+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

RC (runs created): An estimator of how many runs a batter produces for his team, created by Bill James.

“Basic”: ((H + BB) * (1B + (2*2B) + (3*3B) + (4*HR))) / (AB + BB)

“Technical”: ((H + BB - CS + HBP - GDP) * ((1B + (2*2B) + (3*3B) + (4*HR) + (.26 * (BB - IBB + HBP)) + (.52 * (SH + SF + SB)))))/ (AB + BB + HBP + SF + SH)

Which formula and when: FanGraphs employs both formulas depending on what stats are available for individual seasons. Seasons prior to 1955 use the “Basic” formula and any season after and including 1955 uses the “Technical” formula.

Why you should care: Runs Created is a good estimator of how many runs a team should have scored in a given season. When applied to players, it is somewhat less accurate though still a useful estimator of a player’s actual production.

Variations: There are other run estimators that do a better job then Runs Created, yet one of the main advantage of Runs Created is that it’s extremely easy to calculate. Other run estimators include: Batting Runs, Base Runs, Extrapolated Runs, Estimated Runs Produced, Equivalent Runs.

Links and Resources:

Wikipedia: Runs Created
A Brief History of Run Estimation: Runs Created
How Runs are Really Created Part 1, Part 2, Part 3
The Book Wiki: Runs Created
The Book Wiki: Run Estimators

BRAA (batting runs above average): BRAA is the difference in run expectancy (RE) between the start of the play and the end of the play. That difference is then credited/debited to the batter and the pitcher. Over the course of the season, each players’ BRAA for individual plays is added up to get his season total BRAA.

Calculation Example: In game 4 of the 2007 World Series, the RE for the Red Sox to start the inning was .52. When Jacoby Ellsbury doubled off Aaron Cook in the very first at-bat in the game, the Red Sox were then expected to score 1.15 runs for the rest of the inning. The difference or BRAA was .63 runs. Ellsbury was credited +.63 runs and Aaron Cook credited with -.63 runs.

Why you should care: BRAA tells you how many runs a player contributed to his team. It’s similar to WPA (except in runs), but unlike WPA it does not take into account the inning or score of the game. Therefore, it is a more context neutral statistic. It does however take into account how many runners are on base and how many outs are left in the inning.

Variations: REW (run expectancy wins) is BRAA converted to wins.

Links and Resources:

Run Expectancy by Run Environment
The Book Wiki: Run Expectancy

WPA (win probability added): WPA is the difference in win expectancy (WE) between the start of the play and the end of the play. That difference is then credited/debited to the batter and the pitcher. Over the course of the season, each players’ WPA for individual plays is added up to get his season total WPA.

Calculation Example: In game 4 of the 2007 World Series, the WE for the Rockies started out at 50%. When Jacoby Ellsbury doubled off Aaron Cook in the very first at-bat in the game, the Rockies WE declined to 44.2%. The difference or WPA was .058 wins (5.8%). Ellsbury was credited +.058 wins and Aaron Cook credited with -.058 wins.

Why you should care: WPA takes into account the importance of each situation in the game. A walk off home run is going to be weighted more then a home run in a game that has already gotten out of hand. This makes it a great tool for determining how valuable a player was to his team’s win total.

When not to use it: WPA is more of a descriptive statistic and not that great of a predictive statistic. There are better statistics to use in raw player evaluations than WPA.

Links and Resources:

The Hardball Times: The One About Win Probability
The Book Wiki: Win Probability Added
Wikipedia: Win Probability Added

K/9 (strikeouts per 9 innings): The average of how many batters a pitcher strikes out per 9 innings pitched.

Calculated as: (SO * 9) / IP

Why you should care: K/9 is a perfectly suitable way to evaluate a player’s ability to strike batters out.

Current Baselines (2002-2007): The average K/9 for starting pitchers is 6.17 and 7.21 for relievers. For starting pitchers the top and bottom 20th percentile are a K/9 above 7.56 and below 4.89. Relievers top and bottom 20th percentiles are a K/9 above 8.94 and below 5.54.

Variations: Some people prefer to use strikeouts per batter faced (K% or K/G) to express a player’s ability to strike batters out. The difference is minimal and the argument for using K% is that K/9 excludes walked batters and K% does not, suggesting that K/9 may either overstate or understate a pitcher’s overall effectiveness (not pure strikeout ability).

Links and Resources:

Wikipedia: Strikeouts per 9 innings pitched
U.S.S. Mariner: Evaluating Pitcher Talent