A Blog About Workload Management, Biomechanics, and Baseball Analytics
Contract Assessment - 05.09.2020
SMWW Player Assessment: J.T. Realmuto
I recently completed Ari Kaplan's Baseball Analytics course through Sports Management Worldwide and the final requirement was to complete a contract assessment on a player of my choice.
This post examines J.T Realmuto's past performance, 3-year projections, and my assessment of his value to the Phillies. Realmuto is the game's best catcher and will expect his first contract in free agency to reflect that value. My assessment predicts his contract to be in the ballpark of 5 years, $120M.
The course was extremely beneficial and I am happy to recommend it to anyone who is seeking a solid foundation in baseball analytics.
Managing Pitcher Workloads after the Coronavirus Shutdown
Operating under the assumption that the regular season will be shortened, every game will carry increased importance for making the post season. This creates competing motivations for teams. They want to get their top pitchers going as quickly as possible to be competitive but ramping up too quickly could force time on the IL and cause more time to be missed compared to shortened or skipped appearances early on. Fortunately, there is a way teams could estimate workload and help ensure pitchers aren’t being exposed to more workload than they can handle. I will outline that method and point out the challenges/shortcomings it presents to teams.
Increasing Player Value with a Focus on Exit Velocity
As teams continue to focus their player development programs aiming to get the most value out of a player possible it will be important for them to identify which traits they want to focus on and how that will effect the player's value to the team. Working off of this idea I wondered, "What would happen to a player's value if he could add 1 mph of exit velocity to his batted balls?" This post will explain my approach to making this conversion from miles per hour to dollars and show why a batting program that develops increased exit velocity would add tremendous value to a player or major league organization. I will also discuss some approaches to increasing exit velocity I would pursue if developing a program myself.
Batter-Pitcher Matchups: Revisiting ideas from The Book's 1999-2002 Dataset
In The Book: Playing the Percentages in Baseball, the authors investigate batter-pitcher matchups using a number of different statistics to see if choosing matchups using those statistics give either side an advantage. The statistics used are handedness, GB/FB rate, BB/K rate, contact rate, and player quality. This post re-examines those statistics using data from 2017-2019 to see if the trends observed hold true in 2019.
The Houston Astros are a league leader in using data analytics to identify and develop talent. Recently, their use of spin rate has become more public and the success they have had finding pitching talent speaks for itself. There is more to the Astros' success than finding players with certain spin rate characteristics, but it is likely the first step they take when evaluating pitchers. With the 2020 season wrapping up I wanted to look ahead to the free agent pitchers and see if any are good fits when using this method of assessment.
Based on my analysis, the top 5 free agent pitchers in 2020 with high spin pitch profiles are: 1 - Rick Porcello 2 - Tommy Hunter 3 - Edinson Volquez 4 - Yu Darvish 5 - Jake Arrieta
Each play in the playoffs holds extra weight compared to the regular season. An error can change a game and a loss can doom a series. In close games and series, it is often the team that executes the small plays that comes out on top.
A particular play in Game 2 of the NLDS between Washington and Los Angeles stood out in this context: Asdrúbal Cabrera singled to right field, driving in Ryan Zimmerman. However, the throw from the outfield held up Kurt Suzuki at third base and Cabrera was thrown out trying to advance to second base on the throw. Although the Nationals still won the game, the base running error was not inconsequential in the series.
ABBS 2020 Inaugural Conference Recap and Video Availability
Notice: This link will take you away from Phanalytics to the American Baseball Biomechanics blog.
Emerging technologies such as markerless motion capture, pitching mounds with embedded force plates, and wearables will continue to increase the accessibility and interest in biomechanics research in the wider baseball community. ABBS has a tremendous opportunity to spread research and establish best practices for these new technologies that will help push the game forward.
Eliminating Extra Innings to Improve a Condensed Season Schedule
In the last few weeks I have heard a number of ways Major League Baseball could adjust the schedule to get as many games in as possible this year. Two schedule changes that seem almost definite are increased double headers and less rest days. With the added toll that will take on players the commissioner’s office is likely exploring how these changes would affect injuries and ways to mitigate that risk. One option I have had proposed to me is the elimination of extra innings.
This post will explore how eliminating extra innings would have changed outcomes over the last five seasons and how much of an impact that has on players and teams.
Does being traded mid-season to a better (or worse) team effect player performance?
Just before the 2019 MLB trade deadline Nick Castellanos was traded from the Detroit Tigers to the Chicago Cubs. With the trade, Castellanos moved from what would ultimately be the worst team in baseball in 2019 to a Cubs team making a postseason push and finishing with 37 more wins than Detroit. Castellanos' wOBA increased by 0.077 and his wRC+ increased from 105 to 154. It appears that moving to a team filled with more talent and more motivation to win caused his performance to improve but is that true for all players such that an increase in performance could be predicted? This post will use all trades from the 2017-2019 MLB trade deadlines to investigate this idea.
Predicting EoY Batting Averages Using Data from March/April
Batting average is not a particularly useful tool for player analysis, but it something that fans and commentators often use. I thought adding an end of season projection to that conversation would be interesting and attempted to build a model that could use data from March and April of a season to predict end of year batting average.
This post will summarize my initial attempt, some things I have learned since then, and ideas to further improve model performance.
Part 2: Solving Major League Baseball's Home Run Problem
Part 1 explained the increasing home run and strikeout rates in Major League Baseball, and how sabermetrics and technology have influenced this change. This post will discuss solutions - both good and bad - and how Major League Baseball can implement them.
Part 1: Investigating Major League Baseball’s Home Run Problem
As home runs hit record levels this season and strike out rates continue to climb, Major League Baseball has a problem that needs to be addressed. This post will examine the trends and explain how we got here.