Major Update to The Baseball Gauge

Today, I relaunched The Baseball Gauge. There are a number of changes, that I’ll try to outline.

Adaptive Design

The biggest and most important change is the site now works well on all screen sizes. It now adjusts to the size and aspect ratio of your screen, whether you are on a mobile phone or 4k monitor. On Android devices, click “Add to Home Screen” to have the website act as a web app.

New Address

While still partners with Seamheads family (Negro Leagues and Ballparks Databases), the site is now hosted at www.thebaseballgauge.com

Championship Win Probability Added

I’ve had cWPA on the site for postseason games over the past few years and have experimented with how to display the statistic on the site. There are now cWPA values for all games in the Retrosheet play-by-play database. This includes over twelve million plays from 1921 to present as well as all postseason games. Deduced games are included, meaning that all games from 1942-present are covered. A Championship Win Probability Added primer will be coming soon.

Color Scheme

This purely cosmetic change goes from the old Green and Brown to Blue and Black.

Re-designed Game Changer

The MLB.tv Game Changer looks different, but it remains mostly the same under the hood. This includes recently added features such as importing/exporting settings.

Interactive Graphs

The Graphs are no longer just displayed as .jpeg images. They are now written in javascript and are interactive.

Current Season Retrosheet Download

For the second straight year, I’m providing a downloadable retrosheet/chadwick style play-by-play database, available in the downloads section.

Customized Metrics

As with the previous version of the site, you can customize Wins Above Replacement, Average, and Greatness to your liking. You can use Baseball-Reference’s WAR, but with a FIP based pitching WAR. Or you can have any combination of RA9 and FIP (50/50, 33/67, 25/75, etc). Similarly, for Fielding WAR with defensive regression analysis and defensive runs saved/total zone.

This isn’t a complete list of changes, just the most significant. And as I have done in the past, I’ll continue to update the site daily and add new features here and there. I hope you enjoy.

Introducing the New Negro Leagues Database

It’s been over five years since we originally launched the Negro Leagues Database. Over that time, there have been significant additions to the database, in terms of new seasons and statistics. But the website and the presentation of these statistics have largely remained the same. In May of 2015, I overhauled the Major League part of The Baseball Gauge, and I’ve wanted to do the same with the Negro Leagues section. Today, we re-launch the award-winning Negro Leagues Database. Here are some of the new features:

Per 162 games

One of the biggest issues with Negro Leagues statistics is that they are incomplete. We don’t have box scores for every game and we currently do not have data for every season and league. Because of this, it’s tough to compare Buck Leonard’s 62 career home runs to Cristóbal Torriente’s 70, the same way we compare Harmon Killebrew (573) to Andre Dawson (438).

To help fix this issue, I’ve included “per 162 games” rates on player and season/career leaderboard pages. Here we’ll see that Buck Leonard averaged 26 home runs per 162 games, while Torriente averaged 11.

Similarity scores

Comparing raw stats from Negro Leagues to Major Leagues is far from perfect. It doesn’t account for league quality, park factors or era. Having said that, we have similarity scores on all player pages, to see which Major Leaguer had the most similar career. Because of the issue described above, “per 162 games” statistics are used instead of career totals. There is also the ability to only compare to Hall of Famers or active players.

The similarity score tool shows us that Oscar Charleston’s most similar Major Leaguer was Rogers Hornsby
Charleston vs Hornsby

Defensive Regression Analysis

These fielding statistics have been available on the Major League site for a few years now and they are finally included in The Negro Leagues Database. Defensive Regression Analysis, created by Michael Humphreys, takes basic fielding statistics and estimates how many runs a player has saved (or allowed) compared to average.

Defensive Regression Analysis shows us that Dick Seay, while a lightweight with the bat (career 51 OPS+), saved 67 runs at second base in the season we have fielding data.

New Wins Above Replacement

The calculation for Wins Above Replacement now matches the Major League site. It uses Base Runs for offense, Defensive Regression Analysis for fielding, and runs allowed (with an adjustment for fielding) for pitching. The replacement level has been set at .294 to be consistent with Baseball-Reference and Fangraphs.

There is also Wins Above Average and Wins Above Greatness if you prefer a different baseline. As with the previous version of the website, Win Shares and Win Shares Above Bench are included.

The career leaders per 162 games contains many familiar names:
WAR per 162

Roster pages

These are available on team, year, franchise, and all-time pages. They contain vitals, uniform #’s, and birth/death information.

Data Coverage

These pages give the user an idea of which statistics we have and which we are missing.

New Logo

We have a beautiful new logo, which was kindly provided by Gary Cieradkowski, creator of the Infinite Baseball Card Set and author of The League of Outsider Baseball.

Finally, we have all the features that were previously available on The Negro Leagues Database as well as the Major League version of The Baseball Gauge.

MLB.tv Game Changer (formerly Dashboard)

I can’t tell you how many times I’ve been watching a game on MLB.tv and all of the sudden, my twitter feed blows up when something big happens, like Giancarlo Stanton hitting another 450+ ft homerun. But I missed it because I didn’t know that he was at bat. So I decided to make something that will allow me to customize my baseball viewing experience. Something that will allow me to see as much of the baseball that I want to see. I got the idea from using Dan Brooks’ (of brooksbaseball.net) MLB.tv RedZone, which switches between the games with the highest leverage index. This allowed me to see the potential of using MLB’s gameday data.

Enter the MLB.tv Dashboard, which allows you to customize what you want to watch the most, and automatically switches between games based on your priorities. Here are some of the things you can customize:

  • Batters: Want to see Bryce Harper, Mike Trout, Giancarlo Stanton, or even Bartolo Colon at the plate? Add it to your list, and the application will switch to their game when they come to the plate.
  • Pitchers: Don’t want to miss Jake Arrieta or Clayton Kershaw pitch? This will switch to their game while they are on the mound and go to another game on your list while their team is batting.
  • Baserunners: It doesn’t get much more exciting than when Billy Hamilton is on the bases. If the runner of your choosing is on 1st or 2nd base with the next base open, the application will make sure you see it.
  • Fantasy Teams: The three settings above allows you add all of the players on your fantasy team to track their progress.
  • Teams: Let’s say you’re a huge Braves fan and that’s mostly what you want to see. But you also want to see each of Manny Machado’s at bats. Put Machado at #1 and Atlanta at #2 and you’ll get to watch your Braves games, but the application switches to Orioles games when Machado comes to the plate. It will switch back when his at bat is over.
  • Leverage Index: If you don’t want to miss a tense moment in any game, set the LI to something like “>= 3.0” and it will switch to any game that meets that criteria.
  • No-hitters: Don’t want to miss a potential no-hitter, but don’t think it’s important until after the 7th inning? You can set that as a priority.
  • Vin Scully: There’s only a few more months left that we get to appreciate the greatest announcer in baseball history. This setting will switch to Dodger games when they are at home, or on the road in San Diego, Anaheim and San Francisco.
  • Position Players Pitching: Who doesn’t love to see a left fielder pitch in a blowout or in the 18th inning? This setting will switch to a game if a non-pitcher is on the mound.
  • Extra Innings: Self-explanatory. This setting will switch to games that are in extra innings.
  • Replay Challenge/Review: Let’s say you are a masochist and you want to see all replay challenges. This will switch to those situations.

Now suppose none of your priority items are met, or your teams are on commercial break. In that event, the application will switch to the game with the current highest leverage index. It will keep switching between these games until any of your priority criteria are met.

In addition to the above priority items, you can also avoid watching any teams of your choosing. If you’re blacked out from seeing a certain team, you can add them to your ignore list and the application will avoid changing to their games since you won’t be able to watch them.

We all know MLB.tv has a delay from the actual live game to when you see it on your screen. You can adjust the delay timer setting if the games are switching too early or late. However, I recommend not changing this setting too much since it can severely alter the experience.

Finally, you can set whether or not you want the application to wait for the current at bat to finish before switching to a higher priority game, or if you want to it change immediately. The default setting is to wait. Changing the setting to switch immediately will allow you to not miss higher priority situations, but beware that you could see a lot of changing at inopportune times.

If you choose to use this, I hope you enjoy it. Let me know what you think.

Game Star Ratings

I’ve added a star rating to each game. It measures the “enjoyability” of a game based on a few different factors. There are many elements to a game that can make it enjoyable to the unbiased fan. I’ve tried to include the most important of these.

The rating system ranges from 0 stars to 5 stars and goes in increments of .25 stars. The average game will be around 2.5 stars.

Leverage Index (aLI)
The first and most important element is leverage index, which measures the importance of each situation in the game. The more crucial a moment in a game is to the outcome, the higher the leverage index will be. A leverage index of “1” is average. A leverage index of “3” is equal to three times as important as the average play. FanGraphs has a primer on LI for those interested.

In the game rating formula, I use average leverage index over the course of the entire game. I could have chosen to just go with the top X plays in a game, or the number of plays over a certain threshold, but I felt the average over the course of the game is best suited to gauge the intensity of the entire game.

Win Expectancy Change (WE+/-)
Next is change in win expectancy per play. Suppose an RBI single increases a team’s win expectancy from 55% to 65%. That would obviously be an increase of 10 percentage points. I calculate the average absolute value of WE change over the course of the entire game and use that number for my rating formula.

The average play in the average game will have a win expectancy change of about 3.3 percentage points. Bigger and more exciting plays will increase this number, while plays in blowout games will do the opposite.

Leverage index and win expectancy change very likely have a high correlation, which would cause these elements to be “double counted”. I have taken that into consideration and am fine with it since they are the most important factors in gauging a game’s intensity.

Championship Leverage Index (CLI)
Championship leverage index is similar to in-game leverage index (above), in that it gauges the importance of a single game as opposed to a single play. The game importance is measured in how much a team’s probability of winning the World Series changes in a win versus a loss.

The average game will have a CLI of 1 and is equal to the average game on opening day. In the 2nd Wild Card era (2012-present), the average game on opening day can change a team’s chances of winning the World Series by 0.59 percentage points.

The CLI used in this ratings formula is the average of the two team’s CLI for this game.

Examples: A team that is already eliminated has a 0% chance of winning the World Series. A win will not increase their chances, so their CLI will be 0. The same goes for a team that has already clinched their division. A division title ensures that a team is 1 of the 8 teams in the postseason tournament, meaning they have a 12.5% chance of winning the World Series. A win or a loss after clinching the division will not change this number. But a one-game playoff for the division (game 163) is a “win or go home” scenario and will have a CLI of around 21, since it is 21 times more important than the average game on opening day.

Comeback (CB)
The final element is comeback, which is defined as the highest win expectancy the losing team reached during the game. A comeback can range anywhere from 50 percentage points to 100 percentage points. A comeback of 100 percentage points means that the losing team had a 100% chance of winning, but still managed to lose the game. A comeback of 50 percentage points means the losing team was never able to increase their win expectancy above the 50% level at the beginning of the game and likely means the game was never much in doubt.

Formula and Weights
Each of the four elements (LI, WE+/-, CLI, CB) are individually compared to a large sample of games ranked in a percentile. These percentiles are then weighted and combined to create the star rating. The weights are:
aLI = 1.5
WE+/- = 1.5
CLI = 1
CB = 1

Example: A game has an average leverage index of 1.25, an average win expectancy change of 4.5 percentage points, a championship leverage index of 1.55, and a 85% comeback. Their percentiles and weights are:
aLI = 70 * 1.5
WE+/- = 82 * 1.5
CLI = 92 * 1
CB = 90 * 1

Their sum is 410. This number is divided by 25 and rounded to the nearest whole number. It is finally divided by 4 to give you the star rating. This game would be a 4 star game (410 / 25 = 16.4 = 16 / 4 = 4).

Elements of a game not currently included in star rating system
Individual game performances and milestones. A player hitting 4 HR in a game is exciting and uncommon and makes each of the at bats more important. A pitcher taking a no-hitter or perfect game late into the game has the same effect. These types of elements are currently not included, but are “on the table” for future versions.

Star Players
One could argue that the more superstar players in a game could make it more enjoyable. This rating system does not take the players superstar status or skill level into account.

Special Games
While Derek Jeter’s final home game was exciting in its own right, I would argue that it was even more enjoyable since it was his final game at Yankee Stadium. This rating system doesn’t take these rare situations into account.

The Home Crowd’s Enjoyment
As mentioned above, this star rating measures the enjoyment for the unbiased fan. The home crowd may have a different definition of an enjoyable game based on whether their team wins, but this system makes no such distinction.

2016 Retrosheet Database

One of the best days of every offseason for a baseball nerd is Retrosheet annual end of season release day. It’s the day one of the best sites on the internet releases the play by play data from the previous season. If you’re like me, you download it immediately and go to town. But one thing I’ve always wanted was the ability to access the data during the current season.

So this past offseason, I designed a way to take mlb’s gameday data and convert it into a Chadwick-style retrosheet database. The database (.csv files) will be available and updated daily* in the downloads section. I’m making it available mainly because I know there are others out there, like me, that are interested in having an in-season pbp database. But also because I’d like to have more than one set of eyes on it, to help iron out the kinks and catch any errors.

Error Checking
I run a few processes to check for errors and to validate the data. But there is still the possibility that errors will come up from time to time. I’d like to make this a forum for error reporting, for those who are interested in helping.

Daily Download
Just as with this website, I intend to have updates available daily. Usually, the site is updated in the morning. But with a full-time job and two toddlers at home, there can sometimes be a delay.

Missing columns
There are a few columns in the events table that I have left blank:
“EVENT_TX”: It turns out that it is a huge process to replicate this. While I believe the “EVENT_TX” column is helpful in quickly identifying the play, I don’t use it in my queries and felt it wasn’t worth the hassle. The same goes for the “BAT_PLAY_TX” and the “RUNX_PLAY_TX” columns.

“BATTEDBALL_LOC_TX”: Gameday does include hit locations for all balls in play, but I have yet to dive into this data. If there is someone who has experience with this data and is willing to assist in converting gameday’s x and y coordinates to Project Scoresheet locations codes, please let me know.

“UMP_ID”: These columns for the six umpires are currently left blank.

“GWRBI_BAT_ID”: This is left blank because game winning RBI’s are no longer officially recorded.

ID’s for players making their Major League debut
Since these players have yet to be assigned official ID’s by retrosheet, I just give them the next available ID for their name. For example, if a John Smith were to make a debut, he would be assigned ID “smitj005”, since 005 would be next in line.

Building a Retrosheet Database
For those who are interested in using the data, but lack experience, David Temple at TechGraphs recently created a helpful two part tutorial.

Donations
If you find this data useful and have some disposable income, please consider donating. I do not get paid for my work on this website and while it is my passion to work with baseball data, it does take a lot of time and money (server costs) to keep it up. I’d like to also suggest donating to David Smith and the Retrosheet team.





Re-introducing Series Win Probability Added

A few years ago, I added postseason series win probability graphs and data. Most people are familiar with win probability graphs, which show each team’s win expectancy based on the inning, score, outs, & bases occupied. For series win probability, I took it one step further and put it in the context of a postseason series.

New Graphs
Recently, I started upgrading the graphs on the site. Previously, the graphs were generated with jpgraphs, which uses php and generates an image on the user’s browser. The new graphs are generated using highcharts, which uses javascript and allows the user to interact with the graph. The biggest difference is the user can now hover their mouse over the plot lines to see the data from each play.

I feel that these new graphs are a big step forward in following the play by play of each postseason series and I hope you feel the same.

Individual Player’s Series Win Probability Added (sWPA)
When I originally added the postseason data, I included a page for the top plays in postseason history. This shows which plays had the biggest difference in sWPA from before and after the play. In the new update, each player is credited/debited sWPA based on their involvement in the play. I decided to use the same method for allocating WPA that is used at Baseball-Reference.

This now allows us to see which players had the biggest impact on a particular series and also which players have accumulated the most sWPA in their postseason careers.

sWPA vs wsWPA
wsWPA is World Series Win Probability Added. This is not just the win probability of winning the series, but winning the World Series. This is calculated as sWPA divided by (# of series before the World Series * 2). Obviously, the sWPA in the World Series would be equal to the wsWPA.

Example: Francisco Cabrera’s walk-off 2-run single in the 1992 NLCS increased the Braves chances of winning the series by 74% (26% -> 100%). Since the NLCS is one series away from the World Series, this is how we calculate wsWPA:
74% / (1 * 2) = 37%
This tells us that Cabrera increased his team’s chances of winning the World Series by 37%.

A 20% sWPA play in the Wild Card game would be divided by six since it is three series away from the World Series:
(20% / (3 * 2)) = 3.33%

wsWPA is a good way to compare the importance of plays from different types of series

Note
I have included regular season tiebreakers in the postseason data. While they are not technically postseason games, they are pivotal in World Series win probability.

The New Baseball Gauge

My project over the winter was to overhaul the site. I believe it is a big improvement and I hope that you will feel the same way. Much of the previous version is included and I have made some pretty big additions.

New Metrics
The biggest addition to the new site are the metric selections. Previously, the user had the choice between WAR, Win Shares, and Win Shares Above Bench. Now there two versions of Wins Above Replacement: rWAR (Baseball-Reference) and gWAR (my calculation). In addition, I have included an “Above Average” (WAA) and “Above Greatness” (WAG) option. I am willing to add other versions of WAR, so long as they are available and updated on a daily basis during the season.

Customized Metrics
The user now has the ability to customize WAR, WAA & WAG. You can mix and match the batting, fielding, & pitching components from both Baseball-Reference and The Baseball Gauge’s version. Or you can just use the average of the two metrics.

Since both Baseball-Reference and The Baseball Gauge’s pitching metrics are based on runs allowed, there is also an option to use a Fielding Independent Pitching based version of pitching WAR.

Finally, there is an option to regress the fielding component to zero. This is for those who feel that fielding should have less of an impact on a player’s overall metric.

*To use customized metrics, it is required that you have cookies enabled in your browser options. They are usually enabled by default.

Pennant & Wild Card Win Expectancies
1964 National League
These are the expectancy that each team will win the division/league/wildcard based on their record that day and the remaining schedule. This is taken from the glossary:

On every day of every season, the remanining schedules are simulated 100,000 times. In the simulations, all teams are equal except for that the home team gets a home field advantage. This home field advantage is the record for all home team’s from five years before and five years after the current season.

For example, when simulating the 1978 season, the home field advantage used is 53.96%. This was the winning percentage for all home team’s from 1973-1983.

For the strike shortened years of 1981 (first half) and 1994, the original schedules were simulated. This is why no team finishes with 100% during those seasons.

100,000 simulations of the remaining schedules from every day in Major League Baseball history is no easy task. There were roughly 1.8 trillion games simulated from 1871-2014.

Bill James Favorite Toy
Jeter 3000 Favorite Toy
Bill James described these as “A method that takes into account a player’s age and performance level in predicted the probability that he will accumulate certain career stats.” They are included in each player’s page as well as each seasons page.

Similarity Scores
These are found in each player’s page. Similarity Scores gauge how similar two players are based on their statistics. The user can choose between career stats and stats up to a certain age.
Mike Trout’s Similarity Scores

Baseball Gauge Awards
The Baseball Gauge Awards are given to the top (or bottom) player in various statistical categories. The method for choosing winners is purely based on statistics, with no voting involved.

The stistical formulas are mostly made up of Components Above Average. To be the leader in a certain category, a player needs to excel in a combination of rate stats AND counting stats
2015 Baseball Gauge Awards (Batting)
2015 Baseball Gauge Awards (Pitching)

Player vs Player
Ted Williams vs Joe DiMaggio
This allows the user to select two players and compare their metrics. It accessed by going to a player’s page and selecting “Player vs Player” in the “Comparison” drop down menu.

JAWS Scores
Developed by Jay Jaffe in 2004 at Baseball Prospectus. It is meant to gauge a player’s Hall of Fame credentials by comparing them to Hall of Famers at the same position. JAWS is the average of a player’s career total Wins Above Replacement and the total of their seven best seasons (peak).

JAWS scores are included in player comparison pages, future hall of fame ballots, and Veterans Committee ballot pages.

Finally, Navigation has been improved. Table filtering will now automatically execute after a selection. There are other various additions and improvements throughout the site that would require to much time to highlight individually.

Enjoy!

Baseball Gauge Power Rankings

I’ve recently added a Power Rankings chart to the front page. The concept is quite simple but there is a lot that goes into the formula.

First off, I look at 3 different time periods and assign different weights for each.

1. Entire season (50% weight)
2. Last 25 games (33% weight)
3. Last 10 games (16.7% weight)

The “Last 10 games” actually have more impact on the final number than just 1/6th since the last 10 games are included in both the last 25 and entire season.

Four numbers go into each of the 3 time periods….

1. Team’s Winning Percentage
2. Team’s Pythagorean Winning Percentage
3. The average Winning Percentage of their opponents
4. The average Pythagorean Winning Percentage of their opponents

I then simply just find the average of these 4 stats for each of the 3 different time periods.

Example:
Let’s say a team goes 8-2 and in their last 10 games, while scoring 45 runs and allowing 45 (Pythagorean win % would be .500). Their opponents over the past 10 games have an average winning percentage of .463 and an average Pythagorean winning percentage of .455. Combining these 4 numbers would give them a .5545 rating over the past 10 games. Even though they won 80% of their games, they didn’t outscore their opponents and their schedule was weak, so their rating is not as high.

Postseason Series Win Probability

I would have liked to add this in October, but I had some things to tweak and I wanted to make sure the 2012 data was included at launch.

Thanks to Retrosheet, every single postseason play, game, and series is included. I’ve also added postseason tiebreakers. Before I get emails about the tiebreakers, I realize they are technically regular season games. I just wanted to include them since they are a “loser goes home/winner advances” format.

What I’ve done is incorporate single game win probability into postseason series, to show how each play impacts the team’s probability of winning the series. I’ve set the Home Field Advantage at .559. 55.9% is simply the Home team’s winning percentage throughout postseason history. The reason I included a home field advantage is to show the importance of having an extra home game during a series.

One of my favorite features is the Top Plays list. I’ve tried to include as many filters as possible to narrow down searches. According to my WPA database, Francisco Cabrera’s game winning single in the 1992 NLCS is the biggest series changing play in postseason history. The Braves chances of winning the series before the play was 27%, jumping to 100% afterwards.

But if you apply the “World Series Probability” filter, Hal Smith’s 3-Run HR in the 1960 World Series is the biggest play in history. This filter shows the team’s change in probability of winning the World Series, not just that particular series. Bill Mazeroski’s homerun an inning later is the only Game 7 walk-off in World Series history, but it was Smith’s homerun that is by far the biggest series changing play.

Some other notes from this database….

–The biggest comeback in postseason history was in the 1986 World Series by the New York Mets. In Game 6, with 2 outs and no runners on, the Red Sox had a 99.3% chance of winning the series.

–The second biggest comeback in history was in the same season’s ALCS. This time the Red Sox came back from the Angels’ 99.1% chance of winning.

–The least eventful series, in terms of average change in win probability per play, was the Giants 4-game sweep in the 1989 World Series. All the “excitement” occurred off the field that year.

–Babe Ruth’s caught stealing to end the ’26 World Series was a 10% swing, which is the largest for a caught stealing in World Series history.

–Dave Roberts stolen base in Game 4 of the 2004 ALCS was just the 10th biggest in the 2004 postseason and the 2nd biggest in that season’s ALCS. It only increased the Red Sox chances of winning the series by 1%.

–Derek Lowe recorded the top 2 postseason strikeouts of all-time, both from the 9th inning in the deciding game of the 2003 NLDS. With a 4-3 lead, Lowe struck out Adam Melhuse and Terrance Long, increasing the Red Sox chances by 28% and 26%.

–There have been 46 postseason walk-off homeruns. 9 of those have been series clinching.

–The first postseason walk-off homerun happened in Game 1 of the ’49 World Series (Tommy Henrich).

–Of the 46 walk-off homeruns, the smallest increase in series win probability was Nelson Cruz’s Grand Slam in the 2011 ALCS, just a 2% increase.

–In the “2-2-1 Format” for a 5-Game Series, the home team is just 5 for 15 (.333 Win%) in game 5’s.

Game 6 of the 2011 World Series has 3 of the top 4 plays from all Non-Clinching World Series games. (1)Freese’s 9th Inning triple, (2)Berkman’s 10th Inning single, (4)Hamilton’s 10th Inning Homerun. David Freese’s 11th Inning walk-off is 22nd on the list.

The Steve Bartman Incident Game 6 of 2003 NLCS
Prior to the play, the Cubs had a 96.0% chance of winning the pennant. Had Moises Alou caught the foul ball, their chances would have increased to 97.6%. Instead, Mark Prior walked Luis Castillo, making their chances 93.6%, a 4% difference. Steve Bartman didn’t walk Luis Castillo, nor did he allow the 8 runs in the inning.

Two batters later, Alex Gonzalez committed an error on a possible double play ball. Before the play, the Cubs chances were 89.7%. Had Gonzalez turned the double play, the inning would have ended with a 97.1% chance. But he committed the error, making the Cubs chances 84.7%, a 12.4% difference.

The Game 6 loss isn’t entirely the fault of Alex Gonzalez, but he had a lot more to do with it than Steve Bartman.

Also, I want to apologize about some of the play descriptions. Most of them are fine, but it’s very difficult to write code for rare plays.

Finally, I don’t currently have data for individual player’s cumulative series WPA. Mainly because I’d like to divide the credit amongst all players without just crediting the batter for offensive plays and pitcher for defensive plays. Unfortunately, I do not have a method for that at the time.