NBA Cup Final Game Report: Milwaukee Bucks vs Oklahoma City Thunder
Ari Cross
2025-04-28
NBA Cup Analyses: Milwaukee Bucks v The Oklahoma City Thunder
Overview
The NBA Cup is a recent addition to regular season play with a tournament debut in 2023. All NBA Cup games contribute to regular season records except the championship game.
The Bucks’ 2024 NBA Cup championship game was played against a team who, in regular season, had the highest offensive and defensive rating in the NBA (compared to our no.11). OKC is a statistically sound team with superstars and MVP nominees like Shai Gilgeous-Alexander accompanied by a deep roster of heavy contributors. During there first match-up of the year, without doubt, the OKC Thunder were expected to walk away with a trophy. What circumstances fostered the Bucks’ success in the NBA Cup finals?
This report addresses the five major findings from this game’s data that led to our success:
League-Wide Norms
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | 43.97 | 1.397 | 31.47 | 7.405e-75 |
| contestLevellightly_contested | 10.2 | 2.398 | 4.251 | 3.416e-05 |
| contestLeveluncontested | 22.83 | 3.521 | 6.484 | 8.4e-10 |
The regression reveals a significant, negative relationship between variables; that across teams, as contest level goes up (i.e uncontested to heavily contested), the probability that a shot goes in goes down. These results are expected since tougher defenses across the NBA will produce lower scoring opportunities for opponents.
Contributions to Our Success
Sticking to Our Strengths
| Team | Avg Shot Make Probability | Actual FG Pct |
|---|---|---|
| MIL | 49.6 | 0.420 |
| OKC | 49.5 | 0.337 |
At a very basic level, we can observe that under the same average shot difficulty, we put in more shots (42% efficiency compared to their 33%). The gap in actual field goal percentage could point to exceptional Bucks defense or cold shooting from OKC.
Shot Distribution
Heat Map
From the heat map and bar chart, we can draw that OKC had greater diversity in their shot selection. However, we had noticeably higher catch and shoot makes, catch and shoots with relocation, stand-still layups, and tips. Both charts display the Bucks’ ability to convert on shots near the rim and in their most effective corners.
Court Region and Shot Type
This chart displays the top 5 most effective shooting regions for each team
| team_nba_off | region | fg_pct | attempts |
|---|---|---|---|
| MIL | left corner two | 1.000 | 2 |
| MIL | left wing three | 0.857 | 7 |
| MIL | right corner three | 0.571 | 7 |
| MIL | key | 0.545 | 15 |
| MIL | ra | 0.500 | 15 |
| OKC | key | 0.524 | 24 |
| OKC | left wing two | 0.500 | 3 |
| OKC | middle two | 0.500 | 6 |
| OKC | ra | 0.500 | 15 |
| OKC | left corner two | 0.333 | 7 |
Bucks
We were extremely effective from high-value zones, (e.i., Left wing three, Right corner three, Restricted area). Our perimeter shooting is notably strong: we shot 85.7% from the left wing three (6 of 7), and 57.1% from the right corner three (4 of 7). Shooting well over league average.
OKC
OKC’s left corner two was notably less effective (33.3%, 2 of 6). Mid-range zones such as the left wing two and middle two each returned 50% FG, though on low volume (3 and 6 attempts respectively).
Reliable Defense
To understand whether the Bucks’ defensive strategy made an impact, I next examined shot contest levels.
Defensive Contest
| Contest Level | Team | FG Pct | Avg Shot Make Prob | Attempts |
|---|---|---|---|---|
| heavily_contested | MIL | 0.367 | 44.6 | 54 |
| heavily_contested | OKC | 0.404 | 43.4 | 53 |
| lightly_contested | MIL | 0.522 | 55.5 | 25 |
| lightly_contested | OKC | 0.179 | 53.1 | 30 |
| uncontested | MIL | 0.444 | 63.7 | 9 |
| uncontested | OKC | 0.455 | 69.4 | 11 |
Uncontested
OKC took slightly more difficult uncontested shots and were more efficient from them, meaning our defense likely slacked off due to the perceived low probability that a shot would go in.
Lightly Contested
Avg shot qSQ difficulty was comparable however, we
converted at a much higher rate! Our ability to convert lightly
contested shots at a higher rate could indicate superior shot selection
and offensive execution.
OKC’s low conversion on lightly contested shots point to a Bucks
defense forcing OKC into shooting from tougher spots. This is confirmed
in scoring and shooting performance by player table where we find
starters like Shai with lower averageqSQ or shot make
probabilities.
Heavily Contested
The average shot difficulty qSQ was similar for both
teams, OKC was able to achieve slightly better shooting performance in
the presence of heavy defense. This points to a high defensive caliber
of both teams; us matching the tenacity of OKC, the NBA’s regular season
leader in defensive rating, with our own resilient defense.
Randomness
| team_nba_def | opponent_fg_pct | avg_qSQ_against | avg_contest_level | attempts_faced |
|---|---|---|---|---|
| MIL | 0.337 | 49.5 | 1.73 | 94 |
| OKC | 0.420 | 49.6 | 1.74 | 88 |
With similar average shot quality and contest level—both of which were uncharacteristic based on regular season data—and a higher volume of shot attempts, the evidence suggests that OKC was simply unlucky. The Bucks seemingly made more shots than expected against OKC, even though they didn’t take higher-quality or less-contested ones.
Catch-and-shoot Effectiveness
| team_nba_off | fg_pct | avg_qSQ | attempts |
|---|---|---|---|
| MIL | 0.438 | 55.2 | 48 |
| OKC | 0.306 | 55.4 | 38 |
The similarity in average shot quality (MIL: 55.2, OKC: 55.4) indicates that both teams were generating similar quality of catch-and-shoot opportunities. The higher attempt rate signifies an successful game plan that created more opportunities through effective ball movement and off-ball player movement. The game strategy focused on leveraging catch-and-shoot opportunities contributed to greater scoring.
Assists and Passing
| player_nba_passer | team_nba_off | Assist_Opps | Assisted_Shots |
|---|---|---|---|
| Antetokounmpo, Giannis | MIL | 19 | 10 |
| Lillard, Damian | MIL | 9 | 4 |
| Portis, Bobby | MIL | 2 | 3 |
| Green, AJ | MIL | 6 | 2 |
| Prince, Taurean | MIL | 2 | 2 |
| Connaughton, Pat | MIL | 6 | 1 |
| Jackson Jr., Andre | MIL | 1 | 1 |
| Lopez, Brook | MIL | 2 | 1 |
| Williams, Jalen | OKC | 8 | 3 |
| Gilgeous-Alexander, Shai | OKC | 7 | 2 |
| Hartenstein, Isaiah | OKC | 7 | 2 |
| Mitchell, Ajay | OKC | 0 | 2 |
| Dort, Luguentz | OKC | 2 | 1 |
| Joe, Isaiah | OKC | 4 | 1 |
| Wallace, Cason | OKC | 4 | 1 |
| Williams, Kenrich | OKC | 2 | 1 |
The assist data highlights contrasting offensive structures between us and OKC. For us, Giannis clearly served as the offensive hub, generating 19 assist opportunities, resulting in 10 assisted shots.
OKC’s playmaking was more distributed, with Jalen Williams, Shai Gilgeous-Alexander, and Isaiah Hartenstein each generating 7–8 assist opportunities, though with fewer converted assists. This spread suggests a more balanced but less efficient scheme in this matchup, contributing to their lower overall offensive efficiency.
Opponent Underperformance
Scoring and shooting performance by team
| team_nba_off | player_nba_shooter | FGA | FG | FG_Percentage | ThreePA | ThreeP | ThreeP_Percentage | Avg_Distance | Avg_qSQ |
|---|---|---|---|---|---|---|---|---|---|
| MIL | Antetokounmpo, Giannis | 19 | 10 | 0.526 | 0 | 0 | NaN | 7.56 | 50.2 |
| MIL | Lillard, Damian | 12 | 6 | 0.500 | 10 | 5 | 0.500 | 24.97 | 43.2 |
| MIL | Lopez, Brook | 12 | 5 | 0.417 | 6 | 3 | 0.500 | 17.95 | 50.9 |
| MIL | Portis, Bobby | 9 | 2 | 0.222 | 3 | 1 | 0.333 | 14.32 | 47.9 |
| MIL | Trent Jr., Gary | 9 | 5 | 0.556 | 6 | 3 | 0.500 | 22.48 | 48.2 |
| MIL | Jackson Jr., Andre | 6 | 1 | 0.167 | 4 | 0 | 0.000 | 17.01 | 60.6 |
| MIL | Prince, Taurean | 6 | 2 | 0.333 | 5 | 2 | 0.400 | 21.98 | 60.4 |
| MIL | Green, AJ | 5 | 3 | 0.600 | 5 | 3 | 0.600 | 24.57 | 47.9 |
| OKC | Gilgeous-Alexander, Shai | 24 | 8 | 0.333 | 9 | 2 | 0.222 | 16.38 | 49.0 |
| OKC | Williams, Jalen | 20 | 8 | 0.400 | 4 | 1 | 0.250 | 12.88 | 45.0 |
| OKC | Hartenstein, Isaiah | 11 | 6 | 0.545 | 0 | 0 | NaN | 8.69 | 55.4 |
| OKC | Joe, Isaiah | 7 | 2 | 0.286 | 6 | 1 | 0.167 | 25.89 | 51.0 |
| OKC | Williams, Kenrich | 6 | 1 | 0.167 | 4 | 1 | 0.250 | 18.24 | 51.1 |
| OKC | Dort, Luguentz | 5 | 1 | 0.200 | 4 | 0 | 0.000 | 22.52 | 56.5 |
| OKC | Mitchell, Ajay | 5 | 1 | 0.200 | 2 | 0 | 0.000 | 11.24 | 50.3 |
| OKC | Caruso, Alex | 3 | 0 | 0.000 | 2 | 0 | 0.000 | 17.31 | 47.5 |
Bucks
Starters like Dame, Giannis and Brook maintained high FG
and ThreeP percentages while steadily creating shots with
low difficulty/ higher make probabilities.
Forwards like Prince creating shots with higher make
probabilities and guards like Green and Trent Jr. shooting
maintaining outstanding ThreeP efficiencies while creating
shots with higher probabilities of makes, compared to starters like
Dame, undoubtedly contributed to the team’s success.
This table’s outcomes show a successful game plan that prioritized player’s strengths and set them up for success.
| attempts | fg_pct | avg_qSQ | catch_shoot_pct | highly_contested_pct |
|---|---|---|---|---|
| 24 | 0.526 | 50.2 | 0.75 | 0.667 |
| attempts | fg_pct | avg_qSQ | catch_shoot_pct | highly_contested_pct |
|---|---|---|---|---|
| 14 | 0.5 | 43.2 | 0.5 | 0.5 |
| attempts | fg_pct | avg_qSQ | catch_shoot_pct | highly_contested_pct |
|---|---|---|---|---|
| 12 | 0.417 | 50.9 | 0.455 | 0.583 |
OKC
Starters like Shai took a larger number of improbable shots thus
converting on only a third of his FG and an even lower
percentage of ThreeP. Other starters like Jalen Williams
suboptimaly averaged low make probabilityqSQ(avg = 45)
likely hurting his field goal percentage.
| attempts | fg_pct | avg_qSQ | catch_shoot_pct | highly_contested_pct |
|---|---|---|---|---|
| 26 | 0.333 | 49 | 0.5 | 0.654 |
| attempts | fg_pct | avg_qSQ | catch_shoot_pct | highly_contested_pct |
|---|---|---|---|---|
| 13 | 0.545 | 55.4 | 0.462 | 0.692 |
| attempts | fg_pct | avg_qSQ | catch_shoot_pct | highly_contested_pct |
|---|---|---|---|---|
| 21 | 0.4 | 45 | 0.25 | 0.619 |
Shot Quality vs. Makes for starters and bench
Kenrich Williams, Ajay Mitchell, and Alex Caruso combined for 2 made field goals on 14 total attempts. With these role players underperforming, it reinforces the notion that OKC’s offensive inefficiency in the game was more about underperformance.
In the midst of OKC’s sub-optimal performance, we had a bench who stepped up. Players like Gary Trent Jr. and AJ Green outperformed most starters when accounting for the amount of minutes played.
Key Takeaways
There are several factors that contributed to our NBA Cup Finals win. OKC is a team with a stacked roster of talent, favored highly on both offensive and defensive ends. Intuitively, there must be a give and take that allowed us to prevail given the circumstances. Here’s what that looks like:
Sticking to Our Strengths - The Bucks benefitted from multiple contributors shooting well within their historical strengths. Brook Lopez and Damian Lillard each shot 50% from three. Notably, bench players like Gary Trent Jr. and Andre Jackson Jr. took shots with comparable or even better expected shot quality than some starters . This suggests our game plan generated good looks for everyone.
Importantly, our bench did not take significantly lower-quality shots. Players like Andre Jackson Jr., Taurean Prince, and AJ Green had some of the highest average shot quality (e.g., Jackson Jr. at 60.6 qSQ, Prince at 60.4), and Green even shot 60% from the field. This suggests that our offensive distribution created good looks regardless of who was taking them.
Reliable Defense - Defense was instrumental in our victory. We forced OKC to take tougher shots. We maintained a consistent defensive effort, effectively contesting shots without sacrificing shot efficiency. The defensive contest levels revealed that while OKC had slightly better shooting performance on uncontested shots, we grossly outperformed OKC in lightly contested shots. Additionally, in heavily contested scenarios, both teams displayed similar defensive intensity, but the Bucks were able to limit OKC’s high-quality scoring opportunities, contributing to their success.
Randomness - League-wide probabilities explain that there is a strong negative relationship between contest level and make probability; with tougher defensive efforts, make probability should go down. This was not the story for OKC’s offense. The Bucks grossly outperformed OKC on lightly contested shots. Nearly a third of OKC’s shot attempts where lightly contested yet converted only 18% of the time compared to our 52% for lightly contested. As such, the outcome of the game may reflect more randomness than entrenched competitive advantages.
Opponent Underperformance - OKC’s primary scorers Shai Gilgeous-Alexander and Jalen Williams combined for 44 FGA but converted just 16, yielding a collective FG% of ~36%, well below regular season efficiency. Despite averaging decent quality shots (e.g., Shai’s qSQ = 49.0), their actual shooting fell short of expectation. This underperformance, especially given the volume, significantly limited OKC’s offensive output. Additionally, Isaiah Joe and Lu Dort, typically relied on for perimeter shooting, went 1-for-10 from three, despite solid average shot quality (qSQ > 50). These figures underscore not just tough shooting luck, but a failure to capitalize on decent opportunities.
Unfamiliarity{#unfamiliarity} - Finally, t’s important to note that the NBA Cup Final marked the first meeting between the Bucks and the Thunder this season. Without prior head-to-head context, both teams entered the matchup without acquaintance or familiarity with each other’s tendencies. This lack of previous exposure may have amplified the role of variance in shooting outcomes and defensive matchups.
The NBA cup final marked a career performance for many Bucks players in the face of a sub-optimal opponent performance. The well thought-out game plan that played to the strengths of each player and also forced opponents to play uncharacteristically contributed to our success in the NBA Cup Final.