motusBATTING vs Hit or Not

motusBATTING is the latest lab-to-field technology developed by motus that will allow you to monitor all aspects of your swing, in each of the 9 hitting zones. Receive feedback and recommendations for becoming a hot hitter in the whole strikezone. Unique Features: ◉ HOT/COLD ZONES help you see where you need the most work and hone in on specific ways to make your cold zones hot. ◉ COACH ACCOUNT allows you to manage your whole team from one login and create custom drill workouts and push them directly to athletes. ◉ VIDEO ANALYSIS brings swing metrics and video playback together for increased awareness and learning ◉ LIVE BP SESSIONS offer better in-game analysis tools that hitting from a tee. ◉ DRILLS provide you with actionable feedback directly based on your weak areas, specific to each zone and swing metric. ◉ LIVE MODE provides depth of insight into each swing, instantly. ◉ ADVANCED GRAPHS reveal trends and patterns on a daily, weekly, and on a swing-by-swing basis. Key Measures: ① Hot/Cold Zones ② Bat Speed ③ Hand Speed ④ Attack Angle ⑤ Bat Rotational Speed ⑥ Swing Time ⑦ Swing Length

The Hit or Not app is designed to contribute to coach, player, and fan evaluations of plate appearances (PA) in baseball. The app also works great for batting cage competitions. To make this app work, I take all available data from the 2023-2026 NCAA Division 1 baseball seasons, and 2022-2026 MLB seasons.For each 1 mph increment in exit velocity (EV) and 1° increment in launch angle (LA), I count all instances of outs, singles (1b), doubles (2b), triples (3b) and home runs (hr). The user also has the option to select an outfield direction for the hit. I do not provide the option to choose an infield direction, because NCAA data for hit direction on ground balls is too inaccurate. When the user selects 'MLB', they can make an additional choice to evaluate hits in any of the 30 MLB parks. With these counts based on user input of League, hit Direction, Park, EV, and LA, I compute the mean batting average (AVG) and slugging percentage (SLG). These values of AVG and SLG are equivalent to 'expected stats (xBA or xSLG) for balls in play (not including strikeouts). For both counts and percentages, I apply 9-cell neighborhood averaging around each {EV,LA} pair to account for the average Trackman/Hawkeye errors in EV and LA observations. Similarly, the hit Direction bins are sampled with 5° overlap. When the user taps the 'Calculate' button, the app selects randomly from the distribution of out, 1b, 2b, 3b, and hr counts and displays an outcome that simulates some of the randomness in baseball, while respecting the statistics of the user's inputs for League, hit Direction, Park, EV, and LA. The user can tap the 'Calculate' button as many times as they like, with each tap representing a reroll of the dice. We often use this feature when players or coaches challenge a HitOrNot result during an indoor scrimmage. The heat map screen takes the same data used in the dashboard, and plots the user selected grid as a heatmap. This tool allows users to interrogate the differences in {EV,LA} outcomes for different Leagues, hit Directions, and Parks. The about screen allows users to update the data in the map to the most recent week of NCAA and MLB play. Thank you to Joe Haumacher, Kyle Bonicki, Scott Bradley, and the Princeton University Baseball Team.