If there's one thing baseball fans are averse to, it's change. Over the MLB's 149-year history, alterations to the game's rules, like lowering the pitcher's mound (1968) or introducing instant replay challenges (2014) came only after years of heated debate between reformers and purists. Maybe the most contentious issue ever to divide these two camps is whether or not to replace notoriously inaccurate human home plate umpires with less fallible machines. Though that was once largely considered out of the bounds of possibility, MLB games officiated by so-called "robot umpires" are now closer to reality than ever before. Starting this week, batters stepping up to the plate during spring training games will have the ability to challenge an umpire's pitch calls and have them immediately reviewed by a computer.

BARCELONA, SPAIN - MAY 09: The Terminator robot is seen in the paddock following qualifying for the ... [ ] Spanish Formula One Grand Prix at the Circuit de Catalunya on May 9, 2009 in Barcelona, Spain. Robotics has been a growing staple across the entertainment industry for some time now. Whether it's enhancing scenes in film and TV through innovative cameras and angles, or through the rides we see at amusement parks, robotics has been steadily becoming more advanced before our eyes. What are the next steps in this growing sector? One area that has been utilised to great success so far has been using robotic stunt doubles on film and TV sets.

There are ample such updates between "The Show 21" to "The Show 22." San Diego Studios has added fielding to the game's custom practice mode. The plate coverage indicator (PCI) now shrinks when batters try to hit pitches thrown outside the strike zone to more realistically correspond to the real-life difficulty of hitting such pitches. Similarly, pitch types meant to be thrown in specific areas (such as sinkers or split finger fastballs, which are most effectively thrown at the bottom of the strike zone) have less accuracy when they're targeted in unrealistic spots. "PCI Anchors" now allow hitters to pin their focus (read: hitting cursor) if a pitcher seems to be emphasizing a particular part of the strike zone. There are new animations for fielding, and perfect throws from the outfield put the player receiving such throws in better position to make a tag.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Ozzie Guillen made clear Sunday he was no fan of robot umpires or automated strike zones coming to Major League Baseball. Robot umpiring was first tested in the independent Atlantic League of Professional Baseball and in the Low-A minor leagues. Triple-A minor league baseball is trying out automated strike zones for the 2022 season.

The field of quantitative analytics has transformed the world of sports over the last decade. To date, these analytic approaches are statistical at their core, characterizing what is and what was, while using this information to drive decisions about what to do in the future. However, as we often view team sports, such as soccer, hockey, and baseball, as pairwise win-lose encounters, it seems natural to model these as zero-sum games. We propose such a model for one important class of sports encounters: a baseball at-bat, which is a matchup between a pitcher and a batter. Specifically, we propose a novel model of this encounter as a zero-sum stochastic game, in which the goal of the batter is to get on base, an outcome the pitcher aims to prevent. The value of this game is the on-base percentage (i.e., the probability that the batter gets on base). In principle, this stochastic game can be solved using classical approaches. The main technical challenges lie in predicting the distribution of pitch locations as a function of pitcher intention, predicting the distribution of outcomes if the batter decides to swing at a pitch, and characterizing the level of patience of a particular batter. We address these challenges by proposing novel pitcher and batter representations as well as a novel deep neural network architecture for outcome prediction. Our experiments using Kaggle data from the 2015 to 2018 Major League Baseball seasons demonstrate the efficacy of the proposed approach.

Umpires will have a little help behind home plate in some minor league games this season – from a "robot ump." Major League Baseball announced Thursday that select games in the Low-A Southeast will use a robot to help call balls and strikes. The use of the technology, called the Automatic Ball-Strike System, will also "ensure a consistent strike zone is called, and determine the optimal strike zone for the system," according to MLB. The robot's use is one of a number of experimental rules announced Thursday, which the league said are "designed to increase action on the basepaths, create more balls in play, improve the pace and length of games, and reduce player injuries." MLB has often tried out rules in the minor leagues it is considering for the majors.

I, for one, will welcome our robot umpire overlords, at least when it comes to calling balls and strikes. The automated strike zone is coming, probably within the next three seasons, and I am here for it. If you've spent any time on Twitter during baseball season, especially the postseason the last few years, you've probably stumbled on fans arguing for #RobotUmpsNow against those who argue for "the human element," two sides of the ongoing debate over whether baseball should move to automated calling of balls and strikes. It came up yet again in the 2019 World Series, when umpire Lance Barksdale missed two obvious calls in Game 5, one of which he openly blamed on Washington catcher Yan Gomes, which led Nationals manager Davey Martinez to yell at Barksdale to "wake up," and another so egregious that the victim, Victor Robles, jumped in anger and tossed his batting gloves after Barksdale called him out on a pitch that never even saw the strike zone. Both calls were bad, and in both cases there was at least the appearance that Barksdale was punishing the Nationals--punishing Gomes for assuming the strike call before it happened, then punishing the whole team later for questioning him in the first place.

Pitching a baseball is about accuracy and speed. A swift ball on target is the goal, allowing the pitcher to strike out the batter. The system uses an NVIDIA Jetson AGX Xavier, fitted with a USB camera running at 100FPS. A Nerf tennis ball launcher is used to fire a ball towards the batter. Once triggered, the AI uses the camera to capture two successive images of the ball in flight.

Its creator says it can predict whether a baseball pitch will land inside or outside of the strike zone. Tipper was developed by Nick Bild, a serial creator who seems to have an unquenchable thirst to create and innovate. He makes apps, trains neural networks, and literally has a gold badge in'problem solving' on HackerRank. He says he was inspired to build Tipper while sitting idle in traffic, pondering the world from an engineer's point of view. A modified Nerf tennis ball launcher is programmatically fired with a solenoid. A 100FPS camera is pointed in the direction of the launcher and captures two successive images of the ball early in flight.

D3.js, and the power of the web to visualize the process of training a model to predict balls (blue areas) and strikes (orange areas) from baseball data. As we go, we'll visualize the strike zone the model understands throughout training. You can run this model entirely in the browser by visiting this Observable notebook. Today's professional sports environment is packed with large amounts of data. This data is being applied to all sorts of use cases by teams, hobbyists, and fans.