Tulsa

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+0.0833rd
  Defense+0.1051st
• 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.
  Offense39.1%16th
  Defense39.3%78th
• Explosiveness

  Average EPA on the successful snaps: how big the good plays were.
  Offense+1.2982nd
  Defense+1.2531st
• Rushing EPA

  EPA per designed run.
  Offense−0.0219th
  Defense0.0066th
• Passing EPA

  EPA per pass play.
  Offense+0.1948th
  Defense+0.2526th
• Standard downs

  Success rate on downs where either a run or a pass makes sense, like 1st and 10 or 3rd and 2.
  Offense42.3%8th
  Defense43.6%84th
• Passing downs

  Success rate on downs that all but force a pass, like 2nd and 8 or longer, or 3rd and 5 or longer.
  Offense33.8%46th
  Defense30.8%71st
• Line yards

  Rushing yards per carry credited to the offensive line, weighting the first few yards most.
  Offense2.519th
  Defense2.8060th
• Stuff rate

  Share of runs stopped at or behind the line of scrimmage.
  Offense22.1%22nd
  Defense20.1%57th
• Havoc

  Share of snaps with a tackle for loss, sack, forced fumble, interception or pass breakup.
  Offense25.0%6th
  Defense18.0%23rd

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 Tulsa 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. L
   MSU
2. W
   SJSU
3. L
   OKST
4. W
   WYO
5. L
   SMU
6. L
   NAVY
7. L
   CIN
8. L
   MEM
9. L
   TULN
10. W
    UCF
11. L
    HOU
12. W
    ECU

Bars above the line: better than expected for Tulsa.

Players, 2019

1. Zach SmithQB438 plays · +0.154 pts/play · 59th of 228 QBs
   1.26WAR0.57 to 2.04
2. Shamari BrooksRB234 plays · +0.136 pts/play · 35th of 349 RBs
   0.60WAR0.39 to 0.78
3. Corey Taylor IIRB114 plays · +0.195 pts/play · 91st of 349 RBs
   0.42WAR0.30 to 0.53
4. Keylon StokesWR100 plays · +0.185 pts/play · 12th of 439 WRs
   0.35WAR0.22 to 0.47
5. Sam CrawfordWR85 plays · +0.131 pts/play · 60th of 439 WRs
   0.21WAR0.09 to 0.32
6. Keenen JohnsonWR65 plays · +0.123 pts/play · 121st of 439 WRs
   0.15WAR0.07 to 0.23
7. Jacob RaineyPK22 plays · +0.332 pts/play · 40th of 114 kickers
   0.14WAR0.07 to 0.20
8. Josh JohnsonWR65 plays · +0.065 pts/play · 269th of 439 WRs
   0.08WAR0.00 to 0.15

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 Tulsa'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.
  42542596th of 130Roster talent: 425, 96th of 130 in FBS
• Recruiting

  Average 247Sports class score over this season's class and the three before it.
  13413494th of 130Recruiting: 134, 94th of 130 in FBS
• Returning production

  Share of last season's production (expected points added) by players still on the roster.
  73%73%37th of 130Returning production: 73%, 37th of 130 in FBS
• SP+ rating

  The result: Bill Connelly's opponent-adjusted rating, points per game better than an average FBS team.
  −2.5−2.573rd of 130SP+ rating: −2.5, 73rd 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 Tulsa'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 0.03 of the 0.09 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 WYOQ3 11:22 4th & 13 at WYO 32, up 7Went for it; the numbers favored a field goal−1.7 pts
2. vs OKSTQ4 9:58 4th & 5 at OKST 6, down 12Went for it; the numbers favored a field goal−1.4 pts
3. at SMUQ3 7:31 4th & 3 at SMU 27, up 21Kicked a field goal; the numbers favored going for it (close call)−0.9 pts
4. vs UCFQ3 10:19 4th & 1 at TLSA 32, down 11Punted; the numbers favored going for it (close call)−0.9 pts
5. at CINQ4 12:12 4th & 4 at CIN 4, down 7Kicked a field goal; the numbers favored going for it (close call)−0.9 pts
6. vs HOUQ4 8:50 4th & 10 at HOU 44, down 10Went for it; the numbers favored punting (close call)−0.5 pts
7. at TULNQ2 4:30 4th & 3 at TULN 15, tiedKicked a field goal; the numbers favored going for it (close call)−0.5 pts
8. vs MEMQ1 8:03 4th & 4 at MEM 42, down 14Punted; the numbers favored going for it (close call)−0.4 pts

Record vs. expectation, 2019

79th of 130 FBS teams at beating the model's pregame expectations

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

1. L

   MSU
2. W

   SJSU
3. L

   OKST
4. W

   WYO
5. L

   SMU
6. L

   NAVY
7. L

   CIN
8. L

   MEM
9. L

   TULN
10. W

    UCF
11. L

    HOU
12. W

    ECU

What moved it, 2019

Every game starts at Tulsa'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−0.97
• Possession & field position−0.01
• Clock−0.11
• Timeouts+0.19
• Less the pregame edge, counted before kickoff+0.96

Game by game

• MSUL 7-28 at MSU6% → 0%Before kickoff −44 · On the field −6: Scoreboard −10
• SJSUW 34-16 at SJSU67% → 99%Before kickoff +17 · On the field +32: Scoreboard +41, Clock +2
• OKSTL 21-40 vs OKST18% → 1%Before kickoff −32 · On the field −18: Scoreboard −27, Clock −1
• WYOW 24-21 vs WYO67% → 92%Before kickoff +17 · On the field +25: Scoreboard +28, Timeouts +2
• SMUL 37-43 at SMU20% → 14%Before kickoff −30 · On the field −6: Scoreboard −30, Timeouts +17
• NAVYL 17-45 vs NAVY56% → 0%Before kickoff +6 · On the field −56: Scoreboard −56, Clock −2
• CINL 13-24 at CIN14% → 2%Before kickoff −36 · On the field −12: Scoreboard −19, Timeouts −1
• MEML 41-42 vs MEM24% → 37%Before kickoff −26 · On the field +13: Scoreboard −22, Clock −3
• TULNL 26-38 at TULN25% → 2%Before kickoff −25 · On the field −23: Scoreboard −27, Timeouts −1
• UCFW 34-31 vs UCF12% → 95%Before kickoff −38 · On the field +82: Scoreboard +39, Timeouts +2
• HOUL 14-24 vs HOU64% → 4%Before kickoff +14 · On the field −60: Scoreboard −55, Clock −3
• ECUW 49-24 at ECU66% → 100%Before kickoff +16 · On the field +34: Scoreboard +39, Clock +1

Points of win probability, from Tulsa's side.

Closest call

UTSA 45-46 TLSA (2024) - Tulsa dropped to a 1.6% win probability before pulling it out.