A few weeks ago, I regressed as a writer. I regressed a lot, actually: twenty years worth of slash line data regressed against twenty years of run scoring data in various ways. But — and this is a dangerous sentence, and usually a bad one — someone asked me a question on Twitter and I want to answer it. Namely: was batting average always the weakest correlation to run scoring among the slash line statistics, or has it only become so recently?

This is going to be a quick hitter. I broke the game down somewhat arbitrarily, using eras defined by OOTP Perfect Team. I started in 1947 and went up until 2000 (the results of the 2000s were in my previous article). Here’s what those 2000s results look like, which should both give you an idea of the correlations today and preview the format for the rest of the article:

Without further ado, let’s get started.

Golden Years, 1947–1960
Now, these weren’t the golden years for me, because I wasn’t alive, but I guess that’s what some people call this era of baseball. Jackie Robinson! Ted Williams! Stan Musial! Willie Mays! Batting average mattered more, but it still didn’t matter:

What do I mean by that? Well, if you predict run scoring with OBP and SLG, you get a 0.908 adjusted r-squared to actual runs scored. Predict run scoring with the entire triple slash line, and you get an adjusted r-squred of 0.91. Batting average did better, on its own, as a run scoring predictor, but using OBP and SLG was the gold standard in the golden years.

Baseball Boom, 1961–1979
This is a broad era that folds in some pitching-dominant years that led to rules changes, the early part of the speed era, and some early-60s home run mania. It’s also an era where, if you know OBP and SLG, you don’t need to know batting average to predict run scoring:

Like the 1947–60 span, using OBP and SLG as predictors does just as well as using all three statistics. More specifically, OBP/SLG had a 0.922 adjusted r-squared to runs scored. The full AVG/OBP/SLG regression checks in at 0.923. Average… if you’re already 99.89% of the there, it’ll get you that last tiny bit of explanatory power. That’s not exactly a ringing endorsement.

Defensive Era, 1980–1992
Even though I wasn’t alive for a big chunk of this era and wasn’t following baseball for the vast majority of it, it’s one of my favorite eras, thanks to Ozzie Smith, my single favorite baseball player and, per my mom, the person I’ve most emulated in my life. I spent countless hours mimicking the defensive plays I saw on my “Ozzie, That’s a Winner” VHS tape, which my uncle had recorded on local access TV in St. Louis. I’m a lefty, so I was doing them backwards and they never led to me becoming a defensive wunderkind, but none of that mattered to me; I just wanted to be like Ozzie. Uh, where were we? Oh, right. Average didn’t matter:

Using the criteria from above, OBP/SLG checks in at 0.863, and an all-three-slash-stats regression checks in at 0.864. It’s interesting to note that OBP and SLG explain the lowest percentage of variation in run scoring in this era, which I attribute to the huge range in team baserunning strategy and effectiveness, but that’s not the point of this study. The point is that if you already know a team’s OBP and SLG, you don’t need to know their batting average to predict how many runs they scored.

The Power Years, 1993–2000
I cut this one off at 2000, since my previous article already covered the 21st century, but OOTP extends it to 2004. Regardless, you guessed it:

This time, the adjusted r-squared is the same whether you look at OBP/SLG or AVG/OBP/SLG. So there you have it: throughout the eras, the correlations have remained the same. If you’re trying to predict a team’s run scoring and already have their on-base percentage and slugging percentage, you can stop there. Batting average won’t add anything to the equation.

With only a few hours to go before the results of the BBWAA’s 2023 Hall of Fame balloting are announced, the widespread assumption is that the voters will pitch their second shutout in three years and their fifth since voters returned to annual balloting in 1966. Not only is there no slam-dunk candidate with the milestones and squeaky-clean reputation that portends a first-ballot election, or a returning candidate who’s the equivalent of a gimme putt away from 75%, but the highest share of the vote from among the 201 ballots published (just over half of the expected total) shows no candidate receiving more than 80.1%. Given that voters who don’t publish their ballots ahead of the announcements tend to be more conservative when filling them out, at best we’ve got a nail-biter ahead of us for the top two candidates. As of Monday evening, Jason Sardell, the top prognosticator for election results for three years running, forecast only about a 13% chance of a candidate being elected. He hasn’t updated the odds in the 21 hours since, which has added just 18 ballots to the pile, but I believe these will suffice:

Just over 24 hours to go until the Baseball Hall of Fame reveal, and here's where we are with 183 ballots in @NotMrTibbs's tracker. Today has been a bad day for Scott Rolen, but Todd Helton continues to slowly climb. pic.twitter.com/xRpCjZR3BL

— Jason Sardell (@sarsdell) January 23, 2023

If you’re looking for a glimmer of hope for Scott Rolen and Todd Helton, I do have one. Here’s a table showing all of the candidates who have received at least 70% via the pre-announcement ballots since 2014 (“The Tracker Era”):

As I noted in my election day preview, of the 14 candidates who received 75% to 85% via ballots published prior to the results, the average differential between those shares and their final results was a drop of 5.6% overall, and 4.4% once you exclude Bonds/Clemens/Schilling, whose baggage created a resistance to their candidacies that doesn’t apply to any of the others here.

While on the one hand just two out of 10 instances in which a candidate received less than 80% resulted in his election that year, the data has been consistent, in that everybody receiving 78.2% or higher has in fact ended up across the finish line. Sardell’s forecasting, which groups voters based upon the number of candidates they include and their electoral stance on PED users, is certainly more sophisticated than this quick-and-dirty table. But as we count down to the announcement, we at least know that there’s something to be said about the possibility of Fred McGriff having company in Cooperstown on July 23.

Player Valuation Analyst

The Pirates Why

The Pittsburgh Pirates are a storied franchise in Major League Baseball who are reinventing themselves on every level. Boldly and relentlessly pursuing excellence by:

• purposefully developing a player and people-centered culture;
• deeply connecting with our fans, partners, and colleagues;
• passionately creating lifetime memories for generations of families and friends; and
• meaningfully impacting our communities and the game of baseball.

At the Pirates, we believe in the power of a diverse workforce and strive to create an inclusive culture centered in Passion, Innovation, Respect, Accountability, Teamwork, Empathy, and Service.

Job Summary

The Pittsburgh Pirates are currently seeking a full-time Analyst to join their Professional Player Valuation team. The Professional Player Valuation team is responsible for producing internal valuations of players and for communicating the insights from their research to others within Baseball Operations. In this role, you will have the ability to influence roster construction and to see the impact of your work on the field. This role will provide candidates with opportunities for growth and the ability to learn from others throughout the organization.

When you submit your application, please include an original piece of research, or a project you have worked on, that you feel is relevant to this position. There is no expectation that this research/project is baseball specific. While not essential for consideration, priority will be given to applicants who submit a sample of their work.

Primary Role Responsibilities:

• Serve as the primary analyst for a subset of the professional player population
• Collaborate with other areas of Baseball Operations on the assessment of professional players
• Assist in the building of models and tools to aid in player skill assessment discussions
• Prepare tools, visualizations, and reports to aid in disseminating information throughout Baseball Operations
• Answer research questions that you think will add value to the organization, as well as those requested by department leadership and other within Baseball Operations

Involvement In:

• Trade deadline meetings.
• Off-season strategy meetings.
• Roster management discussions.

Required:

1. Authorized to work lawfully in the United States.
2. Expertise with R, Python, or Stan
3. Ability to generate insights with testable predictions from complex data sets
4. Experience with programming data visualizations (Rshiny, Ggplot, or equivalent)
5. Demonstrated ability to explain complex models and ideas clearly and succinctly
6. Proficiency in SQL to perform data manipulation with an understanding or relational database structures
7. An understanding of skill-acquisition and development concepts and their applications

Desired:

1. Strong interpersonal skills to communicate effectively with a wide range of individuals throughout the Baseball Operations department
2. Passion for learning, especially in areas outside of individual expertise
3. Ability to apply insights from external fields to baseball. Examples include, but are not limited to, computer science, kinesiology, machine-learning, physics, or psychology
4. Initiative to seek out and perform research on topics of personal interest

To Apply:
To apply, please follow this link.

The content in this posting was created and provided solely by the Pittsburgh Pirates.

Senior Computer Vision Engineer

About Us:
KinaTrax’s mission is to provide professional and collegiate teams with game-changing insights about their most valuable asset: their athletes. We deliver research-grade markerless motion capture technology that allows teams to collect in-game biomechanical performance data on their athletes. KinaTrax camera systems are currently deployed in over 50 stadiums & labs across MLB, MiLB, & NCAA organizations – and expanding rapidly. Our comprehensive data capture & analysis tools are operationalized for daily use by players, GM’s, coaches, trainers, medical staff, and beyond. As the market leader in Major League Baseball, KinaTrax has established itself as a foundational part of the teams’ day to day strategy and decision-making machine. But we aren’t finished. We are constantly innovating and looking to recruit talented teammates to help us continue to revolutionize this space.

What we are looking for:
Currently we are seeking highly motivated software engineers to lead the development of our core technologies that measure athletic performance in competitive environments and deliver game changing insights at the speed of sport. In this role you will help build and refine our computer vision algorithms focused on tracking player movement and biomechanics. Your contributions will focus on bringing the next generation of athlete performance data, across a variety of sports, to teams worldwide.

Required Skills

• PhD/MS degree in Computer Vision, Machine Learning or related field with research publications; alternatively equivalent years of industry experience solving problems which do not have readily available solutions.
• Expertise in 3D computer vision, machine learning, and artificial intelligence
• Experience maintaining and promoting best practices for software development, for example: test driven development/design, unit tests, code coverage, refactoring, gated checkins, code reviews, continuous integration etc.
• Code optimization for processing speed
• Expertise in at least one of these specific areas: 2D/3D object(s) detection and tracking, keypoint detection, human pose recognition, image sequence/ visual/LIDAR-based tracking, and multi-object tracking, optimization, computational geometry
• Track record of driving research projects from start to completion, including conception, problem definition, experimentation, iteration, and finally publication or productization
• Strong verbal and written communication skills

Our Stack

• Machine Learning: Proficient in Python, TensorFlow, PyTorch
• Computer Vision: Very strong in C++, OpenCV
• DevOps: Visual Studio, Github, Bazel, CMake, FFMPEG
• User Interface: Qt

Additional experience that will set you apart

• Strong industry or applied experience, with 3-4+ years at a reputable company
• Creative projects evaluating humans in natural environments
• Demonstrable interest in sports/athletic performance/biomechanics
• Exposure to biomechanics
• Experience with embedded computer vision & machine learning
• Creative sensor fusion projections/solutions
• Experience with pose2image translation
• Deploying cloud computing
• Container eco-systems (Kubernetes)

To Apply:
To apply, please follow this link.

Software Application Engineer – KinaTrax Camera System Software

About Us:
KinaTrax’s mission is to provide professional and collegiate teams with game-changing insights about their most valuable asset: their athletes. We deliver research-grade markerless motion capture technology that allows teams to collect in-game biomechanical performance data on their athletes. KinaTrax camera systems are currently deployed in over 50 stadiums & labs across MLB, MiLB, & NCAA organizations – and expanding rapidly. Our comprehensive data capture & analysis tools are operationalized for daily use by players, GM’s, coaches, trainers, medical staff, and beyond. As the market leader in Major League Baseball, KinaTrax has established itself as a foundational part of the teams’ day to day strategy and decision-making machine. But we aren’t finished. We are constantly innovating and looking to recruit talented teammates to help us continue to revolutionize this space.

What we are looking for:
Currently we are seeking highly motivated software engineers to lead the development of our core technologies that measure athletic performance in competitive environments and deliver game changing insights at the speed of sport. In this role you will help build intuitive and aesthetically pleasing software tools used by our elite level clients to capture data on their athletes daily. Your contributions will focus on bringing the next generation of athlete performance data, across a variety of sports, to teams worldwide.

Our Stack

• Languages: C++, Python
• SDK/APIs: OpenCV, Qt, three.js, Boost
• DevOps: Visual Studio, Github, Docker, Amazon EKS, Lens, FFMPEG

What you bring:

• Experience developing both 2D/3D Imaging Software
• Experience with software design, programming and interactive UI
• Demonstrated proficiency in C++/Qt for latency-critical software
• Deep knowledge of computer architecture fundamentals with an excellent understanding of the interaction between software and hardware
• Deep understanding of operating system concepts, specifically embedded application design and implementation
• Developing and debugging multithreaded applications
• Implementation of named pipes
• Working experience with hardware SDKs, integration, and synchronization for cameras, forceplates, EMG, etc.
• Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
• Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
• Strong verbal and written communication skills
• A strong team player, self-motivated and positive attitude.
• Innovative and creative, you proactively explore new ideas and adapt quickly to change

Relevant Experience

• BS/MS with extensive industry experience or PhD in Computer Science, Computer Engineering or Electrical Engineering, or equivalent experience
• Experience with camera ISP is a plus
• Proficiency with data structure and visualization
• Code optimization for processing speed
• Familiarity with common development and debugging techniques.
• Experience maintaining and promoting best practices for software development, for example: test driven development/design, unit tests, code coverage, refactoring, gated checkins, code reviews, continuous integration etc.
• Familiarity with polling and callback frame grabbing techniques
• Experience with building software in both Windows & Linux environments

Additional experience that will set you apart

• Strong industry or applied experience, with 3-4+ years at a reputable company
• Creative projects evaluating humans in natural environments
• Demonstrable interest in sports/athletic competition
• Exposure to biomechanics/sport performance
• Experience with embedded computer vision & machine learning
• Knowledge of NUMA Architecture
• Experience with Swift language
• Some experience with calibrated imaging systems

To Apply:
To apply, please follow this link.

The content in this posting was created and provided solely by KinaTrax.

KinaTrax Stringer/Operator (Remote, Part-Time)

Job Description
KinaTrax is seeking highly motivated and detail-oriented candidates for Stringer/Operator positions at select Major League Baseball Clubs and affiliates for the 2023 season. These individuals will be responsible for operating the KinaTrax system for games in PST and CST time zones. The number of games varies weekly based on the season schedule. Stringer/Operator(s) will start as soon as possible coinciding with the MLB/affiliate season.

Responsibilities:

• The duties of this role will be fulfilled remotely and require availability at least one hour before first pitch and continue approximately one hour after the final out (Approx. 5-6 hour total shift)
• Responsible for capturing in-game video data on a play-by-play basis using KinaTrax software
• Responsible for monitoring the system, troubleshooting, and logging information during the game 
• Responsible for validating all information and uploading the data after the game
• Work closely with our game-night support staff to ensure proper operation and accuracy of data
• Other reasonable and related duties may be assigned.

Preferred Qualifications:

• Candidate must be motivated, well organized, and detail oriented.
• A firm understanding of baseball is required.
• Candidate must be able to work remotely for games on weeknights and weekends associated with home games.
• Strong computer proficiency (Windows OS and Windows-based software) and the ability to quickly learn and operate new software
• Laptop/computer required with external monitor (multiple or widescreen monitors preferred)
• Strong & stable internet connection (400mbps+ service preferred)
• A "team player" with a great attitude, including but not limited to a willingness to make and learn from mistakes and the ability to work closely and cooperatively (and take direction from) our game-night staff
• Professionalism. It is a fun job and we pay people to watch baseball, but it is also an important job and we want people who will take the responsibility seriously.

Relocation

• Remote, Relocation is not required.

Company Description

• KinaTrax develops a markerless motion capture system that analyzes the 3D movement of a baseball pitcher and hitter in-game. The system is installed in several ballparks throughout the country, and is utilized by professional baseball teams for the purposes of assessing and enhancing player performance and preventing injuries. The company was founded in 2015 and is headquartered in Boca Raton, Florida.

Additional Information

• Type: Part-time
• Experience: Entry level
• Functions: Data Capture, Information Technology
• Industries: Markerless Motion Capture, Biomechanics, Baseball Analytics

To Apply:
To apply, please follow this link.

The content in this posting was created and provided solely by KinaTrax.

About Inside Edge
Inside Edge Scouting Services specializes in data capture and analytics down to the finest details of every Major League game. Major League clubs, media, and other clients subscribe to our real-time pitch-by-pitch data, custom-tailored reports, and advanced analytic tools to gain an edge on their competition. We provide a fun, fast-paced work environment and an opportunity to get started on a career in baseball and differentiate yourself from other job seekers. Past employees have gone on to positions with both Major League clubs and media organizations.

Position Title & Description
Chart Team: Candidates filling this position will serve on Inside Edge’s core charting team, a small group of high level applicants who will be responsible for capturing rich MLB data points like intended locations, defensive ratings, shifts, intangibles, and more.

Key areas of responsibility

• Participate in a rigorous training program before the season starts
• Use Inside Edge software to enter and crosscheck data
• Mark actions to be reviewed by supervisors
• Add, review, and update qualitative player notes
• Review video replay ensuring integrity of charted data

Location
Remote work available in the following states: CA, MN, MO, NC, NV, TX, VA, WI, OH, WA

Wages and term of employment
March 15th (tentatively) through the end of the 2023 MLB season
Starting pay: minimum wage (rate varies depending on the state in which you reside)
Roughly full-time hours (30-40 hrs/wk), day shift (8:00 AM CT start time)

Qualifications
While in-depth training will be provided, candidates need a strong understanding of both the basics and subtleties of baseball games, and will be required to quickly and accurately recognize pitch types, locations, defense, intangibles, and other various data points.

To apply

• Fill out our online screening test at: Chart Team 2023 Screening Test
• Once you have completed the screening, please send an email with your resume to bobbygiller@gmail.com. Feel free to include supplemental information and a quick note on what you’re including. A cover letter is unnecessary.
• Depending on the results and your experience, we’ll contact you to set up an interview.

The content in this posting was created and provided solely by Inside Edge.

TrackMan Data Operations Intern

Description:

Join TrackMan Baseball’s Data Operations team as a paid intern for the 2023 baseball season. You will have a vital role in a growing, fast-moving, entrepreneurial company that is breaking new ground in sports. In this position, you will primarily be responsible for reviewing and verifying TrackMan data from a significant number of major and minor league baseball, NCAA, and international stadiums during the 2023 baseball season.

The internship starts in early February and finishes at the conclusion of the major league baseball season. Interns are expected to work 8 hours a day and 5 days a week, and weekend availability is required. An hourly rate of $15.00 will be offered.

About TrackMan Inc.
TrackMan, Inc. is a US-based subsidiary of TrackMan A/S and is based in Stamford, CT, about 30 miles north of New York City. TrackMan A/S has developed a range of products for the golf market and is considered the gold standard in measurement of ball flight and swing path. TrackMan’s golf products are used by top touring professionals, teaching pros, broadcasters and governing bodies.TrackMan, Inc. introduced 3D Doppler radar technology to the baseball industry and the technology is now used by all major league baseball organizations and is a component of MLB’s StatCast system. TrackMan, Inc. is revolutionizing baseball data and has been featured in publications such as the New York Times, Sports Illustrated, FanGraphs, and ESPN.

Requirements:

• Thorough knowledge of baseball.
• Proficiency in Microsoft Excel.
• Strong attention to detail and ability to work well with others.

Desired Skills and Experience:

• Bachelor or Master’s degree in Statistics, Mathematics or a related field.
• Strong knowledge of databases, SQL, and R statistical software.
• Python or other scripting language experience.

This is a great opportunity for anyone eager to break into the baseball community and acquire valuable experience with data available exclusively to professional baseball franchises. Based on your performance and openings within the company, you will also have the opportunity to continue working with TrackMan after the internship concludes. During the internship, you will work with the entire TrackMan staff and gain further knowledge of how the company operates. Full training will be provided.

To Apply:
To apply, send a resume and cover letter to dpo@trackman.com. No phone calls please.

The content in this posting was created and provided solely by TrackMan Inc.

Lead or Senior Quantitative Analyst, Player Evaluation

Title: Lead or Senior Quantitative Analyst, Player Evaluation
Department: Baseball Research & Development
Reports to: Director, Baseball Research & Development
Status: Regular Full-Time
Location: Philadelphia, PA; also open to Remote

Position Overview:
As a Lead or Senior Quantitative Analyst (QA), Player Evaluation, you help shape the future of Phillies Baseball Operations by building statistical models to forecast player performance and communicating those results to decision-makers. Using analytical rigor and sophisticated statistical modeling techniques, you identify opportunities for the Phillies to improve via the application of forecasts to player development and evaluation. Join a team doing cutting-edge foundational research on biomechanics, human movement, ball-flight physics, and more, with the unique opportunity to apply those findings to player evaluation.

Responsibilities:

• Conduct and oversee statistical forecasting projects in multiple baseball subject areas
• Collaborate with baseball subject matter experts in scouting, development, biomechanics, machine learning, decision science, and more, integrating their expertise into player evaluation models
• Maximize organizational impact of the department’s player evaluation models by advocating model-driven decision-making in various baseball contexts
• Ensure projects conform to best practices for implementing, maintaining, and improving predictive models throughout their life cycles
• Assist and mentor other members of the QA team with their projects by providing guidance and feedback on your areas of expertise within baseball and statistical modeling
• Continually enhance your and your colleagues‘ knowledge of baseball and data science through documentation, reading, research, and discussion with your teammates and the rest of the front office

Required Qualifications:

• 2-5+ years of relevant work or graduate school experience
• Possess or are pursuing a BS, MS or PhD in Statistics or related (e.g., mathematics, physics, or ops research) or equivalent practical experience
• To determine leveling we look at a variety of factors including, but not limited to, years of experience and education. Typically we consider candidates as Lead QA around 2-3 years of experience and Senior QA around 4-5+ years of experience
• Proficiency with scripting languages such as Python, statistical software (R, S-Plus, SAS, or similar), and databases (SQL)
• Demonstrated experience designing, constructing, implementing, and leading technical research projects for use by non-technical stakeholders
• Proven willingness to both teach others and learn new techniques
• Willingness to work as part of a team on complex projects
• Proven leadership and self-direction

Preferred Qualifications:

• Experience with a probabilistic programming language (Stan, PyMC, etc.)
• Experience managing or overseeing the work of other data scientists or analysts
• Experience with model-driven decision-making under uncertainty (eg. a rigorous approach to fantasy sports, poker, etc.)

Interested applicants should submit both their resume and an answer to the following question:

The R&D department has been asked to identify the best defensive catcher in baseball. What models would you build to answer that question, and how would you apply those models to decision-making? (250 word limit)

Tip: There’s no defined right or wrong answer. Responses are used to get some insight into how you approach problem solving and baseball in general.

The Phillies are proud to be an equal opportunity employer, and are committed to growing a workforce diverse in perspective and background. We proudly strive to build a group of employees who represent the fans and communities we currently, and aim to, serve.

To Apply:
To apply, please follow this link.

The content in this posting was created and provided solely by the Philadelphia Phillies.

Lotte Giants (KBO) Analyst, Research & Development

Location: Busan, South Korea

The Lotte Giants, an inaugural member of the KBO (Korean Baseball Organization) League, are looking for an Analyst to work out of the front office at Sajik Stadium in Busan, South Korea. The KBO was founded in 1982 and is the top level of professional baseball in South Korea. Consisting of ten teams, the league is split into two divisions and each team plays a total of 144 games. The Lotte Giants are located in Busan, the country’s second-biggest city, on the southeastern coast of the Korean peninsula.

The Research & Development team is involved in every aspect of the organization, including but not limited to Major League Operations, Player Personnel, Scouting, Advance Scouting, Player Development, and Sports Science. The department is responsible for the oversight and implementation of all data and technology-related processes within the organization. The current department was the first of its kind in the KBO and has set the standard for quantitative analysis in foreign professional baseball leagues. The incoming Analyst will have an opportunity to have an enormous impact on the day-to-day operations of the organization while also continuing to assist in the scaling of the R&D department. The incoming Analyst’s responsibilities consist of the following, but are not limited to:

• Utilize the current player evaluation infrastructure to assist the GM with player personnel decision-making
• Enhance the current player evaluation system
• Propose new project ideas and take the initiative to help improve on the current processes in place
• Lead the R&D departments advance scouting efforts
• Utilize the predictive modeling infrastructure to help players optimize their skillsets
• Maintain and improve the departments back-end data science architecture
• Be the go between the R&D department and first team manager
• Provide feedback to the coaching staff to improve in-game strategy

The ideal candidate primarily uses either R or Python, and SQL, and has some background and knowledge about baseball-specific sabermetrics processes. The department will place a strong priority on candidates with experience creating models and translating raw data into practical, usable information. Some understanding of biomechanics and sports science would be a plus, as would prior experience with roster construction and advance scouting. Critical thinking skills will be highly valued for this position, as the Analyst will serve as an important member of a decision-making group.

There are no specific educational background requirements for this position, though experience in data science, computer science, and/or a related computational field will be considered – whether academic or professional. The ability to speak Korean is not necessary for this role, although the ability to do so is a plus.

To Apply:
If interested in this position, please email your resume, desired salary and any pertinent work samples to lottegiantsjob@gmail.com.

The content in this posting was created and provided solely by the Lotte Giants.

Minor League Pitching Coach

Location: Arlington, TX
Status: Full-Time

It’s fun to work in a company where people truly BELIEVE in what they’re doing!

We’re committed to bringing passion and customer focus to the business.

The Texas Rangers are seeking a Minor League Pitching Coach to join the organization. An affiliate Pitching Coach will be tasked with carrying out organizational pitching philosophies at their given affiliate. You will be asked to effectively monitor & communicate player plan implementation, while adjusting goals based on collaborative communication with the player, front office & field staff.

Essential Functions of Position Include, But Are Not Limited to the Following:

• Develop pitching methods that reinforce the organization’s philosophies.
• Develop and establish a next day game review process that provides feedback to pitchers on their performance.
• Plan daily pitching practice while coordinating with other members of the field staff
• Analyze and create ways to optimize each pitcher’s performance at your affiliate.
• Collaborate with affiliate Strength Coach and Apprentice’s at affiliate to identify and act on solutions related to physical limitations/opportunities for development
• Develop a process that empowers players to abide by organizational advance scouting efforts.
• Assist the staff and players with the implementation of systems and technologies.
• Communicate plans, goals and progress with players and relevant staff members throughout checkpoints.
• Prescribe and monitor a development plan for each pitcher based on areas of opportunity.
• Ability to adapt to Minor League season, schedule, personnel, and
• Outward communication towards supervisors.
• Responsible for documentation of player plans, adjustments, and occurring changes at the affiliate.
• All duties as assigned.

Preferred Qualifications:

• Interest in player development, strength & conditioning, and analytics.
• Ability to understand and advocate for changes due to new information and or tools in baseball.
• Strong interpersonal and communication skills.
• Willingness to use available resources to problem solve.
• Strong computer skills and proficiency in Microsoft Office.
• Hard working and driven to succeed.
• Professional or collegiate playing experience is a plus.
• Coaching experience is a plus.
• Fluency in Spanish is a plus.
• Strength & conditioning related degrees are a plus.

Desired Attributes:

• Character – High Integrity. Hard Working. Empathetic.
• Servant Mindset – The player’s best interests are always the top priority
• Domain Knowledge
• Open Mindedness – Demonstrated track record of growth throughout career
• Data Driven Decision Maker & Committed to Process
• Intellectual Humility
• Clear Communication & Collaboration – Up/Down/Lateral
• High Energy & Passion

The above statements are intended to describe the general nature and level of work being performed by people assigned to this job. They are not intended to be an exhaustive list of all responsibilities, duties and skills required of the job.

If you like wild growth and working with happy, enthusiastic over-achievers, you’ll enjoy your career with us!

To Apply:
To apply, please follow this link.

The content in this posting was created and provided solely by the Texas Rangers.