NBA predictions Tue, Nov 10

Win probabilities, projected scores and totals for every NBA game, plus player box-score lines. Methodology →

6 games on this slate · 5–2 since Nov 8 (model V6) →

Power rankings: every team's model rating →

‹

Updated 43m ago

Tue, Nov 10 · 7:30 PM ET

#27 Bucks

16.7% 103.5

#8 Knicks

83.3% 116.3

ModelMarket

Spread NYK -12.8 NYK -15.5

NYK win 83.3% 89.0%

Total 219.8 220.5

Biggest factor: Home court · NYK +1.7 pts

Tue, Nov 10 · 8:00 PM ET

#2 Celtics

59.1% 110.0

#11 76ers

40.9% 106.9

ModelMarket

Spread BOS -3.0 BOS -1.5

BOS win 59.1% 50.0%

Total 216.9 216.5

Biggest factor: Injuries and absences · PHI +1.9 pts

Tue, Nov 10 · 9:00 PM ET

#21 Clippers

42.3% 111.3

#18 Suns

57.7% 113.9

Projected total 225.2

Biggest factor: Injuries and absences · PHX +2.7 pts

Tue, Nov 10 · 9:00 PM ET

#17 Heat

73.4% 119.0

#30 Jazz

26.6% 110.3

ModelMarket

Spread MIA -8.6 MIA -10.5

Total 229.3 229.5

Biggest factor: Injuries and absences · MIA +3.5 pts

Tue, Nov 10 · 10:00 PM ET

#20 Magic

59.7% 113.3

#28 Kings

40.3% 110.0

ModelMarket

Spread ORL -3.3 ORL -5.5

Total 223.2 222.5

Biggest factor: Home court · SAC +1.7 pts

Tue, Nov 10 · 11:00 PM ET

#7 Thunder

51.6% 112.5

#12 Trail Blazers

48.4% 112.0

ModelMarket

Spread OKC -0.5 OKC -1.5

Total 224.4 226.5

Biggest factor: Injuries and absences · POR +2.0 pts

	MIL	NYK
Offensive ratingpts/100	110.225th of 30	115.48th of 30
Defensive ratingpts/100	117.728th of 30	111.76th of 30
Net ratingpts/100	-7.527th of 30	3.78th of 30
Paceposs/48	100.213th of 30	97.129th of 30

	BOS	PHI
Offensive ratingpts/100	117.42nd of 30	112.321st of 30
Defensive ratingpts/100	110.45th of 30	112.37th of 30
Net ratingpts/100	7.02nd of 30	0.015th of 30
Paceposs/48	96.930th of 30	100.015th of 30

	LAC	PHX
Offensive ratingpts/100	113.119th of 30	113.915th of 30
Defensive ratingpts/100	114.621st of 30	115.223rd of 30
Net ratingpts/100	-1.520th of 30	-1.319th of 30
Paceposs/48	100.114th of 30	99.123rd of 30

	MIA	UTA
Offensive ratingpts/100	113.218th of 30	108.430th of 30
Defensive ratingpts/100	114.218th of 30	116.125th of 30
Net ratingpts/100	-1.018th of 30	-7.729th of 30
Paceposs/48	102.13rd of 30	101.07th of 30

	ORL	SAC
Offensive ratingpts/100	111.322nd of 30	110.226th of 30
Defensive ratingpts/100	114.319th of 30	117.829th of 30
Net ratingpts/100	-3.022nd of 30	-7.728th of 30
Paceposs/48	98.826th of 30	100.79th of 30

	OKC	POR
Offensive ratingpts/100	114.712th of 30	115.211th of 30
Defensive ratingpts/100	109.11st of 30	113.916th of 30
Net ratingpts/100	5.63rd of 30	1.314th of 30
Paceposs/48	99.322nd of 30	101.16th of 30

Points leaders

Shai Gilgeous-Alexander29.6 pts
Giannis Antetokounmpo26.2 pts
Jalen Brunson25.1 pts
Devin Booker24.1 pts
Jayson Tatum23.7 pts

Rank by


MIA @ UTA	26.2	9.2	0.82	5.4	3.23	8.3	4.7	1.08	1.12	3.17
OKC @ POR	29.6	8.4	2.47	5.4	0.52	3.6	5.0	1.18	1.00	2.38
ORL @ SAC	22.1	6.7	1.53	4.0	1.04	4.6	8.4	1.31	0.00	3.67
BOS @ PHI	23.7	5.1	2.26	6.6	0.47	4.6	6.1	0.71	0.58	2.39
MIL @ NYK	25.1	6.0	2.65	5.2	0.74	2.4	4.8	0.79	0.25	1.59
ORL @ SAC	23.2	5.9	2.74	3.2	0.43	2.6	6.8	0.95	0.08	3.13
OKC @ POR	21.0	5.3	1.59	5.6	1.03	4.8	5.8	0.84	0.09	2.76
LAC @ PHX	24.1	6.6	1.94	5.2	0.56	2.3	4.2	1.13	0.19	2.27
MIA @ UTA	22.3	3.1	4.05	3.9	1.35	5.5	3.3	0.93	0.54	2.14
BOS @ PHI	21.7	5.2	2.19	4.8	0.36	2.9	4.6	1.08	0.67	2.04
MIA @ UTA	19.7	4.8	2.40	2.9	1.34	3.2	5.6	1.29	0.78	2.28
MIL @ NYK	17.7	5.0	1.52	3.2	2.60	7.5	2.8	0.78	0.41	2.23
MIA @ UTA	18.2	5.7	1.02	3.8	2.39	5.3	3.0	1.80	0.62	1.80
BOS @ PHI	19.2	5.6	1.67	3.0	1.41	4.4	3.5	1.01	0.45	1.97
BOS @ PHI	16.8	5.0	0.78	4.5	1.81	5.8	3.3	0.71	0.90	2.49
OKC @ POR	20.0	6.7	1.19	3.0	0.89	3.3	3.6	1.36	0.37	1.69
LAC @ PHX	18.3	4.2	2.17	3.5	0.47	1.5	5.7	0.74	0.00	3.99
ORL @ SAC	15.6	3.5	1.73	3.3	1.03	3.9	5.1	1.39	1.02	2.28
MIL @ NYK	18.4	4.0	2.53	2.7	0.90	3.2	4.1	0.84	0.09	1.93
BOS @ PHI	14.5	4.4	1.04	2.5	0.69	4.9	5.5	1.11	0.93	2.60
OKC @ POR	17.6	2.7	2.50	4.6	0.62	2.6	4.1	0.75	0.11	2.17
BOS @ PHI	17.2	3.6	2.68	2.0	0.63	4.0	3.4	1.39	0.54	2.39
ORL @ SAC	11.0	4.1	0.15	2.4	3.11	7.8	5.2	0.88	0.09	2.91
MIA @ UTA	15.3	3.8	2.06	1.6	0.55	1.9	7.4	1.28	0.61	2.57
ORL @ SAC	17.3	2.3	3.29	2.8	0.69	3.4	2.4	1.06	0.81	2.06
MIA @ UTA	15.2	4.1	2.06	0.8	1.81	4.5	2.9	0.94	1.03	2.15
MIL @ NYK	16.3	2.7	2.05	4.7	1.43	2.9	2.2	1.02	0.50	1.46
LAC @ PHX	15.1	3.5	2.01	2.0	1.17	5.1	3.1	1.18	0.61	1.09
ORL @ SAC	12.7	3.2	0.94	3.3	2.37	6.7	1.4	1.09	0.82	1.54
LAC @ PHX	15.9	3.9	1.88	2.5	0.86	2.7	2.4	0.96	0.24	1.66
ORL @ SAC	15.7	3.2	1.92	3.5	0.44	2.2	2.3	0.82	0.33	2.00
LAC @ PHX	13.4	4.1	1.07	1.9	0.52	2.9	3.3	0.86	0.62	2.18
MIA @ UTA	9.9	3.6	0.03	2.5	2.21	5.2	2.9	0.99	1.34	1.91
MIL @ NYK	12.4	2.7	1.40	2.8	2.45	3.5	2.8	0.69	0.24	1.46
LAC @ PHX	14.3	3.4	1.95	1.7	0.84	4.0	1.7	0.85	0.12	1.53
OKC @ POR	9.9	2.9	0.86	1.5	3.21	6.2	1.6	0.52	1.80	1.45
OKC @ POR	13.5	3.1	2.01	1.3	0.94	3.0	2.1	1.13	0.34	1.82
ORL @ SAC	14.6	2.0	3.37	0.5	1.27	2.6	1.9	0.66	0.42	1.27
BOS @ PHI	11.5	2.6	1.58	1.5	0.93	2.3	4.4	0.99	0.59	2.15
BOS @ PHI	11.6	3.0	1.57	0.9	1.17	4.3	2.7	1.23	0.49	1.37
MIL @ NYK	12.3	3.2	1.29	2.0	1.48	3.1	2.5	0.58	0.13	1.50
LAC @ PHX	12.2	3.6	1.07	1.8	1.20	4.1	1.2	0.57	1.09	1.12
OKC @ POR	7.6	3.1	0.07	1.3	2.77	5.9	3.4	1.17	0.30	1.65
OKC @ POR	9.0	3.7	0.04	1.4	2.33	4.4	1.8	0.53	2.12	1.70
ORL @ SAC	12.2	2.3	1.81	2.1	0.77	3.9	1.5	0.56	0.10	1.21
MIA @ UTA	14.1	1.1	3.76	0.6	0.21	2.3	1.9	0.49	0.76	1.12
BOS @ PHI	11.9	2.1	1.85	2.1	0.66	2.1	3.2	0.68	0.19	1.41
OKC @ POR	10.6	1.6	1.85	1.8	0.96	2.7	3.7	0.83	0.17	1.46
MIL @ NYK	10.5	2.3	1.72	0.7	1.17	3.4	2.6	0.89	0.27	1.39
MIA @ UTA	9.9	2.0	1.03	2.9	0.78	2.7	3.0	1.13	0.08	1.45
ORL @ SAC	9.1	3.5	0.34	1.0	2.00	3.9	1.3	1.03	1.37	1.12
BOS @ PHI	8.6	3.2	0.08	2.0	2.17	4.4	1.5	0.38	1.14	0.98
MIL @ NYK	7.7	1.6	1.30	0.5	1.36	4.5	2.6	0.65	2.63	1.53
MIL @ NYK	8.2	1.2	1.72	0.6	0.90	4.9	3.2	1.47	0.24	1.46
LAC @ PHX	10.1	1.3	1.29	3.6	0.48	2.3	2.4	0.54	0.19	1.41
MIL @ NYK	9.2	2.4	1.14	1.0	0.94	2.6	3.4	0.57	0.38	1.66
LAC @ PHX	7.7	1.9	0.81	1.4	0.68	3.2	3.5	1.54	0.49	1.50
OKC @ POR	10.4	1.2	2.38	0.8	1.06	2.9	1.3	0.74	0.19	1.24
OKC @ POR	10.0	1.9	1.25	2.5	0.90	1.5	1.5	0.66	0.13	1.94
MIA @ UTA	7.6	2.5	0.21	2.0	0.46	2.1	4.7	0.42	0.00	2.09
ORL @ SAC	9.3	2.5	1.26	0.6	0.88	1.9	2.9	0.82	0.76	1.06
LAC @ PHX	9.0	2.1	1.25	1.0	1.44	2.7	1.6	0.86	0.82	1.07
ORL @ SAC	9.4	2.2	1.19	1.4	0.29	1.1	3.7	0.56	0.08	1.48
ORL @ SAC	9.9	2.6	0.91	1.8	0.83	1.9	1.3	0.89	0.40	0.67
MIA @ UTA	6.7	2.7	0.00	1.2	2.30	4.2	1.3	0.53	1.20	0.84
LAC @ PHX	7.1	3.0	0.04	1.0	1.95	3.2	1.8	0.81	0.67	1.38
LAC @ PHX	8.9	0.8	2.14	0.8	0.76	2.5	3.0	0.60	0.00	1.08
ORL @ SAC	9.0	1.4	1.58	1.4	0.77	3.4	0.9	0.64	0.12	1.10
LAC @ PHX	9.0	1.5	1.62	1.2	0.53	2.3	1.9	0.78	0.09	1.30
LAC @ PHX	5.9	2.5	0.00	0.9	1.87	3.4	2.9	0.45	0.75	1.60
OKC @ POR	9.3	1.1	1.96	1.3	0.37	1.9	1.9	0.85	0.11	0.97
MIA @ UTA	8.4	2.0	1.29	0.5	1.06	2.3	1.5	0.68	0.89	1.04
LAC @ PHX	7.5	1.0	1.68	0.4	0.74	2.0	3.3	1.44	0.07	1.49
LAC @ PHX	6.5	2.4	0.18	1.2	1.37	3.8	1.2	0.65	0.88	1.15
MIA @ UTA	8.9	1.8	1.42	1.1	0.59	1.9	1.4	0.45	0.20	1.13
MIL @ NYK	7.1	2.0	0.67	1.0	1.98	2.9	0.8	0.41	0.70	0.99
OKC @ POR	6.2	2.7	0.00	0.9	1.76	3.3	1.3	0.63	1.21	0.84
OKC @ POR	7.8	1.2	1.50	0.9	1.19	3.0	1.7	0.54	0.08	0.77
MIL @ NYK	8.1	2.1	1.18	0.5	0.76	1.8	2.0	0.76	0.55	0.87
MIL @ NYK	8.6	0.8	1.95	1.2	0.55	1.6	1.7	0.57	0.17	0.85
MIA @ UTA	6.4	1.2	1.18	0.5	0.68	4.6	1.5	0.64	0.37	0.85
BOS @ PHI	6.8	1.4	0.89	1.4	1.82	2.2	1.3	0.38	0.48	0.75
OKC @ POR	5.9	0.5	1.28	0.9	0.92	3.2	2.8	0.77	0.35	0.76
ORL @ SAC	5.1	2.0	0.00	1.2	1.97	3.0	1.5	0.53	0.98	0.66
BOS @ PHI	5.4	2.2	0.00	0.9	2.24	2.8	0.8	0.55	0.73	0.67
MIL @ NYK	7.2	1.9	0.79	1.1	0.93	2.0	0.8	0.48	0.30	0.72
MIA @ UTA	5.4	1.2	0.65	1.1	1.16	3.0	1.7	0.72	0.17	1.24
LAC @ PHX	6.1	1.6	0.73	0.7	0.90	2.6	1.5	0.66	0.83	0.62
MIL @ NYK	5.3	2.2	0.03	0.9	1.95	3.1	1.0	0.38	0.43	0.57
MIA @ UTA	7.1	1.0	1.44	0.7	0.68	1.8	1.5	0.76	0.00	0.76
LAC @ PHX	6.5	1.1	1.40	0.2	1.16	1.7	1.8	0.83	0.00	0.72
OKC @ POR	6.6	0.2	1.59	1.4	0.53	1.5	1.4	0.49	0.12	0.86
OKC @ POR	5.6	1.4	0.72	0.5	1.68	2.3	0.7	0.83	0.20	0.52
LAC @ PHX	6.6	2.0	0.58	0.9	0.62	1.7	0.7	0.47	0.21	0.66
MIL @ NYK	6.2	0.3	1.74	0.3	0.58	1.9	1.5	0.36	0.29	0.79
BOS @ PHI	5.4	0.9	1.05	0.4	0.91	2.6	1.2	0.58	0.00	0.86
BOS @ PHI	6.3	0.5	1.74	0.1	0.70	2.4	1.1	0.38	0.15	0.48
OKC @ POR	5.1	0.7	0.81	1.4	0.97	1.8	1.7	0.49	0.32	0.62
BOS @ PHI	5.3	1.7	0.25	1.2	1.37	2.4	0.8	0.34	0.00	0.42
BOS @ PHI	6.4	0.7	1.40	0.8	0.37	1.6	1.1	0.47	0.14	0.67
ORL @ SAC	4.9	0.3	1.26	0.4	0.84	2.1	1.5	1.07	0.61	0.46
LAC @ PHX	6.0	0.4	1.53	0.7	0.21	1.5	1.5	0.38	0.00	0.55
MIA @ UTA	5.2	1.0	0.79	0.9	0.87	1.2	1.4	0.69	0.09	0.69
MIL @ NYK	5.6	0.6	1.34	0.5	0.52	1.4	0.9	0.58	0.34	0.47
MIL @ NYK	5.7	2.0	0.43	0.4	0.92	0.8	0.7	0.37	0.33	0.41
MIL @ NYK	4.0	0.6	0.53	1.3	0.46	1.4	2.2	0.36	0.04	1.13
OKC @ POR	3.5	0.5	0.48	1.2	0.80	2.5	0.9	0.37	0.36	0.79
MIA @ UTA	5.2	1.1	0.89	0.3	0.36	0.9	1.1	0.54	0.00	0.62
LAC @ PHX	3.1	0.8	0.14	1.0	0.63	2.1	1.1	0.43	0.83	0.73
OKC @ POR	5.6	1.6	0.71	0.4	0.45	0.4	0.8	0.40	0.00	0.54
MIL @ NYK	3.1	0.6	0.51	0.3	0.38	0.9	3.4	0.42	0.00	0.63
BOS @ PHI	3.5	0.4	0.76	0.4	0.60	1.7	1.1	0.59	0.53	0.53
BOS @ PHI	3.9	0.7	0.73	0.3	0.79	2.1	0.6	0.43	0.08	0.52
BOS @ PHI	2.7	0.4	0.45	0.5	0.94	1.8	1.5	0.86	0.39	0.56
BOS @ PHI	3.9	0.6	0.58	0.9	0.59	1.8	0.6	0.36	0.15	0.45
ORL @ SAC	4.4	0.5	1.04	0.2	0.26	1.2	0.8	0.56	0.20	0.43
MIL @ NYK	3.2	0.2	0.63	0.9	0.72	1.4	1.5	0.34	0.31	0.51
ORL @ SAC	3.3	1.1	0.00	1.1	0.80	1.6	0.6	0.23	0.29	0.31
MIA @ UTA	3.6	1.1	0.26	0.5	0.62	1.1	0.9	0.36	0.00	0.69
MIA @ UTA	3.2	0.6	0.48	0.6	0.48	1.8	0.8	0.35	0.31	0.46
MIA @ UTA	4.3	0.5	0.92	0.6	0.23	0.9	0.5	0.26	0.12	0.39
MIL @ NYK	3.2	0.7	0.53	0.2	0.08	1.0	1.4	0.39	0.31	0.80
ORL @ SAC	3.7	0.3	0.96	0.2	0.19	0.6	1.5	0.46	0.00	0.56
MIL @ NYK	4.0	0.4	0.95	0.4	0.33	0.8	0.7	0.24	0.00	0.28
OKC @ POR	3.1	0.4	0.53	0.7	0.44	1.1	0.5	0.24	0.14	0.43
OKC @ POR	2.9	0.7	0.44	0.2	0.23	1.2	0.6	0.50	0.18	0.45
ORL @ SAC	3.6	0.5	0.78	0.2	0.39	0.9	0.4	0.29	0.06	0.19
ORL @ SAC	2.4	0.5	0.42	0.2	0.43	1.3	1.0	0.19	0.38	0.36
MIL @ NYK	2.1	1.0	0.00	0.2	0.89	1.2	0.4	0.18	0.73	0.28
LAC @ PHX	3.0	0.8	0.33	0.5	0.50	0.6	0.4	0.23	0.22	0.24
MIL @ NYK	2.1	0.3	0.39	0.3	0.25	1.5	0.8	0.29	0.28	0.37
MIA @ UTA	2.7	0.2	0.61	0.5	0.10	1.0	0.8	0.27	0.00	0.43
OKC @ POR	2.3	0.7	0.26	0.2	0.33	1.0	0.6	0.21	0.25	0.37
BOS @ PHI	2.0	0.0	0.58	0.2	0.26	0.7	0.7	0.27	0.57	0.56
ORL @ SAC	1.5	0.4	0.12	0.4	0.54	1.4	0.7	0.40	0.00	0.42
BOS @ PHI	2.7	0.7	0.33	0.3	0.19	0.6	0.4	0.13	0.00	0.35
MIL @ NYK	2.6	0.2	0.64	0.3	0.23	0.5	0.5	0.18	0.00	0.25
OKC @ POR	1.4	0.3	0.02	0.7	0.36	0.9	0.8	0.25	0.17	0.31
LAC @ PHX	2.2	0.8	0.14	0.2	0.32	0.6	0.3	0.11	0.00	0.20
BOS @ PHI	1.9	0.4	0.30	0.2	0.24	0.7	0.5	0.17	0.00	0.34
ORL @ SAC	1.8	0.7	0.00	0.3	0.29	0.6	0.4	0.12	0.19	0.18
MIL @ NYK	1.8	0.6	0.12	0.3	0.22	0.7	0.4	0.18	0.00	0.21
MIA @ UTA	2.4	0.1	0.65	0.1	0.07	0.4	0.2	0.11	0.20	0.15
ORL @ SAC	1.4	0.1	0.28	0.4	0.45	0.6	0.5	0.30	0.00	0.38
LAC @ PHX	1.7	0.5	0.16	0.3	0.20	0.4	0.3	0.15	0.00	0.22
MIL @ NYK	1.3	0.5	0.00	0.3	0.43	0.4	0.4	0.16	0.00	0.24
LAC @ PHX	1.6	0.2	0.30	0.3	0.10	0.5	0.3	0.14	0.12	0.20
BOS @ PHI	1.5	0.4	0.11	0.3	0.18	0.5	0.2	0.08	0.00	0.19
MIL @ NYK	0.6	0.1	0.04	0.2	0.57	0.9	0.5	0.23	0.00	0.28
LAC @ PHX	1.1	0.2	0.17	0.2	0.12	0.4	0.5	0.14	0.00	0.23
MIA @ UTA	0.8	0.3	0.00	0.3	0.29	0.6	0.3	0.12	0.00	0.21
LAC @ PHX	0.8	0.1	0.18	0.1	0.06	0.5	0.4	0.12	0.12	0.23
BOS @ PHI	1.1	0.3	0.13	0.1	0.18	0.3	0.2	0.07	0.00	0.16
BOS @ PHI	1.1	0.5	0.00	0.1	0.22	0.3	0.2	0.06	0.00	0.11
LAC @ PHX	0.8	0.2	0.10	0.2	0.05	0.3	0.4	0.09	0.00	0.19
LAC @ PHX	0.6	0.2	0.00	0.3	0.15	0.2	0.2	0.09	0.00	0.13
MIA @ UTA	0.4	0.1	0.04	0.1	0.10	0.5	0.2	0.06	0.00	0.10
BOS @ PHI	0.2	0.0	0.00	0.2	0.11	0.4	0.3	0.10	0.00	0.15
MIA @ UTA	0.3	0.0	0.00	0.3	0.07	0.3	0.2	0.06	0.10	0.11
BOS @ PHI	0.2	0.0	0.00	0.2	0.09	0.3	0.2	0.07	0.00	0.12
MIL @ NYK	0.1	0.0	0.00	0.1	0.04	0.1	0.1	0.03	0.00	0.05
BOS @ PHI	0.0	0.0	0.00	0.0	0.02	0.1	0.0	0.01	0.00	0.03

NBA predictions Tue, Nov 10

How we got this

Model vs market

How the numbers are built

Team comparison

Who is out

Conditions

How we got this

Model vs market

Movement

How the numbers are built

Team comparison

Who is out

Conditions

How we got this

How the numbers are built

Team comparison

Who is out

Conditions

How we got this

Model vs market

How the numbers are built

Team comparison

Who is out

Conditions

How we got this

Model vs market

How the numbers are built

Team comparison

Who is out

Conditions

How we got this

Model vs market

How the numbers are built

Team comparison

Who is out

Conditions

Points leaders

Games

Model vs market

How the numbers are built

Team comparison

Who is out

Conditions

Model vs market

Movement

How the numbers are built

Team comparison

Who is out

Conditions

How the numbers are built

Team comparison

Who is out

Conditions

Model vs market

How the numbers are built

Team comparison

Who is out

Conditions

Model vs market

How the numbers are built

Team comparison

Who is out

Conditions

Model vs market

How the numbers are built

Team comparison

Who is out

Conditions

Players

Points leaders