UNLV

Profile vs. FBS, 2014

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+0.0925th
  Defense+0.293rd
• 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.
  Offense38.8%16th
  Defense47.5%11th
• Explosiveness

  Average EPA on the successful snaps: how big the good plays were.
  Offense+1.2776th
  Defense+1.2625th
• Rushing EPA

  EPA per designed run.
  Offense+0.1152nd
  Defense+0.274th
• Passing EPA

  EPA per pass play.
  Offense+0.0817th
  Defense+0.3511th
• Standard downs

  Success rate on downs where either a run or a pass makes sense, like 1st and 10 or 3rd and 2.
  Offense42.4%13th
  Defense52.7%9th
• Passing downs

  Success rate on downs that all but force a pass, like 2nd and 8 or longer, or 3rd and 5 or longer.
  Offense31.8%31st
  Defense35.4%28th
• Line yards

  Rushing yards per carry credited to the offensive line, weighting the first few yards most.
  Offense2.9247th
  Defense3.299th
• Stuff rate

  Share of runs stopped at or behind the line of scrimmage.
  Offense15.9%84th
  Defense15.4%11th
• Havoc

  Share of snaps with a tackle for loss, sack, forced fumble, interception or pass breakup.
  Offense3.1%6th
  Defense1.9%47th

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, 2014

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
   ARIZ
2. W
   UNCO
3. L
   NIU
4. L
   HOU
5. L
   SDSU
6. L
   SJSU
7. W
   FRES
8. L
   USU
9. L
   UNM
10. L
    AF
11. L
    BYU
12. L
    HAW
13. L
    NEV

Bars above the line: better than expected for UNLV.

Players, 2014

1. Blake DeckerQB445 plays · +0.220 pts/play · 39th of 199 QBs
   1.84WAR1.19 to 2.53
2. Devonte BoydWR78 plays · +0.225 pts/play · 20th of 469 WRs
   0.33WAR0.20 to 0.47
3. Shaquille Murray-LawrenceRB127 plays · +0.113 pts/play · 85th of 350 RBs
   0.27WAR0.17 to 0.38
4. Logan YunkerP62 plays · +0.223 pts/play · 4th of 94 punters
   0.26WAR0.13 to 0.39
5. Devante DavisWR59 plays · +0.151 pts/play · 102nd of 469 WRs
   0.17WAR0.08 to 0.26
6. Keith WhitelyRB108 plays · +0.080 pts/play · 148th of 350 RBs
   0.16WAR0.07 to 0.23
7. Maika MataeleWR37 plays · +0.176 pts/play · 166th of 469 WRs
   0.12WAR0.08 to 0.17
8. Jonathan LeivaPK15 plays · +0.336 pts/play · 70th of 114 kickers
   0.09WAR0.05 to 0.13

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

Fourth-down calls vs. the numbers, 2014

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 3 clear-cut ones account for 0.09 of the 0.17 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 UNCOQ4 0:15 4th & 4 at UNCO 7, up 1Went for it; the numbers favored a field goal−4.0 pts
2. vs FRESQ2 1:01 4th & 1 at midfield, up 17Punted; the numbers favored going for it−2.7 pts
3. vs AFQ3 13:41 4th & 17 at AF 30, down 24Went for it; the numbers favored a field goal−2.6 pts
4. vs FRESQ3 6:55 4th & 2 at UNLV 27, up 10Punted; the numbers favored going for it (close call)−0.9 pts
5. at SJSUQ2 7:36 4th & 7 at UNLV 28, down 3Went for it; the numbers favored punting (close call)−0.7 pts
6. at HOUQ2 8:54 4th & 2 at midfield, down 6Punted; the numbers favored going for it (close call)−0.6 pts
7. at BYUQ4 6:22 4th & 1 at UNLV 41, down 19Punted; the numbers favored going for it (close call)−0.6 pts
8. at HOUQ2 0:30 4th & 12 at HOU 39, down 6Went for it; the numbers favored a field goal (close call)−0.5 pts

Record vs. expectation, 2014

110th of 128 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

   ARIZ
2. W

   UNCO
3. L

   NIU
4. L

   HOU
5. L

   SDSU
6. L

   SJSU
7. W

   FRES
8. L

   USU
9. L

   UNM
10. L

    AF
11. L

    BYU
12. L

    HAW
13. L

    NEV

What moved it, 2014

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−2.18
• Possession & field position−0.01
• Clock−0.01
• Timeouts−0.27
• Less the pregame edge, counted before kickoff+0.73

Game by game

• ARIZL 13-58 at ARIZ7% → 0%Before kickoff −43 · On the field −7: Scoreboard −10
• UNCOW 13-12 vs UNCO96% → 79%Before kickoff +46 · On the field −17: Scoreboard +6, Timeouts +1
• NIUL 34-48 vs NIU19% → 2%Before kickoff −31 · On the field −18: Scoreboard −28, Clock −2
• HOUL 14-47 at HOU9% → 0%Before kickoff −41 · On the field −9: Scoreboard −12
• SDSUL 17-34 at SDSU12% → 1%Before kickoff −38 · On the field −11: Scoreboard −16
• SJSUL 10-33 at SJSU23% → 0%Before kickoff −27 · On the field −23: Scoreboard −26
• FRESW 30-27 vs FRES28% → 79%Before kickoff −22 · On the field +51: Scoreboard +37, Timeouts −25
• USUL 20-34 at USU12% → 1%Before kickoff −38 · On the field −11: Scoreboard −17, Timeouts −1
• UNML 28-31 vs UNM51% → 18%Before kickoff +1 · On the field −32: Scoreboard −39, Clock −4
• AFL 21-48 vs AF38% → 0%Before kickoff −12 · On the field −38: Scoreboard −43, Clock −1
• BYUL 23-42 at BYU7% → 0%Before kickoff −43 · On the field −7: Scoreboard −11
• HAWL 35-37 at HAW31% → 4%Before kickoff −19 · On the field −27: Scoreboard −27, Timeouts −2
• NEVL 27-49 vs NEV24% → 0%Before kickoff −26 · On the field −24: Scoreboard −31, Clock −2

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.