UTSA

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+0.1358th
  Defense+0.0765th
• 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.
  Offense41.0%25th
  Defense42.3%63rd
• Explosiveness

  Average EPA on the successful snaps: how big the good plays were.
  Offense+1.2466th
  Defense+1.1858th
• Rushing EPA

  EPA per designed run.
  Offense+0.0744th
  Defense+0.0557th
• Passing EPA

  EPA per pass play.
  Offense+0.2367th
  Defense+0.1456th
• Standard downs

  Success rate on downs where either a run or a pass makes sense, like 1st and 10 or 3rd and 2.
  Offense44.1%13th
  Defense47.6%54th
• Passing downs

  Success rate on downs that all but force a pass, like 2nd and 8 or longer, or 3rd and 5 or longer.
  Offense35.2%64th
  Defense32.1%65th
• Line yards

  Rushing yards per carry credited to the offensive line, weighting the first few yards most.
  Offense2.8031st
  Defense3.0733rd
• Stuff rate

  Share of runs stopped at or behind the line of scrimmage.
  Offense20.9%28th
  Defense16.3%17th
• Havoc

  Share of snaps with a tackle for loss, sack, forced fumble, interception or pass breakup.
  Offense19.4%8th
  Defense17.3%66th

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 UTSA 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
   ALST
2. L
   CSU
3. L
   ASU
4. L
   ODU
5. W
   USM
6. W
   RICE
7. L
   UTEP
8. W
   UNT
9. W
   MTSU
10. L
    LT
11. L
    TA&M
12. W
    CLT
13. L
    UNM

Bars above the line: better than expected for UTSA.

Players, 2016

1. Dalton SturmQB292 plays · +0.146 pts/play · 100th of 218 QBs
   0.80WAR0.34 to 1.26
2. Jarveon WilliamsRB179 plays · +0.165 pts/play · 40th of 347 RBs
   0.55WAR0.37 to 0.76
3. Jalen RhodesRB130 plays · +0.141 pts/play · 102nd of 347 RBs
   0.34WAR0.19 to 0.51
4. Yannis RoutsasP47 plays · +0.209 pts/play · 24th of 82 punters
   0.18WAR0.10 to 0.27
5. Jared JohnsonQB44 plays · +0.189 pts/play · 174th of 218 QBs
   0.16WAR0.08 to 0.23
6. Josh StewartWR47 plays · +0.159 pts/play · 147th of 417 WRs
   0.14WAR0.08 to 0.21
7. Kerry Thomas Jr.WR35 plays · +0.127 pts/play · 263rd of 417 WRs
   0.08WAR0.03 to 0.13
8. JaBryce TaylorWR11 plays · +0.174 pts/play · 368th of 417 WRs
   0.04WAR0.02 to 0.05

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 UTSA'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.
  39839896th of 128Roster talent: 398, 96th of 128 in FBS
• Recruiting

  Average 247Sports class score over this season's class and the three before it.
  107107113th of 128Recruiting: 107, 113th of 128 in FBS
• Returning production

  Share of last season's production (expected points added) by players still on the roster.
  64%64%70th of 128Returning production: 64%, 70th of 128 in FBS
• SP+ rating

  The result: Bill Connelly's opponent-adjusted rating, points per game better than an average FBS team.
  −11.2−11.2106th of 128SP+ rating: −11.2, 106th 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 UTSA'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 0.06 of the 0.11 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 CLTQ4 14:54 4th & 13 at UTSA 15, up 13Went for it; the numbers favored punting−2.5 pts
2. vs ASUQ4 1:10 4th & 24 at UTSA 32, down 4Went for it; the numbers favored punting−1.9 pts
3. vs UNMQ4 10:36 4th & 15 at UNM 35, down 10Went for it; the numbers favored a field goal−1.3 pts
4. vs ALSTQ2 1:47 4th & 3 at ALST 35, up 7Punted; the numbers favored going for it (close call)−0.8 pts
5. vs ASUQ2 3:09 4th & 10 at ASU 37, up 8Punted; the numbers favored going for it (close call)−0.8 pts
6. vs ALSTQ4 2:35 4th & 3 at ALST 41, up 13Punted; the numbers favored going for it (close call)−0.6 pts
7. vs CLTQ3 4:41 4th & 4 at CLT 9, up 10Kicked a field goal; the numbers favored going for it (close call)−0.3 pts
8. vs CLTQ1 3:20 4th & 3 at CLT 3, tiedKicked a field goal; the numbers favored going for it (close call)−0.3 pts

Record vs. expectation, 2016

20th of 128 FBS teams at beating the model's pregame expectations

Expected wins adds up UTSA's chance of winning each game as the model saw it at kickoff - a 70% favorite counts 0.7. Based on 12 of 13 games; one has no play-by-play to score.

1. W

   ALST
2. L

   CSU
3. L

   ASU
4. L

   ODU
5. W

   USM
6. W

   RICE
7. L

   UTEP
8. W

   UNT
9. W

   MTSU
10. L

    LT
11. L

    TA&M
12. W

    CLT
13. L

    UNM

What moved it, 2016

Every game starts at UTSA'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+0.70
• Possession & field position−0.02
• Clock+0.04
• Timeouts−0.05
• Less the pregame edge, counted before kickoff+0.65

Game by game

• ALSTW 26-13 vs ALST96% → 98%Before kickoff +46 · On the field +2: Scoreboard +8
• CSUL 14-23 at CSU29% → 3%Before kickoff −21 · On the field −25: Scoreboard −28, Timeouts −1
• ASUL 28-32 vs ASU12% → 4%Before kickoff −38 · On the field −8: Scoreboard −23, Clock −1
• ODUL 19-33 at ODU43% → 1%Before kickoff −7 · On the field −42: Scoreboard −38, Timeouts −2
• USMW 55-32 vs USM17% → 99%Before kickoff −33 · On the field +83: Scoreboard +58, Possession & field position −1
• RICEW 14-13 at RICE51% → 62%Before kickoff +1 · On the field +11: Scoreboard +23, Clock +4
• UNTW 31-17 vs UNT67% → 98%Before kickoff +17 · On the field +31: Scoreboard +32, Timeouts +1
• MTSUW 45-25 at MTSU10% → 99%Before kickoff −40 · On the field +89: Scoreboard +66, Clock +1
• LTL 35-63 at LT13% → 0%Before kickoff −37 · On the field −13: Scoreboard −16
• TA&ML 10-23 at TA&M4% → 1%Before kickoff −46 · On the field −3: Scoreboard −8
• CLTW 33-14 vs CLT67% → 99%Before kickoff +17 · On the field +32: Scoreboard +33, Clock +1
• UNML 20-23 vs UNM30% → 5%Before kickoff −20 · On the field −25: Scoreboard −37, Clock −2

Points of win probability, from UTSA's side.

Closest call

CCU 15-44 UTSA (2024) - UTSA dropped to a 0.0% win probability before pulling it out.