UNLV

Profile vs. FBS, 2016

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+0.1774th
  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.
  Offense42.1%35th
  Defense45.7%25th
• Explosiveness

  Average EPA on the successful snaps: how big the good plays were.
  Offense+1.2465th
  Defense+1.2818th
• Rushing EPA

  EPA per designed run.
  Offense+0.2391st
  Defense+0.1523rd
• Passing EPA

  EPA per pass play.
  Offense+0.1235th
  Defense+0.308th
• Standard downs

  Success rate on downs where either a run or a pass makes sense, like 1st and 10 or 3rd and 2.
  Offense45.8%27th
  Defense50.8%21st
• Passing downs

  Success rate on downs that all but force a pass, like 2nd and 8 or longer, or 3rd and 5 or longer.
  Offense34.9%61st
  Defense35.3%32nd
• Line yards

  Rushing yards per carry credited to the offensive line, weighting the first few yards most.
  Offense3.0358th
  Defense3.307th
• Stuff rate

  Share of runs stopped at or behind the line of scrimmage.
  Offense17.1%76th
  Defense17.5%29th
• Havoc

  Share of snaps with a tackle for loss, sack, forced fumble, interception or pass breakup.
  Offense14.7%70th
  Defense16.6%53rd

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, 2016

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. W
   JKST
2. L
   UCLA
3. L
   CMU
4. L
   IDHO
5. W
   FRES
6. L
   SDSU
7. W
   HAW
8. L
   CSU
9. L
   SJSU
10. W
    WYO
11. L
    BOIS
12. L
    NEV

Bars above the line: better than expected for UNLV.

Players, 2016

1. Dalton SneedQB123 plays · +0.242 pts/play · 123rd of 218 QBs
   0.56WAR0.30 to 0.86
2. Kurt PalandechQB108 plays · +0.209 pts/play · 136th of 218 QBs
   0.42WAR0.22 to 0.62
3. Charles WilliamsRB94 plays · +0.181 pts/play · 113th of 347 RBs
   0.32WAR0.22 to 0.45
4. Lexington ThomasRB95 plays · +0.103 pts/play · 186th of 347 RBs
   0.18WAR0.06 to 0.31
5. Devonte BoydWR48 plays · +0.186 pts/play · 110th of 417 WRs
   0.17WAR0.10 to 0.23
6. Johnny StantonQB68 plays · +0.126 pts/play · 173rd of 218 QBs
   0.16WAR0.07 to 0.25
7. Evan PantelsPK51 plays · +0.134 pts/play · 37th of 116 kickers
   0.13WAR0.04 to 0.22
8. Xzaviar CampbellRB28 plays · +0.131 pts/play · 253rd of 347 RBs
   0.07WAR0.04 to 0.09

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, 2016

• Roster talent

  247Sports' talent composite: the recruiting ratings of every player on the roster, transfers included.
  376376106th of 128Roster talent: 376, 106th of 128 in FBS
• Recruiting

  Average 247Sports class score over this season's class and the three before it.
  118118100th of 128Recruiting: 118, 100th of 128 in FBS
• Returning production

  Share of last season's production (expected points added) by players still on the roster.
  56%56%88th of 128Returning production: 56%, 88th of 128 in FBS
• SP+ rating

  The result: Bill Connelly's opponent-adjusted rating, points per game better than an average FBS team.
  −14.3−14.3114th of 128SP+ rating: −14.3, 114th 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, 2016

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 0.11 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. at HAWQ3 5:10 4th & 1 at HAW 1, tiedKicked a field goal; the numbers favored going for it−4.6 pts
2. vs IDHOQ4 0:46 4th & 6 at IDHO 43, tiedPunted; the numbers favored going for it−4.5 pts
3. vs NEVQ3 14:55 4th & 1 at UNLV 27, down 17Punted; the numbers favored going for it−1.7 pts
4. at SJSUQ2 1:46 4th & 3 at SJSU 20, down 17Kicked a field goal; the numbers favored going for it (close call)−0.8 pts
5. at UCLAQ4 10:51 4th & 3 at UNLV 30, down 14Punted; the numbers favored going for it (close call)−0.7 pts
6. at SDSUQ2 14:53 4th & 1 at UNLV 49, down 10Punted; the numbers favored going for it (close call)−0.7 pts
7. vs CSUQ1 5:14 4th & 1 at CSU 24, down 7Kicked a field goal; the numbers favored going for it (close call)−0.6 pts
8. at SDSUQ3 14:56 4th & 4 at UNLV 47, down 6Punted; the numbers favored going for it (close call)−0.5 pts

Record vs. expectation, 2016

101st 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. W

   JKST
2. L

   UCLA
3. L

   CMU
4. L

   IDHO
5. W

   FRES
6. L

   SDSU
7. W

   HAW
8. L

   CSU
9. L

   SJSU
10. W

    WYO
11. L

    BOIS
12. L

    NEV

What moved it, 2016

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−1.38
• Possession & field position−0.03
• Clock+0.25
• Timeouts−0.45
• Less the pregame edge, counted before kickoff+0.33

Game by game

• JKSTW 63-13 vs JKST99% → 100%Before kickoff +49 · On the field +1: Scoreboard +2
• UCLAL 21-42 at UCLA5% → 0%Before kickoff −45 · On the field −5: Scoreboard −8
• CMUL 21-44 at CMU15% → 0%Before kickoff −35 · On the field −15: Scoreboard −20
• IDHOL 30-33 vs IDHO82% → 11%Before kickoff +32 · On the field −71: Scoreboard −43, Timeouts −19
• FRESW 45-20 vs FRES77% → 99%Before kickoff +27 · On the field +22: Scoreboard +27, Timeouts −1
• SDSUL 7-26 at SDSU13% → 2%Before kickoff −37 · On the field −11: Scoreboard −19, Timeouts +1
• HAWW 41-38 at HAW22% → 95%Before kickoff −28 · On the field +73: Scoreboard +53, Clock +3
• CSUL 23-42 vs CSU53% → 1%Before kickoff +3 · On the field −52: Scoreboard −54, Clock −2
• SJSUL 24-30 at SJSU57% → 4%Before kickoff +7 · On the field −53: Scoreboard −41, Timeouts −2
• WYOW 69-66 vs WYO27% → 79%Before kickoff −23 · On the field +52: Scoreboard +37, Timeouts −25
• BOISL 25-42 at BOIS4% → 1%Before kickoff −46 · On the field −3: Scoreboard −9
• NEVL 10-45 vs NEV66% → 0%Before kickoff +16 · On the field −66: Scoreboard −63, Possession & field position −1

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.