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-conferenceWatsco Center, Coral Gables, FL
Manhattan
Efficiency #328
2025-26: 12-20 · 8-13 conf
@
Miami
Efficiency #30
2025-26: 26-9 · 14-6 conf
Fri, Nov 20 · TBD
67.4
win 5%
Projected
87.0
win 95%
No line posted yet.
Model: MIA −19.7 · 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: Miami 68.7, Manhattan 68.7 → 69.1 possessions
+1.6
Miami offense vs Manhattan defense
+10.1
Manhattan offense vs Miami defense
−5.6
Projected total
154.4
Axis starts at 140 pointsPoints added or removed at each step
Projected margin
Even game
0.0
Miami offense vs Manhattan defense
+10.1
Manhattan offense vs Miami defense
+5.6
Home court+2.9 points per 100 possessions to the home offense, −2.9 to the visitors
+4.0
Miami by 19.7
19.7
Manhattan side ◂▸ Miami 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 Miami.

Pace & total

possessions per 40 minutes
Tempo, possessions per 40 minutes

Projected possessions: 69.1, from each team's opponent-adjusted tempo. On raw 2025-26 season pace, Miami is the faster team (69.2, #137 of 365) and Manhattan the slower (68.1, #195). When tempos clash, the slower team usually pulls the pace harder (study weights 0.80 vs 0.66).

Projected possessions
69.1
per team, 40 min
Projected total
154.4
model
Projected team totals
67.4 / 87.0
Manhattan / Miami
Adjusted tempo, vs an average opponent
68.7#143
Manhattan
68.7#145
Miami

How this game plays

what the study found each side's numbers actually do when these two teams meet
AdvantageMiami
Where Miami scores: shooting
Miami shoots an effective 55.3% (#32); Manhattan allows 53.9% (#297). Expect about 55.0%, 3.7 points above an average D1 matchup — worth roughly +5.2 points per 100 possessions.
GlassMiami
Miami owns the glass
Projected offensive rebound rate of 38.3% is worth roughly +4.8 points per 100 possessions for Miami's offense.
AdvantageManhattan
Where Manhattan struggles: shooting
Manhattan shoots an effective 47.9% (#315); Miami allows 51.7% (#198). Expect about 49.5%, 1.7 points below an average D1 matchup — worth roughly -2.5 points per 100 possessions.
GlassManhattan
Manhattan gets out-rebounded
Projected offensive rebound rate of 26.9% is worth roughly -2.1 points per 100 possessions against Manhattan's offense.
Watch for noise
3-point caution
A notable season 3-point number here (Manhattan's defense) is close to a coin flip going forward — the study's model of next-game 3P% barely beats guessing the D1 average. Shot volume is what actually carries: expect Manhattan at about 38.5% of its shots from three, Miami about 32.4%.
ClashMiami
Style match: paint scoring
Miami scores 51.9% of its points in the paint; Manhattan gives up 46.6% of opponents' points in the paint (D1 average 42.8%). Expect about 46.3% — both habits push the same way.
Manhattan has the ball
OffenseMiami allowsvs D1 avg
47.9% #315
51.7% #198
-2.5 pt/100
15.6% #111
18.2% #88
-0.3 pt/100
28.6% #247
25.9% #19
-2.1 pt/100
34.0% #208
29.5% #50
-0.5 pt/100
79.6% #3
—
+0.6 pt/100
Manhattan net across the four factors + FT%, vs. a D1-average matchup: -4.8 pts/100
Miami has the ball
OffenseManhattan allowsvs D1 avg
55.3% #32
53.9% #297
+5.2 pt/100
16.2% #150
16.7% #175
+0.5 pt/100
37.2% #14
37.0% #358
+4.8 pt/100
37.1% #125
25.7% #11
-0.6 pt/100
68.3% #320
—
-0.4 pt/100
Miami net across the four factors + FT%, vs. a D1-average matchup: +9.7 pts/100

Shot diet

Where each offense shoots from and where each defense lets teams shoot · 2025-26 numbers
Manhattanoffense vsMiamidefense
Share of shots by zone
Manhattan shoots
Miami allows
D-I average
FG% by zone
ZoneD-I avg
Rim50.2%#36059.8%#21958.6%
Short 2 (≤9 ft)43.8%#8544.3%#27739.5%
Mid 2 (10 ft+)42.9%#3837.3%#13838.0%
Three32.0%#28035.8%#29633.7%
Miamioffense vsManhattandefense
Share of shots by zone
Miami shoots
Manhattan allows
D-I average
FG% by zone
ZoneD-I avg
Rim64.2%#3458.5%#16658.6%
Short 2 (≤9 ft)35.6%#27542.5%#23739.5%
Mid 2 (10 ft+)42.0%#5442.7%#32638.0%
Three34.9%#12736.6%#33233.7%
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
Manhattan
+0.3extra scoring chances per game vs a D-I average team#168 of 365
Off reb %
28.6%
#247
Def reb %
63.0%
#358
Turnover %
15.6%
#111
Forced TO %
16.7%
#175
FT rate
34.0%
#208
FT allowed
25.7%
#11
Where the chances come from (per game)
Offensive rebounding−0.6
Defensive rebounding−2.0
Ball security+0.8
Forcing turnovers0.0
Foul shots drawn−0.2
Foul shots allowed+2.4
Miami
+7.2extra scoring chances per game vs a D-I average team#7 of 365
Off reb %
37.2%
#14
Def reb %
74.1%
#19
Turnover %
16.2%
#150
Forced TO %
18.2%
#88
FT rate
37.1%
#125
FT allowed
29.5%
#50
Where the chances come from (per game)
Offensive rebounding+2.2
Defensive rebounding+1.5
Ball security+0.4
Forcing turnovers+1.0
Foul shots drawn+0.5
Foul shots allowed+1.5

Miami has more extra scoring chances per game: +7.2 (#7 of 365) to Manhattan’s +0.3 (#168).

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

Manhattan2025-26 numbers
#PlayerPosClHtGP-GSMPGPPGRPGAPGTOVeFG%3P%3PA/gFT%UsageStatus
6Ben Tweedyat ColgateGJr6'4"33-3329.49.43.12.61.355.9%38.5%3.370.3%19.0%
12Josiah Sabinoat NiagaraGSr6'4"28-1021.07.14.60.91.349.3%24.5%1.890.9%22.6%
10Quron ElliottGSo6'2"29-012.03.61.20.90.547.3%31.8%0.870.8%20.0%
16Ognjen Stankovicat AkronFSo6'8"18-06.62.31.70.50.755.4%20.0%0.358.8%22.1%
23Jackson DelaneyGSr6'5"18-05.00.70.70.20.242.9%36.4%0.60.0%11.9%
1Kevin Kearneyat Saint Joseph'sFSo6'7"14-04.10.61.00.10.318.8%0.0%0.650.0%22.5%
11Asier MiguelGSo6'4"9-04.92.30.40.30.359.4%30.0%1.150.0%26.6%
87Liam McChesneyat CreightonFSr6'10"3-02.70.70.70.00.725.0%0.0%0.7—42.1%
20Jaeden RobleyGFr6'5"0-00.00.00.00.00.0——0.0——
9Jameson BorodawkaGFr5'10"0-00.00.00.00.00.0——0.0——
22Nick LukmannGFr6'4"0-00.00.00.00.00.0——0.0——
13Anthony PacciarelliGSo6'5"0-00.00.00.00.00.0——0.0——
3DaSean LewisGJr5'10"0-00.00.00.00.00.0——0.0——
7Dusan EricFFr6'8"0-00.00.00.00.00.0——0.0——
2Isaac JohnsonFJr6'7"0-00.00.00.00.00.0——0.0——
0Jahzar GreeneGFr5'11"0-00.00.00.00.00.0——0.0——
14Novak ManojlovicFFr6'8"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.
Transfers in: Kevin Kearney (from Saint Joseph's), Liam McChesney (from Creighton), Josiah Sabino (from Niagara), Ognjen Stankovic (from Akron), Ben Tweedy (from Colgate)

Tale of the tape

national rank toned left for away, right for home
ManhattanMiami
Efficiency
105.7
115.9
115.7
103.2
68.7
68.7
—
—
Shooting
47.9%
55.3%
47.8%
56.7%
32.0%
34.9%
79.6%
68.3%
34.0%
37.1%
Ball control & glass
15.6%
16.2%
16.7%
18.2%
28.6%
37.2%
37.0%
25.9%
8.2%
9.7%
10.7%
10.2%
Style
37.1%
31.9%
41.4%
51.9%
11.1%
14.2%
49.6%
54.4%
28.1%
25.0%
Roster
3.05
3.04
6'4"
6'5.9"
7.4%
42.5%

Offseason

roster movement into this season — descriptive, not a model input
Manhattan
Returning minutes: 7.4%Returning production: 5.3%
Lost
  • Jaden Winston 35.3 mpg, 15.3 ppg · not on a D1 roster yet
  • Devin Dinkins 33.6 mpg, 12.1 ppg · not on a D1 roster yet
  • Fraser Roxburgh 30.6 mpg, 11.0 ppg · not on a D1 roster yet
  • Terrance Jones 28.2 mpg, 12.7 ppg · not on a D1 roster yet
  • Anthony Isaac 23.8 mpg, 9.5 ppg · not on a D1 roster yet
  • Marko Ljubicic 20.0 mpg, 5.8 ppg · not on a D1 roster yet
Added
  • Ben Tweedy from Colgate · 29.4 mpg, 9.4 ppg last season
  • Josiah Sabino from Niagara · 19.6 mpg, 6.6 ppg last season
  • Ognjen Stankovic from Akron · 3.4 mpg, 1.2 ppg last season
  • Kevin Kearney from Saint Joseph's · 1.6 mpg, 0.3 ppg last season
  • Liam McChesney from Creighton · 0.2 mpg, 0.1 ppg last season
  • Jameson Borodawka no D1 minutes last season

Returns 7% of last season's minutes; lost 5 of its top 5. Teams returning under 40% of last season's minutes have historically fallen about 0.8 points of AdjEM (n=716 team-seasons, 2022-2026).

Miami
Returning minutes: 42.5%Returning production: 45.1%
Lost
  • Tre Donaldson 34.1 mpg, 16.4 ppg · not on a D1 roster yet
  • Ernest Udeh Jr. 26.7 mpg, 6.3 ppg · not on a D1 roster yet
  • Tru Washington 24.3 mpg, 10.6 ppg · not on a D1 roster yet
  • Timotej Malovec 14.1 mpg, 3.9 ppg · not on a D1 roster yet
  • Noam Dovrat 7.3 mpg, 2.9 ppg · not on a D1 roster yet
  • Salih Altuntas 7.1 mpg, 1.5 ppg · not on a D1 roster yet
Added
  • Brent Bland from Saint Peter's · 33.1 mpg, 13.9 ppg last season
  • DeSean Goode from Robert Morris · 31.0 mpg, 15.2 ppg last season
  • Acaden Lewis from Villanova · 30.5 mpg, 12.2 ppg last season
  • Nick Dorn from Indiana · 21.8 mpg, 7.6 ppg last season
  • Somto Cyril from Georgia · 21.1 mpg, 9.3 ppg last season
  • Robert Miller III from LSU · 18.8 mpg, 5.9 ppg last season

Returns 42% of last season's minutes; lost 3 of its top 5.

Head-to-head

this season and last
No meetings the last two seasons.

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.