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-conferencePetersen Events Center, Pittsburgh, PA
Buffalo
Efficiency #186
2025-26: 17-15 · 7-12 conf
@
Pitt
Efficiency #120
2025-26: 13-20 · 6-14 conf
Thu, Nov 5 · TBD
70.2
win 31%
Projected
76.1
win 69%
No line posted yet.
Model: PITT −5.9 · total 146.2 · 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: Pitt 65.9, Buffalo 67.7 → 65.3 possessions
−6.5
Pitt offense vs Buffalo defense
+3.4
Buffalo offense vs Pitt defense
+1.2
Projected total
146.2
Axis starts at 135 pointsPoints added or removed at each step
Projected margin
Even game
0.0
Pitt offense vs Buffalo defense
+3.4
Buffalo offense vs Pitt defense
−1.2
Home court+2.9 points per 100 possessions to the home offense, −2.9 to the visitors
+3.8
Pitt by 5.9
5.9
Buffalo side ◂▸ Pitt side

Steps are rounded to one decimal, so they can differ from the total by 0.1: the total steps add to 146.3 against a projected 146.2, and the margin steps add to +6.0 against +5.9. The margin is the same build seen from the home side; positive numbers help Pitt.

Pace & total

possessions per 40 minutes
Tempo, possessions per 40 minutes

Projected possessions: 65.3, from each team's opponent-adjusted tempo. On raw 2025-26 season pace, Buffalo is the faster team (67.6, #215 of 365) and Pitt the slower (64.5, #340). When tempos clash, the slower team usually pulls the pace harder (study weights 0.80 vs 0.66).

Projected possessions
65.3
per team, 40 min
Projected total
146.2
model
Projected team totals
70.2 / 76.1
Buffalo / Pitt
Adjusted tempo, vs an average opponent
67.7#229
Buffalo
65.9#346
Pitt

How this game plays

what the study found each side's numbers actually do when these two teams meet
AdvantageBuffalo
Where Buffalo scores: shooting
Buffalo shoots an effective 55.4% (#30); Pitt allows 53.4% (#279). Expect about 54.7%, 3.4 points above an average D1 matchup — worth roughly +4.9 points per 100 possessions.
Pace
Half-court grind
Pitt averages 64.5 possessions per 40, Buffalo 67.6. Slower teams pull the pace toward themselves more than fast teams push it (0.80 vs 0.66 in five seasons of games), so expect about 65 — more possessions mean more points in the total and more chances for the better team to separate.
AdvantagePitt
Where Pitt scores: shooting
Pitt shoots an effective 51.0% (#193); Buffalo allows 55.1% (#333). Expect about 53.1%, 1.8 points above an average D1 matchup — worth roughly +2.6 points per 100 possessions.
GlassBuffalo
Buffalo gets out-rebounded
Projected offensive rebound rate of 27.0% is worth roughly -2.1 points per 100 possessions against Buffalo's offense.
Watch for noise
3-point caution
A notable season 3-point number here (Buffalo) 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 Buffalo at about 45.3% of its shots from three, Pitt about 43.6%.
GlassPitt
Pitt owns the glass
Projected offensive rebound rate of 33.2% is worth roughly +1.7 points per 100 possessions for Pitt's offense.
Buffalo has the ball
OffensePitt allowsvs D1 avg
55.4% #30
53.4% #279
+4.9 pt/100
16.2% #151
16.2% #208
+0.9 pt/100
27.1% #291
28.1% #68
-2.1 pt/100
39.6% #65
30.9% #82
-0.0 pt/100
73.6% #151
—
+0.1 pt/100
Buffalo net across the four factors + FT%, vs. a D1-average matchup: +3.7 pts/100
Pitt has the ball
OffenseBuffalo allowsvs D1 avg
51.0% #193
55.1% #333
+2.6 pt/100
17.3% #225
17.9% #101
-1.2 pt/100
33.9% #72
31.1% #209
+1.7 pt/100
30.4% #300
31.4% #90
-0.6 pt/100
67.4% #336
—
-0.4 pt/100
Pitt net across the four factors + FT%, vs. a D1-average matchup: +2.0 pts/100

Shot diet

Where each offense shoots from and where each defense lets teams shoot · 2025-26 numbers
Buffalooffense vsPittdefense
Share of shots by zone
Buffalo shoots
Pitt allows
D-I average
FG% by zone
ZoneD-I avg
Rim59.8%#12460.0%#22558.6%
Short 2 (≤9 ft)41.9%#12444.3%#27839.5%
Mid 2 (10 ft+)40.0%#10938.8%#19338.0%
Three37.9%#2035.2%#26333.7%
Pittoffense vsBuffalodefense
Share of shots by zone
Pitt shoots
Buffalo allows
D-I average
FG% by zone
ZoneD-I avg
Rim57.3%#21762.0%#30058.6%
Short 2 (≤9 ft)35.4%#28155.6%#36539.5%
Mid 2 (10 ft+)43.1%#3542.2%#31038.0%
Three33.4%#20635.1%#25233.7%
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
Buffalo
+2.2extra scoring chances per game vs a D-I average team#92 of 365
Off reb %
27.1%
#291
Def reb %
68.9%
#209
Turnover %
16.2%
#151
Forced TO %
17.9%
#101
FT rate
39.6%
#65
FT allowed
31.4%
#90
Where the chances come from (per game)
Offensive rebounding−1.1
Defensive rebounding−0.2
Ball security+0.4
Forcing turnovers+0.8
Foul shots drawn+1.2
Foul shots allowed+1.0
Pitt
+1.2extra scoring chances per game vs a D-I average team#131 of 365
Off reb %
33.9%
#72
Def reb %
71.9%
#68
Turnover %
17.3%
#225
Forced TO %
16.2%
#208
FT rate
30.4%
#300
FT allowed
30.9%
#82
Where the chances come from (per game)
Offensive rebounding+1.1
Defensive rebounding+0.8
Ball security−0.3
Forcing turnovers−0.3
Foul shots drawn−1.2
Foul shots allowed+1.1

Buffalo has more extra scoring chances per game: +2.2 (#92 of 365) to Pitt’s +1.2 (#131).

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

Buffalo2025-26 numbers
#PlayerPosClHtGP-GSMPGPPGRPGAPGTOVeFG%3P%3PA/gFT%UsageStatus
21Drew Steffeat S IllinoisGSr6'5"32-3230.89.93.12.51.649.1%29.9%5.167.2%21.0%
2J.J. Starlingat SyracuseGSr6'4"30-3029.010.92.72.41.445.7%28.9%2.854.8%26.6%
25Dylan Ducommunat N IllinoisGSo6'4"26-2630.610.82.93.42.942.7%28.9%7.370.0%28.1%
1Jalen Pitreat Tennessee StFSr6'8"33-3324.36.46.60.91.162.7%16.7%0.283.9%13.4%
9Antoine Lorick IIIat Tennessee StFSr6'8"33-2421.59.85.11.11.554.7%25.6%1.265.7%26.2%
10Mikkel Tyneat RichmondGSr5'10"31-2819.06.21.21.41.052.0%39.4%3.564.7%21.0%
3Tristan Kuskaat 108942FSo6'9"3-317.35.72.70.71.356.7%25.0%1.3—20.7%
7Harrison Reedeat Incarnate WordGSr6'1"25-310.95.10.80.10.178.8%53.2%3.266.7%17.8%
12Kyle JonesGSr6'5"18-010.52.22.20.30.250.0%0.0%0.266.7%12.4%
0Matas ButeliauskasFSo6'7"8-04.90.81.10.00.160.0%40.0%0.60.0%10.1%
20Daniel ShaoGSr5'10"2-01.50.00.00.00.5——0.0—21.9%
35Evan RomanoGSo6'3"0-00.00.00.00.00.0——0.0——
22JJ Frakesat OregonGSo6'5"0-00.00.00.00.00.0——0.0——
5Boluwasefe JohnFJr6'8"0-00.00.00.00.00.0——0.0——
24Jahrel VigoGFr6'3"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.
Transfers in: Dylan Ducommun (from N Illinois), JJ Frakes (from Oregon), Tristan Kuska (from 108942), Antoine Lorick III (from Tennessee St), Jalen Pitre (from Tennessee St), Harrison Reede (from Incarnate Word), J.J. Starling (from Syracuse), Drew Steffe (from S Illinois), Mikkel Tyne (from Richmond)

Tale of the tape

national rank toned left for away, right for home
BuffaloPitt
Efficiency
111.2
110.2
111.9
107.6
67.7
65.9
—
—
Shooting
55.4%
51.0%
54.1%
51.7%
37.9%
33.4%
73.6%
67.4%
39.6%
30.4%
Ball control & glass
16.2%
17.3%
17.9%
16.2%
27.1%
33.9%
31.1%
28.1%
9.8%
9.5%
8.1%
9.2%
Style
45.5%
42.6%
37.8%
41.5%
14.1%
12.6%
54.4%
52.3%
24.2%
22.7%
Roster
3.67
2.02
6'4.6"
6'3.6"
3.6%
55.7%

Offseason

roster movement into this season — descriptive, not a model input
Buffalo
Returning minutes: 3.6%Returning production: 2.6%
Lost
  • Ryan Sabol 36.2 mpg, 18.8 ppg · not on a D1 roster yet
  • Noah Batchelor 30.8 mpg, 7.3 ppg · not on a D1 roster yet
  • Angelo Brizzi 29.8 mpg, 14.5 ppg · transferred to North Carolina
  • Derrick Talton Jr. 26.0 mpg, 5.3 ppg · not on a D1 roster yet
  • Daniel Freitag 23.9 mpg, 14.3 ppg · not on a D1 roster yet
  • Tim Oboh 21.0 mpg, 8.5 ppg · not on a D1 roster yet
Added
  • Drew Steffe from S Illinois · 30.8 mpg, 9.9 ppg last season
  • J.J. Starling from Syracuse · 27.2 mpg, 10.3 ppg last season
  • Dylan Ducommun from N Illinois · 26.5 mpg, 9.3 ppg last season
  • Jalen Pitre from Tennessee St · 24.3 mpg, 6.4 ppg last season
  • Antoine Lorick III from Tennessee St · 21.5 mpg, 9.8 ppg last season
  • Mikkel Tyne from Richmond · 18.4 mpg, 6.0 ppg last season

Returns 4% of last season's minutes; lost 5 of its top 5 (1 transferred). Teams returning under 40% of last season's minutes have historically fallen about 0.8 points of AdjEM (n=716 team-seasons, 2022-2026).

Pitt
Returning minutes: 55.7%Returning production: 50.3%
Lost
  • Cameron Corhen 33.8 mpg, 13.6 ppg · not on a D1 roster yet
  • Barry Dunning Jr. 31.8 mpg, 12.5 ppg · not on a D1 roster yet
  • Nojus Indrusaitis 23.6 mpg, 9.1 ppg · transferred to California
Added
  • Benjamin Mayhew no D1 minutes last season
  • Henry Lau no D1 minutes last season
  • Jajuan Nelson no D1 minutes last season
  • Amdy Ndiaye no D1 minutes last season
  • Dishon Jackson no D1 minutes last season

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

Returns 56% of last season's minutes; lost 2 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.