Stanford

Profile vs. FBS, 2018

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+0.1671st
  Defense+0.1247th
• 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.
  Offense44.2%57th
  Defense44.4%34th
• Explosiveness

  Average EPA on the successful snaps: how big the good plays were.
  Offense+1.2464th
  Defense+1.1278th
• Rushing EPA

  EPA per designed run.
  Offense−0.049th
  Defense+0.0842nd
• Passing EPA

  EPA per pass play.
  Offense+0.3690th
  Defense+0.1942nd
• Standard downs

  Success rate on downs where either a run or a pass makes sense, like 1st and 10 or 3rd and 2.
  Offense49.5%66th
  Defense49.3%31st
• Passing downs

  Success rate on downs that all but force a pass, like 2nd and 8 or longer, or 3rd and 5 or longer.
  Offense33.3%48th
  Defense33.8%40th
• Line yards

  Rushing yards per carry credited to the offensive line, weighting the first few yards most.
  Offense2.5110th
  Defense3.0328th
• Stuff rate

  Share of runs stopped at or behind the line of scrimmage.
  Offense24.1%9th
  Defense17.6%31st
• Havoc

  Share of snaps with a tackle for loss, sack, forced fumble, interception or pass breakup.
  Offense20.6%4th
  Defense17.0%65th

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, 2018

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 Stanford 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
   SDSU
2. W
   USC
3. W
   UCD
4. W
   ORE
5. L
   ND
6. L
   UTAH
7. W
   ASU
8. L
   WSU
9. L
   WASH
10. W
    ORST
11. W
    UCLA
12. W
    CAL
13. W
    PITT

Bars above the line: better than expected for Stanford.

Players, 2018

1. K.J. CostelloQB385 plays · +0.333 pts/play · 7th of 225 QBs
   2.40WAR1.82 to 3.07
2. Bryce LoveRB180 plays · +0.187 pts/play · 31st of 351 RBs
   0.63WAR0.47 to 0.79
3. JJ Arcega-WhitesideWR85 plays · +0.256 pts/play · 2nd of 434 WRs
   0.41WAR0.28 to 0.52
4. Cameron ScarlettRB68 plays · +0.128 pts/play · 237th of 351 RBs
   0.16WAR0.12 to 0.21
5. Trenton IrwinWR65 plays · +0.130 pts/play · 81st of 434 WRs
   0.16WAR0.08 to 0.24
6. Jake BaileyP55 plays · +0.141 pts/play · 8th of 91 punters
   0.15WAR0.08 to 0.21
7. Kaden SmithTE49 plays · +0.151 pts/play · 7th of 204 TEs
   0.14WAR0.05 to 0.24
8. Trevor SpeightsRB53 plays · +0.134 pts/play · 254th of 351 RBs
   0.13WAR0.10 to 0.17

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 Stanford'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, 2018

• Roster talent

  247Sports' talent composite: the recruiting ratings of every player on the roster, transfers included.
  78778717th of 130Roster talent: 787, 17th of 130 in FBS
• Recruiting

  Average 247Sports class score over this season's class and the three before it.
  22922922nd of 130Recruiting: 229, 22nd of 130 in FBS
• Returning production

  Share of last season's production (expected points added) by players still on the roster.
  87%87%11th of 129Returning production: 87%, 11th of 129 in FBS
• SP+ rating

  The result: Bill Connelly's opponent-adjusted rating, points per game better than an average FBS team.
  +13.0+13.026th of 130SP+ rating: +13.0, 26th 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, 2018

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 Stanford's own. A call that differs gives up the gap to the best option; gaps under one point are close calls, and the 9 clear-cut ones account for 0.20 of the 0.26 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 CALQ4 3:43 4th & 1 at CAL 3, up 7Kicked a field goal; the numbers favored going for it−3.3 pts
2. vs WSUQ3 6:16 4th & 1 at WSU 22, up 4Kicked a field goal; the numbers favored going for it−2.8 pts
3. at WASHQ4 9:00 4th & 3 at WASH 3, down 10Kicked a field goal; the numbers favored going for it−2.8 pts
4. at PITTQ4 3:49 4th & 8 at PITT 41, up 1Punted; the numbers favored going for it−2.8 pts
5. at UCLAQ4 3:42 4th & 1 at STAN 45, up 7Punted; the numbers favored going for it−2.2 pts
6. at OREQ3 7:14 4th & 1 at STAN 15, down 17Punted; the numbers favored going for it−2.1 pts
7. at ASUQ2 3:40 4th & 3 at ASU 3, down 3Kicked a field goal; the numbers favored going for it−1.2 pts
8. at ASUQ2 11:31 4th & 1 at STAN 44, down 3Punted; the numbers favored going for it−1.2 pts

Record vs. expectation, 2018

39th of 130 FBS teams at beating the model's pregame expectations

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

1. W

   SDSU
2. W

   USC
3. W

   UCD
4. W

   ORE
5. L

   ND
6. L

   UTAH
7. W

   ASU
8. L

   WSU
9. L

   WASH
10. W

    ORST
11. W

    UCLA
12. W

    CAL
13. W

    PITT

What moved it, 2018

Every game starts at Stanford'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+1.16
• Possession & field position−0.03
• Clock−0.07
• Timeouts+0.17
• Less the pregame edge, counted before kickoff−0.63

Game by game

• SDSUW 31-10 vs SDSU85% → 100%Before kickoff +35 · On the field +14: Scoreboard +19
• USCW 17-3 vs USC65% → 99%Before kickoff +15 · On the field +35: Scoreboard +34, Timeouts +1
• UCDW 30-10 vs UCD97% → 100%Before kickoff +47 · On the field +2: Scoreboard +4
• OREW 38-31 at ORE55% → 90%Before kickoff +5 · On the field +35: Scoreboard +44, Timeouts +15
• NDL 17-38 at ND36% → 0%Before kickoff −14 · On the field −36: Scoreboard −37, Timeouts +1
• UTAHL 21-40 vs UTAH62% → 1%Before kickoff +12 · On the field −62: Scoreboard −59, Clock −3
• ASUW 20-13 at ASU55% → 83%Before kickoff +5 · On the field +28: Scoreboard +42, Possession & field position −4
• WSUL 38-41 vs WSU60% → 15%Before kickoff +10 · On the field −45: Scoreboard −40, Possession & field position −3
• WASHL 23-27 at WASH23% → 13%Before kickoff −27 · On the field −9: Scoreboard −20, Timeouts −1
• ORSTW 48-17 vs ORST94% → 100%Before kickoff +44 · On the field +6: Scoreboard +9
• UCLAW 49-42 at UCLA71% → 95%Before kickoff +21 · On the field +25: Scoreboard +38, Clock +2
• CALW 23-13 at CAL57% → 96%Before kickoff +7 · On the field +39: Scoreboard +45, Clock +3
• PITTW 14-13 at PITT68% → 95%Before kickoff +18 · On the field +27: Scoreboard +38, Clock +2

Points of win probability, from Stanford's side.

Closest call

STAN 46-43 COLO (2023) - Stanford dropped to a 0.9% win probability before pulling it out.