変化球　Henkakyuu

Position Players by WAR: Free Agency

2012-11-09T06:27:00.000-08:00

These articles were originally posted on FanGraphs a year ago. I am just posting them here for posterity.

The Expansion Era saw the Major Leagues spread west and go from 16 teams to 24 26 by 1977:

The pitching mound had been lowered, the designated hitter added, and baseball was looking like the modern game save for one thing: Free Agency. In the 19th century, players started getting paid more than the average worker. In order to control salaries, baseball created the Reserve Clause in 1879 which said that even if a player’s contract expired, the team that contract was with still retained rights to their services. Players were given one-year contracts, and if they refused to sign, they couldn’t sign with another team.

The Federal League was formed in 1914 to compete with the Major Leagues, but only lasted two seasons. After 1915, the Major Leagues bought out most of the owners of the Federal Leagues, giving them ownership in Major League teams, or other considerations. The owner of one of those teams, the Baltimore Terrapins, refused to be bought out and brought suit against the National League under the Sherman Anti-trust Act. That came to a head in 1922 in Federal Baseball Club vs. National League which created the MLB Anti-Trust exemption. Apparently because baseball was an amusement, it didn’t fall under the same rules for interstate commerce.

That all changed in 1975 when an arbitrator struck down the reserve clause and granted Free Agency to two pitchers: Andy Messersmith and Dave McNally. From 1976, Free Agency was born, and players were no longer bound by the Reserve Clause.

A quick synopsis: in 1969, Curt Flood fought against being traded. He gave up his career in baseball. He believed that the Reserve Clause was cause for collective bargaining, and the first collective bargaining agreement between MLB and the Major League Baseball Players Association was reached by Marvin Miller in 1970. That started the ball rolling for Free Agency to be granted by the arbitrator in 1975.

Here are the players who came out of that era:

And the second half:

Who jumps out to you?

Player list (career WAR in parentheses):

Rickey Henderson (114.1)	Mike Schmidt (110.5)
Cal Ripken (99.7)	Wade Boggs (94.8)
George Brett (91.6)	Eddie Murray (78.8)
Rafael Palmeiro (75.5)	Paul Molitor (75.2)
Carlton Fisk (74.4)	Lou Whitaker (74.3)
Bobby Grich (74.1)	Robin Yount (74.1)
Gary Carter (72.5)	Dwight Evans (71.4)
Tim Raines (71)	Mark McGwire (70.6)
Ozzie Smith (70.3)	Barry Larkin (69.8)
Alan Trammell (69.5)	Tony Gwynn (67.9)
Willie Randolph (67.9)	Darrell Evans (67.8)
Dave Winfield (67.7)	Buddy Bell (66.6)
Ryne Sandberg (62.6)	Andre Dawson (62.3)
Keith Hernandez (61.8)	Fred McGriff (61.3)
Ron Cey (59.4)	Chet Lemon (56.9)
Jim Rice (56.1)	Jose Cruz (55.2)
Will Clark (54.4)	Brian Downing (54.1)
Jack Clark (53.9)	Fred Lynn (53.8)
Tony Phillips (51.5)	Toby Harrah (50.9)
George Foster (50.8)	Ken Singleton (50.2)
Kirby Puckett (49.4)	Julio Franco (48.6)
Lance Parrish (48)	Tony Fernandez (47.7)
Matt Williams (47.4)	Dale Murphy (47.3)
Brett Butler (46.3)	Davey Lopes (46.1)
Devon White (46)	Jose Canseco (45.9)
Don Mattingly (45.8)	Dave Parker (45.7)
Harold Baines (45.3)	Dave Concepcion (44.8)
Doug DeCinces (44.3)	Gary Gaetti (44.3)
Albert Belle (44.2)	Paul O’Neill (43.8)
Willie Wilson (43.5)	Darrell Porter (43.4)
Darryl Strawberry (43.2)	Andy Van Slyke (43.1)
Jesse Barfield (42.6)	Dusty Baker (42.6)
Steve Garvey (42.5)	Kent Hrbek (42)
Chili Davis (41.7)	Lenny Dykstra (41.4)
Jim Sundberg (41.4)	Tim Wallach (40.6)
Wally Joyner (40.4)	Jay Bell (40.3)
Kirk Gibson (39.5)	Carney Lansford (39.1)
Cecil Cooper (38.6)	Greg Luzinski (38.1)
Bill Madlock (38.1)	Pedro Guerrero (37.9)
Bob Boone (37.1)	Lonnie Smith (37)
Eric Davis (36.8)	Ken Griffey (36.8)
Bob Watson (36.6)	Don Money (36.5)
Frank White (36.2)	Dwayne Murphy (35.7)
Bobby Bonilla (35.6)	Andres Galarraga (35.4)
Ron Gant (35.3)	Don Baylor (34.9)
Gary Matthews (34.6)	Mike Hargrove (34.5)
Travis Fryman (33.7)	Hal McRae (33.6)

References:

Fangraphs Career WAR Leaders

Position Players by WAR: Liveball Era

2012-11-04T11:06:00.000-08:00

These articles were originally posted on FanGraphs a year ago. I am just posting them here for posterity.

Last week we covered the position players of the Deadball Era. Our next stop is the Liveball Era. Although the names of the era make it obvious – the Deadball Era was characterized by low run scoring – the Liveball Era saw rise to the home run, Babe Ruth, and an offensive explosion that changed the way the game was played:

The Liveball Era players are ones I am much more familiar with. One fifth of the players in the Hall of Fame are from the Liveball era. There should be a good reason for that. The Hall of Fame was originally established in 1936. The first players inducted were people from the Liveball Era. A special committee of experts was created to select the best players on the 19th century for induction, but the whole process was botched. Initially, the voters averaged about 10 players per ballot, but the Hall of Fame folks only wanted to vote in five players. So they counted each vote as half a vote.

If you need 75% for induction, and each vote only counts for half, there isn’t much of a chance anyone will actually get inducted. So instead of getting five people in the Hall from the 19th century, none of them got in. As a result, the backlog of players that needed to be inducted made things slower in future elections. Instead of inducting a lot of the Liveball Era greats, the 19th century players took up space on the ballot, and diluted the voting. The Veterans’ Committee picked up the slack, resulting in 48 total players from this era being inducted to the Hall of Fame. Unfortunately, the Hall of Merit thinks only 33 of those players were actually worthy.

The end of the Liveball Era was the start of WWII. Over 500 MLB players fought in the war, and the talent level dropped from 1941-1945, as did scoring.

Here is the first half of the Liveball Era:

And here’s the second half:

When I updated the methodology behind the ordering, I remade the graphs. The previous versions are available here and here

If we had to do it all over again, who would we elect from this time period? If you had a ballot, who would you vote for?

Player list (career WAR in parentheses):

Babe Ruth (177.7)	Rogers Hornsby (134.9)
Lou Gehrig (125.9)	Mel Ott (116.1)
Jimmie Foxx (112.3)	Joe DiMaggio (92)
Charlie Gehringer (88.1)	Luke Appling (84.7)
Paul Waner (79.2)	Frankie Frisch (78.8)
Al Simmons (78.5)	Harry Heilmann (78.1)
Joe Cronin (75.4)	Arky Vaughan (74.3)
Goose Goslin (71.9)	Johnny Mize (71.8)
Lou Boudreau (69.8)	Hank Greenberg (68.2)
Joe Gordon (67.2)	Bob Johnson (66.4)
Bill Dickey (63.8)	George Sisler (62.8)
Bill Terry (61.1)	Stan Hack (59)
Sam Rice (58.4)	Billy Herman (56.9)
Tony Lazzeri (56.7)	Joe Medwick (56.3)
Kiki Cuyler (56.2)	Gabby Hartnett (56.1)
Mickey Cochrane (55.9)	Earl Averill (55)
Edd Roush (54.7)	Joe Sewell (54.3)
Dave Bancroft (53.8)	Joe Judge (52.9)
Buddy Myer (51.4)	Charlie Keller (50.4)
Ben Chapman (50.1)	Travis Jackson (49.6)
Heinie Manush (49.4)	Dixie Walker (48.5)
Harlond Clift (47.1)	Chuck Klein (46.5)
Earle Combs (46.3)	Babe Herman (46.2)
Augie Galan (46.1)	Dick Bartell (45.7)
Hack Wilson (45.6)	Bill Nicholson (45.5)
Ken Williams (45.5)	Dolph Camilli (45.4)
Rudy York (44.7)	Tommy Henrich (44.6)
Jack Fournier (44.4)	Wally Berger (44.3)
Ernie Lombardi (43.8)	Jeff Heath (43.8)
Jimmie Dykes (42.8)	Jim Bottomley (42.5)
Max Bishop (42.3)	Pie Traynor (42.2)
Ken Keltner (41.8)	Lonny Frey (41.2)
Cy Williams (41)	Wally Moses (40.9)
Phil Cavarretta (38.9)	Wally Pipp (38.8)
Hal Trosky (38.6)	George Grantham (38.4)
George Burns (38.1)	Ross Youngs (38)
Lu Blue (38)	Joe Kuhel (37.4)
Willie Kamm (37.4)	Roy Cullenbine (36.7)
Riggs Stephenson (36.7)	Pinky Higgins (36.5)
Frank McCormick (36.4)	Frankie Crosetti (35.8)
Chick Hafey (35.4)	Cecil Travis (34.5)
Marty Marion (34.4)	Freddie Lindstrom (34.4)
Sam West (34.3)	Marty McManus (34.2)
Rick Ferrell (34.1)	Tony Cuccinello (33.8)
Bob Meusel (33.7)

References:

Fangraphs Career WAR Leaders

Rethinking the Standings Again

2012-10-31T17:06:00.002-07:00

These articles were originally posted on FanGraphs a year ago. I am just posting them here for posterity.

On Tuesday I talked about how we could Rethink the Standings in a visual way. And you guys responded with helpful reasons. For the most part, I agreed with what you said, and sought out to fix it and make it better.

I realized from your comments that I had failed to mention that I never meant to animate it; I just got carried away and animated it without explaining what I was after. Oops. My goal had been to just make it a single stationary image like this:

Also, when working on the improved version, I found two big problems with my logic: 1) I had totally forgotten about double headers, so my graph showed Detroit with a whopping 156 games last year, and 2) I was calculating Wild Card standings wrong. I had just taken the 4th best record in the league, but the real formula is the best non-division-leading record in the league. So there was plenty of room for improvement on my part.

The biggest comments from you folks were as follows:

To my eye, it would be easier to follow if the individual teams had fixed lateral positions instead of the first-place team moving to the left — the vertical position indicates that already, so it seems superfluous. Also fixing the lateral positions would reduce the model’s dependence on colour, which, of course, benefits the colour-blind and also makes it more useful in situations where it won’t print in colour (such as most newspapers).

- Darien

In short, drop the horizontal movement and reduce the use of color. Done and done.

The only thing I would add might be value-keys next to each dot indicating (what the graph already indicates) the # of games back in the division… it might be hard to see if my team is 3 or 3.5 games back since the locations are so close together (or put them down at the bottom under the team abbreviations).
I’m also a fan of the idea of leaving the teams in the same order to begin the season (alphabetical seems easiest) so that I don’t have to keep track of what column my team happens to be in on any one day. With the dots clustered by division, it should still be readily visually apparent which team leads the division.
Another “graphic” thing might be to use the team logos instead of dots… in any case, superb work.

- Adam D

So add some labels so we can do more than just guesstimate the gap between teams, fix the order alphabetically so it’s easier to find your team each day, and use the logos instead of dots. Unfortunately, logos are a no-go. Excel doesn’t handle images well at all, and it just doesn’t look good (trust me — it’s really ugly).

1. I wanted to see when teams were eliminated. The baseballrace site used a moving line, which on your version would appear at the bottom and move up the graph; that might work. Or maybe just a big X riding on top of an eliminated team’s dot.
1a. Come to think of it, showing when teams clinch would also be useful.
2. I wonder whether I could highlight my favorite team. Putting teams in the same horizontal position will help, but it would be so great if I could click a button to highlight, say, the Twins, and then they stand out in some way — a larger circle, “MIN” in bold text, something.

- ScooterPie

There were a lot more comments, but these were the biggies. Here is the revised version:

Teams that are eliminated (see Baltimore) get their name greyed out at the top, and their circle becomes a lighter shade of grey. Teams that clinch their division are in black, and teams that have clinched a playoff spot are outlined in black like Tampa Bay and New York in this shot:

And this is what the season looks like, animated:

I also got a lot of requests to share the Excel file, so here it is. I will let you figure out the method to my madness, but feel free to ask in the comments if you have any questions. And if there are still ways to improve it, please let me know.

Rethinking the Standings

2012-10-31T16:57:00.001-07:00

These articles were originally posted on FanGraphs a year ago. I am just posting them here for posterity.

Every morning when I was a kid, I would wake up and grab the sports pages to see how my team was doing. Millions of people across the country do the same — they wake up and reach for a paper to sift through something like this:

I always go straight for my division and my team, and see how many games behind they are. I then look longingly at the Wild Card standings to get an idea of how far behind they are. But most of the information gets completely ignored as I eat my breakfast. I’m guessing that millions of people across the country are doing the same.

Chris Spurlock over at Beyond the Box Score made a noble attempt to animate a division race here. But a few things didn’t sit well with me. First of all, it was out of context — you can see how your team is doing in comparison to the division leader, but not in relation to the rest of the league.

My suggestion was this:

But I still have the same problem. It shows how each team is doing in relation to the AL East, but how is the rest of the AL doing? Not to mention that it looks almost exactly like the graphs Studes used to update on The Hardball Times. Though that isn’t a bad thing, it also means that I didn’t add anything at all to the conversation.

So I got thinking even harder about it. What do we really want to see in the standings? We want to know how our team is doing. We want to know how far behind they are in the division. We want to know how they are doing in relation to the other teams in the league when it comes to the Wild Card Standings. Also, we probably want to see what direction they’re headed in. Some newspapers show the record over the last 10 games and/or the current streak (3L, or 4W, etc.). Studes created sparklines to do the same thing.

This is my attempt at making the divisional race better (the file is 6 megabytes, so please give it a little bit of time to load):

Each division is on the same graph, so we can compare across the entire AL in a glance. Whoever is furthest left in their division is in first. Each team also has a “trail” of 5 days to show what direction they are moving in. When a line pops up between two teams, it means that they have changed places in the standings.

Unlike my usual graphs, this was made entirely in Excel, and then was turned into a .gif file, so the image quality isn’t so great. Please be understanding. I also don’t handle ties too well, and don’t show which team is actually first in the Wild Card race.

I want your feedback. Does this graph work for you? Would looking at a chart like this every morning with your coffee be a good replacement for the traditional standings?

Position Players by WAR: Modern Era

2012-10-31T16:38:00.004-07:00

These articles were originally posted on FanGraphs a year ago. I am just posting them here for posterity.

I think we’re all familiar with the Modern Era, so I’m not going in to too much detail:

I drew the line wrong though. I should have started it in 1993, with the introduction of two new teams: the Colorado Rockies and the Florida Marlins, and the huge boost in runs scored per game. Instead, I set it to 1995 because I had noted the wrong date for the two expansion franchises. They were joined by the Tampa Bay Rays and the Arizona Diamondbacks in 1998. For the most part, it doesn’t change much, but as I’ve learned doing this series, you guys catch everything, so it’s better to be up-front about it.

Here are the players that move in to the Modern Era if I change the cutoff to 1993:

By strong request of Big Jgke I have updated the images to include these players.

Here is the first half of the Modern Era, almost all retired:

The second half finishes up with some players still starting their careers but are already in the top 500 for WAR. Just look at Pujols!

None of these players are in the Hall of Fame (yet). How many of them do you think are deserving? Who do you think has a shot, even though they may still be playing?

Player list (career WAR in parentheses):

Barry Bonds (169.7)	Alex Rodriguez (107.4)
Ken Griffey Jr. (85.6)	Chipper Jones (85.5)
Jeff Bagwell (83.9)	Albert Pujols (80.6)
Frank Thomas (79.1)	Jim Thome (73.5)
Ivan Rodriguez (73.4)	Manny Ramirez (72.2)
Larry Walker (72.2)	Scott Rolen (71.6)
Edgar Martinez (71.6)	Andruw Jones (70.5)
Derek Jeter (70.4)	Craig Biggio (70.1)
Roberto Alomar (68.2)	Mike Piazza (68.2)
Jim Edmonds (68.1)	Gary Sheffield (65.8)
Kenny Lofton (65.1)	Sammy Sosa (64.6)
John Olerud (62.4)	Jeff Kent (61.9)
Vladimir Guerrero (61.7)	Bobby Abreu (61.5)
Robin Ventura (61.3)	Todd Helton (60.6)
Luis Gonzalez (60.1)	Brian Giles (57.8)
Lance Berkman (56.2)	Jason Giambi (54.3)
Carlos Beltran (54.1)	Moises Alou (53)
Jorge Posada (51.9)	Adrian Beltre (50.8)
Ichiro Suzuki (50.7)	Mike Cameron (50.4)
Carlos Delgado (49.1)	Mark Grace (48.7)
Omar Vizquel (48.4)	Ellis Burks (48.1)
Bernie Williams (47.8)	J.D. Drew (46.9)
Chase Utley (44.3)	Miguel Tejada (44)
Nomar Garciaparra (43.2)	Steve Finley (43)
Chuck Knoblauch (42.6)	David Justice (42.4)
Jason Kendall (42.2)	Ray Lankford (42.2)
Magglio Ordonez (41.8)	Johnny Damon (41.8)
Reggie Sanders (41.4)	Juan Gonzalez (38.8)
Derrek Lee (38.3)	Ken Caminiti (38)
Edgar Renteria (37.7)	Placido Polanco (37.4)
Miguel Cabrera (37.3)	Tim Salmon (37.1)
Jimmy Rollins (36.7)	David Wright (36.6)
Mark Teixeira (36.3)	Brady Anderson (36.2)
Jeff Cirillo (36.1)	Troy Glaus (36)
B.J. Surhoff (35.8)	David Ortiz (35.6)
Shawn Green (35.1)	Javy Lopez (34.6)
Carl Crawford (34.5)	Tino Martinez (33.8)
Mo Vaughn (33.7)

I think you commenters are the best copy writers. You’ve kept me honest. Even when I make dumb mistakes.

I’d like to apologize heartily to Andre Dawson for forgetting him. As fredsbank so eloquently put it:

so I know FG hates that Dawson is in the Hall, but to blatantly omit it seems a little over the top

(An additional thanks to My echo and bunnymen and ofMontreal who said the same, but were kinder about it)

Additional apologies to Mark Belanger (caught by Joe of Chop-n-Change, and Rick Ferrell (caught by SF 55 for life).

Thank you Rich for letting me know that just because they share a name, the American Association and the American League didn’t actually have a direct connection. Thank you Sam, Dash, Richard Gadsden, fredsbank, Rafael J, hunterfan, and Rally for letting me know that I should really have made my cutoffs better, specifically in regards to Ty Cobb (as stated by Rally):

I can see using those cutoffs Cobb gets lumped into the liveball era. But you know that isn’t right – he exemplified the way to play baseball in the deadball era. His peak years were all deadball.

Thank you to ibn Bob for reminding me there was more than one strike-cancelled World Series, dorasaga for reminding me of the proper year when the Cubs last won the World Series (happy 103rd birthday!), and ofMontreal for pointing out that the Red Sox are not in the National League (bet they wish they were though).

And a super-special thanks to bcp33sox and James III for helping me improve the graphs and WAR lists at the end of each article to make the experience less painful for you people reading.

Making mistakes is the best way of improving. Thank you all for taking the time to read, and especially for those of you who take the time to comment and correct me. All the mistakes brought to my attention should (theoretically) be fixed now. Let me know if you find any more.

References:

Fangraphs Career WAR Leaders

Position Players by WAR: Expansion Era

2012-10-31T16:27:00.005-07:00

These articles were originally posted on FanGraphs a year ago. I am just posting them here for posterity.

The Expansion Era saw the Major Leagues grow from 16 teams in 1959 to 24 teams in 1975. The Washington Senators moved to Minnesota in 1960, and MLB decided to expand earlier than planned at risk of losing its anti-trust exemption. The new Washington Senators would become the Texas Rangers in 1972. They were joined by the Los Angeles Angels in the same year. 1962 saw the Houston Colt .45s and the New York Mets join the Majors, followed by the Kansas City Royals, Seattle Pilots (later to become the Milwaukee Brewers), San Diego Padres, and the international addition of the first Canadian Team, the Montreal Expos.

I cut off the Expansion Era at 1975, right before Free Agency took hold, but there were two new teams formed in 1977: the Seattle Mariners, and the Toronto Blue Jays.

If you looked at a map of the MLB in 1950, this is what it would look like:

The same map in 1977 after Expansion was finished would look like this:

The Expansion era also saw a couple rule changes. The pitching mound was lowered in 1968, and the Designated Hitter rule was introduced in 1973. Baseball looks more and more like the modern game. Integration sees the rise of all-time great non-white players like Hank Aaron and Willie Mays. Let’s take a look at those players:

Part II:

Who jumps out to you?

Player list (career WAR in parentheses):

Willie Mays (163.2)	Hank Aaron (150.5)
Frank Robinson (116.3)	Carl Yastrzemski (108.7)
Joe Morgan (108)	Al Kaline (101.9)
Brooks Robinson (94.6)	Pete Rose (91.4)
Roberto Clemente (91.3)	Johnny Bench (81.5)
Reggie Jackson (81.4)	Rod Carew (80.3)
Ron Santo (79.3)	Harmon Killebrew (78.4)
Willie McCovey (75.7)	Reggie Smith (71.8)
Graig Nettles (71.8)	Willie Stargell (70.9)
Joe Torre (70.8)	Billy Williams (69.7)
Dick Allen (67.9)	Tony Perez (67.8)
Norm Cash (64.8)	Luis Aparicio (63.6)
Bobby Bonds (63.3)	Willie Davis (63.3)
Ken Boyer (63.3)	Sal Bando (62.7)
Ted Simmons (61.1)	Jimmy Wynn (60.7)
Rocky Colavito (59)	Orlando Cepeda (58.3)
Rusty Staub (56.6)	Vada Pinson (56.4)
Cesar Cedeno (55.2)	Jim Fregosi (53.9)
Boog Powell (53.8)	Bert Campaneris (53.7)
Lou Brock (53.4)	Bill Freehan (52.8)
Al Oliver (50.2)	Tony Oliva (48.6)
Roy White (47.5)	Gene Tenace (47.4)
Frank Howard (46.8)	Felipe Alou (45.7)
Rico Petrocelli (45.7)	Amos Otis (45.6)
Dick McAuliffe (44.6)	Thurman Munson (44.4)
Roger Maris (44.3)	Bob Allison (43.9)
Maury Wills (43.7)	Ron Fairly (43.1)
George Scott (43)	Curt Flood (42.5)
Paul Blair (42.1)	Johnny Callison (41.6)
Dick Groat (41.6)	Don Buford (41.1)
Richie Hebner (40.9)	Mark Belanger (40.5)
Bill White (40.1)	Bill Mazeroski (39.5)
Rico Carty (39.4)	Elston Howard (39)
Bobby Murcer (38.6)	Bob Bailey (38.1)
Ken McMullen (38)	Clete Boyer (38)
Rick Monday (38)	Garry Maddox (37.3)
Chris Speier (36.1)	Davey Johnson (35.6)
Willie Horton (34.7)	Tom Haller (34.4)
Ron Hansen (34.4)	Ron Hunt (34.4)
Leo Cardenas (34.4)	Lee May (34.3)

References:

Fangraphs Career WAR Leaders

Position Players by WAR: Post-War Era

2012-10-31T16:23:00.001-07:00

These articles were originally posted on FanGraphs a year ago. I am just posting them here for posterity.

I mentioned in the Liveball Era article that over 500 MLB players served in WWII. Those gaps are a lot more apparent in many of the post-war players. Not only did players miss years (like Ted Williams), but they also had partial seasons due to service in the military. With so many players going off to serve, the quality of the Major Leagues dropped, and scoring dropped as a result. While I’m jumping from the Liveball Era to the Post-War Era, please keep in mind that players on the edges were more likely to be affected by the war.

If anyone has a full list of players that served, and the years they served, I would love to add that information to the charts to make the impact more obvious.

After the war, baseball started to change again. In 1947, Jackie Robinson was the first black player since the 19th century to play in the National League. He was joined 11 weeks later by Larry Doby in the American League, and baseball started integrating. 1947 also saw the first televised World Series. Baseball’s popularity soared after the war. During the Liveball Era it had been relatively stable, but the end of the war brought far more spectators than ever:

I don’t know what caused it. There were no new teams, stadiums weren’t really holding more people than they did before, but more people were coming to see the games. It could have been post-war bliss, TV increasing baseball’s visibility, integration, or just the great players from this era like Ted Williams, Stan Musial, and Mickey Mantle.

Part of it had to do with the relocation of teams. The Boston Braves moved to Milwaukee in 1953. The St. Louis Browns moved to Baltimore in 1954 and became the Baltimore Orioles. The Philadelphia Athletics became the Westernmost team in the Majors when they moved to Kansas City in 1955. In ~~1957~~ 1958, the Brooklyn Dodgers were moved to the West Coast along with the New York Giants to become the Los Angeles Dodgers and San Fransisco Giants.

Editor’s note: Night games spread to 15 out of 16 teams (the Cubs being the sole exception) by 1949. I’ll bet you that had something to do with the increase.

Regardless of the reason, the Post-War Era’s increase in popularity led MLB to expand during the 60′s Expansion era, which we will cover next.

Post-War Era Part I:

Post-War Era Part II:

If we had to do it all over again, who would we elect from this time period? If you had a ballot, who would you vote for?

Player list (career WAR in parentheses):

Ted Williams (139.8)	Stan Musial (139.3)
Mickey Mantle (123.1)	Eddie Mathews (107.2)
Ernie Banks (74.1)	Duke Snider (71.7)
Yogi Berra (71.4)	Pee Wee Reese (69.7)
Richie Ashburn (67.5)	Jackie Robinson (62.7)
Bobby Doerr (60.9)	Minnie Minoso (58.1)
Enos Slaughter (57.6)	Larry Doby (56.6)
Bob Elliott (56.2)	Nellie Fox (55.3)
Vern Stephens (53.7)	Ralph Kiner (53.4)
Gil Hodges (50.4)	Red Schoendienst (47.4)
Phil Rizzuto (47.2)	Eddie Yost (45.3)
Mickey Vernon (44.5)	Gil McDougald (44.2)
Alvin Dark (44)	Sherm Lollar (43.7)
George Kell (43.4)	Roy Campanella (43.1)
Joe Adcock (42.7)	Eddie Stanky (42.6)
Andy Pafko (42)	Jim Gilliam (41.9)
Sid Gordon (41.7)	Dom DiMaggio (41.1)
Carl Furillo (39.7)	Smoky Burgess (39.6)
Gene Woodling (39.3)	Tommy Holmes (38.9)
Bobby Thomson (38.8)	Al Rosen (38.8)
Vic Wertz (38.5)	Johnny Logan (38.2)
Eddie Joost (38.1)	Jackie Jensen (37.5)
Del Ennis (37.5)	Johnny Pesky (36.8)
Earl Torgeson (35.9)	Bill Skowron (35.7)
Del Crandall (35.2)	Pete Runnels (35.1)
Jim Piersall (34.7)	Roy Sievers (34.3)
Walker Cooper (34.3)	Ted Kluszewski (34.2)
Harvey Kuenn (33.9)

References:

Fangraphs Career WAR Leaders

Position Players by WAR: Deadball Era

2012-10-31T16:15:00.000-07:00

These articles were originally posted on FanGraphs a year ago. I am just posting them here for posterity.

Last week we covered the position players of the 19th century. Our next stop is the Deadball Era. In 1901, the American League was formed, making it the second major league along with the National League. That is the beginning of the two-league system we still enjoy today.

As we know, the game itself has changed over the last century. Originally, foul balls were not counted as strikes. Intrepid batters (like King Kelly) would foul off pitches until they could draw a walk. In 1894, the rules were changed to call fouled-off bunts as strikes. In 1901, all fouls became strikes in the National League, followed by 1903 for the American league. That year would also see the first World Series, held every year (except 1904, and other strike years).

But the owners were cheap back then. The balls the pitchers used were supposed to last an entire game, as they were quite expensive, and apparently some owners would hire security guards specifically to retrieve foul balls. That cheapness led to the first players’ strike in 1908, and may have contributed to the Black Sox scandal in 1919. Further, the environment that these players grew up in was advantageous to pitching.

Check below:

There were still tremendous players in that era. Of the 65 Position Players in the top 500 Wins Above Replacement, 21 are in the Hall of Fame. I’ve heard of some of them, like John McGraw (though he played half his career in the 19th century), Frank Chance (from the “Tinker to Evers to Chance” combo that marks the last time the Cubs won the World Series in ~~1907~~ 1908), Nap Lajoie, Honus Wagner, and Sam Crawford. But that leaves 16 players in the Hall of Fame that I can’t recognize by name, and another 50 players or so that are among the 500 best that I have never heard of. With no further adieu, here is the graph showing the best position players of the Deadball Era:

When I changed the methodology, I updated the above graphic. The old one is here Also, a hearty thanks to bcp33bosox for suggesting outlines to give a point of reference for each shade. I’ve updated this (and the previous graph) based on his suggestion. If you have ways you think I can improve this graphic, please let me know.

If you had a Hall of Fame ballot for this period, which 21 players would you pick? Did the writers and the Veteran’s committee make the right choice? Did any of these players catch your eye as particularly good or bad choices?

Next week we will tackle the Liveball Era, the emergence of Babe Ruth, and the first Hall of Fame balloting process.

Links to player pages (career WAR in parentheses):

Ty Cobb (163.9)	Honus Wagner (149.8)
Tris Speaker (142.6)	Eddie Collins (134.2)
Nap Lajoie (108.2)	Fred Clarke (81.6)
Sam Crawford (76.5)	Sherry Magee (74.1)
Zack Wheat (70)	Bobby Wallace (68.4)
Joe Jackson (67)	Max Carey (66.6)
Frank Baker (65.8)	Jimmy Sheckard (65)
Harry Hooper (62.3)	Joe Tinker (62.2)
Tommy Leach (60.8)	Elmer Flick (59.4)
Ed Konetchy (59.2)	Johnny Evers (57.6)
Larry Doyle (56.8)	Jimmy Collins (54.6)
Heinie Groh (54.2)	Frank Chance (53.3)
Bobby Veach (52.8)	Larry Gardner (52.5)
Roy Thomas (51.8)	Fred Tenney (51.6)
George Burns (50.7)	Rabbit Maranville (50.5)
Jake Daubert (49.7)	Art Fletcher (49.6)
Fielder Jones (49.3)	Roger Peckinpaugh (48.9)
Del Pratt (48.4)	Miller Huggins (48.1)
Wally Schang (47.1)	Roger Bresnahan (46.8)
Donie Bush (46.5)	Art Devlin (45.8)
Clyde Milan (45.1)	Harry Davis (44.1)
Dan McGann (41)	Heinie Zimmerman (40.9)
Gavvy Cravath (40.2)	Cy Seymour (40.1)
Bill Bradley (39.8)	Ginger Beaumont (39.5)
John Titus (39.4)	Danny Murphy (39)
Topsy Hartsel (38.8)	Claude Ritchey (38.4)
Benny Kauff (38.4)	Mike Donlin (38)
Frank Schulte (38)	Freddy Parent (37.4)
Terry Turner (37.1)	Stuffy McInnis (36.6)
Jimmy Williams (36)	Dode Paskert (36)
Harry Steinfeldt (36)	Chick Stahl (35.4)
Kid Elberfeld (35.2)	Buck Herzog (34.4)
Ray Chapman (34.3)

References:

Fangraphs Career WAR Leaders

Position Players by WAR: 19th Century

2012-10-31T16:08:00.002-07:00

These articles were originally posted on FanGraphs a year ago. I am just posting them here for posterity.

Admittedly, I don’t know a lot about players from long ago, save the ones who are famous for one reason or another (I hope I’m not alone). If they weren’t in Ken Burns’ “Baseball” documentary, then I probably don’t know much much about them. Sometimes I’ll see an article like this one by Steve Treder that brings an older player to light. And sometimes, usually around this time of the year, someone will talk about Hall of Fame snubs, like in this article by Adam Darowski about the best players not in the Hall of Fame. But for the most part, I’m willfully ignorant.

Looking at all the players in baseball is a daunting task. There are a lot of them. There are a lot of eras. How do you judge people who played back in the 19th century when you are only familiar with the players of more modern eras? Even if I look at the player page for someone like Cupid Childs, what can I really figure out? He is totally removed from context; how can I judge a guy who played so long ago against his peers if I’m not familiar with them? I get lost easily in the numbers. I can look and stare at stats until the cows come home, but not get anywhere because of the lack of context.

Over the next several weeks, I want to take a look at the top 500 position players of all time by career Wins Above Replacement. I have split them in to seven different eras:

Baseball Prehistory (1871-1900)
Deadball Era (1900-1920)
Liveball Era (1921-1945)
Post-war Era (1945-1960)
Expansion Era (1961-1976)
Free Agent Era (1976-1995)
Modern Era (1995-Present)

Here are the eras shown by runs scored per game:

From 1871-1900, baseball was a wild and crazy changing game. There were five different leagues: National Association (which evolved into the National League in 1876), the American Association (which evolved into the American League in 1901), the Union Association, and the Players League. The number of games in each season varied wildly by league and by team, and the whole era is just a giant mess to try to sift through. There are eleven Hall of Fame position players from that era, and the only one I’ve ever heard of is Cap Anson.

In addition to the eleven Hall of Famers, there are twenty other position players in the top 500 for Wins Above Replacement. The following chart shows all 31 players and their results by age. The darker the square, the more WAR they received that season. I intentionally left out total WAR for each player because the goal isn’t to rank these players, but to see how they matched up to each other, in the context of the great position players of their day.

I edited the images when I updated the methodology, the old image is available here.

Above is what the best position players of the 19th century looked like. If you had a Hall of Fame vote, which 10 players would you put on your ballot? In many ways, this is a lot easier than later eras. There was no World Series (started from 1903), no MVP Award (started from 1931), no All-Star Game (started from 1933), and no Gold Glove (started from 1957). All we have to go on is the record they left on the field. So let your eyes wander, come to your own conclusions, and let me know if you find anything.

Links to Player Pages (Career WAR in Parentheses):

Cap Anson (88.7)	George Davis (86.4)
Roger Connor (86.3)	Dan Brouthers (80.1)
Bill Dahlen (80)	Jesse Burkett (72.8)
Ed Delahanty (71.7)	Billy Hamilton (68.9)
Jake Beckley (59.6)	Willie Keeler (59.3)
Jack Glasscock (58.7)	Joe Kelley (57.5)
Jim O’Rourke (52.2)	Bid McPhee (51.4)
John McGraw (50.7)	Cupid Childs (48.4)
Harry Stovey (47.7)	Buck Ewing (47.5)
Hugh Duffy (46.5)	Paul Hines (45.4)
King Kelly (45)	Hughie Jennings (44.9)
Lave Cross (44.5)	Hardy Richardson (43.7)
Jimmy Ryan (43.4)	George Van Haltren (43.2)
Mike Griffin (43.2)	Mike Tiernan (42.9)
George Gore (42.8)	Deacon White (42.4)
Pete Browning (41.6)	Sam Thompson (41.2)
Ed McKean (40.2)	Charlie Bennett (39.6)
John Ward (39.1)	Kip Selbach (38.8)
Herman Long (37.6)	Fred Dunlap (37.6)
Ned Williamson (37.3)	Dummy Hoy (35.2)
Billy Nash (33.8)	Bill Joyce (33.7)

References:

Fangraphs Career WAR Leaders

Top 500 Position Players by WAR

2011-02-04T15:00:00.000-08:00

My series of top 500 Position Players by WAR is finished and up on Fangraphs. If you missed it, please take a minute to check it out if it interests you:

A Correction for Jon Peltier

2011-01-27T17:46:00.000-08:00

The way the web works is funny. I have been reading Jon Peltier's blog for several years now, and learned a lot of what I can do in Excel, and about principles of design from his blog (and ones like it). I was shocked when he commented on my blog when I wrote about basic principles of data visualization for baseball. I was possibly more shocked when I read his comment and realized that he was very right and I'd made a charting faux-pas.

Here is the chart he takes issue with:

Jon doesn't like pie charts much. In the data visualization community, there's been a lot of back and forth over whether pie charts are useful. Since they are used everywhere, people are familiar with them, so I look on them a bit more favorably than Jon does, but that wasn't the essence of his complaint.

You should never use multiple pie charts if you're going to compare them. It's just no easy to compare the size of slices across multiple pie charts.

So instead, Jon recommends using bar charts, like this:

And I think I have to admit it looks a lot better, and is a lot easier to compare between the various players this way. I still think pie charts are okay if you keep them simple, but I didn't follow my own advice and keep them simple enough. This is probably more the proper simplicity:

Rules of Thumb for Complex Visualization

2011-01-11T03:09:00.000-08:00

Last time I discussed some simple graphing techniques that will make visualizations easier to grasp. This time I want to discuss some slightly more complicated techniques involving graphing more complicated data.

Basic graphs usually tackle only a few pieces of information:

Bar Graph:

#1: Players
#2: Home Runs

Pie Graphs:

#1: Players
#2: Plate Appearance Outcomes

Both of these graphs were simple. The players are constants, so they don't really need much special treatment (they are just labels), so we're really only graphing a single variable for each.

Line Graphs:

#1: Players
#2: Home Runs
#3: Over Time

This one is slightly more complicated, but we are all so used to seeing graphs over time like this that most of us wouldn't find it too complicated since while time varies, it only varies in a predictable way (we all know when tomorrow will be).

Lots of times we have to deal with more complicated data sets. Instead of just graphing a single unpredictable variable, what if we have to graph two of them?

To give a simple baseball example, let's say you want to see how many wins each team got in 2010, and how much money they paid in salaries.

There are two main ways you can do it. You can label the various points with colors and leave a legend, or you can label the points on the chart and use the same color.

Here are examples:

Looks like confetti, doesn't it?

When dealing with this much data, there are just too many different colors to handle a different color for each, so if the point is to show each team, then it's probably better to go with labels. Note that the labels are a lighter color and pretty small -- if I made them black, they would draw attention away from the data, and focus the eyes on the labels -- that would make it harder to see the trends.

So when is color good? Let's say you wanted to make this chart to show the differences between AL and NL teams. That would be great for color.

The benefit of adding color here is that it tells a story. The AL has a much wider gap between the top teams in salary and the bottom teams. Boston and New York are just way ahead of the group, and the top NL teams in payroll are between #2 and #3 in the AL. Clearly there's a gap between the leagues.

You can adjust the colors to tell whatever story you want. Let's say we just want to focus on the AL East and how absurdly unbalanced they are.

I just made the AL East jump out by making them really dark, and all the other teams grey. This tells the story of how incredibly well Toronto and Tampa did considering their financial disadvantage. It also shows the huge gap between the "Haves" and the "Have Nots" in the game's most competitive division.

But let's say we want to make this even more complicated. Let's say we want to not only show data for one year, but show data for 3 years. If we throw all that data on a single graph, we get a giant mess.

Too many labels, no real trending, just a giant blotch of stuff. It becomes pretty hard to tell what is what. So we need to really focus on what we want to say rather than just dumping data on a graph.

For instance, if we want to take a look at AL vs. NL from 2008-2010 in payroll and wins, we can do that. But time doesn't really come into the picture if we do it this way, it just looks like a giant mess of dots, and doesn't help us with trending.

One way we can make things a bit clearer is by changing our colors around. The more recent the year, the more solid we make the color -- that way we can see trending visually over time.

For another example, if we just want to see where the AL East is flying around to in the grand scheme of things, we can color them in, connect the dots, and give an idea of how the division is trending.

That shows us a bit more. Baltimore and Toronto are cutting salaries but have improved their win totals in 2010. Tampa Bay is spending more and more each year. The Yankees are essentially treading water. And the Red Sox made a huge jump in salary for 2010.

The other teams are all grey, and they aren't color coded by year. That is a judgment call. Is that information important to your story? In fact, if you only care about the AL East teams, you could even remove all of the excess dots and show just the AL East information. This shows how the division is working internally without cluttering it up with all those extra details.

Another alternative is to make them even less visible so that the AL East data stands out more, but the other information is still out there.

This all comes back to the first rule of graphing: what story are you trying to tell? You should leave in information that is important to your story, and take out anything that doesn't matter. Personally I think that showing Toronto as a more-or-less middle of the pack team, Baltimore as among the perennial losers, and Tampa Bay as the thrifty winners is useful, so I like the last graph best. That tells the story of the AL East best in my mind. But always decide for yourselves.

The basic lesson here is that you can use color to add extra information to a graph, but if you have too much information jumbled together, no amount of color will save you.

Think about your message, make sure that the message is the most obvious thing in the graph, and play around until you find something that works well for you.

If you want to code more data than this, you need to move into the world of interactive graphing like Gapminder.org

But that's a story for another day...

References:

Win Data from Baseball Reference
2010 Salary Data from CBS Sports
2008-2009 Salary Data from About.com

As a side note, I will be on vacation in the US for the next couple weeks, so there won't be many blog posts here. I'll try to check in regularly with twitter and e-mail and the like, and have two scheduled columns going up at Fangraphs each Friday.

Rules of Thumb for Visualization

2011-01-07T02:47:00.000-08:00

If you're going to make a visualization of any data (baseball or otherwise), here are a few recommendations to make it more effective.

#1 Know Your Story

Every graph should tell a story. If it's not, then why are you making it? What story are you trying to tell? What do you want the reader to get out of it? If you aren't clear about what the reader should take away from the graph, usually it's time to think about what you're trying to express before trying to make a graph of it. So for this example, I am going to compare the three career home run leaders of all time.

#2 Pick an Appropriate Graph Type

There are three main graph types:
- Bar Graphs
- Line Graphs
- Pie Charts

Bar Graphs are a good default. 98% of the time, if you're comparing things, a bar graph is a good place to start. When you use bar graphs, there is one major rule that you have to follow: always start the main axis at 0. We judge the categories by the height of the bar, so if you don't start from 0, it warps the data. Here's an example:

Wow! Bonds DESTROYED Ruth's record, didn't he? Only he didn't. Here's the same graph with the axis starting at 0:

Tells quite a different story, doesn't it?

One of the times you don't want to use bar graphs is when you're talking about something that happened over time. For instance, if you are going to look at how many home runs each of those players had by age, a line graph is a better bet:

If I do it with a bar graph, it gets messy, and harder to read:

Pie charts are the oddball of the group. Humans don't judge areas of circles too well, so pie charts are really only good if you're dealing with 2-3 categories, so that it's easy to eyeball. And the percentages always have to add up to 100%. For instance, if we want to look at how often a player strikes out, walks, or hits the ball, a pie graph is a mighty fine choice:

#3 Don't Overdo It

Sometimes you have a lot of data, and you get tempted to try to put it all on one graph. Let's say you want to show how many home runs each player got along with how many plate appearances he had each season. You could throw them on the same graph like this:

Now the problem is that the message gets muddied. What exactly are you trying to say with that added data? If you want to show the different in PA/HR by age, then maybe it's a better bet to make a second graph that focuses just on that:

More graphs isn't necessarily a bad thing if it helps you tell your message better.

#4 Focus the Message

When you create a chart in Excel, it really makes it ugly. Look at the first graph I created using Excel Defaults:

Here are some of the problems:

The background is too dark
The gridlines are too strong
The axis is messed up (it doesn't start from zero!)
The title and legend duplicate information
The player names are tiny
The bar graphs all have shadows for some reason

What is the story of this graph? It shows how many home runs the three home run leaders have. So we want to focus on those three blue bars, who did them, and what they mean. So I eliminated the background of the graph, fixed the axis, lightened the grid lines, gave a proper title, and made the names of each player more visible:

#5 Add Color (where appropriate)

If you'll notice, most of my graphs use the same color. When we look at a graph and see a lot of color, we assume that the color means something. So color is another tool to help us tell the story. If I made the three bars in the career home run graphs different colors, our brain would tell us "Hey, the bars must be a different color for a reason!" and it would spend a little time trying to figure out what the color is telling us.

When there's a reason to add color (for instance, in the pie graph) it's a good idea to be consistent and to make sure that the color adds to the story, rather than to distract it. I used red for strikeouts because they are "bad", blue for walks because they are "good", and grey for the rest because they are neutral (and not part of the story, but need to be there to make sure the pies add up to 100%).

#6 Resist the Urge to Pretty-up the Graph with Chart Junk

A lot of the graphs we see on a daily basis are prettied up. People add 3D effects, drop shadows, gradients, etc. But those things don't typically add to the story we're trying to tell. They just "look cool" so people want to use them. Adding chart junk to your graphs is like putting a spoiler on a Honda Civic -- maybe it looks like it should go faster, but it's really just weighing the car down.

For instance, let's say I get the urge to pretty-up my pie graphs. What was pretty obvious before suddenly becomes a lot less obvious:

It's a lot harder to compare the slices in 3D, because 3D graphs distort data. We aren't so good at adjusting perspective in our head, so we lose a lot of ability to analyze the data. And the gradients add absolutely nothing but headaches since we lose the center of the grey slice making it even harder to figure out the angle (and with the angle, the size of the slices).

These are just the basics, with a really simple example. The above suggestions are just that -- suggestions. The most important thing is to keep the message at the center of the graph. Find what works best with your audience, and displays the data best, and you'll do fine.

All of the above examples were created using Microsoft Excel. Graphs from Excel can be copy-pasted into a vector data editing program like Adobe Illustrator or Inkscape to have better control over how it looks.

A Glimpse at Pitcher WAR

2011-01-04T20:58:00.000-08:00

Happy New Year!

There has been a lot of hubbub by Adam Darowski over at Beyond the Box Score on Hall of Fame Wins Above Replacement, and how to better get an idea of peak value and career value for people who weight them differently.

You can see his work here and here (it is awesome and interactive).

Personally I believe a lot in the power of the eye and what it sees when it takes a look. Design a graphic using your brain, and let your eyes soak it in and let your gut come to a conclusion about the data. While I love Adam's graphs, I don't know if "Weighted WAR" is the way to go -- it is quite arbitrary, and doesn't really measure peak performance as much as it measures performance over a certain level. A player who has an incredible 10 WAR season at age 22 and a 10 WAR season at age 32 will look like they have the same peak (8 WAR above MVP) as a player who has 7 WAR from 10 straight seasons.

So I thought to myself, "How can we make that distinction?" I diddled with a lot of data, and I came up with this:

(Click for a larger version)

The graph includes all the players in the top 50 for career pitching WAR, as well as any player in either the Hall of Fame or the Hall of Merit.

Let your eyes come to your own conclusions, but here is what I noticed:

Early careers were a lot shorter than they were later on
The best pitchers jump right out -- Young, Nichols, Johnson
Pitchers in the modern era are starting their careers a lot later, and ending them earlier than they did in the Expansion Era

If you have any suggestions, improvements, etc., let me know. I will be doing a version for batters on FanGraphs in the near future.

References:

Data collected from Baseball Reference via Baseball Projection
Raw Data in a Google Spreadsheet

Created using Excel for initial formatting, and Adobe Illustrator for prettying up. Licensed under Creative Commons Attribution, Non-Commercial License.

Merry Christmas

2010-12-25T14:00:00.000-08:00

I spent far more time than I ever intended on a reply to Jinaz's Hall of Fame WAR Comparison post over at Beyond the Boxscore. But what came out was interesting, so I'm sticking it up here and explaining it.

Back in the day, I made my Cumulative WAR by Age Graph in response to the same graphs that Jinaz used. Basically, the goal was to show the same data in a slightly different way, to tell a slightly different story. While the "nth best" season works great for normal progressions, it doesn't work so well when a player has an odd career (like one marred by injuries, or one interrupted by war). In cases like that, the cumulative WAR by age tells a much different story. Look at Bonds vs. Aaron for example.

Well this year, Adam Darowski introduced a tremendous Interactive 2011 Hall of Fame Ballot. So lots of people want to look at other careers in a similar way -- by tremendous (6+ WAR), good (3+ WAR), and better than nothing (0+ WAR) seasons. Sky Kalkman came up with a version combining Jinaz's line graph with Adam's radar graph.

Something about it struck me as a bit off, and counter-intuitive. I couldn't figure out why exactly, so in trying to explain it, I worked out a solution of my own:

Seasons with negative WAR have been removed. There are some good points and bad points about this graph. The big negative is that you can only compare two players. On the plus side, it shows not only how many 6+ WAR a player had, but also how many seasons with over 6 WAR a player had. That can help you get another dimension in the data -- did the player have a long peak of consecutive seasons over 6 WAR? Did they have a tremendous fluke of a 10 WAR season and sink back to 5+ after that?

I don't know how much use this will actually have to most people, but it is a different way to compare players, so I figured I'd put it up.

References:

WAR Figures from Rally's Baseball Projection Site

Image is licensed under a Creative Commons Attribution, Non-Commercial License. Original graph was created in Excel and then prettied up in Adobe Illustrator.

Free Agent Signings by Year

2010-12-22T00:04:00.000-08:00

In his "The Two Markets" post on Fangraphs, Dave Cameron talked about how the trade market and the free agent market relate to each other, and if the decrease in trade returns may be related to the increase in free agent spending.

I wondered if there was any indicator of what is going on with the free agent market, decided to grab some data and see if I could figure out anything. To be honest, I didn't know what (if anything) I would find, and just figured I'd dive into the data and see what there is to see. As a quick note, I took away international signings from the pool -- they really muck things up (players from Cuba come over pretty young and throw off averages since they are complete outliers). I also ignored minor league deals.

First thing I looked at was the average age of free agents. My thought was, "Maybe the recent trend of signing players before their arbitration runs out has reduced the pool of young free agents, and increased the free agent age -- that would reduce supply of younger talented players, and increase costs of the remaining free agents."

Well, the data didn't exactly bear that out. It looks like the average age of free agents has had a consistent downward trend. So I thought, "Well, maybe that's because guys like Julio Franco refused to retire and drove up the average age. Maybe we should be looking at the average age of free agents who were signed!"

Again, we see the same trend, there's almost no difference. So it looks like the talent pool, for some reason, is getting younger in the MLB from 2006 to today. It looked to be time to take it from a different angle.

I thought about contract length next. How many contracts of each type are they handing out? And what age are players in each bracket? Are they giving more 1-year contracts to younger players who had a bad year, injury concerns, or something to prove?

The darker the color, the more recent the year (2006-2010). As you can see, for the most part, the contracts are granted to younger players pretty-much across the board, regardless of contract length, with longer contracts being given to progressively younger players. That makes sense. So I looked at the average age that a contract ends, so that we get an idea of when teams want to cut ties, rather than the age they want to make them:

Lighter colors are shorter contracts (from 1-5+ years). A couple things I noticed here are that the younger the player (and the longer the contract), the older the player seems to be when he ends. I wonder if this is a premium on getting premier talent signed long-term -- you are almost required to keep the player longer than you would otherwise like to. We don't see the same pattern in 2-year or 3-year contracts, but we see it in 4-year contracts for 2008 (Derek Lowe, 41 and Ryan Dempster, 37), and we see it in 5+ year contracts from 2008 through 2010.

But unfortunately, I'm still nowhere near answering my original question. Why the heck are we seeing such a decline in free agent ages?

I looked at how many years of contracts teams were giving to free agents, and how many players they gave them to. The closer the two lines get, the shorter each contract will be. If they're on top of each other, it would mean that they are only handing out 1-year contracts.

So through all this, I never really figured it out. What I can say is that it looks like teams are getting a lot more conservative about big free agent signings, and that something around 2007-2008 seemed to cause it. It could be that the huge amount of contracts signed in 2006 flooded teams with players that are still under contract, and we will see a small rebound as those players go back on the market leaving more openings on each team. Or it could be a new understanding of aging, causing teams to be more conservative about signing aging players. Or it could be something I'm not seeing.

I'm sorry that this is so long and drawn out, but I think there's something hidden in the data trying to jump out. I fiddled around with it a dozen ways over many hours, and spent quite a bit of time thinking of different ways to tackle the data, but whatever is hidden didn't jump out at me.

I think that it may be hidden in data I haven't put in the spreadsheet. Perhaps the poor free agent signings of 2006 (Soriano, Zito, Carlos Lee, Juan Pierre, etc.) made people think twice about spending big bucks on the Free Agent market. Or maybe the Financial Crisis starting from 2007-2008 had an impact on team finances. Or maybe there just weren't good players on the market. Or...

If you can figure it out, please, let me know.

References:

Data from ESPN
Raw Data in a Google Spreadsheet

Note: For reasons unknown, Google doesn't do the formulas right. If you download as an Excel file, then you should be able to see a lot fewer #DIV/0 errors. Graphics were created in Excel and then imported into Adobe Illustrator. There's nothing special about them, but if you do want to use them, feel free under a Creative Commons Attribution, Non-commercial license.

Writing at Fangraphs

2010-12-20T21:12:00.000-08:00

Sorry for the lack of updates over the past week. I have several things I am working on, but I am intentionally not posting them here because I have been added to the writing staff at Fangraphs. I still plan to post some things here, but I will be focusing a lot more on writing for the bigger audience there.

Some things I am working on:

Finishing the graphical game summary
Using the Japanese NPB stats I posted to do some analysis on the difference with the MLB
Creating a "Baseball Basics" presentation geared toward non-sabermetric fans explaining about why they should care about how baseball works

A Work in Progress

2010-12-14T07:04:00.000-08:00

I am rethinking the way we look at game summaries. Most sites have a play by play, box score, and even a WPA graph for the game. Isn't there a better way to get an idea of how the game flowed without sifting through the text?

I figure there must be, so I'm trying to come up with something, but it's a process. And it's nowhere near done, but I may as well show what I have to get feedback, or at least to put it out there in case someone else has use for it in its current form.

Pardon the lack of polish, it was created solely in Excel (turned to PNG in Illustrator):

(Click for a larger version)

What I want to do is to give a nice graphical review of what happened on the field, coupled with the run expectancy for each part. Each base-out state at the start of the at-bat is shown by the graphic. Red is no outs, yellow one out, blue two outs. To the left of each base graphic is a white dot showing the expected runs (0 to 3) before the at-bat, and to the right is the expected runs (0 to 3) after the at-bat. If the at-bat ended the inning, the dot is black (at zero). Each player's contribution is the gap between the left and the right.

But there's still a lot of stuff missing:

Color coding the base-out states is ugly. I want to change the background color to correspond to the number of outs (white for 0, light grey for 1, grey for 2, and black for 3 outs)
I need to show when a player scored runs in his at-bats. For instance, the 3-run HR by Sardinha in the 4th inning makes it look like he dropped the run expectancy (1st and 2nd with 2 outs to bases empty 2 outs). In reality he scored 3 runs minus that drop in expectancy
I need to figure out a better way of dealing with stolen bases. There is one in the 9th, but there's no way of knowing that it happened, or who did it. That's going to be a challenge (stupid non-discrete events in an otherwise discrete game!)

My ultimate goal is to be able to give a nice simple way to tally up the expected runs and scored runs in the middle (simple addition across), and a nice simple way to tally up the run expectancy added by each player (simple addition downward). I'd be much happier if it also looked nice, but I'll focus on getting the hard work of making it useful first.

If anyone has any input, please let me know. And if there are any Excel geniuses out there who know how I can use a custom marker for XY charts with a transparent background (transparent turns black when you add it to the chart), I'd be eternally grateful.

References:

Game Log from Fangraphs

Image is licensed under Creative Commons attribution, non-commercial license. Feel free to use it as you'd like, and if you'd like the file I've used for it, just ask.

Nippon Pro Baseball Data (2005-2010)

2010-12-12T21:07:00.000-08:00

I have spent a bit of time putting together the batting and pitching stats from Japanese pro baseball from 2005-2010. I've uploaded it onto Google Documents so that you can use it as well. The English names are all pretty-much wrong (I guess there are a few correct ones in there), because there are over 1000 different last names alone, and translating them would have taken a long time. However, the names there should be usable for doing stats, and searching for the name of any player on google by copy-pasting the characters should get you a site with the proper English name.

Also, Japan tracks "Hold Points" which are holds + relief wins. Useless stat, but it's in there.

All data was retrieved from the NPB homepage.

Please feel free to use it however you'd like, and I will start delving into it myself in the near future. You can access the Google spreadsheet here.

2011 Red Sox Salary Commitments and Projected Wins

2010-12-12T01:43:00.000-08:00

Beyond the Box Score asked for its readers to make graphs showing a team's salary commitments and projected wins in a way that you can understand with a glance. Unfortunately, that's a lot of information with one graph, and I cheated by making an entire sheet covering all the information in several graphs:

(Click for a larger image, or download as a PDF)

The top graph shows the projected Wins Above Replacement projected for the team. The projections were taken from Fangraphs and fiddled with a bit. I am not passing judgment on the actual projections (in other words, take them with a grain or six of salt), just putting down what was projected. Replacement level was set at 50 wins (which is why the Y-axis starts at 50).

The second graph shows projected salaries for each player, taken from Cot's Contracts. For players who have under 3 years of service time, I had them paid 110% of the league minimum, which was $400,000 in 2010, and I projected to increase by $10,000 a year. For players with more than 3 years, but less than 6 (the 3 arbitration years) I had them paid 40% of their previous year's value the first year, 60% of their value the second, and 80% value the third (estimates taken from tangotiger and/or fangraphs).

The bottom portion shows the legend for the mess of colors, and a table of the data for the players projected to be on the roster for 2011. Since there are only 21 players, 4 more will be added, but since I don't know who they are or how much they will be paid, I just left the spots blank.

There are also thick black lines on the first two graphs. For budget, I took the 2011 salaries, and calculated an 8% increase every year to create a projected budget (thick line in the second graph). Using that projected budget, and the projected cost of a win ($4.75 million in 2011, 8% inflation every year after that), I calculated how many wins the remaining budget minus salary costs could buy on the free agent market, and put it as a thick line on the top half of the graph.

So what?

Well, looking at the graph a bit, a few things popped out at me. The first is the importance of cost-controlled players. Despite the Red Sox having a lot of talent projecting to an unreasonable amount of wins (114 in 2011 -- again, grain of salt), if they don't get more cost controlled players that can make a contribution at low cost, they will drop down to an average team in 5 years.

The second thing I noticed was the Red Sox long-term commitments to homegrown players like Youkilis, Pedroia, and Lester. Their contributions are quite cheap for the amount of wins they are projected for. All three deals were signed before free agency, so the players accepted a discount for job security. That helps a lot.

The third thing I noticed was the amount of starting pitching they have signed. Lester, Beckett, and Lackey all have 4 years left under contract. Buccholz has 5 years left of service time (I think, but I've never been too bright). Even Matsuzaka is still under contract for 2 more years, and Tim Wakefield the knuckle-baller is also there for another year when/if someone gets injured.

Anyway, this is just an exercise, and I would love to do it for all 30 teams, but I'm afraid it would be a full-time job. Feel free to use the format if you want, and I will be happy to support you if you need it.

References:

Contract Data from Cot's Contracts
WAR Data from Fangraphs
Player photos from Yahoo Sports

The graph was initially made in Excel, then imported into Adobe Illustrator. Licensed under Creative Commons, Attribution, Non-Commercial License (save for the player photos which are copyrighted).

Ted Williams Rolling wOBA per 150 Games

2010-12-07T23:13:00.000-08:00

I wanted to try a new take on seeing the progression of players. Rather than looking at wOBA by year and injuries/playing time separately, I wanted to add them all into one graph so you can see both how they've performed, and how much they've performed.

My first attempt uses Ted Williams. The red line is his career wOBA, the blue line is the league average wOBA over that period, and the black line shows how he performed over the past 150 games. As the line fades further from black, that means he played fewer of the past 150 games.

What does this tell us? Well, the darker the line, the more confident we should be that his performance is based on skill, and not just random fluctuations.

In the future I want to use this type of graph to compare multiple players and see what their performance and injury history looks like to give a quick glance evaluation between similar free agents, or trade candidates.

References:

Raw Data as an Excel Spreadsheet (8.1 MB)
Ted Williams Game Logs from Baseball Reference
Ted Williams Career wOBA from Fangraphs

This graph was created in Excel and then edited in Adobe Illustrator. It is licensed under a Creative Commons Attribution, Non-Commercial license.

wOBA by Ball-Strike Count

2010-12-06T22:34:00.000-08:00

I am a big fan of graphs and baseball. Fangraphs made me excited because putting complex data into reasonably easy to understand graphs helps open up sabermetrics to more fans. I'm a big fan of statistical analysis, but after a while, a table full of numbers just starts running together and stops making sense. That's what makes graphs such an effective tool.

I've dabbled in graphs myself. When people were creating the WAR graphs to compare hall of famers, I made a sample graph showing cumulative WAR by age on Tom Tango's Book Blog:

(click for a larger image)

Of course, soon after Fangraphs came out with a far better looking one, saving me the headache of figuring out how to automate it.

Here is my latest foray into the world of graphs, looking at wOBA by count:

(click for a larger image)

Let me explain the mess you see above. The horizontal X-axis shows the amount of pitches. The first pitch is all the way to the left, and a full count is all the way to the right. The vertical Y-axis shows the wOBA for all at-bats that go through that count.

Since all at-bats go through the first pitch, the average wOBA is .330 (league average). The higher on the graph, the more likely a player is going to do something good. As you can see, the best count for hitters is 3-0, and the worst count is 0-2. On 3-0 the average hitter is better than 2001-2002 Barry Bonds, and on 0-2 they're batting more like Adam Wainwright in 2010.

The size of the counts (by area) are the amount of times that count has happened. There were 936,848 PA in my sample, so the first pitch is the biggest. There were only 47,488 3-0 counts, so that is the smallest. Each of the counts is a graph in and of itself showing what happened at that count.

Blue is ball, red is strike, and gray means the play ended. As you can see, with 2 strikes the play ends with another, so there are only balls and ended at-bats.

So What?

I made this graph for my own use. It is a nice easy-reference tool to track what's happening each pitch. I can follow and see if a batter's chances went up or down, and how likely the at-bat is going to end on each pitch (really roughly).

Ideally I would make one for each team, so that you can get one for your own team and use it when you're watching games, or even for each player so that you can compare and contrast Vladimir Guerrero with Kevin Youkilis, or the Twins and the Yankees, etc. And there's a good chance that there are things that you can think of to use this graph for, so please let me know what they are in the comments.

References:

Raw Data in a Google Spreadsheet
Data from Baseball Reference Split Data (2006-2010 MLB)
wOBA Numbers Calculated using Tango Tiger's wOBA Data (2008 MLB)

The graph was initially made in Excel to get the bubble positions and sizes, then imported into Adobe Illustrator to add the pie graphs, connecting lines, etc. Images are licensed under Creative Commons, Attribution, Non-Commercial License.

Run Expectancy by Base-Out State (2010 MLB)

2010-12-06T22:09:00.000-08:00

How many runs does a team score on average when there is nobody on and no outs?

The answer to that question is available by run expectancy charts, like this one provided by Baseball Prospectus. But to most people, that's just a mess of numbers, and it's difficult to follow.

Here is the same information in a chart form:

(click for a larger version)

The Red base diagrams show the runs expected with zero outs. Orange with one out. Blue with two outs. The first column is with the bases empty. Moving to the right, it shows the runs expected with 1 runner on, 2 runners on, or the bases loaded. As you can see, the more people on base, the more runs you are expected to score, and the fewer outs, the more runs you are expected to score.

References:

Spreadsheet with Raw Data
Run Expectancies from Baseball Prospectus Run Expectancy Chart
Data was collected using Baseball Reference Batting Event Finder

Image was editing using Adobe Illustrator. Vector data is available on request. This image is available under a Creative Commons Attribution, Non-Commercial License.

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Position Players by WAR: Free Agency

Position Players by WAR: Liveball Era

Rethinking the Standings Again

Rethinking the Standings

Position Players by WAR: Modern Era

Position Players by WAR: Expansion Era

Position Players by WAR: Post-War Era

Position Players by WAR: Deadball Era

Position Players by WAR: 19th Century

Top 500 Position Players by WAR

A Correction for Jon Peltier

Rules of Thumb for Complex Visualization

Rules of Thumb for Visualization

A Glimpse at Pitcher WAR

Merry Christmas

Free Agent Signings by Year

Writing at Fangraphs

A Work in Progress

Nippon Pro Baseball Data (2005-2010)

2011 Red Sox Salary Commitments and Projected Wins

Ted Williams Rolling wOBA per 150 Games

wOBA by Ball-Strike Count

So What?

Run Expectancy by Base-Out State (2010 MLB)

変化球　Henkakyuu