UNLV

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+0.0939th
  Defense+0.229th
• 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.
  Offense42.2%36th
  Defense45.4%27th
• Explosiveness

  Average EPA on the successful snaps: how big the good plays were.
  Offense+1.3084th
  Defense+1.3512th
• Rushing EPA

  EPA per designed run.
  Offense+0.1575th
  Defense+0.1132nd
• Passing EPA

  EPA per pass play.
  Offense+0.0216th
  Defense+0.359th
• Standard downs

  Success rate on downs where either a run or a pass makes sense, like 1st and 10 or 3rd and 2.
  Offense48.9%57th
  Defense49.0%36th
• Passing downs

  Success rate on downs that all but force a pass, like 2nd and 8 or longer, or 3rd and 5 or longer.
  Offense28.9%14th
  Defense38.1%11th
• Line yards

  Rushing yards per carry credited to the offensive line, weighting the first few yards most.
  Offense3.0667th
  Defense2.8458th
• Stuff rate

  Share of runs stopped at or behind the line of scrimmage.
  Offense17.4%74th
  Defense19.0%44th
• Havoc

  Share of snaps with a tackle for loss, sack, forced fumble, interception or pass breakup.
  Offense16.5%45th
  Defense15.9%44th

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 UNLV 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
   USC
2. W
   UTEP
3. W
   PV
4. L
   ARST
5. L
   UNM
6. L
   USU
7. L
   AF
8. L
   SJSU
9. L
   FRES
10. W
    SDSU
11. L
    HAW
12. W
    NEV

Bars above the line: better than expected for UNLV.

Players, 2018

1. Armani RogersQB147 plays · +0.271 pts/play · 106th of 225 QBs
   0.75WAR0.48 to 1.02
2. Lexington ThomasRB179 plays · +0.161 pts/play · 51st of 351 RBs
   0.54WAR0.38 to 0.70
3. Max GilliamQB201 plays · +0.119 pts/play · 146th of 225 QBs
   0.45WAR0.13 to 0.80
4. Brandon PresleyWR52 plays · +0.139 pts/play · 120th of 434 WRs
   0.14WAR0.06 to 0.21
5. Xzaviar CampbellRB33 plays · +0.205 pts/play · 259th of 351 RBs
   0.13WAR0.11 to 0.15
6. Hayes HickenPK48 plays · +0.105 pts/play · 67th of 112 kickers
   0.09WAR0.04 to 0.14
7. Evan PantelsPK6 plays · +0.393 pts/play · 100th of 112 kickers
   0.04WAR0.03 to 0.06
8. Darren Woods Jr.WR26 plays · +0.077 pts/play · 350th of 434 WRs
   0.04WAR0.00 to 0.07

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 UNLV'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.
  40140196th of 130Roster talent: 401, 96th of 130 in FBS
• Recruiting

  Average 247Sports class score over this season's class and the three before it.
  12612698th of 130Recruiting: 126, 98th of 130 in FBS
• Returning production

  Share of last season's production (expected points added) by players still on the roster.
  78%78%31st of 129Returning production: 78%, 31st of 129 in FBS
• SP+ rating

  The result: Bill Connelly's opponent-adjusted rating, points per game better than an average FBS team.
  −11.1−11.1106th of 130SP+ rating: −11.1, 106th 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 UNLV'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 0.03 of the 0.06 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 HAWQ3 10:35 4th & 1 at UNLV 25, up 15Punted; the numbers favored going for it2.6 pts
2. vs NEVQ2 7:42 4th & 2 at NEV 49, down 9Punted; the numbers favored going for it (close call)0.7 pts
3. vs AFQ4 6:36 4th & 8 at AF 30, down 6Went for it; the numbers favored a field goal (close call)0.5 pts
4. vs AFQ3 5:45 4th & 5 at AF 16, down 20Went for it; the numbers favored a field goal (close call)0.5 pts
5. at ARSTQ4 1:52 4th & 19 at UNLV 25, down 7Went for it; the numbers favored punting (close call)0.3 pts
6. at USCQ2 13:12 4th & 1 at UNLV 34, down 2Punted; the numbers favored going for it (close call)0.2 pts
7. vs PVQ3 8:38 4th & 4 at PV 6, up 7Kicked a field goal; the numbers favored going for it (close call)0.2 pts
8. vs FRESQ1 15:00 4th & 1 at UNLV 30, tiedPunted; the numbers favored going for it (close call)0.1 pts

Record vs. expectation, 2018

74th of 130 FBS teams at beating the model's pregame expectations

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

1. L

   USC
2. W

   UTEP
3. W

   PV
4. L

   ARST
5. L

   UNM
6. L

   USU
7. L

   AF
8. L

   SJSU
9. L

   FRES
10. W

    SDSU
11. L

    HAW
12. W

    NEV

What moved it, 2018

Every game starts at UNLV'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−1.22
• Possession & field position−0.04
• Clock−0.01
• Timeouts+0.04
• Less the pregame edge, counted before kickoff+0.84

Game by game

• USCL 21-43 at USC6% → 0%Before kickoff −44 · On the field −6: Scoreboard −9
• UTEPW 52-24 vs UTEP94% → 100%Before kickoff +44 · On the field +6: Scoreboard +9
• PVW 46-17 vs PV97% → 90%Before kickoff +47 · On the field −7: Scoreboard +7, Possession & field position +1
• ARSTL 20-27 at ARST27% → 2%Before kickoff −23 · On the field −26: Scoreboard −29, Possession & field position −1
• UNML 14-50 vs UNM71% → 0%Before kickoff +21 · On the field −71: Scoreboard −66, Clock −1
• USUL 28-59 at USU5% → 0%Before kickoff −45 · On the field −5: Scoreboard −7
• AFL 35-41 vs AF28% → 9%Before kickoff −22 · On the field −19: Scoreboard −37, Clock −2
• SJSUL 37-50 at SJSU39% → 1%Before kickoff −11 · On the field −38: Scoreboard −39, Timeouts +1
• FRESL 3-48 vs FRES5% → 0%Before kickoff −45 · On the field −5: Scoreboard −9
• SDSUW 27-24 at SDSU7% → 92%Before kickoff −43 · On the field +85: Scoreboard +48, Clock +3
• HAWL 28-35 at HAW36% → 4%Before kickoff −14 · On the field −32: Scoreboard −32, Possession & field position −1
• NEVW 34-29 vs NEV17% → 94%Before kickoff −33 · On the field +77: Scoreboard +41, Timeouts +3

Points of win probability, from UNLV's side.

Closest call

UNM 20-31 UNLV (2022) - UNLV dropped to a 0.6% win probability before pulling it out.