Categorysports

Baseball Managers by Position Played

Mike Matheny, a former catcher, was just named manager of the St. Louis Cardinals. This got me thinking, knowing that Bruce Bochy and Joe Girardi were both former catchers, what’s the distribution of former positions of MLB managers.

It turns out catchers are by far the most popular former position for a manager, with ten of the twenty eight managers being former catchers. I was surprised there are only two pitchers.

Position Count %
C 10 35.7%
OF 6 21.4%
SS 4 14.3%
1B 3 10.7%
3B 2 7.1%
P 2 7.1%
2B 1 3.6%

Note: Positions based on play in Major or Minor league, several managers did not make it to the majors.

Current Managers in MLB as of Nov 17, 2011, 28, due to two empty positions at this time.

Data from Baseball Reference and Wikipedia

Baseball Wins vs. Attendance, which fans are loyal? fair weather?

I discovered the Baseball-Databank, a database of wonderful baseball stats that you can download. So now my math+data geek and baseball geek can merge until an uber dork. So don’t be surprised over the next several weeks as the baseball season is about to get underway and hope is in the air, to see plenty of stats about our national pastime.

First up, in today’s post I plotted a Wins vs. Attendance correlation for each team. This allows us to answer a few interesting questions: do wins contribute to attendance? who has the most fair weather fans? most loyal? Does a new ballpark have a bigger impact on attendance than wins?

How to read the graphs

The x-axis is wins and the y-axis is attendance. So the top-right corner is a high attendance and a high number of wins. The bottom left low attendance and low number of wins.

A loyal team plot would look like high horizontal line on the graph regardless of number of wins attendance stays high. A fair weather team would have a diagonal line, as wins increase, so does attendance. A disloyal fan base would have a low plot regardless of the number of wins.

What stories does the data tell

A couple of interesting things I gleamed out of the data:

  • The San Francisco Giants fan base was a pretty fair weather bunch until the new stadium in 2000 and then became one of the most loyal.

  • It appears the Minnesota Twins are the most fair weather fans, which seems appropriate for the Minnesota weather. The plot for the Twins is almost a perfect diagonal line, high correlation between wins and attendance.

  • The Baltimore Orioles graph looks to me as the most optimistic fans, there is a extremely step diagonal line, it is almost like they are saying, please just give us a few wins and we are there.

  • A few plots that are confusing to me; the Texas Rangers, Seattle Mariners and Colorado Rockies both have a surprisingly consistent number of wins, but attendance varies greatly.

  • As far as most loyal, tough to call but it might be the Atlanta Braves, it looks like they have a good base of attendance at 2.5m. The least loyal is fairly obvious, the Florida Marlins, regardless of some fairly high win seasons, the attendance does not go up.

Take a look, what stories can you tell?


Anaheim Angels

Arizona Diamondbacks

Atlanta Braves

Baltimore Orioles

Boston Red Sox

Chicago Cubs

Chicgo White Sox

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Cleveland Indians

Colorado Rockies

Detriot Tigers

Florida Marlins

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Kansas City Royals

Los Angeles Dodgers

Milwaukee Brewers

Minnesota Twins

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New York Yankees

Oakland A’s

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St. Louis Cardinals

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About

Greatest Quarterbacks of Modern Era

My era begins with Joe Montana. I enjoyed the Pittsburgh Steelers and Oakland Raiders as a kid, but I didn’t really know what I was watching. So sorry, Bradshaw and Plunkett this list starts in 1980.

Ranking the top Quarterbacks based on Winning Percentage, Championship Game Record and Super Bowl Record. It’s all about the W’s. How do you see them shaking out? Would Brady top the list if he won the last one? Who am I missing?

Rank Quarterback Winning Pct Championship Superbowl
1 Joe Montana 117-47 (71.3%) 4-2 4-0
2 Tom Brady 87-24 (78.4%) 4-1 3-1
3 Peyton Manning 117-59 (66.5%) 2-1 1-0*
4 John Elway 148-82 (64.3%) 5-1 2-3
5 Jim Kelly 101-59 (63.1%) 4-1 0-4
6 Steve Young 94-49 (65.7%) 1-3 1-0
9 Troy Aikman 94-71 (57.0%) 3-1 3-0
7 Brett Favre 169-100 (62.8%) 2-3 1-1
8 Dan Marino 147-93 (61.2%) 1-2 0-1
10 Kurt Warner 57-44 (56.4%) 3-0 1-2
11 Donovan McNabb 82-45-1 (64.6%) 1-4 0-1


[1] Data collected prior to 2010 Superbowl

Fantasy Football 2007 Wrap-up: Receivers vs. Running Backs

For the first time in recorded history, I won our Fantasy Football league. woo hoo! Oddly enough, I had horribly running backs this year, sorry Maroney you may go undefeated but you were a fantasy bust. None of my running backs were in the top 20 based on yardage. However, my receiving core was solid with two receivers in the top 20, one in the top 5. Was that the difference or just luck? Having Brady as my starting QB didn’t hurt, and that was luck.

I thought maybe I also did better since you play three receivers instead of two running backs. However, in previous years I’ve had good receivers and never did well. So what is different about this NFL season, did this year prefer receivers over running backs, or did backs under perform across the board. Let’s look at some numbers.

2007

Running Back Yards
L.Tomlinson 1,474 yds
A.Peterson 1,341 yds
B.Westbrook 1,333 yds
W.Parker 1,316 yds
J.Lewis 1,304 yds
Total 6,768 yds
Receivers Yards
R.Wayne 1,510 yds
R.Moss 1,493 yds
C.Johnson 1,440 yds
L.Fitzgerald 1,409 yds
T.Owens 1,355 yds
Total 7,207 yds

This is very different than seasons past, which the top yard carrier has been running backs. Last year even the 5th best running back had more yards than the top receiver

2006

Running Back Yards
L.Tomlinson 1,815 yds
L.Johnson 1,789 yds
F.Gore 1,695 yds
T.Barber 1,662 yds
S.Jackson 1,528 yds
Total 8,492 yds
Receivers Yards
C.Johnson 1,369 yds
M.Harrison 1,366 yds
R.Williams 1,310 yds
R.Wayne 1,310 yds
D.Driver 1,295 yds
Total 6,650 yds

The top 5 rushers had +1,842 yds more than the top 5 receivers, that is a significant change to this year. Now yards aren’t everything, running backs tend to score more touchdowns and they also get yards receiving. So looking at the fantasy points the last two years:

Fantasy Points for 2007

Running Back Points
L.Tomlinson 293 pts
B.Westbrook 269 pts
J.Addai 222 pts
A.Peterson 222 pts
C.Portis 210 pts
Receivers Yards
R.Moss 280 pts
T.Owens 218 pts
B.Edwards 212 pts
R.Wayne 198 pts
L.Fitzgerald 188 pts

The top tunning backs still outscored the top receivers in fantasy points, but compare those fantasy points to 2006:

Fantasy Points for 2006

Running Back Points
L.Tomlinson 410 pts
L.Johnson 317 pts
S.Jackson 314 pts
F.Gore 249 pts
W.Parker 246 pts
Receivers Yards
M.Harrison 199 pts
T.Owens 189 pts
R.Wayne 181 pts
D.Driver 173 pts
C.Johnson 172 pts

The receivers scored about the same points, but the running back points took a significant drop from the previous year. Randy Moss and Tomlinson record years being the anomalies.

Did NFL teams rely on the pass more this year than the run? Yes. As you can see in this chart of total NFL yardage, by passing and rushing for the past 7 years. Note: 2001 there was 1 less team in the league, so total yardage is down. It was only Houston so not down too much.

Chart: NFL Yardage Passing vs. Rushing
NFL Yardage Passing vs. Rushing 2001-2007

The real question is, what will the NFL do next year and how should I pick my draft. I was hoping the chart would give an overall trend, but it doesn’t seem consistent enough. Oh well, I guess Brady and Moss as my first two picks next year wouldn’t be too bad.

Sept 23rd, 2012 – A-Rod becomes Home Run King!

Following up on my previous article with the prediction, Alex Rodriquez will become the all time home run leader with his 760th home run on Sept 23rd, 2012. He’ll pass Barry Bonds’ mark of 759 home runs also set on Sept 23rd, 5 years previous.

Prediction for A-Rod’s next five seasons leading up to the record:

Year Home
Runs
Career
Total
2007 58 522
2008 52 574
2009 53 627
2010 50 677
2011 45 722
2012 38 760

Home Run Kings – Barry Bonds, A-Rod and Pujols

Barry Bonds is probably going to break the home run record in the next few months. Though I’m not sure why people are making such a big deal about it, the record is going to be about as short lived as McGwire’s single season record. A-Rod is clearly going to be breaking Barry’s record in a few years and a good chance Pujols can break A-Rod’s record a few years after that.

Here are some numbers:

Alex Rodriquez started this season with 464 home runs at age 31. He has averaged 41 home runs a year for each full year he’s played. If he does the same for the next 7 years, he will hit another 287 home runs, giving him 751 career home runs at age 38. Bonds at age 38 was just past 600 home runs, and still at his physical peak. If A-Rod can perform in his later years as well as Bonds then he could expect an additional 150 home runs which would put him around 900 career home runs. If he falls off in his later years, maybe Alex will only hit 800.

Albert Pujols is on the same pace as A-Rod. In Pujols first 6 seasons he has hit 250 home runs, averaging 41 home runs a year and he’s still only 27 years old. The 2006 season was his best to date hitting 49 home runs, so it appears he is getting warmed up, which will make him down right frightful when he gets to his prime in his early 30’s.

But remember, Bonds has walked a major league record 2,432 times, once every 4 at bats for his career. If his walk ratio was on par with A-Rod’s which is once every 8 at bats, Bonds would have an additional 1,200 at bats. Considering Bonds’ career ratio is a home run every 13 at bats, that would have given him an additional 92 home runs, making it a bit harder for Alex to catch him. But at A-Rod’s current pace he might just hit 92 home runs this year.

The Odds of a Perfect Bracket

It’s March Madness time again which means time to fill out the tournament brackets. So I’m sure we are all wondering what the odds are to pick a perfect bracket, all 64 games picked exactly right.

The straight odds calculations is relatively easy, for each 64 games you must pick the right outcome out of the 2 possible outcomes (win or loss). So our Statisitics 101 class tells us that is two to the power of 64 (2^64) different possible combinations. Only one those will be the perfect bracket.

You have 1 in 18 quintillion chance to pick a perfect bracket

Oh and if you didn’t calculate that, 2^64 = 1.8 x 1019 or more precisely  18,446,744,073,709,551,616. This means you have a 1 in 18 quintillion chance of picking the perfect bracket. You have a better chance of winning the lottery two days in a row then picking the perfect bracket.[1]

However, I don’t buy the straight odds calculation. There has never been a #1 seed beat by a #16 seed, which is 4 games that are practically gimmies. The lowest seed to win the championship is an #8 seed, the lowest seed to make it to the final four is a #11 seed, to the elite eight #12 seed and sweet sixteen is a #14 seed.[2]

So it is relatively obvious that the rankings do give a distinct advantage to be able to pick a perfect bracket.

Here’s how I’m going to divide up the odds for the top seed to win a game in the first round:

 #1 vs. #16 = 0.95
 #2 vs. #15 = 0.80
 #3 vs. #14 = 0.75
 #4 vs. #13 = 0.70
 #5 vs. #12 = 0.65
 #6 vs. #11 = 0.60
 #7 vs. #10 = 0.55
 #8 vs. #9 = 0.50

Multiplying each of these gives us a 4% chance to pick a perfect round 1 for a regional bracket. There are four regionals so that would be 2.56 x 10^-6 (1 in 390,625 to pick a perfect round 1)

The second round is not quite as easy to give odds to, the #1 seeds would still have a distinct advantage over a #8 or #9, but the odds would be tough for say a #3 vs. #6. Let’s say the favored team for round 2 has a 55% chance of winning. For the 16 round 2 games this would be 0.55^16 = 7.0 x 10-5

Let’s say for the remaining rounds the odds are even, though they wouldn’t be but I want the calculation to be relatively conservative. So the remaining 15 games, actually 16 including the play-in game gives the odds as 0.50^16 = 1.5 x 10-5

Combining all the rounds gives us a total odds of 2.69 x 10-15 which is a 1 in 371 trillion chance, quite a bit easier than the straight odds but still a really really long shot.

So good luck with your picks, don’t feel bad if you miss a couple. :)

[1] – Assuming a 1 in 30 million chance of winning the lottery; to win the lottery two days in a row would be (3 x 10^6) * (3 x 10^6) = 9 x 10^12, or 1 in 9 trillion chance.

[2] – Seed information from Wikipedia

Instant Replay, Google and the BCS

Fuzziness is what makes the world interesting. We seem have too many demands for precision, too much black and white. The fuzziness and gray area gives us something to talk about, something interesting, points for debate.
Instant replays – why does every call have to be right? A referee missing or making the wrong call is part of the game. Just like a player dropping a pass or striking out. Stopping the game, waiting around for 5 minutes may be great for commercials but it is not any fun to watch. Plus kills the instant excuses of what might have been.

Could you imagine having instant replay in baseball? Or even worse the automated pitch detector to call balls and strikes, after a close pitch no one would get upset, there would be no “COME ON BLUE! Get your eyes checked!!”, it would be another case of shrugging and saying well that’s what the computer says. Which reminds me of a recent customer service call to United but I’ll save that for another day.

Google – the great organizer and provider of the world’s information is similar in many regard to instant replay. It seems whenever I find myself in a lively discussion with a group, someone, myself included, will always want to look up the “correct answer” on Google. This usually ends up killing the discussion.

B.C.S. – each bowl season everyone always seems to get up in arms about why we must have a college football playoffs. How can we possibly know who the National Champion is? The BCS tried to implement a national championship game against the top two teams, which isn’t enough. How do we know they really are the top two teams, we must have playoffs.

My thought on this is to get rid of the whole BCS and championship game format. It obviously can’t decide it anyways. Even if you have an eight team playoffs, the ninth team will feel slighted. Let the bowls go to how they were, which was good for 50+ years. And let people have lively debates on why Boise State should be the national champion or why USC is overrated.

Having a computer decide the answers to everything is just boring. The debate and differing view points and people being passionate now that’s interesting. Who cares if there is no right or wrong, I like the fuzziness.

Greatest Day in Fantasy Football

Tonight I needed over 40 pts out of Steven Jackson to move into second place in my fantasy league. Obviously, it didn’t happen so I ended up in third which is still in the playoffs and I play the same team either way. But it got me thinking what was the best fantasy day by any player. Here’s what I found.

In recent times, when people were actually playing fantasy football:

  • Jerry Rice (WR San Francisco 49ers) – Oct 14, 1990 vs. Atlanta – 41 pts, 5 receiving TDs, 225 yds

  • Clinton Portis (RB Denver Broncos) – Dec 7, 2003 vs. Kansas City – 41 pts, 5 rushing TDs, 218 yds rushing, 33 yds receiving

  • Shaun Alexander (RB Seattle Seahawks) – Sept 30, 2002 vs. Minnesota – 40 pts, 5 rushing TDs, 139 yds rushing, 92 yds receiving

  • Peyton Manning (QB Indianapolis Colts) – Nov 25, 2004 vs. Detroit Lions – 40 pts, 6 passing TDs, 236 yds passing

Prior to fantasy football, plus multi-talented players:

  • Gale Sayers (RB Chicago Bears) – Dec 12, 1965 vs. San Francisco – 45 pts, 5 rushing TDs, 1 receiving TD, 1 punt return TD, 113 rushing yds, 89 receiving yds

  • Dub Jones (RB Cleveland Browns) – Nov 25, 1951 vs. Chicago Bears – Minimum 43 pts, 4 rushing TDs, 2 receiving TDs. I couldn’t find total yards but he had the following yards just from his touchdowns: 83 yds rushing, 77yds

  • Cookie Gilchrist (RB Buffalo Bills) – Dec 8, 1963 vs. NY Jets – 42 pts
    5 rushing TDs, 243 rushing yds

And probably the greatest fantasy day in NFL history is incomplete. Ernie Nevers (RB, K – Chicago Cardinals) on Nov 28th, 1929 vs. Chicago Bears rushed for 6 TDs and kicked 4 extra points. (He missed two) Unfortunately, I can not find how many yards he rushed for so I can’t complete his score. Minimum 40 pts

Scoring Note: My league gives 6 pts per touchdown, 1 pt per 20 yds rushing, 1 pt per 20 yds receiving, 1 pt per 50 yds passing.

What might have been, Part I

With Barry Bonds about to pass Babe Ruth’s career home run record, home runs and records are getting a lot of talk. I’m a baseball enthusiast and all the stats and records appeal to the math geek in me. I recently heard an interesting tid bit that needs a little exploring: Willie Mays missed almost two full years of baseball due to military service.

Mays finished with 660 career home runs, which was 3rd all time until Barry passed him. Let’s look at Mays home runs by years early in his career, which is when he went into the military.

Willie Mays Home Runs

Year HR At Bats
1951 20 464
1952 4 127
1953 0 0
1954 41 565
1955 51 580
1956 36 578

Averaging out HR per at-bat for Mays first few years works out to be, Mays hit a home run each 15.2 at-bats. Averaging out how many at-bats Mays had per year in full years he played is 546 at-bats.

So in 1952 he missed out on 419 at-bats and in 1953 he missed out on all 546 at-bats. Using one home run every 15.2 at bats works out to be 27 more home runs in 1952 and 36 home runs in 1953 which is an additional 63 home runs.

Babe Ruth finished with 714 career home runs; if Mays didn’t serve in the military it is very likely he would of had 63 more home runs, giving him a career total of 723 home runs.

Stats Courtesy of Baseball Almanac
Photo Credit from Academy of Achievement

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