Texas Tech

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+0.1453rd
  Defense+0.1919th
• 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.
  Offense46.1%74th
  Defense43.1%47th
• Explosiveness

  Average EPA on the successful snaps: how big the good plays were.
  Offense+1.1115th
  Defense+1.362nd
• Rushing EPA

  EPA per designed run.
  Offense+0.1270th
  Defense+0.1040th
• Passing EPA

  EPA per pass play.
  Offense+0.1741st
  Defense+0.3211th
• Standard downs

  Success rate on downs where either a run or a pass makes sense, like 1st and 10 or 3rd and 2.
  Offense50.2%71st
  Defense45.3%78th
• Passing downs

  Success rate on downs that all but force a pass, like 2nd and 8 or longer, or 3rd and 5 or longer.
  Offense36.6%74th
  Defense38.5%9th
• Line yards

  Rushing yards per carry credited to the offensive line, weighting the first few yards most.
  Offense3.1881st
  Defense2.8656th
• Stuff rate

  Share of runs stopped at or behind the line of scrimmage.
  Offense17.3%71st
  Defense19.0%44th
• Havoc

  Share of snaps with a tackle for loss, sack, forced fumble, interception or pass breakup.
  Offense18.8%57th
  Defense19.5%43rd

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 Texas Tech 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
   MTST
2. W
   UTEP
3. L
   ARIZ
4. L
   OU
5. W
   OKST
6. L
   BAY
7. L
   ISU
8. L
   KU
9. W
   WVU
10. L
    TCU
11. L
    KSU
12. L
    TEX

Bars above the line: better than expected for Texas Tech.

Players, 2019

1. Jett DuffeyQB371 plays · +0.205 pts/play · 45th of 228 QBs
   1.43WAR0.83 to 2.04
2. SaRodorick ThompsonRB151 plays · +0.187 pts/play · 51st of 349 RBs
   0.53WAR0.41 to 0.63
3. Alan BowmanQB45 plays · +0.217 pts/play · 188th of 228 QBs
   0.18WAR0.11 to 0.27
4. Erik EzukanmaWR47 plays · +0.152 pts/play · 143rd of 439 WRs
   0.13WAR0.08 to 0.19
5. Armand ShyneRB53 plays · +0.134 pts/play · 239th of 349 RBs
   0.13WAR0.10 to 0.16
6. Trey WolffPK16 plays · +0.443 pts/play · 42nd of 114 kickers
   0.13WAR0.09 to 0.17
7. R.J. TurnerWR52 plays · +0.136 pts/play · 149th of 439 WRs
   0.13WAR0.06 to 0.21
8. Ta'Zhawn HenryRB49 plays · +0.135 pts/play · 245th of 349 RBs
   0.12WAR0.09 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 Texas Tech'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.
  57757760th of 130Roster talent: 577, 60th of 130 in FBS
• Recruiting

  Average 247Sports class score over this season's class and the three before it.
  17917958th of 130Recruiting: 179, 58th of 130 in FBS
• Returning production

  Share of last season's production (expected points added) by players still on the roster.
  58%58%70th of 130Returning production: 58%, 70th of 130 in FBS
• SP+ rating

  The result: Bill Connelly's opponent-adjusted rating, points per game better than an average FBS team.
  +2.7+2.753rd of 130SP+ rating: +2.7, 53rd 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 Texas Tech'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 0.09 of the 0.16 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 KUQ4 5:12 4th & 1 at TTU 34, tiedPunted; the numbers favored going for it−6.3 pts
2. vs OKSTQ3 2:48 4th & 6 at OKST 38, up 13Punted; the numbers favored going for it−1.5 pts
3. at KUQ3 5:39 4th & 2 at KU 9, up 10Kicked a field goal; the numbers favored going for it−1.4 pts
4. vs OKSTQ2 11:37 4th & 3 at OKST 26, up 13Kicked a field goal; the numbers favored going for it (close call)−0.9 pts
5. vs KSUQ4 8:54 4th & 6 at TTU 42, down 10Went for it; the numbers favored punting (close call)−0.7 pts
6. at WVUQ2 6:38 4th & 6 at WVU 36, up 25Punted; the numbers favored going for it (close call)−0.6 pts
7. at KUQ2 14:56 4th & 2 at KU 6, up 7Kicked a field goal; the numbers favored going for it (close call)−0.6 pts
8. vs ISUQ3 8:45 4th & 4 at ISU 4, down 13Kicked a field goal; the numbers favored going for it (close call)−0.5 pts

Record vs. expectation, 2019

116th of 130 FBS teams at beating the model's pregame expectations

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

1. W

   MTST
2. W

   UTEP
3. L

   ARIZ
4. L

   OU
5. W

   OKST
6. L

   BAY
7. L

   ISU
8. L

   KU
9. W

   WVU
10. L

    TCU
11. L

    KSU
12. L

    TEX

What moved it, 2019

Every game starts at Texas Tech'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−1.60
• Possession & field position−0.05
• Clock−0.12
• Timeouts+0.13
• Less the pregame edge, counted before kickoff+0.39

Game by game

• MTSTW 45-10 vs MTST96% → 100%Before kickoff +46 · On the field +4: Scoreboard +6
• UTEPW 38-3 vs UTEP98% → 100%Before kickoff +48 · On the field +2: Scoreboard +3
• ARIZL 14-28 at ARIZ56% → 1%Before kickoff +6 · On the field −55: Scoreboard −44, Timeouts −2
• OUL 16-55 at OU4% → 0%Before kickoff −46 · On the field −4: Scoreboard −6
• OKSTW 45-35 vs OKST28% → 97%Before kickoff −22 · On the field +69: Scoreboard +45, Timeouts +2
• BAYL 30-33 at BAY22% → 14%Before kickoff −28 · On the field −8: Scoreboard −29, Timeouts +17
• ISUL 24-34 vs ISU29% → 4%Before kickoff −21 · On the field −24: Scoreboard −37, Clock −2
• KUL 34-37 at KU69% → 4%Before kickoff +19 · On the field −64: Scoreboard −39, Timeouts −3
• WVUW 38-17 at WVU60% → 100%Before kickoff +10 · On the field +40: Scoreboard +45, Clock +2
• TCUL 31-33 vs TCU37% → 19%Before kickoff −13 · On the field −18: Scoreboard −33, Clock −2
• KSUL 27-30 vs KSU54% → 12%Before kickoff +4 · On the field −42: Scoreboard −45, Clock −3
• TEXL 24-49 at TEX24% → 0%Before kickoff −26 · On the field −24: Scoreboard −25, Clock −1

Points of win probability, from Texas Tech's side.

Closest call

TTU 23-22 ISU (2024) - Texas Tech dropped to a 9.3% win probability before pulling it out.