 3 at CAL 9, up 7Kicked a field goal; the numbers favored going for it−1.1 pts
5. vs COLOQ2 6:10 4th & 4 at COLO 43, up 4Punted; the numbers favored going for it (close call)−0.8 pts
6. vs ASUQ4 7:37 4th & 23 at ASU 35, up 10Punted; the numbers favored a field goal (close call)−0.7 pts
7. vs KSUQ4 12:40 4th & 2 at UCLA 29, down 11Punted; the numbers favored going for it (close call)−0.7 pts
8. vs KSUQ2 4:56 4th & 6 at midfield, up 10Punted; the numbers favored going for it (close call)−0.4 pts

Record vs. expectation, 2017

89th 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. W

   TA&M
2. W

   HAW
3. L

   MEM
4. L

   STAN
5. W

   COLO
6. L

   ARIZ
7. W

   ORE
8. L

   WASH
9. L

   UTAH
10. W

    ASU
11. L

    USC
12. W

    CAL
13. L

    KSU

What moved it, 2017

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−0.95
• Possession & field position+0.02
• Timeouts−0.04
• Less the pregame edge, counted before kickoff−0.24

Game by game

• TA&MW 45-44 vs TA&M69% → 90%Before kickoff +19 · On the field +21: Scoreboard +28, Timeouts −2
• HAWW 56-23 vs HAW95% → 100%Before kickoff +45 · On the field +5: Scoreboard +8
• MEML 45-48 at MEM60% → 6%Before kickoff +10 · On the field −53: Scoreboard −37, Timeouts −3
• STANL 34-58 at STAN33% → 1%Before kickoff −17 · On the field −33: Scoreboard −34, Clock −1
• COLOW 27-23 vs COLO71% → 86%Before kickoff +21 · On the field +15: Scoreboard +21, Timeouts +2
• ARIZL 30-47 at ARIZ60% → 0%Before kickoff +10 · On the field −60: Scoreboard −50, Timeouts −2
• OREW 31-14 vs ORE69% → 98%Before kickoff +19 · On the field +29: Scoreboard +31, Timeouts +1
• WASHL 23-44 at WASH11% → 0%Before kickoff −39 · On the field −11: Scoreboard −16
• UTAHL 17-48 at UTAH29% → 0%Before kickoff −21 · On the field −29: Scoreboard −31
• ASUW 44-37 vs ASU59% → 96%Before kickoff +9 · On the field +38: Scoreboard +35, Timeouts +2
• USCL 23-28 at USC15% → 4%Before kickoff −35 · On the field −11: Scoreboard −19, Timeouts −2
• CALW 30-27 vs CAL75% → 79%Before kickoff +25 · On the field +4: Scoreboard +12, Possession & field position +2
• KSUL 17-35 vs KSU37% → 1%Before kickoff −13 · On the field −36: Scoreboard −43, Clock −2

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.