The SportsWire

Texas Tech

2026 · Last game: week 5 vs Colorado · bye in week 6

Updated Tue Oct 13, 7:55 PM CT

Next up projections

vs Arizona State

Week 7 · Sat Oct 17, 2:30 PM CT / 3:30 PM ET · Jones AT&T Stadium (Lubbock) · Arizona State (FBS, Big 12)

Our call: Texas Tech by 18.9Strong

All three models favor Texas Tech. The line, shown for comparison only, has Texas Tech by 12.5.

The average of SP+, Elo and Our model; the line is left out and kept as a benchmark. Toss-up under 3 points, Lean 3 to 7, Strong 7 and over. More ›

Game page: vs Arizona State ›

Regression watch: Flattered last game: the 22-point win ran 22 points ahead of the play. In a 2022–25 study, teams flattered by more than 7 fell about 3 points short of their season form the next game. More ›

Schedule: Played the 85th-toughest schedule in FBS so far (average opponent rating -6.1); 61st-toughest remaining (+2.9). More ›

Opponents by our model's rating 5 played · 7 left
  • Week 1 vs Abilene Christian (-32.8) · FCS, pooled
  • Week 2 at Oregon State (+4.1)
  • Week 3 vs Houston (+12.1)
  • Week 4 vs Sam Houston (-12.2)
  • Week 5 at Colorado (-1.5)
  • Remaining:
  • Week 7 vs Arizona State (+0.7)
  • Week 8 at Cincinnati (+0.5)
  • Week 9 vs Arizona (+9.3)
  • Week 10 vs West Virginia (-2.6)
  • Week 11 at Oklahoma State (-1.0)
  • Week 12 at Baylor (+6.7)
  • Week 13 vs TCU (+6.8)

Each opponent's rating from our model's week 7 snapshot, in points against an average FBS team; the averages are ranked among the 138 FBS teams. Played means through week 6. Experimental, like the Our model card.

Series: tied 3–3 · last: Arizona State 26–22, 2025

Series history 6 meetings since 1999
  • All-time: tied 3–3 (6 meetings since 1999)
  • Last meeting: Arizona State 26–22, 2025, Mountain America Stadium
  • Streak: none
  • At Jones AT&T Stadium: Texas Tech leads 2–0 (2 meetings)
  • Biggest margins: Texas Tech 37–23 (2013) · Arizona State 31–13 (1999)
  • Since 2000: Texas Tech leads 3–2 (5 meetings)
SeasonResultSite
2025Arizona State 26–22Mountain America Stadium
2024Texas Tech 30–22Jones AT&T Stadium
2017Texas Tech 52–45Jones AT&T Stadium
2016Arizona State 68–55Mountain America Stadium
2013Texas Tech 37–23neutral site
RatingTexas TechArizona State
SP+ +21.6 #10 +3.6 #59
Elo 1862 1464

Projected margin for Texas Tech · three models and the line, side by side

SP+
+20.5
Texas Tech +21.6 vs Arizona State +3.6
avg miss 10.6
closest 47/79
Elo
+18.4
Texas Tech 1862 vs Arizona State 1464
avg miss 12.3
closest 33/79
Line · benchmark
+12.5
pregame line
avg miss 11.1
Our model · experimental
+17.7
Texas Tech +15.9 vs Arizona State +0.7
prior 55%
avg miss 11.8
closest 14/40
How to read this
  • SP+ is a rating of how good a team is, in points better or worse than an average team, built from its play-by-play, so the gap between two teams' ratings is a projected margin.
  • Elo is a rating that goes up with every win and down with every loss, more for beating a strong team, less for beating a weak one. The gap between two ratings is divided by 25 to turn it into points.
  • The line is the pregame line, the margin the market expected, shown from Texas Tech's side. It is not one of the models: it is shown for comparison only, as the benchmark the models and Our call are measured against.
  • Our model is this site's own experimental rating, built from each team's play-by-play adjusted for who it has played, blended with a preseason estimate from last season, returning players and recruiting. "prior 67%" means 67 percent of the rating is still that preseason estimate; the share shrinks as games are played.
  • In SP+, Elo and our model the home team gets 2.5 points (Texas Tech); the line already includes the market's own home edge. The conversion to points is approximate.
  • "avg miss 9.8" under a card is how far that model's projections have been from the actual margins this season, on average, in points. "closest 10/39" is how many of the season's 39 finished games that model came closest on (a tie counts for every tied model); the line, a benchmark, shows its average miss only. Shown once a model has 20 finished games.
  • Our call, above the cards, is the site's prediction: the simple average of the SP+, Elo and our model margins. The pregame line is left out of it and kept as a benchmark to measure it against. Toss-up means the average is under 3 points, Lean 3 to 7, Strong 7 or more. It is made only when all three of those models have a number, and it is scored after the game like the models.
  • Each card is one method's estimate, shown side by side; when they disagree, that is worth knowing.
  • After a game, the sentence under the score shows every model and the line against the actual margin: ✓ when a model named the winner, ✗ when it did not (the line, a benchmark, gets no mark), and Closest: the model nearest the final margin among those present, never the line (an exact tie goes to the first listed). The "closest 10/39" counts keep the accuracy report's scoring of SP+ and Elo, where a tie credits both.
  • Metrics explained: the four models and their track record ›

Efficiency matchup · season to date · Texas Tech 5 games, Arizona State 5 games

Edge: Texas Tech wins 4 of 5

Texas Tech moves the ball better, stops plays better, is more disruptive and allows fewer big plays. Arizona State has the edge on big plays.

Texas Techlonger bar = betterArizona State

Run game, head to head · season to date

Texas Tech's runs work 50% of the time; Arizona State's run defense allows 40%. Arizona State's runs work 45% of the time; Texas Tech's run defense allows 33%.

More numbers
MeasureTexas TechArizona StateEdge
Off. success rate46.7%44.9%Texas Tech
Off. explosiveness1.141.19Arizona State
Def. success rate allowed37.2%39.5%Texas Tech
Def. explosiveness allowed1.291.36Texas Tech
Havoc created16.7%14.7%Texas Tech
  • Run game, success rate: Texas Tech offense 49.7% · Arizona State defense allows 40.4% · edge: Texas Tech
  • Run game, success rate: Arizona State offense 44.9% · Texas Tech defense allows 33.1% · edge: Texas Tech
  • Run game, PPA per play: Texas Tech offense +0.18 · Arizona State defense allows +0.08 · edge: Texas Tech
  • Run game, PPA per play: Arizona State offense +0.02 · Texas Tech defense allows -0.11 · edge: Texas Tech
  • Success rate: how often a play does its job, gaining enough yards to keep the offense on schedule.
  • Explosiveness: how big a team's successful plays are on average; a higher number means more big gains.
  • Havoc: how often the defense blows up a play with a sack, a tackle for loss, a takeaway or a pass broken up.
  • PPA (predicted points added): how many points an average play is worth to the offense; above zero is good for the offense, below zero good for the defense.

How to read this

  • Each row compares the two teams on one measure of how well they have played this season, offense and defense, using every game so far with garbage time (plays late in a decided game) left out.
  • The run game rows pair one team's rushing offense with the other's run defense. The row goes to whichever side is further from the FBS baseline this season, the offense by how far it runs above it, the defense by how much less it allows (baseline: run success rate 43%, PPA per play +0.08, the midpoint of what FBS offenses gain and FBS defenses allow, since both also play FCS teams), so two strong sides or two weak ones are judged by which is the more so. Edges within 3 points of success rate or 0.05 PPA are even. A strong run offense against a weak run defense is where a team can control the game on the ground.
  • In the five rows at the top the longer bar is the better number: bars are scaled within each row, and in the two "allowed" rows the lower number gets the longer bar.
  • Success rate is the share of plays that kept the offense on schedule (half the yards needed on first down, most of them on second, all of them on third or fourth). Explosiveness is how big the successful plays were, on average, in points. Havoc is the share of the opponent's snaps the defense wrecked: a tackle for loss, a sack, a takeaway or a pass broken up.
  • "Allowed" rows are what each defense gave up, so the lower value wins.
  • These are not adjusted for who each team has played, so a light schedule flatters a team, and early in the season they rest on very few games.
  • Metrics explained: success rate, explosiveness and havoc ›

Injuries AI-assisted 1

Gathered by an AI model from public reporting, not from CollegeFootballData. Not used in any number on this page. Verify against the linked sources.

"AI-assisted" means gathered by an AI model from public reporting. When a conference publishes an official availability list for Texas Tech's game (the SEC, Big Ten and ACC do, for conference games), this section shows that instead, labeled "Official report". More ›

  • Cameron Dickey RBinjuryOut
    high-ankle sprain
    Noted Wed Oct 7, 6:32 AM CT, for the game vs Arizona State, Sat Oct 17.
    Coach Joey McGuire said he anticipates Cam Dickey will miss through the Cincinnati game after a high-ankle sprain suffered against Colorado.
    Sources (3): [1] [2] [3] · as of Wed Oct 7, 6:32 AM CT / 7:32 AM ET

Coverage (AI-assisted) · 17

Gathered by an AI model from public posts, not from CollegeFootballData. Not used in any number on this page. Verify against the linked sources. Beat coverage checked Mon Oct 12, 7:30 PM CT.

2 entries about games now final not shown.

Score and result

Texas Tech 29, Colorado 7W

Game page: the call before kickoff and how each model did ›

Projected: SP+ +21.0 ✓ · Elo +18.8 ✓ · Our model +17.5 ✓ · the line (benchmark) +14.5 · actual +22 · Closest: SP+

Texas Tech was as low as 20% and won Texas Tech was as low as 20% and won Q1 Q2 Q3 Q4 100% 50% 0% 29–7
  1. Q4 0:08 · 1st & 10 · 100% → 53%
    (00:08) No Huddle-Shotgun #28 A.Rowden rush middle for 8 yards gain to the TTU12 (#99 D.Manuel; #17 L.Lefau)
  2. Q3 3:07 · 2nd & 11 · 51% → 70%
    (03:07) Shotgun #15 W.Hammond pass complete short right to #7 T.Carter Jr. caught at COLO00, for 11 yards to the COLO00 TOUCHDOWN, clock 03:01. The previous play is under automatic review - "Pass completion". CALL UPHELD #15 W.Hammond pass attempt failed
  3. Q4 7:15 · 1st & 10 · 85% → 100%
    (07:15) Shotgun #16 I.Wilson pass intercepted by #6 J.Curry at COLO00, End Of Play TOUCHDOWN, clock 07:10 #48 S.Harrington kick attempt good (H: #49 W.Karoll, LS: #89 J.Llewellyn)

Away at Folsom Field · Sat Oct 3, 6:30 PM CT / 7:30 PM ET · vs Colorado (FBS, Big 12)

Opponent for every game-level number: Colorado (FBS, Big 12).

Verdict

The scoreboard flattered Texas Tech: the +22 margin ran 22 points ahead of the -0 implied by per-play quality (PPA (expected points added per play) -0.22 vs -0.18, success rate 30% vs 40%) (Colorado turned it over 4 times).

The verdict compares the score with the margin the plays earned. How it is reached ›

Efficiency

Edge: Colorado won 3 of 4

Colorado moved the ball better, got more from each play and was more disruptive. Texas Tech hit the bigger plays.

Texas Techlonger bar = betterColorado
More numbers

Texas Tech ran 64 plays, Colorado 80. Each number is that team's offense; Colorado's column is also what Texas Tech's defense allowed.

MeasureTexas TechColoradoEdge
PPA / play-0.22-0.18Colorado
Total PPA-14.4-14.4Colorado
Success rate29.7%40.0%Colorado
Explosiveness1.111.05Texas Tech
Havoc created12.5%24.2%Colorado
  • Success rate: how often a play did its job, gaining enough yards to keep the offense on schedule.
  • Explosiveness: how big a team's successful plays were on average; a higher number means more big gains.
  • Havoc: how often the defense blew up a play with a sack, a tackle for loss, a takeaway or a pass broken up. Counted from the play-by-play, so it is approximate.
  • PPA (predicted points added): how many points an average play was worth to the offense. Total PPA adds up every play, and is what the Verdict compares with the score.

How to read this

  • The four rows at the top compare the two offenses in this game; the longer bar is the better number, scaled within each row. A number at or below zero draws no bar.
  • A check marks the better side of a row. The line above the rows counts them: it describes how the game was played, not who won it.
  • Metrics explained ›
Top performers

Texas Tech players by cumulative PPA in this game. Usage is the share of the team's plays of that type involving the player. Receiving is passing-play PPA for non-quarterbacks.

Passing

PlayerPass usagePPA/play
Will Hammond QB 93% -4.5 -0.17

Rushing

PlayerRush usagePPA/play
Will Hammond QB 9% +2.8 +0.95
Ashton Rowden RB 3% +0.5 +0.49
Cameron Dickey RB 12% +0.3 +0.07
Quinten Joyner RB 26% -2.4 -0.27

Receiving

PlayerTarget usagePPA/play
Micah Hudson WR 7% +1.0 +0.50
Terrance Carter TE 32% +1.0 +0.11
Coy Eakin WR 7% +0.9 +0.45
J'Koby Williams RB 11% +0.4 +0.12
By play type
MeasureTexas TechColoradoEdge
Rushing PPA-0.29-0.51Texas Tech
Rushing success35.3%25.8%Texas Tech
Passing PPA-0.12+0.18Colorado
Passing success25.0%52.2%Colorado
Standard downs PPA-0.31-0.07Colorado
Standard downs success34.9%42.3%Colorado
Passing downs PPA-0.04-0.38Texas Tech
Passing downs success19.0%35.7%Colorado

Havoc detail — Texas Tech defense: TFL 3, sacks 1, takeaways 4, breakups 2 on 80 snaps. Colorado defense: TFL 9, sacks 2, takeaways 0, breakups 4 on 62 snaps.

Drives

A scoring opportunity is a drive that reaches the opponent's 40-yard line or scores; points per opportunity is the points those drives produced divided by their number.

◀ Texas TechColorado ▶
  • 12Drives13
  • 9Scoring opportunities7
  • 2.4Points per opportunity1.0
  • own 33Avg. starting field positionown 25
By half

Shown because the game changed between halves: the per-play edge swung by 0.70 points a play (Texas Tech -0.40 in the first half, +0.29 in the second). Overtime counts with the second half.

Texas TechColorado
1st2nd1st2nd
PPA / play-0.29-0.13+0.11-0.43
Success rate34%27%49%36%
Plays32303742

Recruiting

Texas Tech's 2026 class: #18 · 250.26 points.

MeasureTexas TechColorado
Talent composite 767.9
#29 of 138
731.6
#39 of 138
Transfer portal · 23 in, 21 out

Arrivals (23)

PlayerFrom★Elig.Date
Brendan Sorsby QB Cincinnati 5 Immediate Jan 5, 2026
Mateen Ibirogba DL Wake Forest 4 Immediate Jan 7, 2026
Adam Trick EDGE Miami (OH) 4 Immediate Jan 7, 2026
Malcolm Simmons WR Auburn 4 Immediate Jan 12, 2026
Jordan Church IOL Louisville 4 Immediate Jan 15, 2026
Amarie Fleming EDGE Allen University 3 Immediate Nov 19, 2025
Austin Romaine LB Kansas State 3 Immediate Jan 4, 2026
Jalen Jones WR Alabama State 3 Immediate Jan 4, 2026
Donte Lee Jr. WR Liberty 3 Immediate Jan 4, 2026
Julien Laventure DL Akron 3 Immediate Jan 5, 2026

+13 more not shown (23 total).

Departures (21)

PlayerTo★Elig.Date
Justin Horne LB undecided 3 Immediate Dec 25, 2025
Kelby Valsin WR undecided 3 Immediate Dec 26, 2025
Maurion Horn CB Oklahoma State 3 Immediate Jan 6, 2026
T.J. West WR UTSA 3 Immediate Jan 7, 2026
Dylan Spencer EDGE UTSA 3 Immediate Jan 7, 2026
Macho Stevenson CB Tulane 3 Immediate Jan 7, 2026
Braylon Rigsby DL Oklahoma State 3 Immediate Jan 8, 2026
Mitch Griffis QB East Carolina 3 Immediate Jan 9, 2026
Cheta Ofili EDGE TCU 3 Immediate Jan 9, 2026
Eddy Smith DL UTSA 3 Immediate Jan 9, 2026

+11 more not shown (21 total).

2026 commits · 22
PlayerHometown★Rating
LaDamion Guyton EDGE Savannah, GA 5 0.9872
Felix Ojo OT Mansfield, TX 4 0.9791
Chase Campbell WR Wolfforth, TX 4 0.9560
S'Vioarean Martin CB Palestine, TX 4 0.9441
Bryce Gilmore OT Prosper, TX 4 0.9163
Matt Ludwig TE Billings, MT 4 0.9118
Kaegan Ash ATH Mount Enterprise, TX 4 0.9049
Donovan Webb S Frisco, TX 4 0.8944
Ashton Rowden RB New Boston, TX 4 0.8942
Jerald Mays IOL Pflugerville, TX 3 0.8817
Jacob Crow OT Alcoa, TN 3 0.8767
Aaron Bradshaw S Fort Worth, TX 3 0.8756
Cord Nolan LB Bixby, OK 3 0.8756
Krush Johnson DL Amarillo, TX 3 0.8744
Stephen Cannon QB Savannah, GA 3 0.8744

+7 more not shown (22 total).

Returning production

Share of last season's production returning for Texas Tech in 2026.

Unit% PPAPPAUsage
Overall53.4%231.656.7%
Passing26.8%41.423.3%
Receiving57.0%118.153.2%
Rushing98.8%72.193.4%

A figure above 100% or below 0% is expected when last season's returning players had negative PPA in that phase, since PPA can be negative.