変化球　Henkakyuu

I Was Wrong

2011-06-22T04:24:00.000-07:00

It's been a while. Over 3 months since the quake, a lot of new information on the issues at Fukushima have been released. I can clearly say that I was incredibly wrong.

I still believe in Nuclear Power. I believe that the graph from this site says it best:

Nuclear has clear dangers. We have seen that. And the damage is incredibly scary. But there are also clear dangers from coal and oil. They are just less sudden and scary.

I'm sorry it took so long to admit my mistake, but my mistake has to be corrected. I apologize for any confusion, and hope that people take this horrible accident as an opportunity to educate themselves about the dangers of power generation, regardless of the source. We depend on electricity, and we need to generate it. We also need to manage the risks.

That is our job as consumers of electricity. Let's get to it.

A Brief Intermission on Nuclear Power

2011-03-14T05:29:00.000-07:00

Full Disclosure: I work for a Japanese manufacturer that creates nuclear power plants (among all sorts of other wonderful things, though not the manufacturer of the plants in question). I strongly believe in the safety of nuclear power. After the massive earthquake and Tsunami in Eastern Japan, I was utterly shocked by the doom and gloom journalism from the US, especially with newscasters throwing around the word "Meltdown" as a segue to calling Fukushima a potential Chernobyl.

There is virtually no possibility of any of the Fukushima Reactors turning in to Chernobyl

There's this thing called the International Nuclear Event Scale that measures how bad a nuclear accident is. 0 is finding a gum wrapper on the floor. 7 is Chernobyl. So where is a "Meltdown"? Well, it isn't on the scale. See, a "Meltdown" is when the reactor of the plant melts. It can melt some (Three Mile Island) or melt a lot and then explode (Chernobyl). The scale indicates what's important to us -- what effect does the incident have on the people and the environment?

From all the information I've read on the Fukushima incidents, this would be rated as a 3 currently. That's less than Three Mile Island (INES 5), and certainly less than Chernobyl (INES 7). The official report isn't out as the incident isn't over, but so far there have been no deaths and no exposure to the deadliest radioactive materials in the reactor.

Japanese plants have been really good up until now. If you can read Japanese, you can read the reports on all incidents that rank on the INES scale on the Japanese NUCIA resource. The worst up until now on the INES scale was a 4 at Tokaimura where two workers died.

If you want technical details on the accident, I suggest reading this article.

What is a "Meltdown"?

Nuclear power plants have Uranium in them that fire off neutrons in a reaction. Those neutrons are slowed down by water so that they can hit more Uranium and create more neutrons. And so on. That reaction creates a lot of heat. That heat is used to turn water to steam and generate electricity in giant turbines. The water, in turn, helps cool the reactor.

When nuclear power plants want to stop, they insert "control rods" which catch the neutrons flying around to slow down the reaction. That is not instant. It takes days for the reaction to totally stop. When the earthquake hit, the reactor automatically inserted the control rods to stop the reaction.

While the reaction slows, it still generates heat. Water is still circulated to cool the reactor. When the electricity went off, the pumps stopped working. The backup is a set of diesel generators. The tsunami damaged the generators. The third backup is 8 hours of battery power. After 8 hours, they could no longer circulate water to the core, so it started heating up.

The building the reactor is a really thick steel pressure vessel center to contain the reactor and the nuclear material. The nuclear material is contained in fuel rods. As long as those rods stay sealed (and they are designed to withstand over 2000 degrees celsius), the really radioactive stuff is contained.

If the reactor gets hot enough, those fuel rods melt. That is the start of a meltdown. When the rods melt, the radioactive stuff inside is released. In this case it was Iodine and Cesium. In addition, when the temperature gets that high, water gets separated into hydrogen and oxygen gas. Both of those are explosive, so to prevent an explosion, pressure was released from the reactor into the atmosphere.
Along with the gases was some of the iodine and cesium. That is bad, of course, but the levels were not high enough to cause lasting health consequences.

The fear is if the core continues to melt. If all attempts to cool the core are unsuccessful and the fuel rods melt entirely and continue to react after they melt, then theoretically they could melt through the giant steel container holding them, and release the fuel (uranium/plutonium) into the atmosphere. That would be bad, but it is predicated on so many worst case scenarios. While this started because of a worse-than-worst case scenario, the likelihood of things continuing to go that bad is very very small.

This reactor was built 40 years ago using 40+ year-old technology. It was designed to withstand a Magnitude 8.0 quake. It has been hit by a 9.0 quake, a several meter tsunami, and the accident has still be contained within the safety measures it was designed with.

Nuclear Power is Safe

Yes, nuclear accidents are really really scary. The potential of Chernobyl is a huge scary cloud looming over our heads. And at times when the news is showing explosions and radiation released to the atmosphere, it's hard to think of nuclear power as safe.

But it is also unfair to nuclear plants. In the US the worst accident was Three Mile Island. Nobody died in that accident, yet no nuclear power plants have been built in the US since. By comparison, 50 people died from coal mining in the US in 2010 alone. Coal accounts for almost 50% of US electricity generation.

Nuclear Power is CO2-emission and NOx-emission free. It has an incredible capacity for base load generation, and countries like France are able to produce over 80% of their electricity from Nuclear alone. MIT conducted a great study on the future of Nuclear Power in 2003 if you'd like to learn more.

Please educate yourself rather than making a knee-jerk reaction. Electricity demand is growing as the population of the world increases, and that means a need to match that demand. Nuclear is one option we need to consider seriously as an emission-free source of power.

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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