doRedis: A parallel back end for R/foreach using Redis.

Bryan Lewis recently did a Vignette using RStudio to run some financial data using R/foreach and doRedis. Parallelizing R continues to become more important as users want to run computations on larger and larger quantities of data in reasonable amounts of time particularly on clusters or the cloud. doRedis includes the following features:

• Support for dynamic pools of parallel workers during running computations.
• Simple cross-platform parallel computing, including at least Windows, GNU/Linux and OS X.
• Fault-tolerant 

doRedis Vignette

Redis

Would we have found out San Diego Basketball through Analytics

This week the San Diego State Basketball program had a number of its players arrested for shaving points in a game in February of 2010 and trying to do the same thing in a game against UCR in February of 2011 (News Story). It appears that these guys were caught by human intelligence. This type of cheating is bad for both the sports organizations (NCAA, NFL, NBA, MLB) and the major betting community. It is bad for the sports organizations because who, except WWE fans, are going to watch fixed match. It is bad for the betting community because they really make their money on the Juice they charge gamblers. The betting community wants fair games that split the money evenly over the spread. Anything that shifts that is a problem for their business model. People believing that the games are fixed could reduce the amount of money bet on games. Also bad for the Bookies.

In the book Freakomonics by Levitt and Dubner, they expose match fixing in sumo matches using statistical analysis. It was a fun read and showed that analytics have the ability to expose cheating in sports without human intelligence. It was also a safe sport to look at because Americans do not really care about sumo nor do they bet on it.

It is interesting to me that there exists so much data and analysis with a goal of prediction on sport, but I have found nothing on using predictive analytics to discover point shaving or game fixing. I realize that doing this kind of work in team sports would be more completed than something like sumo. I just feel that it might be another tool to add to the effort to deter this kind of problem. At first pass it seems the most likely times there is potential cheating is when a players statistics in a game are an outlyer, and the team did not beat the spread. The problem that I see is the sparsity of known point shaving in games. For example I only know of one alleged fixed game in the NCAA basketball season in 2010 out of something like 5,000 games. I do not think that is enough to be useful. Sad to say that if there were more fixed games we might be able to build a better model. Someone suggest to me that I would get better data on game fixing if I looked at Italian football. However, I call it soccer and care Italian football about as much as I do about sumo.

So I have no data to present or model to put forth. I just hate cheaters, and this story has bothered me since it came out on April 11th.

Analytics, Sabermetrics, Data Mining...Why can't we all just get along?

Sabermetrics was a term coined by Bill James to describe the analysis of baseball through objective evidence. Saber, or more accurately SABR, stands for the Society for American Baseball Research. With Bill James as its advocate. Sabermetrics has changed the way baseball is played. No easy task in a sport so encumbered by tradition. Baseball probably collects more data during a game than any other sport and each team plays at least 162 games a year. Rich data territory compared to the 16 regular season games played in the NFL.  Sabermetrics has taken a hard look at the core beliefs of what statistics make a good baseball player or team and runs them against the cold judgement of analytics. The results showed that some previously treasured statistics like batting average were not as important statistics as once thought, but others like on base percentage were better indicators. This is predictive analytics at it best. So it is time to call Sabermatrics what it is analytics.

It is funny for all the impact Sabermetrics has had on baseball I believe it is still limited by the traditions of baseball. Let me give you some examples.

The Blog Sabermetic Research talks about Buck Showalter changing the way his base runners play to gain 5 runs per year which he claims is worth $10 million dollars. Makes sense if the data he is using is good, but the key here is the decision is claimed to be made solely on the numbers.

Pitching is another story. In baseball a starting pitcher must pitch five full innings in order to earn a decision (win/loss).  Many talk about the difference between ERAs of starting versus relief pitchers. The data clearly shows that relief pitchers, even when they are the same person,  have an overall ERA .50 lower than starting pitcher or better. Tango on Baseball touches on the subject in this article. My question is that if relief pitchers have a better ERA than stating pitchers, and starters are generally accepted to be better pitchers than relievers why aren't starters being used like relievers? The impact would be huge! A quick pass says this .50 ERA reduction in starting pitchers would result in 40 less runs allowed by a team over the course of a season! Using Showalter math that is $80 million dollars. I believe the reason that this is not looked at as a solution is because of tradition. If starting pitchers where used like relievers they would never pitcher 5 innings, and therefore would never get  a decision. This would be a fundamental change in the way baseball is played.

In defense of Sabermetricians, there has been some discussion that ERA, like BA, is not a very useful statistic. This would mean that conclusions drawn from those statistics may not be as useful as they appear. I have not seen anything on starters versus relievers in terms of CERA, dERA, DICE or DIPS.

The Most Boring day in the Last hundred Years

I was driving in to work this morning and listening to the radio because I feel that distractions make me a better driver. A news article comes on telling me that a Cambridge researcher has found out that April 11, 1954 is the most boring day in the last 100+ years. I had a good laugh thinking that it isn't only the US government that gives out silly grants for pointless research ( remember the which came first the chicken or the egg paper).

So I dig a little deeper. Turns out this is a software guy who just launched his "smart" search engine. I could not find any specifics on how the engine works which would have been cool, but then I thought this is maybe even cooler than another algorithm. Here is a guy who got this new web site rolled out as a news story in three major newspapers and NPR just by asking an interesting question about the most boring day.  It would be really amazing if the search engine gave him the question after he queried "the most likely answer to a question to be picked up by major news organizations".

The Most Boring Day Article
True Knowledge
Tunstall-Pedoe and his search engine

A Fun Problem to play with....

I am always on the lookout for interesting brain teasers, and this is a fun one. It comes from a blog by Phil Birnbaum which I found a few days ago and I just love. His Sabermetrics stuff is awesome!

A king decides that his country has too many men and not enough women. So he issues a decree: once a couple has a boy, they're not allowed to have any more babies.

The king reasons as follows: no family will have more than one boy. But some families will have two girls, or three girls, or even six girls before they have a boy. So there will wind up being a lot more girls than boys.

Is the king's reasoning correct?

The answer: the king's reasoning is not correct. There will still be approaching 50 percent boys, and 50 percent girls. There are many ways to figure this out. 

I believe this assumes that the chances of having a boy or girl are equally likely. and independent.

Sabermetricresearch

What Operating Systems do R users use?

I went to the Greater Boston R meetup last night where the Rstudio guys did a presentation on their IDE. It was well attended, and Josh and JJ did a great job.

At the end of the talk JJ said that the breakdown of Operating Systems for the Rstudio downloads was roughly 60% windows, 30% mac and 10% linux. Frankly I was surprised by those numbers. I always felt that the majority of people who use open source R would run it on an open source Operating System. Rstudio's results would seem to strongly counter that thought. After the talk, I remembered a survey done by KDnuggets in September of 2007 on what Operating system did people use for their analytics work. It also showed about 60% Windows users but 30% linux and 10% Mac. I believe this suggests an increase in the number of people using Macs to do analytics in the last four years.

I am not sure the data from downloads of an IDE or a survey of Operating Systems by KDnuggets is a good indicator of what is the relative popularity of the various Operating Systems being used by the R community. I do believe it does show the importance of supporting Windows which is consistantly shown as the dominate platform among people surveyed and downloads at least Rstudio. It also showed that if you make an IDE that is not supported on the Mac you could be missing 30% of the potential users.

The recent KDnuggets survey on which R interfaces which was interesting. For example, I would not have expect so many people to respond that they use Tinn-R or R commander but they do.  I always enjoyed the KDnuggets surveys.

KDnuggets Operating System survey
KDnuggets R interface poll

Why are the Red Sox better today? Sabremetrics or Construction?

I saw an article this morning from an MIT professor that predicted the Red Sox would win 100 games this year. That is a pretty bold statement since the Red Sox have only won 100 games in a season three times (1912, 1915 and 1946). However, it got me to wondering how have the Red Sox become so good in recent history. I often heard comments the claim that it is the payroll or the genius of Theo Epstein. Whenever I am with statistics guys, it is the hiring of Bill James and the use of Sabremetrics that made the difference. I have a third theory to put forth as the major reason for the improvement of the Red Sox in recent history, construction at Fenway. Oddly, this started the same year that Bill James was hired by the Red Sox, 2003.

From 1995 to 2002 the Red Sox had a combined record of 695-582 winning 54.42% of their games. From 2003 to 2010 the Red Sox had a combined record of 749-547 winning 57.79% of their games.

Year	W	L	Winning %		Year	W	L	Winning %
2010	89	73	54.94%		2002	93	69	57.41%
2009	95	67	58.64%		2001	82	79	50.93%
2008	95	67	58.64%		2000	85	77	52.47%
2007	96	66	59.26%		1999	94	68	58.02%
2006	86	76	53.09%		1998	92	70	56.79%
2005	95	67	58.64%		1997	78	84	48.15%
2004	98	64	60.49%		1996	85	77	52.47%
2003	95	67	58.64%		1995	86	58	59.72%
	749	547	57.79%			695	582	54.42%

So they are a got better after 2003 and Theo is a genius and Sabremetrics rules baseball. I am not so sure, and I think we reach those numbers based on a Simpson's paradox. Let me explain. If Sabremetrics had been the driving reason for the improvement the Red Sox. they would have gotten better not only at home but away as well. They did not. In fact the Red Sox improved massively at home, but got worse on the road. So what is the factor that explains this? In 2003, the same year Bill James was hired by the Red Sox, additional seating was added the Fenway park for the first time since it was 1946. While it was was always known that Fenway was helpful to certain types of hitters and pitchers and the Red Sox teams have always emphasized those players. I believe that construction made the park even more baised than it was before.



During the period 1995 to 2002 the Red Sox had a better away record than they did from 2003-2010.

Away Record W L % W L % 2010 40 41 49.38% 2002 51 30 62.96% 2009 39 42 48.15% 2001 41 39 51.25% 2008 39 42 48.15% 2000 43 38 53.09% 2007 40 41 49.38% 1999 45 36 55.56% 2006 35 46 43.21% 1998 41 40 50.62% 2005 41 40 50.62% 1997 39 42 48.15% 2004 43 38 53.09% 1996 38 43 46.91% 2003 42 39 51.85% 1995 43 28 60.56% total 319 329 49.23% 341 296 53.53%

For Home games it is a very Different story: Home Record W L % W L % 2010 49 32 60.49% 2002 42 39 51.85% 2009 56 25 69.14% 2001 41 40 50.62% 2008 56 25 69.14% 2000 42 39 51.85% 2007 56 25 69.14% 1999 49 32 60.49% 2006 51 30 62.96% 1998 51 30 62.96% 2005 54 27 66.67% 1997 39 42 48.15% 2004 55 26 67.90% 1996 47 34 58.02% 2003 53 28 65.43% 1995 43 30 58.90% total 430 218 66.36% 354 286 55.31%

MIT economist says Red Sox will win 100 games in 2011