North Texas

Profile vs. FBS, 2018

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+0.1353rd
  Defense+0.0182nd
• 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.
  Offense45.1%68th
  Defense39.9%73rd
• Explosiveness

  Average EPA on the successful snaps: how big the good plays were.
  Offense+1.2147th
  Defense+1.2722nd
• Rushing EPA

  EPA per designed run.
  Offense0.0023rd
  Defense+0.0175th
• Passing EPA

  EPA per pass play.
  Offense+0.2672nd
  Defense+0.0284th
• Standard downs

  Success rate on downs where either a run or a pass makes sense, like 1st and 10 or 3rd and 2.
  Offense49.6%67th
  Defense45.0%69th
• Passing downs

  Success rate on downs that all but force a pass, like 2nd and 8 or longer, or 3rd and 5 or longer.
  Offense35.4%67th
  Defense30.0%77th
• Line yards

  Rushing yards per carry credited to the offensive line, weighting the first few yards most.
  Offense2.6722nd
  Defense2.4891st
• Stuff rate

  Share of runs stopped at or behind the line of scrimmage.
  Offense22.6%19th
  Defense25.8%95th
• Havoc

  Share of snaps with a tackle for loss, sack, forced fumble, interception or pass breakup.
  Offense15.9%53rd
  Defense18.8%82nd

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, 2018

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 North Texas 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
   SMU
2. W
   UIW
3. W
   ARK
4. W
   LIB
5. L
   LT
6. W
   UTEP
7. W
   USM
8. L
   UAB
9. W
   RICE
10. L
    ODU
11. W
    FAU
12. W
    UTSA
13. L
    USU

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

Players, 2018

1. Mason FineQB451 plays · +0.251 pts/play · 18th of 225 QBs
   2.12WAR1.46 to 2.83
2. DeAndre TorreyRB163 plays · +0.160 pts/play · 59th of 351 RBs
   0.49WAR0.38 to 0.61
3. Loren EaslyRB69 plays · +0.197 pts/play · 175th of 351 RBs
   0.25WAR0.21 to 0.30
4. Rico Bussey Jr.WR92 plays · +0.100 pts/play · 66th of 434 WRs
   0.17WAR0.03 to 0.32
5. Jalen GuytonWR62 plays · +0.136 pts/play · 81st of 434 WRs
   0.16WAR0.07 to 0.24
6. Nic SmithRB56 plays · +0.150 pts/play · 241st of 351 RBs
   0.16WAR0.12 to 0.20
7. Michael LawrenceWR44 plays · +0.149 pts/play · 135th of 434 WRs
   0.12WAR0.06 to 0.19
8. Cole HedlundPK17 plays · +0.374 pts/play · 38th of 112 kickers
   0.12WAR0.08 to 0.16

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 North Texas'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, 2018

• Roster talent

  247Sports' talent composite: the recruiting ratings of every player on the roster, transfers included.
  369369107th of 130Roster talent: 369, 107th of 130 in FBS
• Recruiting

  Average 247Sports class score over this season's class and the three before it.
  122122107th of 130Recruiting: 122, 107th of 130 in FBS
• Returning production

  Share of last season's production (expected points added) by players still on the roster.
  74%74%45th of 129Returning production: 74%, 45th of 129 in FBS
• SP+ rating

  The result: Bill Connelly's opponent-adjusted rating, points per game better than an average FBS team.
  +2.1+2.165th of 130SP+ rating: +2.1, 65th 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, 2018

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 North Texas's own. A call that differs gives up the gap to the best option; gaps under one point are close calls, and the 5 clear-cut ones account for 0.18 of the 0.24 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 UTEPQ4 2:56 4th & 1 at UNT 27, up 3Punted; the numbers favored going for it6.9 pts
2. vs FAUQ4 8:07 4th & 3 at FAU 43, up 3Punted; the numbers favored going for it4.4 pts
3. vs FAUQ3 2:27 4th & 1 at UNT 40, up 3Punted; the numbers favored going for it3.6 pts
4. vs LTQ4 12:36 4th & 2 at LT 24, down 2Kicked a field goal; the numbers favored going for it1.6 pts
5. vs USMQ3 6:49 4th & 2 at UNT 30, up 3Punted; the numbers favored going for it1.4 pts
6. at ARKQ3 9:26 4th & 3 at ARK 3, up 24Kicked a field goal; the numbers favored going for it (close call)0.9 pts
7. vs RICEQ3 0:04 4th & 4 at RICE 6, tiedKicked a field goal; the numbers favored going for it (close call)0.6 pts
8. vs SMUQ2 0:48 4th & 5 at SMU 29, up 17Went for it; the numbers favored a field goal (close call)0.6 pts

Record vs. expectation, 2018

69th of 130 FBS teams at beating the model's pregame expectations

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

1. W

   SMU
2. W

   UIW
3. W

   ARK
4. W

   LIB
5. L

   LT
6. W

   UTEP
7. W

   USM
8. L

   UAB
9. W

   RICE
10. L

    ODU
11. W

    FAU
12. W

    UTSA
13. L

    USU

What moved it, 2018

Every game starts at North Texas'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+0.69
• Clock+0.01
• Less the pregame edge, counted before kickoff−1.08

Game by game

• SMUW 46-23 vs SMU59% → 100%Before kickoff +9 · On the field +40: Scoreboard +39, Timeouts +1
• UIWW 58-16 vs UIW99% → 100%Before kickoff +49 · On the field +1: Scoreboard +2
• ARKW 44-17 at ARK37% → 100%Before kickoff −13 · On the field +63: Scoreboard +61, Clock +2
• LIBW 47-7 at LIB78% → 100%Before kickoff +28 · On the field +21: Scoreboard +27, Clock +1
• LTL 27-29 vs LT68% → 8%Before kickoff +18 · On the field −60: Scoreboard −50, Clock −4
• UTEPW 27-24 at UTEP96% → 92%Before kickoff +46 · On the field −4: Scoreboard +14, Clock +1
• USMW 30-7 vs USM70% → 100%Before kickoff +20 · On the field +29: Scoreboard +30, Timeouts +1
• UABL 21-29 at UAB43% → 1%Before kickoff −7 · On the field −42: Scoreboard −40
• RICEW 41-17 vs RICE97% → 99%Before kickoff +47 · On the field +3: Scoreboard +7
• ODUL 31-34 at ODU84% → 15%Before kickoff +34 · On the field −69: Scoreboard −24, Timeouts −2
• FAUW 41-38 vs FAU63% → 88%Before kickoff +13 · On the field +25: Scoreboard +26, Timeouts +2
• UTSAW 24-21 at UTSA95% → 84%Before kickoff +45 · On the field −11: Scoreboard +16, Clock +2
• USUL 13-52 vs USU34% → 0%Before kickoff −16 · On the field −34: Scoreboard −39

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

Closest call

UNT 33-30 WMU (2025) - North Texas dropped to a 1.3% win probability before pulling it out.