Data Analytics and technology have changed baseball as we know it. From the increase in defensive shifts to teams using cameras in the outfield to steal signs, teams will try anything to win. One way to gain an edge in baseball is to figure out what pitches a pitcher will pitch. Pitch prediction is a popular task to try to accomplish with all the data that baseball provides. Most methods involve using situational data like the ball and strike count. In this paper, we try a different method of predicting pitch type by only looking at the pitcher's pose in the set position. We do this to find a pitcher's tell or "tip". In baseball, if a pitcher is tipping their pitches, they are doing something that gives away what they will pitch. This could be because the pitcher changes the grip on the ball only for some pitches or something as small as a different flex in their wrist. Professional baseball players will study pitchers before they pitch the ball to try to pick up on these tips. If a tip is found, the batters have a significant advantage over the pitcher. Our paper uses pose estimation and object detection to predict the pitch type based on the pitcher's set position before throwing the ball. Given a successful model, we can extract the important features or the potential tip from the data. Then, we can try to predict the pitches ourselves like a batter. We tested this method on three pitchers: Tyler Glasnow, Yu Darvish, and Stephen Strasburg. Our results demonstrate that when we predict pitch type at a 70\% accuracy, we can reasonably extract useful features. However, finding a useful tip from these features still requires manual observation.