Sign Commemorating Forbes Field

Given that this year is the 50th anniversary, there were quite a few more people there than usual.

People gathered at the wall

So many people were expected that the city closed off the street that runs by the wall in order to accommodate the festivities.

Street Closed

There were some interesting characters in the crowd.

Super 'stache

Arrr, matey!

There were plenty of reporters and cameramen.

Whoa, I definitely should not have had that burrito for lunch.

People brought along souvenirs and mementos…

Maz and the '60 Bucs

…and maybe a beer or two.

Yinz got an arn for me?

When 3:36 rolled around, it was time to celebrate.

Yes!!

High five!

It was a great event, an opportunity for those of us who weren’t there to experience the thrill of it, and a chance to share it with the next generation.

We had 'em all the way!

• decent offense
• adequate defense
• worst starting pitching in the National League
• worst relief pitching in the National League
• trading away good players for more prospects
• tied the record for the most consecutive losing seasons (16)

The Pirates are now the team with the longest currently active streak of consecutive losing seasons in any major American sport. The last year they had a winning season was 1992, which is more than half of my lifetime ago. If they don’t have a winning season next year (which, let’s face it, does not look very likely), they will take over sole possession of one of the most disgraceful records in professional sports.

As I mentioned in my post on the sports graph paper I developed, I have been charting the Pirates’ win fraction for the last few seasons. With this year’s season at a close, I can summarize my findings: despite a bunch of changes in the Pirates’ organization over the last few years – a new GM, new manager, new coaches, and many new players – the Pirates’ final win fraction has not changed significantly over the last four seasons. Specifically, they have finished with a win fraction of between 0.410 and 0.420 in each of the last four seasons. That means that over the last 4 years, they have either won 66, 67, or 68 games per season. Evidently, all the changes they have made have done precisely nothing to help the team win.

From the graph above, it’s clear that despite vastly different performance early in the season, the Pirates always manage to regress back to approximately 0.415 by the end of the year. If you’d like to play around with the data I used to create the above plot, take a look at this tab-delimited text file of the Pirates’ total wins and win fraction vs. number of games completed. You might have fun or you might get depressed. You’ve been warned.

I heard yesterday that the Pirates traded Nady and Marte to the Yankees. This made me upset. I feel like this season the Pirates have a legitimate shot at getting above .500. However, without Nady and Marte, their likelihood of having a winning season is a lot lower. The trading away of talented players for prospects reminded me of a t-shirt I had seen on eBay that said “Pittsburgh Baseball: Rebuilding since 1992″. In the wake of the Nady/Marte trade, I thought that maybe I should buy this shirt. So I went to eBay to see if I could find it. It had been sold by a Pittsburgh-based T-shirt maker for quite a while, so I thought I’d be able to find it. No such luck.

Not able to find it on eBay, I tried to remember what the seller’s name was in order to see if I could find it through some other channel. I finally found that it was “River City Shirts”. So, I decided to see if they had a website. Google had it indexed but it’s no longer online. I found a MySpace page for “River City Shirts” but the account had been deleted. This seemed very strange. Then, I found an entry on auction-safety.org – a complaint against River City Shirts for having sold a shirt and then never delivered the item. From the complaint, I found the seller’s eBay id and I checked out eBay’s page for the seller. It indicated that despite having achieved power seller status and having compiled a ton of positive reviews, this seller is “No longer registered as a user”. In addition, this page featured a number of angry comments from customers claiming to have not received the merchandise they had ordered. Those keeping score at home will note that we now have a defunct website, a deleted MySpace account, a deregistered eBay account, and multiple complaints on multiple sites.

Basically, the entire online presence of River City Shirts has been deleted. This led me to wonder if perhaps the guy running it had died. From the auction-safety.org complaint, I had the guy’s name and address. So, I did a Google search for his name plus the word “obituary”. This search returned 3 hits. Only one pertained to a person from Pittsburgh. It was a report of a wedding that occurred June 22, 2008. It turns out that our River City Shirts proprietor is not dead; rather, he was a groomsman at the wedding. Furthermore, I went to high school with the bride. It took me a while to realize it, though. I looked at her name and thought that it seemed familiar. I seemed to recall her having been a year or two younger than me. I looked at my school district’s alumni database but I couldn’t find her in it, so I started to doubt that my initial inclinations had been correct. However, as I read through the article again, I took note of the names of the bridesmaids. It turns out that I’m sure that some of them went to high school with me. So, I now have no doubt that the bride went to high school with me.

In any case, having found this article about the wedding does nothing to help me figure out what happened to River City Shirts. I still want my “Pittsburgh Baseball: Rebuilding since 1992″ shirt. Maybe I should contact this girl I went to high school with to see if she can hook me up.

Before long, I had figured out how to basically fill the graph with these curves. If I drew a set of curves that plotted a team’s win fraction assuming they won their first W games (with values of W varying from 1 to 161) and then lost all subsequent games, that gave me half of the curves I needed – the curves that governed a team’s win fraction trajectory when it lost games. In order to get the curves that governed the team’s win fraction trajectory when it won games, I needed curves that assumed that the team lost their first L games and then won all their subsequent games. I implemented this in Excel, but Excel balked at the idea of plotting 322 (2*161) curves. So, I decided to try writing a Gnuplot script to do the same thing. The functions I used in my Excel spreadsheet were a little kludgy but I gradually figured out that the simplest representation of the functions I needed to plot were F(G) = (W/G) and F(G) = (L/G) = 1-(W/G) where W represents the total number of wins (and L represents the total losses) a team has accumulated over its G games played and F is the win fraction. (Note that W+L = G or L = G-W.) Using these functions, I was able to get everything plotted using Gnuplot. However, Gnuplot didn’t do a good job of plotting the F = 1/G curve. Plus, I was having a hard time getting everything formatted the way I wanted. So, I scrapped Gnuplot and tried using Mathematica.

I’ve used Mathematica quite a bit for classes that I’ve taken and TA’ed, so I’m reasonably familiar with it. It turns out I was able to create the graphs easily and in only one line (it spans multiple lines, but it’s really just one compound command). Mathematica is slower than Gnuplot but I find that making formatting changes is easier (mostly because I’m more familiar with Mathematica and Mathematica has a better help browser). In any case, I decided to make “graph paper” that a fan could use to easily follow a favorite team’s win percentage as the season unfolds.

The graph paper necessary for an entire (regular) season of baseball is pretty involved, since there are 162 games in the season. It’s easier to see the important features of this graph paper if we produce graph paper for the regular NFL season instead, since there are only 16 games.

Let me explain what the various curves represent. The green curves are what might be called iso-win lines. That is, each green curve represents the team’s win percentage as a function of the number of games played if the team has won the number of games that appears at the top of the graph where the curve terminates. As long as the team keeps losing (that is, its number of wins doesn’t change), the win fraction trajectory stays coincident with this curve. The red curves might be called iso-loss lines. That is, they represent the win percentage as a function of the number of games played given that the team has lost the number of games that appears at the bottom of the graph where the curve terminates. As long as the team keeps winning (that is, its number of losses doesn’t change), the win fraction trajectory remains coincident with this curve. However, if the team loses a game, then its win fraction trajectory stays on the same iso-win curve but jumps from one iso-loss line to the next one to the right. For example, the 2007 Pittsburgh Steelers won games 6, 7, 8, and 9 of the season. Prior to game 6, they had lost 2 games. Thus, after games 6, 7, 8, and 9, their win fraction trajectory was coincident with the 2-loss iso-loss curve. However, they lost their 10th game of the season. Up to that point they had won 7 games. So, as a result of the loss, they moved down the 7-win iso-win curve and jumped from the 2-loss iso-loss curve to the 3-loss iso-loss curve.

This description may seem pretty arcane. Let me explain how this works in practice. If you want to plot your favorite team’s progress over the course of a season, you start by putting a point at the top left or the bottom left of the plot, depending on whether they won (top left) or lost (bottom left) their first game. If they won their first game and keep on winning, then the line tracking their win fraction will remain at the top of the plot. However, if they lose a game, you then follow the appropriate green curve down to the first intersection with a red curve. After each subsequent game is completed, if it was a win, move upward along the red curve to the next green-red intersection. If the game was a loss, move downward along the green curve to the nearest green-red intersection. Repeat this process until the end of the season.

The value of this graph paper lies in the information it gives you after you have performed the above procedure, or even in the middle of the season. The x coordinate of a given red-green intersection gives the total number of games the team has played and the y coordinate of the intersection gives the win fraction the team has earned to that point. Furthermore, if you follow the green curve back to its terminus, the number there will tell you how many wins the team has had and doing the same procedure with the red curve will tell you how many losses the team has had.

Furthermore, this graph paper exhibits all the behavior we would expect of win fractions. For example, a team’s win fraction changes dramatically with each additional win or loss at the beginning of the season but by the end of the season each win or loss has only a small effect on the win fraction. This is seen clearly in win fraction trajectories plotted using this graph paper. In addition, if a team alternates between winning and losing for a few games, the average rate of change over those games in the win fraction depends on whether the starting win fraction was above or below 0.5. If the starting win fraction was below 0.5, then a team that wins 50% of its subsequent games will improve its win fraction. However, if the team started with a win fraction above 50% and it subsequently wins half of its games, its win fraction will decrease, approaching 0.5 asymptotically. This behavior can be seen easily by plotting such a trajectory on this graph paper. Obviously, teams that start with a win fraction of 0.5 and subsequently win 50% of their games will maintain, on average, their 0.5 win fraction.

It’s certainly possible that other people before me have reported using this type of graph paper to easily visualize the win fraction of a sports team over the course of its season; however, I have never seen any such report. It may also be possible that such a scheme has been proposed to plot some other similar quantity in a non-sports context, but, again, I have not seen anything like this. I expect that this type of behavior is well-known to mathematicians, but I hope that its application to sports represents an original contribution. The links below will allow you to download the PDF version of the graph paper for the NFL (16-game), NBA/NHL (81-game), and MLB (162-game) regular seasons. If you would like the files in SVG format, let me know.  WordPress isn’t letting me upload them, so I might have to email them to you.  In addition, I have included a link to a PDF copy of the Mathematica notebook I used to create the plots. After creating them, I saved them to the SVG format and imported them into Inkscape, which I used to resize and massage them a bit, before saving them to PDF. However, with the Mathematica notebook, you can create your own or improve on mine. Please let me know if you have any difficulties or if you have any suggestions on how these might be improved.

NFL Graph Paper (PDF)

NBA/NHL Graph Paper (PDF)

MLB Graph Paper (PDF)

Graph Paper Mathematica Notebook (PDF)

An interesting tidbit that I learned yesterday is that (as of yesterday) the Pirates’ starting outfielders (Jason Bay, Nate McLouth, and Xavier Nady) collectively have 20 home runs and 73 RBI, which is better than any trio of outfielders in all of baseball.  Nice job, guys.  Of course, I’ve said before that Nate McLouth and Xavier Nady are awesome, but I was impressed at how well the Pirates outfield compares with the rest of the league.

By creating another starting position, the designated hitter rule necessitates larger club payrolls, which reduces the ability of small market teams to compete. Major League clubs pay large salaries for their starting infield, outfield, and (generally) 5 starting pitchers. However, AL teams also have to pay for a DH. While both NL and AL teams have only 25 players on their roster for most of the season, the non-starting players generally cost substantially less than the starting players. In addition, because the DH is a specialist position often filled by players who are at the peak of their earning potential, some clubs pay huge amounts of money to acquire elite DHs, while teams who can’t afford such players find themselves at a distinct disadvantage. For example, Jim Thome, the DH for the Chicago White Sox, will earn $15.67M this year, making him the highest paid player on his team. In fact, if we take an average over all the teams that have a full-time DH (that is, they do not use a platoon at the position), which is half of the AL, the DH is the 3rd highest paid player on the average team (including starting pitchers) and is paid over $10M per year. For small market teams whose entire payroll may be in the realm of $50M, paying $10M for a single player who plays no defense is just not feasible. However, not having that $10M hitting specialist (among other deficiencies) often leaves them in an uncompetitive position.

Does the presence of the DH position really make payrolls higher? To answer this question we observe that the average payroll for an AL team for 2008 is $97.49M while the average NL payroll is $83.28M – a difference of about $14M. This averages across all teams – both large and small markets – in each league. If you object that the AL average is skewed by the Yankees, let’s look at the median. For the AL, the median payroll is $88.81M and in the NL, it is $78.95M – a difference of about $10M. While I won’t claim that the DH position is solely responsible for that $10M difference, it is certainly a large factor in the disparity. In general, it is good for the league if every team can be competitive. However, the lack of a serious salary cap or revenue sharing plan in baseball means that small market teams generally can’t compete with teams from larger markets. The DH rule exacerbates the problem by requiring each team to have 9 starting-caliber offensive players rather than 8 in order to be competitive – something the small market teams can ill afford.

My second and final practical objection to the designated hitter rule comes back to symmetry. The designated hitter rule destroyed the symmetry of the two leagues. With the designated hitter rule in place, the NL is perpetually bested by the AL in Interleague play. Not only that, in the last 20 seasons (1987-2007) the NL has won the World Series only 7 times. Furthermore, in World Series games since 1987 the NL has won 44 games while the AL has won 64. Some may claim that the designated hitter rule can’t explain this difference because the AL team can’t use the DH in NL parks. That is true. However, in AL parks, the NL team just uses some guy off their bench as the DH, while the AL team has a $10M+ elite slugger filling the DH role. While NL teams have only 8 starting-caliber offensive players, the DH rule requires that AL teams have 9 in order to be competitive. Thus, in AL parks, NL teams are overmatched. The only advantage that NL teams might have in NL parks is that they are more used to playing without a DH but this seems like a meager advantage. Furthermore, when AL teams play in NL parks, they often bench one of their normal position players in order to keep their DH in the lineup. While the DH often represents a liability on defense, many managers feel that their DH’s superiority on offense more than makes up for his weakness on defense. Thus, the AL team may still have some advantage in NL parks.

So, to sum up, I object to the DH rule because it disrupts the symmetry of baseball and gives a competitive advantage to the AL in general and to large market AL teams in particular.

Fans of the DH rule would probably suggest that there is a way for the NL to compete with the AL again – that the NL introduce the DH rule themselves. They may say that we can’t have our cake and eat it too. We can either hang onto our beloved “pure” baseball or we can be competitive, but not both. That may be true, but I find it unfortunate. The DH rule was introduced at a time when offense in the AL was a joke. Offensive numbers were way down and the owners wanted a way to attract fans through increased offensive production. The DH rule, which had been discussed since the early 1900s, was adopted as a quick fix. Now, it is unlikely to ever be revoked. It would put a number of players out of jobs and owners wouldn’t like it because fans would miss big offensive numbers. So, it’s not clear what the resolution will be. Will the NL and its fans just grow accustomed to being the weaker league or will league officials eventually relent and adopt the DH rule themselves? I hope that it is neither but I’m afraid that there might be no third option.

This seems a little unfair to me. He came into the game with a 1-run lead and the bases loaded. Any mistake, including a walk or even a passed ball, would earn him a blown save. By the time the inning was over, he had allowed 3 runs to score, so he probably earned the blown save. However, in general, I think assigning credit or blame to pitchers is a fool’s errand. I have commented on this at length in the past so I won’t go into it again. However, this situation highlighted to me that wins and losses aren’t the only types of credit/blame quantities assigned to pitchers that are problematic in their execution.

1. The Pirates won again tonight (2nd in a row) after losing 6 straight, including being swept again by the Cubs. Matt Morris and Adam LaRoche are officially terrible. Nate McLouth, Xavier Nady, and Ryan Doumit are awesome.
2. I had thought about starting a new segment on my blog where I post a line from a song to see if people can figure out what song it’s from. Then, I realized that, in the age of the internet, that task is trivially easy. I had planned on using the line “Who put the shield around the ‘k’?”, from “K for Karnival” by Nothing Painted Blue but just typing the line into Google gives you the answer. Curse you, internet.
3. I also thought about introducing a repeating segment in which I would post a picture as a RAW file and have people submit their take on it. I may still do this if people are interested.
4. On Patriot’s Day our family had an outing to see the Boston Marathon. On our way home, I saw this:

    

   

   That’s right, a “Wii Sports” jacket. Now, I like my Wii as much as the next guy, but I’m not about to wear something like this.  That said, the “Wii Sports” jacket is a step above the bowling pin costume I saw the same day:

   

   Alright.  I think that’s enough for now.  I’ll post more when the mood strikes.