Next 25 developer keynote recap

In a field of dreams

Next up, presenters took a break from Paige’s kitchen remodel to tackle another high-value problem: how to throw a pitch.

With all the data that Major League Baseball processes with Google Cloud — 25 million data points per game — pitching technique is a problem that’s ripe for AI.

Jake DiBattista, winner of the recent Google Cloud x MLB Hackathon, started by analyzing a video of a great left-handed pitcher, Clayton Kershaw. He pre-processed the video using a computer vision library, and stored it in Google Cloud, using selections such as pitch type and game state to pull MLB data. Finally, after sending all this information to the Gemini API, he got his answer: Kershaw threw his signature curveball with nearly no deviation from his ideal.

Impressive, but how well does it work for those of us who aren’t pros? Jake created an “amateur mode” for less experienced players, and used a video of our host, Richard, throwing a pitch! After some prompt engineering to adapt from the professional model for Kershaw to an amateur model for Richard, the results were a little more prescriptive: He has potential, he just needs to tighten up his arm a little, and use more leg drive to maximize his power.

Jake shared the inspiration for his project: As a shot putter in college, he wanted to measure the accuracy of his throwing technique. How can you improve if you don’t know what you’re doing wrong – or right? Back then, having this kind of data would have been incredibly valuable for his development. 

But what’s truly amazing is that Jake built this fully customizable prompt generator for analyzing pitches in just one week. “This essentially worked out of the box,” Jake said. “I didn’t need to implement a custom model or build overly complex datasets.”