San Diego State

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+0.0419th
  Defense−0.0798th
• 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.
  Offense40.9%28th
  Defense37.1%93rd
• Explosiveness

  Average EPA on the successful snaps: how big the good plays were.
  Offense+1.042nd
  Defense+1.0893rd
• Rushing EPA

  EPA per designed run.
  Offense−0.086th
  Defense−0.1899th
• Passing EPA

  EPA per pass play.
  Offense+0.2153rd
  Defense+0.0191st
• Standard downs

  Success rate on downs where either a run or a pass makes sense, like 1st and 10 or 3rd and 2.
  Offense44.9%21st
  Defense41.9%89th
• Passing downs

  Success rate on downs that all but force a pass, like 2nd and 8 or longer, or 3rd and 5 or longer.
  Offense33.7%45th
  Defense28.6%85th
• Line yards

  Rushing yards per carry credited to the offensive line, weighting the first few yards most.
  Offense2.455th
  Defense2.2999th
• Stuff rate

  Share of runs stopped at or behind the line of scrimmage.
  Offense24.0%9th
  Defense27.3%98th
• Havoc

  Share of snaps with a tackle for loss, sack, forced fumble, interception or pass breakup.
  Offense20.9%34th
  Defense26.4%95th

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 San Diego State 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
   WEB
2. W
   UCLA
3. W
   NMSU
4. L
   USU
5. W
   CSU
6. W
   WYO
7. W
   SJSU
8. W
   UNLV
9. L
   NEV
10. W
    FRES
11. L
    HAW
12. W
    BYU
13. W
    CMU

Bars above the line: better than expected for San Diego State.

Players, 2019

1. Ryan AgnewQB386 plays · +0.254 pts/play · 24th of 228 QBs
   1.83WAR1.23 to 2.50
2. Juwan WashingtonRB133 plays · +0.133 pts/play · 133rd of 349 RBs
   0.33WAR0.22 to 0.46
3. Chase JasminRB93 plays · +0.146 pts/play · 165th of 349 RBs
   0.25WAR0.18 to 0.31
4. Chance BellRB87 plays · +0.151 pts/play · 169th of 349 RBs
   0.25WAR0.18 to 0.30
5. Matt AraizaPK24 plays · +0.485 pts/play · 7th of 114 kickers
   0.22WAR0.15 to 0.28
6. Jesse MatthewsWR60 plays · +0.184 pts/play · 64th of 439 WRs
   0.21WAR0.14 to 0.27
7. Jordan ByrdRB76 plays · +0.127 pts/play · 209th of 349 RBs
   0.18WAR0.12 to 0.24
8. Kobe SmithWR84 plays · +0.070 pts/play · 190th of 439 WRs
   0.11WAR0.01 to 0.19

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 San Diego State'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.
  50450477th of 130Roster talent: 504, 77th of 130 in FBS
• Recruiting

  Average 247Sports class score over this season's class and the three before it.
  15015079th of 130Recruiting: 150, 79th of 130 in FBS
• Returning production

  Share of last season's production (expected points added) by players still on the roster.
  59%59%68th of 130Returning production: 59%, 68th of 130 in FBS
• SP+ rating

  The result: Bill Connelly's opponent-adjusted rating, points per game better than an average FBS team.
  +2.0+2.055th of 130SP+ rating: +2.0, 55th 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 San Diego State's own. A call that differs gives up the gap to the best option; gaps under one point are close calls, and the 6 clear-cut ones account for 0.15 of the 0.25 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 WYOQ4 14:54 4th & 1 at SDSU 30, up 5Punted; the numbers favored going for it−5.0 pts
2. vs NEVQ4 10:26 4th & 2 at SDSU 45, down 7Punted; the numbers favored going for it−3.0 pts
3. at UNLVQ4 5:57 4th & 1 at SDSU 34, up 3Punted; the numbers favored going for it−2.8 pts
4. at UCLAQ2 3:47 4th & 3 at UCLA 39, up 3Punted; the numbers favored going for it−1.8 pts
5. vs USUQ3 10:47 4th & 14 at USU 14, down 20Went for it; the numbers favored a field goal−1.5 pts
6. vs USUQ4 10:03 4th & 16 at USU 25, down 20Went for it; the numbers favored a field goal−1.1 pts
7. vs CMUQ3 13:21 4th & 5 at CMU 41, up 17Punted; the numbers favored going for it (close call)−0.9 pts
8. at HAWQ2 9:01 4th & 5 at HAW 39, down 7Punted; the numbers favored going for it (close call)−0.8 pts

Record vs. expectation, 2019

16th of 130 FBS teams at beating the model's pregame expectations

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

1. W

   WEB
2. W

   UCLA
3. W

   NMSU
4. L

   USU
5. W

   CSU
6. W

   WYO
7. W

   SJSU
8. W

   UNLV
9. L

   NEV
10. W

    FRES
11. L

    HAW
12. W

    BYU
13. W

    CMU

What moved it, 2019

Every game starts at San Diego State'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+2.22
• Possession & field position+0.01
• Clock+0.01
• Timeouts+0.07
• Less the pregame edge, counted before kickoff−0.62

Game by game

• WEBW 6-0 vs WEB85% → 96%Before kickoff +35 · On the field +11: Scoreboard +20, Timeouts +1
• UCLAW 23-14 at UCLA31% → 94%Before kickoff −19 · On the field +63: Scoreboard +54, Clock +2
• NMSUW 31-10 at NMSU87% → 99%Before kickoff +37 · On the field +13: Scoreboard +21, Clock +1
• USUL 17-23 vs USU39% → 5%Before kickoff −11 · On the field −34: Scoreboard −42, Clock −2
• CSUW 24-10 at CSU69% → 99%Before kickoff +19 · On the field +30: Scoreboard +37, Clock +2
• WYOW 26-22 vs WYO62% → 93%Before kickoff +12 · On the field +31: Scoreboard +32, Possession & field position −1
• SJSUW 27-17 at SJSU71% → 95%Before kickoff +21 · On the field +24: Scoreboard +37, Clock +1
• UNLVW 20-17 at UNLV77% → 93%Before kickoff +27 · On the field +16: Scoreboard +34, Clock +2
• NEVL 13-17 vs NEV87% → 9%Before kickoff +37 · On the field −78: Scoreboard −50, Clock −3
• FRESW 17-7 vs FRES59% → 97%Before kickoff +9 · On the field +39: Scoreboard +36, Timeouts +2
• HAWL 11-14 at HAW47% → 6%Before kickoff −3 · On the field −41: Scoreboard −37, Timeouts +1
• BYUW 13-3 vs BYU38% → 97%Before kickoff −12 · On the field +58: Scoreboard +43, Timeouts +2
• CMUW 48-11 vs CMU63% → 100%Before kickoff +13 · On the field +37: Scoreboard +37, Timeouts −1

Points of win probability, from San Diego State's side.

Closest call

TOL 14-17 SDSU (2022) - San Diego State dropped to a 13.4% win probability before pulling it out.