Bucks vs. Pacers Game 2 Results According to NBA 2K25

According to our NBA 2K25 simulation, the Indiana Pacers will defeat the Milwaukee Bucks in Game 2 109-99. Just like our last sim, the Pacers nearly blew a double digit lead, but played well enough in the final minutes to prevent a comeback. Meanwhile, we decided to start Damian Lillard on the chance he does play tonight. However, his solid performance wasn’t enough to help Milwaukee win.

Pascal Siakam earned the game ball for his performance. Overall, he scored the most points on his team (27), tied for most rebounds (12), and added three assists to his sheet. Meanwhile, Tyresse Haliburton added another 21 points while earning five rebounds, six assists, and a steal. Additionally, Indiana played well on defense, especially in the 3rd quarter where they only allowed 19 points.

Giannis Antetokounmpo led the Bucks in points (33), rebounds (16), and assists (5). Furthermore, he even earned two blocks, but he went 0-4 on three-point attempts. We did decide to start Damian Lillard, who scored 26 points and earned two steals in the loss. However, even putting him back in the lineup wasn’t enough for Milwaukee to win Game 2.

Additionally, check out some of game stats between the Bucks and Pacers for our Game 2 Sim:

With the win, the Pacers now take a 2-0 lead in the series, which is great for them. The sooner they end this series, the more time they have to rest and recover for the next round. Furthermore, less games, also hopefully means less chance for injury to their core players. However, they still need to win two more games to close this one out.

Meanwhile, the Bucks now trail 0-2 in the series. Even with Damian Lillard returning to action (at the time of writing he’s still considered a Game-Time Decision), the Bucks struggled to keep up with the Pacers. However, the series isn’t over yet, and Milwaukee has the talent to come back and shock the league.

That wraps up our Bucks vs. Pacers Game 2 results according to NBA 2K25. We hope you enjoyed our simulation. These sims also provide a way for us to improve these predictions by experimenting with CPU sliders. We still want to improve a few things, but we’re getting more satisfied with each sim. We hope to create more accurate sims in the future!

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