Justin 2.0
Justin 2.0 arrived in Cargo Champs as one of the most immediately dangerous throwers of the modern era. With only a handful of recorded appearances, he has already demonstrated an unusual combination of speed, control, and natural command of the truck. His first eight throws produced four elite-level scores and some of the fastest unloading speeds of the 2026 season, including a blistering 38.16 boxes per minute.
What separates Justin 2.0 is the absence of a traditional learning curve. He did not slowly develop into a capable thrower; he appeared fully formed. Whether maintaining control through a longer lightweight truck or overwhelming a featherweight with raw speed, he has shown that he can win through more than one style.
Justin 2.0’s legacy is still being written, but his identity is already clear: explosive, adaptable, and built for the position. Few throwers have established such a high ceiling so quickly.
| Throwbriquet | Seasons Active |
|---|---|
| The Governor | — |
| First Truck | Last Truck |
|---|---|
| — | — |
| Nationality | Home Store |
|---|---|
| 🇺🇸 | Walmart #2031 (Union City, CA, USA) |
| Trucks Thrown | Championships | Podiums | Career Points |
|---|---|---|---|
| — | — | — | — |
| Sub-Hour Throws | Two-Truck Days | Legendary Throws |
|---|---|---|
| - | - | — |
| Record | Metric | Held |
|---|
⭐ + bold dates/seasons indicate a currently-held record.
The following charts track three key performance metrics that measure a thrower's effort, pace, and competitive edge.
Together, these metrics strip away noise like truck size or teammate mix. They measure what’s truly in the thrower’s control.
⭐ indicates a thrower's personal best MPP, BPM, and Speed Bonus.
ℹ️ Click to Learn More
The graphs represent the running cumulative average of a thrower's metrics across time and include only trucks where the thrower or throwing team threw 90%+ of the truck.
Trendlines attempt to reflect the behavior of a particular performance metric.
Minutes Per Panel (MPP) uses a LOESS trendline, a locally weighted regression that smooths short-term fluctuations while preserving the natural, nonlinear “learning-curve” shape of skill improvement over time.
Boxes Per Minute (BPM) uses a moving-average trendline, which filters random noise from day-to-day variation and highlights changes in throughput consistency and stamina.
Speed Bonus uses a linear-regression trendline, showing the athlete’s overall direction of improvement relative to normalized truck size and peer averages.
Together these trendlines attempt to balance clarity and realism, revealing long-term progress without distorting the underlying data.