North Texas

Profile vs. FBS, 2015

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−0.132nd
  Defense+0.238th
• 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.
  Offense38.7%15th
  Defense49.5%5th
• Explosiveness

  Average EPA on the successful snaps: how big the good plays were.
  Offense+1.021st
  Defense+1.1758th
• Rushing EPA

  EPA per designed run.
  Offense−0.077th
  Defense+0.1416th
• Passing EPA

  EPA per pass play.
  Offense−0.172nd
  Defense+0.386th
• Standard downs

  Success rate on downs where either a run or a pass makes sense, like 1st and 10 or 3rd and 2.
  Offense46.3%35th
  Defense51.7%14th
• Passing downs

  Success rate on downs that all but force a pass, like 2nd and 8 or longer, or 3rd and 5 or longer.
  Offense25.3%2nd
  Defense43.9%1st
• Line yards

  Rushing yards per carry credited to the offensive line, weighting the first few yards most.
  Offense2.7531st
  Defense3.239th
• Stuff rate

  Share of runs stopped at or behind the line of scrimmage.
  Offense23.1%19th
  Defense15.4%13th
• Havoc

  Share of snaps with a tackle for loss, sack, forced fumble, interception or pass breakup.
  Offense1.4%88th
  Defense1.3%6th

Percentiles among the 128 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, 2015

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. L
   SMU
2. L
   RICE
3. L
   IOWA
4. L
   USM
5. L
   PRST
6. L
   WKU
7. L
   MRSH
8. W
   UTSA
9. L
   LT
10. L
    TENN
11. L
    MTSU
12. L
    UTEP

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

Players, 2015

1. DaMarcus SmithQB102 plays · +0.217 pts/play · 141st of 205 QBs
   0.41WAR0.24 to 0.59
2. Andrew McNultyQB118 plays · +0.170 pts/play · 148th of 205 QBs
   0.38WAR0.22 to 0.57
3. Jeffrey WilsonRB80 plays · +0.145 pts/play · 114th of 333 RBs
   0.22WAR0.16 to 0.26
4. Carlos HarrisWR50 plays · +0.135 pts/play · 160th of 411 WRs
   0.13WAR0.05 to 0.20
5. Eric KeenaP31 plays · +0.184 pts/play · 38th of 92 punters
   0.11WAR0.05 to 0.17
6. Antoinne JimmersonRB41 plays · +0.098 pts/play · 227th of 333 RBs
   0.08WAR0.05 to 0.10
7. Trevor MoorePK10 plays · +0.345 pts/play · 100th of 115 kickers
   0.06WAR0.04 to 0.09
8. Chris LovingTE9 plays · −0.019 pts/play · 40th of 189 TEs
   0.00WAR−0.02 to 0.01

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, 2015

• Roster talent

  247Sports' talent composite: the recruiting ratings of every player on the roster, transfers included.
  38138197th of 128Roster talent: 381, 97th of 128 in FBS
• Recruiting

  Average 247Sports class score over this season's class and the three before it.
  116116100th of 128Recruiting: 116, 100th of 128 in FBS
• Returning production

  Share of last season's production (expected points added) by players still on the roster.
  72%72%50th of 127Returning production: 72%, 50th of 127 in FBS
• SP+ rating

  The result: Bill Connelly's opponent-adjusted rating, points per game better than an average FBS team.
  −25.0−25.0128th of 128SP+ rating: −25.0, 128th of 128 in FBS

Ranks and bars among the 128 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, 2015

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 1 clear-cut one accounts for 0.01 of the 0.05 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 UTEPQ3 5:27 4th & 3 at UNT 40, down 3Punted; the numbers favored going for it1.1 pts
2. at USMQ2 12:08 4th & 1 at UNT 13, down 7Punted; the numbers favored going for it (close call)0.8 pts
3. vs UTEPQ2 0:39 4th & 14 at UTEP 42, up 3Went for it; the numbers favored a field goal (close call)0.7 pts
4. vs RICEQ4 6:24 4th & 16 at RICE 43, down 14Went for it; the numbers favored a field goal (close call)0.6 pts
5. at SMUQ1 8:05 4th & 1 at UNT 40, down 4Punted; the numbers favored going for it (close call)0.4 pts
6. at IOWAQ1 5:56 4th & 1 at UNT 44, down 14Punted; the numbers favored going for it (close call)0.2 pts
7. at USMQ3 6:35 4th & 9 at USM 16, down 35Went for it; the numbers favored a field goal (close call)0.2 pts
8. vs UTSAQ1 7:24 4th & 1 at UNT 23, tiedPunted; the numbers favored going for it (close call)0.1 pts

Record vs. expectation, 2015

109th of 128 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. L

   SMU
2. L

   RICE
3. L

   IOWA
4. L

   USM
5. L

   PRST
6. L

   WKU
7. L

   MRSH
8. W

   UTSA
9. L

   LT
10. L

    TENN
11. L

    MTSU
12. L

    UTEP

What moved it, 2015

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−1.82
• Possession & field position−0.01
• Clock−0.07
• Timeouts+0.01
• Less the pregame edge, counted before kickoff+0.60

Game by game

• SMUL 13-31 at SMU32% → 8%Before kickoff −18 · On the field −24: Scoreboard −25, Clock −1
• RICEL 24-38 vs RICE32% → 2%Before kickoff −18 · On the field −30: Scoreboard −40, Clock −1
• IOWAL 16-62 at IOWA5% → 0%Before kickoff −45 · On the field −5: Scoreboard −8
• USML 14-49 at USM17% → 0%Before kickoff −33 · On the field −17: Scoreboard −21
• PRSTL 7-66 vs PRST52% → 0%Before kickoff +2 · On the field −52: Scoreboard −54, Clock −2
• WKUL 28-55 vs WKU5% → 0%Before kickoff −45 · On the field −5: Scoreboard −9
• MRSHL 13-30 at MRSH3% → 1%Before kickoff −47 · On the field −3: Scoreboard −8, Clock +1
• UTSAW 30-23 vs UTSA24% → 97%Before kickoff −26 · On the field +73: Scoreboard +45, Timeouts +1
• LTL 13-56 at LT4% → 0%Before kickoff −46 · On the field −4: Scoreboard −5
• TENNL 0-24 at TENN1% → 0%Before kickoff −49 · On the field 0: Scoreboard −3
• MTSUL 7-41 at MTSU6% → 0%Before kickoff −44 · On the field −6: Scoreboard −8
• UTEPL 17-20 vs UTEP66% → 10%Before kickoff +16 · On the field −56: Scoreboard −47, Clock −4

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.