Stanford

Profile vs. FBS, 2016

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+0.1359th
  Defense+0.0861st
• 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.
  Offense44.0%54th
  Defense41.7%67th
• Explosiveness

  Average EPA on the successful snaps: how big the good plays were.
  Offense+1.1425th
  Defense+1.2525th
• Rushing EPA

  EPA per designed run.
  Offense+0.1783rd
  Defense+0.1039th
• Passing EPA

  EPA per pass play.
  Offense+0.0831st
  Defense+0.1069th
• Standard downs

  Success rate on downs where either a run or a pass makes sense, like 1st and 10 or 3rd and 2.
  Offense47.9%46th
  Defense45.9%68th
• Passing downs

  Success rate on downs that all but force a pass, like 2nd and 8 or longer, or 3rd and 5 or longer.
  Offense35.8%66th
  Defense34.4%43rd
• Line yards

  Rushing yards per carry credited to the offensive line, weighting the first few yards most.
  Offense3.2080th
  Defense3.1321st
• Stuff rate

  Share of runs stopped at or behind the line of scrimmage.
  Offense14.6%93rd
  Defense16.4%17th
• Havoc

  Share of snaps with a tackle for loss, sack, forced fumble, interception or pass breakup.
  Offense15.7%56th
  Defense16.6%53rd

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, 2016

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
   KSU
2. W
   USC
3. W
   UCLA
4. L
   WASH
5. L
   WSU
6. W
   ND
7. L
   COLO
8. W
   ARIZ
9. W
   ORST
10. W
    ORE
11. W
    CAL
12. W
    RICE
13. W
    UNC

Bars above the line: better than expected for Stanford.

Players, 2016

1. Christian McCaffreyRB244 plays · +0.200 pts/play · 7th of 347 RBs
   0.91WAR0.68 to 1.15
2. Keller ChrystQB135 plays · +0.169 pts/play · 135th of 218 QBs
   0.43WAR0.20 to 0.66
3. Bryce LoveRB94 plays · +0.176 pts/play · 118th of 347 RBs
   0.31WAR0.22 to 0.40
4. Ryan BurnsQB193 plays · +0.073 pts/play · 154th of 218 QBs
   0.26WAR−0.06 to 0.61
5. Conrad UkropinaPK25 plays · +0.521 pts/play · 6th of 116 kickers
   0.24WAR0.16 to 0.32
6. Jake BaileyP43 plays · +0.249 pts/play · 21st of 82 punters
   0.20WAR0.13 to 0.27
7. Trenton IrwinWR51 plays · +0.128 pts/play · 171st of 417 WRs
   0.12WAR0.07 to 0.18
8. Michael RectorWR43 plays · +0.138 pts/play · 194th of 417 WRs
   0.11WAR0.05 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, 2016

• Roster talent

  247Sports' talent composite: the recruiting ratings of every player on the roster, transfers included.
  77877817th of 128Roster talent: 778, 17th of 128 in FBS
• Recruiting

  Average 247Sports class score over this season's class and the three before it.
  23923917th of 128Recruiting: 239, 17th of 128 in FBS
• Returning production

  Share of last season's production (expected points added) by players still on the roster.
  40%40%106th of 128Returning production: 40%, 106th of 128 in FBS
• SP+ rating

  The result: Bill Connelly's opponent-adjusted rating, points per game better than an average FBS team.
  +16.4+16.413th of 128SP+ rating: +16.4, 13th 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, 2016

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 2 clear-cut ones account for 0.11 of the 0.14 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 UCLAQ4 4:40 4th & 1 at STAN 39, down 4Punted; the numbers favored going for it−7.4 pts
2. at UNCQ4 14:52 4th & 1 at STAN 35, down 1Punted; the numbers favored going for it−3.8 pts
3. vs ORSTQ3 11:29 4th & 2 at ORST 13, up 6Kicked a field goal; the numbers favored going for it (close call)−0.8 pts
4. vs ORSTQ2 1:33 4th & 3 at ORST 3, up 3Kicked a field goal; the numbers favored going for it (close call)−0.7 pts
5. vs USCQ4 4:20 4th & 1 at STAN 43, up 17Punted; the numbers favored going for it (close call)−0.4 pts
6. at NDQ4 4:00 4th & 7 at ND 47, up 7Punted; the numbers favored going for it (close call)−0.3 pts
7. vs KSUQ4 4:19 4th & 8 at KSU 37, up 11Punted; the numbers favored going for it (close call)−0.3 pts
8. vs KSUQ3 5:58 4th & 3 at STAN 36, up 11Punted; the numbers favored going for it (close call)−0.1 pts

Record vs. expectation, 2016

18th of 128 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

   KSU
2. W

   USC
3. W

   UCLA
4. L

   WASH
5. L

   WSU
6. W

   ND
7. L

   COLO
8. W

   ARIZ
9. W

   ORST
10. W

    ORE
11. W

    CAL
12. W

    RICE
13. W

    UNC

What moved it, 2016

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+1.98
• Possession & field position+0.01
• Clock+0.11
• Timeouts+0.04
• Less the pregame edge, counted before kickoff−0.60

Game by game

• KSUW 26-13 vs KSU86% → 99%Before kickoff +36 · On the field +12: Scoreboard +18, Timeouts +1
• USCW 27-10 vs USC65% → 99%Before kickoff +15 · On the field +33: Scoreboard +34, Timeouts +2
• UCLAW 22-13 at UCLA53% → 95%Before kickoff +3 · On the field +43: Scoreboard +49, Clock +2
• WASHL 6-44 at WASH37% → 0%Before kickoff −13 · On the field −37: Scoreboard −37
• WSUL 16-42 vs WSU78% → 0%Before kickoff +28 · On the field −78: Scoreboard −71, Possession & field position +1
• NDW 17-10 at ND37% → 90%Before kickoff −13 · On the field +53: Scoreboard +47, Clock +3
• COLOL 5-10 vs COLO59% → 12%Before kickoff +9 · On the field −47: Scoreboard −44, Clock −2
• ARIZW 34-10 at ARIZ43% → 100%Before kickoff −7 · On the field +56: Scoreboard +55, Clock +3
• ORSTW 26-15 vs ORST86% → 98%Before kickoff +36 · On the field +12: Scoreboard +19, Timeouts +1
• OREW 52-27 at ORE52% → 100%Before kickoff +2 · On the field +48: Scoreboard +49, Clock +1
• CALW 45-31 at CAL79% → 98%Before kickoff +29 · On the field +19: Scoreboard +30, Clock +2
• RICEW 41-17 vs RICE98% → 100%Before kickoff +48 · On the field +2: Scoreboard +5
• UNCW 25-23 at UNC57% → 95%Before kickoff +7 · On the field +37: Scoreboard +44, Clock +3

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.