Texas Tech

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+0.1571st
  Defense+0.1147th
• 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.
  Offense44.5%64th
  Defense45.5%28th
• Explosiveness

  Average EPA on the successful snaps: how big the good plays were.
  Offense+1.2152nd
  Defense+1.1471st
• Rushing EPA

  EPA per designed run.
  Offense+0.0236th
  Defense−0.0380th
• Passing EPA

  EPA per pass play.
  Offense+0.2677th
  Defense+0.2520th
• Standard downs

  Success rate on downs where either a run or a pass makes sense, like 1st and 10 or 3rd and 2.
  Offense49.3%70th
  Defense49.3%28th
• 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%49th
  Defense35.1%31st
• Line yards

  Rushing yards per carry credited to the offensive line, weighting the first few yards most.
  Offense2.8847th
  Defense2.9243rd
• Stuff rate

  Share of runs stopped at or behind the line of scrimmage.
  Offense19.7%51st
  Defense14.7%6th
• Havoc

  Share of snaps with a tackle for loss, sack, forced fumble, interception or pass breakup.
  Offense14.6%73rd
  Defense14.4%24th

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 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
   EWU
2. W
   ASU
3. W
   HOU
4. L
   OKST
5. W
   KU
6. L
   WVU
7. L
   ISU
8. L
   OU
9. L
   KSU
10. W
    BAY
11. L
    TCU
12. W
    TEX
13. L
    USF

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

Players, 2017

1. Nic ShimonekQB491 plays · +0.238 pts/play · 12th of 226 QBs
   2.18WAR1.23 to 3.10
2. Keke CouteeWR109 plays · +0.205 pts/play · 1st of 426 WRs
   0.42WAR0.28 to 0.57
3. Tre KingRB130 plays · +0.139 pts/play · 82nd of 352 RBs
   0.34WAR0.24 to 0.44
4. Justin StocktonRB150 plays · +0.110 pts/play · 93rd of 352 RBs
   0.31WAR0.18 to 0.41
5. Dylan CantrellWR97 plays · +0.125 pts/play · 24th of 426 WRs
   0.23WAR0.13 to 0.32
6. Dominic PanazzoloP55 plays · +0.104 pts/play · 16th of 95 punters
   0.11WAR0.01 to 0.21
7. T.J. VasherWR48 plays · +0.117 pts/play · 170th of 426 WRs
   0.11WAR0.06 to 0.15
8. Cameron BatsonWR63 plays · +0.088 pts/play · 176th of 426 WRs
   0.10WAR0.04 to 0.17

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, 2017

• Roster talent

  247Sports' talent composite: the recruiting ratings of every player on the roster, transfers included.
  57557550th of 130Roster talent: 575, 50th of 130 in FBS
• Recruiting

  Average 247Sports class score over this season's class and the three before it.
  19119142nd of 130Recruiting: 191, 42nd of 130 in FBS
• Returning production

  Share of last season's production (expected points added) by players still on the roster.
  34%34%108th of 128Returning production: 34%, 108th of 128 in FBS
• SP+ rating

  The result: Bill Connelly's opponent-adjusted rating, points per game better than an average FBS team.
  +4.8+4.852nd of 128SP+ rating: +4.8, 52nd 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 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 4 clear-cut ones account for 0.07 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. vs TCUQ3 5:55 4th & 2 at TCU 2, down 7Kicked a field goal; the numbers favored going for it−2.9 pts
2. at HOUQ4 11:07 4th & 4 at midfield, up 10Punted; the numbers favored going for it−1.6 pts
3. at TEXQ3 10:06 4th & 2 at TEX 16, down 10Kicked a field goal; the numbers favored going for it−1.1 pts
4. vs OKSTQ4 5:30 4th & 4 at TTU 37, tiedPunted; the numbers favored going for it−1.0 pts
5. vs KSUQ4 3:40 4th & 2 at KSU 13, up 8Kicked a field goal; the numbers favored going for it (close call)−0.8 pts
6. vs ASUQ3 3:06 4th & 2 at ASU 21, up 11Kicked a field goal; the numbers favored going for it (close call)−0.6 pts
7. vs ISUQ4 15:00 4th & 5 at ISU 9, down 11Went for it; the numbers favored a field goal (close call)−0.4 pts
8. at WVUQ2 14:55 4th & 3 at WVU 26, up 4Kicked a field goal; the numbers favored going for it (close call)−0.4 pts

Record vs. expectation, 2017

69th 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

   EWU
2. W

   ASU
3. W

   HOU
4. L

   OKST
5. W

   KU
6. L

   WVU
7. L

   ISU
8. L

   OU
9. L

   KSU
10. W

    BAY
11. L

    TCU
12. W

    TEX
13. L

    USF

What moved it, 2017

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−0.09
• Possession & field position+0.03
• Clock+0.17
• Timeouts−0.18
• Less the pregame edge, counted before kickoff+0.22

Game by game

• EWUW 56-10 vs EWU83% → 100%Before kickoff +33 · On the field +17: Scoreboard +19
• ASUW 52-45 vs ASU66% → 97%Before kickoff +16 · On the field +31: Scoreboard +31, Timeouts +2
• HOUW 27-24 at HOU33% → 95%Before kickoff −17 · On the field +62: Scoreboard +54, Clock +3
• OKSTL 34-41 vs OKST22% → 5%Before kickoff −28 · On the field −17: Scoreboard −32, Clock −2
• KUW 65-19 at KU84% → 100%Before kickoff +34 · On the field +16: Scoreboard +22
• WVUL 35-46 at WVU36% → 2%Before kickoff −14 · On the field −33: Scoreboard −34
• ISUL 13-31 vs ISU63% → 1%Before kickoff +13 · On the field −62: Scoreboard −59, Clock −2
• OUL 27-49 at OU7% → 0%Before kickoff −43 · On the field −7: Scoreboard −11
• KSUL 35-42 vs KSU58% → 23%Before kickoff +8 · On the field −35: Scoreboard −36, Timeouts −20
• BAYW 38-24 at BAY73% → 99%Before kickoff +23 · On the field +26: Scoreboard +34, Clock +2
• TCUL 3-27 vs TCU26% → 0%Before kickoff −24 · On the field −26: Scoreboard −32, Clock −2
• TEXW 27-23 at TEX27% → 93%Before kickoff −23 · On the field +66: Scoreboard +55, Clock +3
• USFL 34-38 at USF41% → 20%Before kickoff −9 · On the field −21: Scoreboard −19, Possession & field position +2

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.