As I’m sure many of you have noticed, tRA has come to FanGraphs. I’m going to try to describe it as concisely as possible, so here goes.

tRA can be seen as an extension of FIP including batted ball types, namely line drives, ground balls, and the different types of flyballs. The idea of using these is to separate defense from pitching while still incorporating some measurement of how ‘fieldable’ the contact a pitcher induces is. Line drives are a little trickier for defenders to handle than ground balls, and tend to lead to more runs scoring. This is reflected in the runs/outs data we have for batted ball types, which leads the way quite neatly to tRA.

If you aggregate the tRA outcomes (K, BB, HBP, HR, + batted balls), and apply run/out values, you end up with expected runs (xR) and expected outs (xO). We can easily convert this to runs per nine innings by taking xR/xO*27. That’s tRA. Note that it is not on the familiar ERA scale, as I believe a defensive neutral statistic should expect defenders to have a league average error rate. League average tRA is typically in the high 4s.

Why use tRA? Well, it’s an interesting tool to supplement FIP with if you want to look at how hard a pitcher is being hit. It’s not a FIP killer by any means, and the difference between StatCorner tRA (using MLB AM classifications) and FanGraphs tRA (using BIS classifications) should tell you why: batted ball types are pretty subjective. However, they’re not wildly inaccurate, and using tRA, especially alongside pitcher batted ball information will give a better understanding of what exactly a pitcher is doing.

I’m very pleased tRA has made its way to FanGraphs, and I can’t say enough thanks to David Appelman for making it possible. I hope everyone finds it useful.

There are alot of questions in various threads in the forums and in the blog entries over the past few months as to what all these stats mean, especially those in which I’ve played a role. And David has a great series of “getting to know”, and he posts references, etc.

The intent of this thread is for me to capture all those questions, and provide a more complete and nuanced set of responses.

In this thread, no question is too simple or too complex. The question itself doesn’t even have to make sense. The only criteria to posting here is that you are confused.

Think of me as “Dear Abby”.

Fire away, and I’ll answer as I can…

Update: Just to let you know I have more answers starting here. You can make more comments in this thread.

O-Swing% (outside swing percentage): The percentage of pitches a batter swings at outside the strike zone.

Why you should care: When a batter swings at a pitch thrown outside the strike zone his chances of success are severely decreased. The ability of a batter to differentiate pitches inside or outside the strike zone is often referred to as plate discipline and O-Swing% is a good measure of true plate discipline. Likewise, pitchers try to get batters to swing at pitches outside the strike zone and O-Swing% for pitchers shows how adept a pitcher is at tricking batters to swing at pitches outside the strike zone.

Current Baselines: The major league average O-Swing% changes slightly from year to year. The chart below shows the major league averages from 2005-2008 for both batters and pitchers.

2002          18.1%
2003          22.2%
2004          16.6%
2005          20.3%
2006          23.5%
2007          25.0%
2008          25.4%

Batters: A lower O-Swing% is preferable for batters.
Pitchers: A higher O-Swing% is preferable for pitchers.

Links and Resources:
Dissecting Plate Discipline: Part 1
Dissecting Plate Discipline: Part 2
More on Plate Discipline
Pitchers, Pitch by Pitch
Expanding the Strike Zone

F-Strike% (first pitch strike percentage): The percentage of plate appearances (for batters) or batters faced (for pitchers) that the first pitch was a strike. This includes anytime that the count after the first pitch was 0-1, or anytime the ball was put into play on the first pitch of a plate appearance.

Why you should care: Getting the first strike on a batter significantly decreases the batter’s chance of success and likewise increases a pitcher’s chance of success.

Current Baselines: The major league average F-Strike% for all players from 2005-2008 is 59%. There is very little variation in the major league average from year to year.

Batters: A lower F-Strike% is preferable for batters.
Pitchers: A higher F-Strike% is preferable for pitchers.

Links and Resources:
Hardball Times: The Importance Of Strike One (Part One)
Hardball Times: The Importance Of Strike One (Part Two)

Pitch Type Abbreviations:

FB – fastball
SL – slider
CT – cutter
CB – curveball
CH – changeup
SF – split-fingered
KN – knuckleball
XX – unidentified
PO – pitch out

About split-fingered pitches: Split fingered pitches include splitters and forkballs.

About the percentages: All pitch type percentages for identified pitches are calculated as a percentage of only identifiable pitches. Unidentified pitches are calculated as a percentage of all pitches.

About the velocity: Next to the percentage in parentheses is the average velocity for the pitch type. If it reads 00.0, it means there is not enough data to calculate the average velocity.

About the leaderboards: On the leaderboards, pitch type percentage and pitch type velocity are broken out into two separate columns for each pitch. The percentage columns are labeled “%” and the velocity columns are labeled “v”.

About the data: All pitch type data is collected and provided by Baseball Info Solutions.

+WPA (win advancement): The amount of positive wins a player contributed to his team, including only the plays where he increased his team’s win expectancy.

-WPA (loss advancement): The amount of negative wins a player contributed to his team, including only the plays where he decreased his team’s win expectancy.

How it’s calculated: It’s calculated exactly the same as WPA, but it only includes the positive (+WPA) or negative (-WPA) results.

Why you should care: It further breaks down WPA letting you understand how big a positive or negative contribution a player made to his team. A player who has a WPA of 1.25 could have made both huge positive and huge negative contributions to his team.

Links and Resources:

Win Shares and Loss Shares

WE (win expectancy): The percent chance a particular team will win based on the score, inning, outs, runners on base, and the run environment.

Assumptions: Win expectancy as it’s currently calculated assumes that each team has an equal chance of winning at the start of a game.

Specifics: FanGraphs uses Tangotiger’s most current win expectancy tables which are available for 3.0 to 6.5 run environments in increments of .5 runs. The league average run environment is used to calculate win expectancy. When the run environment falls in between a .5 increment, the tables are then weighted accordingly to achieve the correct win expectancy.

Links and Resources:

Hardball Times: The One About Win Probability
Walk Off Balk: Win Expectancy Finder
The Book Wiki: Win Expectancy

Clutch: A measurement of how much better or worse a player does in high leverage situations than he would have done in a context neutral environment.

How it’s calculated: WPA / pLI – WPA/LI

Why you should care: Unlike tradition clutch statistics (close & late), Clutch is a much more comprehensive statistic taking into account all situations that may or may not have been high leverage. Additionally, instead of comparing a player to the rest of the field, it compares a player to himself. A player who hits .300 in high leverage situations when he’s an overall .300 hitter is not considered Clutch.

Links and Resources:

All About Clutch
Baseball Fever Forum: SABR Matt

WPA/LI (context neutral wins / game state linear weights): How many wins a player contributes to his team with the Leverage Index aspect removed, invented by Tom Tango.

Calculating WPA/LI: WPA is divided by LI for each individual play attributed to a specific player and then the WPA/LI for the individual plays is then added up to create WPA/LI for an entire season. This is considerably different then taking a player’s WPA and dividing it by pLI.

Why you should care: Unlike standard linear weights, WPA/LI does take into account the situation. So at times when a walk would be just as valuable as a home run, WPA/LI accurately weights the walk and the home run, where linear weights would still give .13 wins to the home run and the walk .03 wins.

Links and Resources:

Unleveraging Win Probability
The Book Wiki: Linear Weights

LI (leverage index): A measure of how important a particular situation is in a baseball game depending on the inning, score, outs, and number of players on base, created by Tom Tango.

Baselines: The average LI is 1 and is considered a neutral situation. 10% of all real game situations have a LI greater than 2, while 60% have a LI less than 1.

Why you should care: Because LI puts a single number on the importance of a situation, it creates a much simpler and specific way of determining which situations in games are important. It can also be applied to players. See below for various LI player stats:

pLI: A player’s average LI for all game events.
phLI: A batter’s average LI in only pinch hit events.
gmLI: A pitcher’s average LI when he enters the game.
inLI: A pitcher’s average LI at the start of each inning.
exLI: A pitcher’s average LI when exiting the game.

See Critical Situations: Part 3 for more details

Additional Links and Resources:

Critical Situations Part 1, Part 2, Part 3
Leverage Index Tables