Nightly Stats & Highlights

Once again, the Blazers were in on it, Deni Avdija (35 points, 9 rebounds, 7 assists) having even given them a five-point lead three minutes from the end among the Pistons. Once again, they gave in in the money time, finally giving in to Cade Cunningham (29 points, 9 assists) and his gang.

No suspense in New York, where Knicks started with a 23-0, the biggest run to start a match since 1997. Enough to spend a quiet evening against the Jazz, Guerschon Yabusele taking the opportunity to score 11 points in 9 minutes while Mohamed Diawara compiled 2 points, 2 rebounds and 3 assists in 12 minutes.

Brandon Ingram transparent, Raptors failed to keep pace with the Hornets of Could Knueppel (21 points at 5/9 from a distance)… and Tijdane Salaün (21 points at 5/6 from a distance), which sets its points record in the NBA!

The start of the season Bulls is definitively forgotten, and they record a sixth defeat in a row against the Pacers of a big Pascal Miss (36 points, 10 rebounds), well supported by Bennedict Mathurin (28 points).

With its 28 points, Kevin Durant became the 8th player to reach the 31,000 point mark in the NBA, and the Rockets made short work of the Sunsto take second place in the Western Conference.

THE Clippers They can’t keep it together. After their victory in Atlanta, the teammates of Nicolas Batum (6 points, 3 rebounds, 4 assists) give in in the money time on the floor of Grizzlies.

Finally, the Bucks showed all their limits in the face of Sixers. Now without Giannis Antetokounmpo, Doc Rivers’ players did not keep up with the Sixers. Tyrese Maxey (12 points) didn’t even have to force, leaving the Philadelphia bench (Quentin Grimes, Jabari Walker, Adem Bona) make the difference.

Boston – LA Lakers
Orlando – Miami
Atlanta – Denver
Cleveland – San Antonio
Oklahoma City – Dallas

Detroit – Portland : 122-116

How to read the stats? Min = Minutes; Shots = Successful shots / Attempted shots; 3pts = 3-points / 3-points attempted; LF = free throws made / free throws attempted; O = offensive rebound; D=defensive rebound; T = Total rebounds; Pd = assists; Fte: Personal fouls; Int = Intercepts; Bp = Lost balls; Ct: Against; +/- = Point differential when the player is on the field; Pts = Points; Eval: player evaluation calculated from positive actions – negative actions.

New York – Utah : 146-112

How to read the stats? Min = Minutes; Shots = Successful shots / Attempted shots; 3pts = 3-points / 3-points attempted; LF = free throws made / free throws attempted; O = offensive rebound; D=defensive rebound; T = Total rebounds; Pd = assists; Fte: Personal fouls; Int = Intercepts; Bp = Lost balls; Ct: Against; +/- = Point differential when the player is on the field; Pts = Points; Eval: player evaluation calculated from positive actions – negative actions.

Toronto – Charlotte : 86-111

How to read the stats? Min = Minutes; Shots = Successful shots / Attempted shots; 3pts = 3-points / 3-points attempted; LF = free throws made / free throws attempted; O = offensive rebound; D=defensive rebound; T = Total rebounds; Pd = assists; Fte: Personal fouls; Int = Intercepts; Bp = Lost balls; Ct: Against; +/- = Point differential when the player is on the field; Pts = Points; Eval: player evaluation calculated from positive actions – negative actions.

Chicago–Indiana: 105-120

How to read the stats? Min = Minutes; Shots = Successful shots / Attempted shots; 3pts = 3-points / 3-points attempted; LF = free throws made / free throws attempted; O = offensive rebound; D=defensive rebound; T = Total rebounds; Pd = assists; Fte: Personal fouls; Int = Intercepts; Bp = Lost balls; Ct: Against; +/- = Point differential when the player is on the field; Pts = Points; Eval: player evaluation calculated from positive actions – negative actions.

Houston – Phoenix : 117-98

How to read the stats? Min = Minutes; Shots = Successful shots / Attempted shots; 3pts = 3-points / 3-points attempted; LF = free throws made / free throws attempted; O = offensive rebound; D=defensive rebound; T = Total rebounds; Pd = assists; Fte: Personal fouls; Int = Intercepts; Bp = Lost balls; Ct: Against; +/- = Point differential when the player is on the field; Pts = Points; Eval: player evaluation calculated from positive actions – negative actions.

Memphis – LA Clippers : 107-98

LA Clippers / 98 Shots Rebounds
Players Min Shots 3pts LF O D T Pd Fte Int Bp Ct Pts Eval
Kawhi Leonard 34 10/23 2/9 2/2 0 8 8 4 2 1 1 1 24 24
John Collins 25 6/14 2/8 2/2 1 1 2 2 2 1 3 0 16 10
Ivica Zubac 35 4/6 0/0 2/2 1 5 6 1 3 1 2 0 10 14
Kris Dunn 21 0/1 0/0 0/0 0 3 3 3 1 2 1 0 0 6
James Harden 36 6/13 3/7 3/4 0 2 2 7 4 0 4 1 18 16
Nicolas Batum 31 2/7 2/7 0/0 1 2 3 4 2 2 2 0 6 8
Kobe Sanders 27 3/6 2/4 0/1 1 6 7 0 4 1 2 0 8 10
Kobe Brown 21 3/7 2/6 5/6 0 4 4 1 0 3 1 0 13 15
Cam Christie 10 1/3 1/3 0/0 1 1 2 0 0 0 0 0 3 3
Total 35/80 14/44 14/17 5 32 37 22 18 11 16 2 98

How to read the stats? Min = Minutes; Shots = Successful shots / Attempted shots; 3pts = 3-points / 3-points attempted; LF = free throws made / free throws attempted; O = offensive rebound; D=defensive rebound; T = Total rebounds; Pd = assists; Fte: Personal fouls; Int = Intercepts; Bp = Lost balls; Ct: Against; +/- = Point differential when the player is on the field; Pts = Points; Eval: player evaluation calculated from positive actions – negative actions.

Milwaukee – Philadelphie : 101-116

How to read the stats? Min = Minutes; Shots = Successful shots / Attempted shots; 3pts = 3-points / 3-points attempted; LF = free throws made / free throws attempted; O = offensive rebound; D=defensive rebound; T = Total rebounds; Pd = assists; Fte: Personal fouls; Int = Intercepts; Bp = Lost balls; Ct: Against; +/- = Point differential when the player is on the field; Pts = Points; Eval: player evaluation calculated from positive actions – negative actions.

Sofia Reyes

Sofia Reyes covers basketball and baseball for Archysport, specializing in statistical analysis and player development stories. With a background in sports data science, Sofia translates advanced metrics into compelling narratives that both casual fans and analytics enthusiasts can appreciate. She covers the NBA, WNBA, MLB, and international basketball competitions, with a particular focus on emerging talent and how front offices build winning rosters through data-driven decisions.

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