Robot umpires have been given a promotion and will be just one step from the major leagues this season. Major League Baseball is expanding its automated strike zone experiment to Triple-A, the highest level of the minor leagues. MLB's website posted a hiring notice seeking seasonal employees to operate the Automated Ball and Strike system. MLB said it is recruiting employees to operate the system for the Albuquerque Isotopes, Charlotte Knights, El Paso Chihuahuas, Las Vegas Aviators, Oklahoma City Dodgers, Reno Aces, Round Rock Express, Sacramento River Cats, Salt Lake Bees, Sugar Land Skeeters and Tacoma Rainiers. The independent Atlantic League became the first American professional league to let a computer call balls and strikes at its All-Star Game in July 2019 and experimented with ABS during the second half of that season. It also was used in the Arizona Fall League for top prospects in 2019, drawing complaints of its calls on breaking balls.
I have nothing against progress. Some of my best friends are traveling shoe salesmen, and I can't tell you how many times my stone hand ax has come in handy around the cave. But I can't shake the feeling we've gone a tad too far with technology. The latest assault on our humanity came Thursday, when news broke that Major League Baseball would use an automated strike zone at Triple-A this season. It means robot umpires will be one heartbeat from the big leagues -- a ''heartbeat'' being that thing once used to deduce whether a ''person'' was alive.
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Robotic umpires that use an automated system for determining ball and strike calls will now be used in Triple-A baseball for the 2022 season, MLB officials announced. This puts the Automated Ball and Strike (ABS) system, which has seen success after experimental adoption by some ballparks in the minor leagues, just one level below the major leagues. MLB'S SNAIL-PACED LOCKOUT TALKS TO RESUME WITH UNION OFFER MLB is currently seeking personnel to operate the system at ballparks for the Albuquerque Isotopes, Charlotte Knights, El Paso Chihuahuas, Las Vegas Aviators, Oklahoma City Dodgers, Reno Aces, Round Rock Express, Sacramento River Cats, Salt Lake Bees, Sugar Land Skeeters and Tacoma Rainiers, FOX 13 of Seattle reported.
I have been always a fan of using analogies and learning examples instead of complicated statistics and math functions in order to understand a concept in Machine learning. That's being said let's look at this situation. You just bought a new football club. Your new football club does not have any players and there are already 3 teams in the league. Team A has conceded 0 goals all seasons thus it is concluded that Team A has the best defense mechanism.
Adobe and Major League Baseball (MLB), North America's most historic professional sports league, announced a major expansion of their partnership to reimagine fan engagement and continue to bring America's favorite pastime to the next generation of fans – powered across Adobe Experience Cloud, Adobe Creative Cloud, and Adobe Sign. The partnership will empower MLB to bring new personalized, seamless experiences to its millions of fans. Fans will feel a part of the ballpark atmosphere from wherever they enjoy baseball – be it at home, on the go, or at the park itself. The league and its Clubs will be able to collaborate more seamlessly with advanced tools for signing contracts, sharing creative assets and engaging directly with their fans. New, fan-friendly features can include personalized promotions and notifications tailored to individual fans – at the ballpark potentially highlighting which entrances will offer the fastest journey to their seats, VIP parking promotions or discounts on grab-and-go concessions.
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.
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Why catch a fly ball with your hands when you can catch one with your leg? TikTok user Shannon Frandreis amazed her fellow fans when she caught a fly ball at a recent Chicago White Sox game with her prosthetic leg. The surrounding crowd stood up and cheered for Frandreis as she hoisted the leg up in the air, celebrating with a big smile on her face. Chicago White Sox's Yoan Moncada, left, celebrates with third base coach Joe McEwing after hitting a two-run home run during the eighth inning of a baseball game against the Detroit Tigers in Chicago, Saturday, Oct. 2, 2021.
We consider two or more forecasters each making a sequence of predictions over time and tackle the problem of how to compare them -- either online or post-hoc. In fields ranging from meteorology to sports, forecasters make predictions on different events or quantities over time, and this work describes how to compare them in a statistically rigorous manner. Specifically, we design a nonasymptotic sequential inference procedure for estimating the time-varying difference in forecast quality when using a relatively large class of scoring rules (bounded scores with a linear equivalent). The resulting confidence intervals can be continuously monitored and yield statistically valid comparisons at arbitrary data-dependent stopping times ("anytime-valid"); this is enabled by adapting recent variance-adaptive confidence sequences (CS) to our setting. In the spirit of Shafer and Vovk's game-theoretic probability, the coverage guarantees for our CSs are also distribution-free, in the sense that they make no distributional assumptions whatsoever on the forecasts or outcomes. Additionally, in contrast to a recent preprint by Henzi and Ziegel, we show how to sequentially test a weak null hypothesis about whether one forecaster outperforms another on average over time, by designing different e-processes that quantify the evidence at any stopping time. We examine the validity of our methods over their fixed-time and asymptotic counterparts in synthetic experiments and demonstrate their effectiveness in real-data settings, including comparing probability forecasts on Major League Baseball (MLB) games and comparing statistical postprocessing methods for ensemble weather forecasts.
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Jose Siri and Chas McCormick hit back-to-back home runs in the eighth inning, rallying the AL West-leading Houston Astros over the Arizona Diamondbacks 7-6 on Sunday. Carlos Correa also homered as the Astros held their comfortable division lead over Oakland. Houston won for the fourth time in five games and cut Tampa Bay's lead for the best record in the AL to 3 ½ games.
Large pre-trained language models have repeatedly shown their ability to produce fluent text. Yet even when starting from a prompt, generation can continue in many plausible directions. Current decoding methods with the goal of controlling generation, e.g., to ensure specific words are included, either require additional models or fine-tuning, or work poorly when the task at hand is semantically unconstrained, e.g., story generation. In this work, we present a plug-and-play decoding method for controlled language generation that is so simple and intuitive, it can be described in a single sentence: given a topic or keyword, we add a shift to the probability distribution over our vocabulary towards semantically similar words. We show how annealing this distribution can be used to impose hard constraints on language generation, something no other plug-and-play method is currently able to do with SOTA language generators. Despite the simplicity of this approach, we see it works incredibly well in practice: decoding from GPT-2 leads to diverse and fluent sentences while guaranteeing the appearance of given guide words. We perform two user studies, revealing that (1) our method outperforms competing methods in human evaluations; and (2) forcing the guide words to appear in the generated text has no impact on the fluency of the generated text.