Projecting X 2.0: How to Forecast Baseball Player Performance

By Mike Podhorzer

*The eagerly anticipated follow up to the groundbreaking first version of Projecting X!*

In Projecting X 2.0: How to Forecast Baseball Player Performance, FanGraphs expert Mike Podhorzer takes you on a journey through the process of projecting baseball player performance. As he walks you through an assortment of both basic and advanced metrics, citing various pieces of relevant research along the way, you will learn the whats, whys, and hows of each statistic.

Once you become an expert on analyzing the statistics and metrics used in forecasting, you will discover how to use those newly acquired skills. A complete step-by-step tutorial on developing a Microsoft Excel spreadsheet to perform the calculations is included, as well as examples of the forecasting process in action as Mike guides you through a hitter and pitcher projection.

***New in Version 2.0***
• New research and more effective methods for projecting BABIP, RBI and runs scored, stolen bases, and pitcher strikeout and walk percentages
• Pitcher Quality Start projections!
• Even more screenshots of relevant data in tabular and graphical form
• Discussions about the usefulness of Spring Training stats and how new technology continues to help move the statistical revolution forward
• Excel tips and frequently asked questions to ensure your spreadsheet is immaculate

Table of Contents:

Introduction
Projection Systems
Projection Methodology
Spring Training
New Technology and Statcast
Chapter 1: Projecting Hitter Performance
Plate Appearances (PA)
Doubles and Triples (2B and 3B)
Strikeout Percentage (K%)
Ground Ball, Line Drive, Fly Ball Rates (GB%/LD%/FB%)
Batting Average on Balls in Play (BABIP)
Home Run per Fly Ball Rate (HR/FB)
Runs Batted In and Runs Scored (RBI and R)
Stolen Bases (SB)
Chapter 2: Projecting Pitcher Performance
Innings Pitched (IP)
Strikeout Percentage (K%)
Walk Percentage (BB%)
Ground Ball, Line Drive, Fly Ball Rates (GB%/LD%/FB%)
Batting Average on Balls in Play (BABIP)
Home Run per Fly Ball Rate (HR/FB)
Saves (SV)
Chapter 3: Creating an Excel Spreadsheet to Project Hitter Performance
Chapter 4: Developing a Hitter Projection
Chapter 5: Creating an Excel Spreadsheet to Project Pitcher Performance
Chapter 6: Developing a Pitcher Projection
Chapter 7: Putting a Bow on It
Chapter 8: Frequently Asked Questions
About the Author
Support Me!

• Language: English
• Publisher: Mike Podhorzer
• Release date: Jan 5, 2016
• ISBN: 9781311180100

Mike Podhorzer

I never dreamed that I would someday publish a book, whether physical or electronic. It all started in the late 1990s when I picked up a free copy of Ron Shandler's Baseball Forecaster at a sports card show. After reading the annual publication from cover to cover, I would never look at baseball statistics the same way again. Having been completely immersed in the exciting world of fantasy baseball, I began forecasting baseball player performance and producing projections of my own, and have been doing so ever since. Following years of spreadsheets and calculations, I finally made the epic decision to share my process with the world.I am the 2015 Fantasy Sports Writers Association (FSWA) Baseball Writer of the Year and currently write for the baseball statistics and analysis website FanGraphs. I was also named a finalist by the FSWA for Best Article on the Web in 2013 and Writer of the Year in 2014. I compete in the Tout Wars and LABR fantasy baseball expert leagues and won the 2013 Tout Wars mixed draft league. Aside from my passion for number crunching, I also enjoy traveling, taking photos during those travels, attending concerts and comedy shows, and munching on falafel sandwiches.

Book preview

Projecting X 2.0: How to Forecast Baseball

Player Performance

By Mike Podhorzer

Copyright © 2016 by Mike Podhorzer

All rights reserved. No part of this publication may be reproduced, distributed, or transmitted in any form or by any means without the express written permission of the publisher except in the case of brief quotations embodied in critical articles and reviews.

Printed in the United States of America

Second Edition, Smashwords Edition: January 2016

www.ProjectingX.com

Table of Contents

Introduction 

Projection Systems

Projection Methodology 

Spring TrainingNew Technology and Statcast 

Chapter 1: Projecting Hitter Performance 

Plate Appearances (PA)Doubles and Triples (2B and 3B) 

Strikeout Percentage (K%)

Ground Ball, Line Drive, Fly Ball Rates (GB%/LD%/FB%) 

Batting Average on Balls in Play (BABIP) 

Home Run per Fly Ball Rate (HR/FB) 

Runs Batted In and Runs Scored (RBI and R) 

Stolen Bases (SB) 

Chapter 2: Projecting Pitcher Performance 

Innings Pitched (IP) 

Strikeout Percentage (K%)

Walk Percentage (BB%) 

Ground Ball, Line Drive, Fly Ball Rates (GB%/LD%/FB%) 

Batting Average on Balls in Play (BABIP) 

Home Run per Fly Ball Rate (HR/FB) 

Saves (SV) 

Chapter 3: Creating an Excel Spreadsheet to Project Hitter Performance 

Chapter 4: Developing a Hitter Projection 

Chapter 5: Creating an Excel Spreadsheet to Project Pitcher Performance 

Chapter 6: Developing a Pitcher Projection 

Chapter 7: Putting a Bow on It

Chapter 8: Frequently Asked Questions 

About the AuthorSupport Me!

Introduction

Welcome to Projecting X 2.0, the follow-up to the original Projecting X. Think of this edition as the pizza with even more of your favorite toppings, the ice cream sundae with an extra drizzle of delectable syrup, or the baseball game that treats you to a rare fireworks display after the final out is made (because, ya know, we all love fireworks, right?).

Though this version is not completely revised, it is most certainly updated with statistics through the 2015 season. Furthermore, and this is where your toppings, syrup, and fireworks make their highly anticipated appearances, 2.0 is chock full of new research, new metrics, new ideas, and new methods for projecting baseball player performance. And of course, the new metrics and methods are all explained in the included step-by-step guide to creating your own projections in Microsoft Excel.

Throughout these digital pages, you will find many links to websites, articles, and research studies. I encourage you to visit the content I have linked to in order to expand your knowledge base even further. The following sites play a vital role in my projection process and research:

FanGraphs – my primary source for statistics

Baseball-Reference – for several advanced pitching metrics

Pro Sports Transactions – injury and disabled list data

Baseball Heat Maps – hitter batted ball data

Brooks Baseball – PITCHf/x player cards

Roster Resource – depth charts and projected lineups/batting orders

Projection Systems

ZiPS. Steamer. Marcel. PECOTA. These are not the names of my pet hamsters or the latest fad diet trends. They are the names of some of the most well-known baseball player projection systems around. All of them derive their forecasts by mixing historical data in a blender and cooking up a projected performance line. These systems are pretty darn good, but for the majority of them, precious little is shared about their inner workings, making them essentially black boxes.

Accordingly, at some point, you might get the urge to project players yourself. And because you are reading this book, that urge may have already been felt. Why might you decide to project players on your own rather than rely on one of the aforementioned systems? Well for one, you may not agree with every projection a system generates. If you were to project a player yourself, you would hold full control over every individual statistical measure in the forecast (strikeout and walk percentages, BABIP, etc.) and be granted the opportunity to instantly discover how a change in one metric affects another.

Furthermore, the forecasting process requires a combination of both art and science. Since the projection systems are built using sophisticated computer models, they have the science part down quite well. However, that also means that these models involve no manual human intervention and are completely objective. But, the art, or subjective, component falls a bit short.

Computers have come a long way since their invention. But sadly, they are still unable to watch the games or read the news and promptly parse the gobs of data out there to incorporate any new actionable pieces of information into a player’s projection. As a result, these systems are prone to miss various dynamics that could affect a player’s projection.

There are several categories of examples in which a human has the advantage:

1) Lineup Spot Changes – A hitter changing spots in the lineup could greatly affect his counting stats, but a computer is unaware of where a hitter is slotted to bat. If it’s the same spot as the previous season, fantastic, but if not, its various counting stat forecasts will require tweaking.

2) Changes to Team’s Lineup – We all know that a hitter is going to score more runs and drive in more runners in a lineup surrounded by above average players rather than replacement level players. So if a team signs the top offensive free agent on the