Tulsa

Profile vs. FBS, 2017

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+0.1364th
  Defense+0.263rd
• 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.
  Offense42.4%43rd
  Defense47.0%16th
• Explosiveness

  Average EPA on the successful snaps: how big the good plays were.
  Offense+1.2466th
  Defense+1.357th
• Rushing EPA

  EPA per designed run.
  Offense+0.2394th
  Defense+0.1519th
• Passing EPA

  EPA per pass play.
  Offense−0.0313th
  Defense+0.432nd
• Standard downs

  Success rate on downs where either a run or a pass makes sense, like 1st and 10 or 3rd and 2.
  Offense47.0%46th
  Defense49.4%26th
• Passing downs

  Success rate on downs that all but force a pass, like 2nd and 8 or longer, or 3rd and 5 or longer.
  Offense32.6%40th
  Defense41.0%5th
• Line yards

  Rushing yards per carry credited to the offensive line, weighting the first few yards most.
  Offense2.9253rd
  Defense3.334th
• Stuff rate

  Share of runs stopped at or behind the line of scrimmage.
  Offense17.3%74th
  Defense16.2%19th
• Havoc

  Share of snaps with a tackle for loss, sack, forced fumble, interception or pass breakup.
  Offense15.4%59th
  Defense12.9%9th

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, 2017

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
   OKST
2. W
   UL
3. L
   TOL
4. L
   UNM
5. L
   NAVY
6. L
   TULN
7. W
   HOU
8. L
   CONN
9. L
   SMU
10. L
    MEM
11. L
    USF
12. L
    TEM

Bars above the line: better than expected for Tulsa.

Players, 2017

1. D'Angelo BrewerRB274 plays · +0.167 pts/play · 5th of 352 RBs
   0.86WAR0.62 to 1.07
2. Chad PresidentQB192 plays · +0.226 pts/play · 95th of 226 QBs
   0.81WAR0.41 to 1.12
3. Luke SkipperQB148 plays · +0.134 pts/play · 140th of 226 QBs
   0.37WAR0.08 to 0.65
4. Shamari BrooksRB81 plays · +0.165 pts/play · 131st of 352 RBs
   0.25WAR0.19 to 0.32
5. Justin HobbsWR80 plays · +0.127 pts/play · 46th of 426 WRs
   0.19WAR0.11 to 0.29
6. Redford JonesPK13 plays · +0.520 pts/play · 40th of 110 kickers
   0.13WAR0.07 to 0.18
7. Keenen JohnsonWR49 plays · +0.055 pts/play · 340th of 426 WRs
   0.05WAR0.00 to 0.09
8. Nigel CarterWR28 plays · +0.089 pts/play · 349th of 426 WRs
   0.05WAR0.02 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 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, 2017

• Roster talent

  247Sports' talent composite: the recruiting ratings of every player on the roster, transfers included.
  42342380th of 130Roster talent: 423, 80th of 130 in FBS
• Recruiting

  Average 247Sports class score over this season's class and the three before it.
  12712788th of 130Recruiting: 127, 88th of 130 in FBS
• Returning production

  Share of last season's production (expected points added) by players still on the roster.
  26%26%118th of 128Returning production: 26%, 118th of 128 in FBS
• SP+ rating

  The result: Bill Connelly's opponent-adjusted rating, points per game better than an average FBS team.
  −10.7−10.7103rd of 128SP+ rating: −10.7, 103rd of 128 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, 2017

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 3 clear-cut ones account for 0.06 of the 0.11 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 TOLQ3 9:10 4th & 1 at TOL 8, up 7Kicked a field goal; the numbers favored going for it−3.4 pts
2. at CONNQ4 13:43 4th & 18 at CONN 34, down 17Went for it; the numbers favored a field goal−1.6 pts
3. at SMUQ3 1:08 4th & 6 at SMU 34, up 3Kicked a field goal; the numbers favored going for it−1.3 pts
4. vs UNMQ2 1:50 4th & 4 at TLSA 40, down 3Punted; the numbers favored going for it (close call)−0.9 pts
5. vs TEMQ2 1:55 4th & 2 at TEM 30, down 14Kicked a field goal; the numbers favored going for it (close call)−0.7 pts
6. at USFQ3 5:32 4th & 2 at TLSA 28, down 10Punted; the numbers favored going for it (close call)−0.6 pts
7. at USFQ4 4:19 4th & 9 at USF 21, down 10Kicked a field goal; the numbers favored going for it (close call)−0.5 pts
8. vs NAVYQ2 15:00 4th & 3 at TLSA 43, up 14Punted; the numbers favored going for it (close call)−0.4 pts

Record vs. expectation, 2017

125th 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

   OKST
2. W

   UL
3. L

   TOL
4. L

   UNM
5. L

   NAVY
6. L

   TULN
7. W

   HOU
8. L

   CONN
9. L

   SMU
10. L

    MEM
11. L

    USF
12. L

    TEM

What moved it, 2017

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−1.92
• Possession & field position+0.05
• Clock−0.08
• Timeouts−0.02
• Less the pregame edge, counted before kickoff+0.45

Game by game

• OKSTL 24-59 at OKST8% → 0%Before kickoff −42 · On the field −8: Scoreboard −11
• ULW 66-42 vs UL83% → 100%Before kickoff +33 · On the field +16: Scoreboard +20, Clock +1
• TOLL 51-54 at TOL26% → 5%Before kickoff −24 · On the field −21: Scoreboard −27
• UNML 13-16 vs UNM70% → 7%Before kickoff +20 · On the field −63: Scoreboard −54, Clock −3
• NAVYL 21-31 vs NAVY30% → 2%Before kickoff −20 · On the field −28: Scoreboard −39, Clock −2
• TULNL 28-62 at TULN33% → 0%Before kickoff −17 · On the field −33: Scoreboard −34
• HOUW 45-17 vs HOU16% → 100%Before kickoff −34 · On the field +84: Scoreboard +66, Possession & field position −1
• CONNL 14-20 at CONN57% → 20%Before kickoff +7 · On the field −37: Scoreboard −23, Possession & field position +4
• SMUL 34-38 at SMU17% → 4%Before kickoff −33 · On the field −13: Scoreboard −21, Timeouts −1
• MEML 14-41 vs MEM12% → 0%Before kickoff −38 · On the field −12: Scoreboard −18, Clock −1
• USFL 20-27 at USF5% → 4%Before kickoff −45 · On the field −1: Scoreboard −12
• TEML 22-43 vs TEM36% → 0%Before kickoff −14 · On the field −36: Scoreboard −41, Clock −2

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.