San Diego State

Profile vs. FBS, 2023

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+0.0734th
  Defense+0.1819th
• 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.
  Offense41.9%48th
  Defense46.8%5th
• Explosiveness

  Average EPA on the successful snaps: how big the good plays were.
  Offense+1.082nd
  Defense+1.1977th
• Rushing EPA

  EPA per designed run.
  Offense+0.0329th
  Defense+0.233rd
• Passing EPA

  EPA per pass play.
  Offense+0.1938th
  Defense+0.2443rd
• Standard downs

  Success rate on downs where either a run or a pass makes sense, like 1st and 10 or 3rd and 2.
  Offense48.8%62nd
  Defense52.6%5th
• Passing downs

  Success rate on downs that all but force a pass, like 2nd and 8 or longer, or 3rd and 5 or longer.
  Offense27.2%26th
  Defense32.1%27th
• Line yards

  Rushing yards per carry credited to the offensive line, weighting the first few yards most.
  Offense3.0843rd
  Defense3.4310th
• Stuff rate

  Share of runs stopped at or behind the line of scrimmage.
  Offense15.3%64th
  Defense12.4%9th
• Havoc

  Share of snaps with a tackle for loss, sack, forced fumble, interception or pass breakup.
  Offense14.6%75th
  Defense15.6%34th

Percentiles among the 133 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, 2023

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
   OHIO
2. W
   IDST
3. L
   UCLA
4. L
   ORST
5. L
   BOIS
6. L
   AF
7. W
   HAW
8. L
   NEV
9. L
   USU
10. L
    CSU
11. L
    SJSU
12. W
    FRES

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

Players, 2023

1. Jalen MaydenQB368 plays · +0.248 pts/play · 39th of 250 QBs
   1.71WAR1.01 to 2.53
2. Jaylon ArmsteadRB85 plays · +0.140 pts/play · 118th of 383 RBs
   0.22WAR0.09 to 0.31
3. Jack BrowningPK67 plays · +0.174 pts/play · 1st of 113 kickers
   0.22WAR0.14 to 0.30
4. Kenan Christon IIRB103 plays · +0.089 pts/play · 162nd of 383 RBs
   0.17WAR0.08 to 0.28
5. Mekhi ShawWR33 plays · +0.221 pts/play · 127th of 457 WRs
   0.14WAR0.06 to 0.21
6. Mark RedmanTE40 plays · +0.165 pts/play · 40th of 212 TEs
   0.12WAR0.04 to 0.21
7. Cam DavisRB57 plays · +0.076 pts/play · 262nd of 383 RBs
   0.08WAR0.04 to 0.11
8. Brionne PennyWR24 plays · +0.059 pts/play · 342nd of 457 WRs
   0.03WAR−0.02 to 0.07

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, 2023

• Roster talent

  247Sports' talent composite: the recruiting ratings of every player on the roster, transfers included.
  440440103rd of 130Roster talent: 440, 103rd of 130 in FBS
• Recruiting

  Average 247Sports class score over this season's class and the three before it.
  16116174th of 130Recruiting: 161, 74th of 130 in FBS
• Returning production

  Share of last season's production (expected points added) by players still on the roster.
  78%78%18th of 128Returning production: 78%, 18th of 128 in FBS
• SP+ rating

  The result: Bill Connelly's opponent-adjusted rating, points per game better than an average FBS team.
  −10.3−10.3102nd of 130SP+ rating: −10.3, 102nd 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, 2023

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 1 clear-cut ones account for 0.02 of the 0.08 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 OHIOQ4 13:32 4th & 3 at OHIO 4, up 4Kicked a field goal; the numbers favored going for it−1.9 pts
2. at CSUQ3 10:10 4th & 3 at CSU 5, down 15Kicked a field goal; the numbers favored going for it (close call)−0.9 pts
3. at SJSUQ4 8:54 4th & 4 at SJSU 4, down 7Kicked a field goal; the numbers favored going for it (close call)−0.8 pts
4. vs NEVQ2 5:29 4th & 6 at NEV 45, down 3Punted; the numbers favored going for it (close call)−0.6 pts
5. vs NEVQ2 5:29 4th & 6 at NEV 45, down 3Punted; the numbers favored going for it (close call)−0.6 pts
6. vs USUQ2 3:59 4th & 9 at USU 13, up 4Went for it; the numbers favored a field goal (close call)−0.6 pts
7. at HAWQ2 6:06 4th & 1 at SDSU 21, up 10Punted; the numbers favored going for it (close call)−0.5 pts
8. vs NEVQ1 2:47 4th & 3 at NEV 40, down 3Punted; the numbers favored going for it (close call)−0.4 pts

Record vs. expectation, 2023

108th of 133 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

   OHIO
2. W

   IDST
3. L

   UCLA
4. L

   ORST
5. L

   BOIS
6. L

   AF
7. W

   HAW
8. L

   NEV
9. L

   USU
10. L

    CSU
11. L

    SJSU
12. W

    FRES

What moved it, 2023

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−1.11
• Possession & field position−0.03
• Clock−0.06
• Timeouts+0.01
• Other+0.01
• Less the pregame edge, counted before kickoff+0.24

Game by game

• OHIOW 20-13 vs OHIO59% → 87%Before kickoff +9 · On the field +28: Scoreboard +27, Possession & field position −3
• IDSTW 36-28 vs IDST98% → 97%Before kickoff +48 · On the field 0: Scoreboard +7, Timeouts +1
• UCLAL 10-35 vs UCLA22% → 0%Before kickoff −28 · On the field −21: Scoreboard −27, Clock −1
• ORSTL 9-26 at ORST6% → 1%Before kickoff −44 · On the field −5: Scoreboard −11
• BOISL 31-34 vs BOIS33% → 5%Before kickoff −17 · On the field −28: Scoreboard −40, Clock −3
• AFL 10-49 at AF28% → 0%Before kickoff −22 · On the field −28: Scoreboard −31, Timeouts +1
• HAWW 41-34 at HAW68% → 96%Before kickoff +18 · On the field +28: Scoreboard +39, Clock +2
• NEVL 0-6 vs NEV83% → 4%Before kickoff +33 · On the field −78: Scoreboard −57, Clock −3
• USUL 24-32 vs USU49% → 47%Before kickoff −1 · On the field −1: Scoreboard −13, Clock −3
• CSUL 19-22 at CSU36% → 5%Before kickoff −14 · On the field −31: Scoreboard −30, Timeouts −3
• SJSUL 13-24 at SJSU16% → 2%Before kickoff −34 · On the field −14: Scoreboard −20, Timeouts −1
• FRESW 33-18 vs FRES42% → 99%Before kickoff −8 · On the field +57: Scoreboard +45, Timeouts +2

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.