CBB Matchups

Every D1 game with tempo-free ratings, four factors and market lines, one place.

College basketball is being built for the 2026-27 season (tip-off Nov 3). What's here fills in as games are played.
Non-conferenceThomas & Mack Center, Las Vegas, NV
Stanford
Efficiency #77
2025-26: 20-13 · 9-10 conf
@
UNLV
Efficiency #124
2025-26: 18-17 · 12-10 conf
Fri, Nov 13 · TBD
76.6
win 46%
Projected
77.8
win 54%
No line posted yet.
Model: UNLV −1.3 · total 154.4 · ratings as of Sat, Oct 3
Preseason ratings start from 2025-26's final adjusted ratings, regressed toward the D1 average. The stats below are 2025-26's too; both update once 2026-27 games are played.

How the projection is built

each step, from an average D-I game to this one
Projected total
Average D-I game
148.2
PaceAdjusted tempo: UNLV 69.9, Stanford 68.1 → 69.7 possessions
+3.0
UNLV offense vs Stanford defense
+0.2
Stanford offense vs UNLV defense
+2.9
Projected total
154.4
Axis starts at 145 pointsPoints added or removed at each step
Projected margin
Even game
0.0
UNLV offense vs Stanford defense
+0.2
Stanford offense vs UNLV defense
−2.9
Home court+2.9 points per 100 possessions to the home offense, −2.9 to the visitors
+4.0
UNLV by 1.3
1.3
Stanford side ◂▸ UNLV side

Steps are rounded to one decimal, so they can differ from the total by 0.1: the total steps add to 154.3 against a projected 154.4. The margin is the same build seen from the home side; positive numbers help UNLV.

Pace & total

possessions per 40 minutes
Tempo, possessions per 40 minutes

Projected possessions: 69.7, from each team's opponent-adjusted tempo. On raw 2025-26 season pace, UNLV is the faster team (70.5, #75 of 365) and Stanford the slower (67.9, #209). When tempos clash, the slower team usually pulls the pace harder (study weights 0.80 vs 0.66).

Projected possessions
69.7
per team, 40 min
Projected total
154.4
model
Projected team totals
76.6 / 77.8
Stanford / UNLV
Adjusted tempo, vs an average opponent
68.1#201
Stanford
69.9#55
UNLV

How this game plays

what the study found each side's numbers actually do when these two teams meet
DefenseUNLV
UNLV's shot-blocking
UNLV ranks #33 nationally in block rate — one of the few defensive numbers that travels. Expect about 11.7% of Stanford's two-point attempts to get blocked against this defense.
AdvantageUNLV
Where UNLV scores: shooting
UNLV shoots an effective 53.6% (#81); Stanford allows 51.0% (#158). Expect about 52.4%, 1.2 points above an average D1 matchup — worth roughly +1.7 points per 100 possessions.
Stanford has the ball
OffenseUNLV allowsvs D1 avg
51.5% #173
52.3% #231
+0.9 pt/100
15.5% #103
17.4% #126
+0.4 pt/100
32.5% #109
32.0% #256
+1.3 pt/100
35.5% #165
44.9% #349
+0.8 pt/100
73.2% #163
—
+0.1 pt/100
Stanford net across the four factors + FT%, vs. a D1-average matchup: +3.5 pts/100
UNLV has the ball
OffenseStanford allowsvs D1 avg
53.6% #81
51.0% #158
+1.7 pt/100
17.1% #209
18.0% #97
-1.2 pt/100
30.2% #189
29.8% #149
-0.3 pt/100
40.7% #51
40.3% #305
+0.7 pt/100
67.7% #331
—
-0.4 pt/100
UNLV net across the four factors + FT%, vs. a D1-average matchup: +0.6 pts/100

Shot diet

Where each offense shoots from and where each defense lets teams shoot · 2025-26 numbers
Stanfordoffense vsUNLVdefense
Share of shots by zone
Stanford shoots
UNLV allows
D-I average
FG% by zone
ZoneD-I avg
Rim53.8%#32158.5%#16558.6%
Short 2 (≤9 ft)39.0%#19326.2%#239.5%
Mid 2 (10 ft+)42.1%#5342.7%#32338.0%
Three35.2%#10335.2%#25933.7%
UNLVoffense vsStanforddefense
Share of shots by zone
UNLV shoots
Stanford allows
D-I average
FG% by zone
ZoneD-I avg
Rim60.3%#11158.2%#15358.6%
Short 2 (≤9 ft)40.0%#17138.8%#15539.5%
Mid 2 (10 ft+)42.0%#5839.9%#24238.0%
Three35.2%#10232.9%#11833.7%
RimShort 2 (≤9 ft)Mid 2 (10 ft+)Three

Zones from play-by-play shot descriptions; garbage time removed. Bars show the share of field-goal attempts in each zone; where a slice is too thin for its number, the shares are listed under the bar. FG% ranks: #1 = best for that side (most accurate shooting, stingiest defense).

Shot volume

Extra scoring chances from rebounds, turnovers and the foul line · 2025-26 numbers
Stanford
+1.5extra scoring chances per game vs a D-I average team#121 of 365
Off reb %
32.5%
#109
Def reb %
70.2%
#149
Turnover %
15.5%
#103
Forced TO %
18.0%
#97
FT rate
35.5%
#165
FT allowed
40.3%
#305
Where the chances come from (per game)
Offensive rebounding+0.7
Defensive rebounding+0.3
Ball security+0.9
Forcing turnovers+0.9
Foul shots drawn+0.1
Foul shots allowed−1.3
UNLV
−1.2extra scoring chances per game vs a D-I average team#244 of 365
Off reb %
30.2%
#189
Def reb %
68.0%
#256
Turnover %
17.1%
#209
Forced TO %
17.4%
#126
FT rate
40.7%
#51
FT allowed
44.9%
#349
Where the chances come from (per game)
Offensive rebounding−0.1
Defensive rebounding−0.4
Ball security−0.2
Forcing turnovers+0.5
Foul shots drawn+1.4
Foul shots allowed−2.5

Stanford has more extra scoring chances per game: +1.5 (#121 of 365) to UNLV’s −1.2 (#244).

Grade = extra shots and foul-line trips per game compared with a D-I average team, built from six season rates. Cell shading is the national rank for each rate (greener = better for that team).

Rotation

Stanford2025-26 numbers
#PlayerPosClHtGP-GSMPGPPGRPGAPGTOVeFG%3P%3PA/gFT%UsageStatus
5Benny GealerGSr6'1"33-3129.510.92.51.70.760.3%41.0%6.489.5%18.7%
52Aidan CammannFSo6'10"33-1521.76.53.81.41.150.0%26.8%1.270.5%18.5%
11Ryan AgarwalGJr6'6"32-1922.35.94.10.81.246.0%34.9%3.469.6%19.5%
4AJ RohosyFSr6'9"33-2820.57.65.60.71.055.4%—0.037.8%23.5%
25Jeremy Dent-SmithGSr6'1"33-420.18.21.81.51.249.0%37.9%4.682.9%26.6%
10Chisom OkparaFSr6'8"17-1728.513.93.92.22.644.2%30.4%3.373.0%32.3%
15Oskar GiltayFFr6'10"32-514.63.34.10.30.762.7%—0.057.5%13.8%
2Donavin YoungFSo6'8"21-915.42.52.70.70.445.6%29.6%1.350.0%12.5%
24Jaylen ThompsonFSo6'7"20-611.22.31.20.30.350.0%21.1%0.966.7%14.5%
20Cameron GrantFSo6'7"20-010.42.11.30.40.336.0%24.5%2.6100.0%17.8%
12Kristers SkrindaFFr6'10"9-06.10.81.60.10.138.9%25.0%0.4—10.2%
33Evan StinsonFSo6'7"4-05.31.80.50.30.350.0%25.0%1.0100.0%18.5%
3Tallis ToureFSo6'11"2-03.50.01.00.00.00.0%—0.0—7.4%
13Ethan KitchGSo6'2"3-01.01.00.00.00.075.0%50.0%0.7—37.9%
0Anthony Batson Jr.GSo6'3"0-00.00.00.00.00.0——0.0——
6Myles JonesGFr6'3"0-00.00.00.00.00.0——0.0——
Rosters come from ESPN and are still filling in; incoming transfers and freshmen may be missing.

Tale of the tape

national rank toned left for away, right for home
StanfordUNLV
Efficiency
112.4
111.3
106.0
108.8
68.1
69.9
—
—
Shooting
51.5%
53.6%
50.5%
54.0%
35.2%
35.2%
73.2%
67.7%
35.5%
40.7%
Ball control & glass
15.5%
17.1%
18.0%
17.4%
32.5%
30.2%
29.8%
32.0%
7.3%
9.2%
9.5%
7.8%
Style
44.0%
36.4%
40.3%
46.9%
12.9%
13.8%
45.8%
51.3%
33.7%
29.1%
Roster
3.04
2.76
6'6.2"
6'5.4"
83.6%
—

Offseason

roster movement into this season — descriptive, not a model input
Stanford
Returning minutes: 83.6%Returning production: 73.5%
Lost
  • Ebuka Okorie 33.0 mpg, 21.8 ppg · not on a D1 roster yet
Added
  • Myles Jones no D1 minutes last season
  • Anthony Batson Jr. no D1 minutes last season

No incoming D1 transfers are listed on ESPN's roster yet — until they are, returning shares read high.

Returns 84% of last season's minutes; lost 1 of its top 5. Teams returning at least 65% of last season's minutes have historically gained about 2.2 points of AdjEM (n=156 team-seasons, 2022-2026).

UNLV
Returning minutes: 100.0%Returning production: 100.0%
ESPN hasn't posted UNLV's roster for this season yet — no movement to show.

Returns 100% of last season's minutes. Teams returning at least 65% of last season's minutes have historically gained about 2.2 points of AdjEM (n=156 team-seasons, 2022-2026). (ESPN hasn't posted this season's roster yet -- this may still be last season's list.)

Head-to-head

this season and last
DateSeasonResult
Sun, Dec 72025-26UNLV 75 – 74 Stanford

The CBB Matchups tab lays out the day's Division I slate: matchup, tip time, records and rank, and — once posted — Pinnacle's spread and total (the median across books when Pinnacle has no line). Three views switch the table between projected scores, pace and totals, and each team's offensive and defensive tendencies. Each game opens into opponent-adjusted possessions, efficiency and four-factor tendencies for both teams, plus recent form, rotation and head-to-head history. Every game also carries our projected score, spread and total, built bottom-up from those same numbers: expected possessions times each side's opponent-adjusted points per possession, plus home court.

What these numbers mean: line shopping.