All-Star Plots?

Here are some star plots for major league baseball batters, pitchers, and ball parks.  The star plots represent the outcomes of a particular at bat for a hitter, a pitcher, or at a given ball park.  For each plot, batter, pitcher, and ball park was varied, while the other two parameters were filled in with the average value.  For instance, all batters outcomes are calculated as if they were facing J. Kinney at Wrigley Field; Pitchers data was calculated as if they were facing K. Medlen at Wrigley; and Park factors were calculated as the outcome of J. Kinney vs K. Medlen at different ball parks.  The data use to calculate these were downloaded from baseball-reference.com and includes the results every single plate appearance so far this season (about 125,000 so far) and where the game was played.  Six outcomes to an at bat were considered: out, walk, single, double, triple, and home run.  The probability of each of these events was estimated creating a vectors of probabilities with six elements corresponding to each of the six outcome considered.  I’ve chosen to display this data using the star plots below.  The key to the star plot can be found in the lower left corner of each plot and displays the probabilities of each outcome relative to other batters.  For instance, a large blue pie piece on the left indicates that batter’s plate appearance ends with a HR more often relative to other players.  Likewise, a large red pie on the right indicates that the batter’s plate appearance ends in an out more often than other players.

I’ve chosen 100 batters based on their wide range of hitting styles.  In the first row, you’ll players who make outs at the lowest rates relative to other players.  These include players like Joey Votto, Andrew McCutchen, David Wight, and Mike Trout.  Further down, you’ll start to see players who you might describe as single’s hitters.  These include players like Ruben Tejada, Derek jeter, B. Revere, and Juan Pierre.  Finally, towards the bottom row, you’ll see the players who are primarily power hitters like Adam Dunn and Jose Bautista with large blue, for home run,s and orange, for walks, pie pieces, with significant red for outs.  Other players on this row like Saltalamacchia, Plouffe, and Rosario have the large blue and significant red pieces, but they lack walks.

The star plot for pitchers is below.  The first thing we need to say here is that Justin Verlander is very, very good at pitching a baseball.  Some other interesting pitchers here are Yu Darvish, Edison Volquez, and Carlos Zambrano.  They seem to give up relatively few hits, but they give up many more walks that the average pitcher.

These plots are ordered from highest to lowest probability that an out will be made in a given plate appearance.  Pittsburgh, Seattle, and San Francisco lead the way in pitching friendly parks.  These are the same as the bottom three  according to ESPNs measure of Park factor.  The most hitter friendly park is, no surprise, Coors field in Colorado.  Other hitter friendly parks include Target and Chase field in Minnesota and Arizona, respectively.  Arizona is expected here, but Minnesota is a little bit surprising.  It looks like, while it is rare to make an out, most hits are only singles, which don’t generate as many runs are their extra base counterparts.  Home run friendly parks include Coors, Chase, Camden Yards, Miller, Comisky, and Yankee Stadium.  Fenway park is solidly in the hitter category, but it gets that way, rather than by giving up many homeruns, by yielding a greater percentage of doubles than any other park.

 

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A novel way to gamble on the NCAA tournament…

I saw a talk at JSM where I was introduced to a fun new (well, new to me) game to play during the NCAA tournament.  First, teams are assigned a price based on their seed.  This can be done in many ways, but it was set in the talk that the one seeds cost 25 cents, the two seeds cost 19 cents, all the way down to the 15 and 16 seeds which were a penny each.  The goal is to choose a set of teams, that costs, in total, one dollar, that will win the most number of games in the NCAA tournament.  So picking all the number one seeds, which will cost exactly one dollar, but the most wins they can earn is 19 (4 each to the final four and then one each for the two semifinals and one for the championship).  So, according to the speaker, this usually won’t get you the win.  First of all, this game is awesome.  Once you can stop thinking about how awesome this game is, the next logical question is: How do you choose the optimal set of teams?

Douglas Noe and his student Geng Chen used an evolutionary algorithm to optimize the selection of teams, and they used Ken Pomeroy’s rankings as a guide to the probability that one team will beat another team in the tournament.  Now, I don’t think I ever heard of evolutionary algorithms, and, if I have, I’ve totally forgotten about them.  But they are wicked cool.    Here is the wikipedia page for evolutionary algorithms, and it’s worth checking out.  Does anyone have any suggestions as to a good resource for an introduction to evolutionary algorithms?

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Infovis vs. Statistical Graphics

From Gelman’s blogInfovis vs. Statistical Graphics

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Michael Lugo's avatarGod plays dice

Which country wins the Olympics? Right now China is in the lead for most medals (73: 34 gold, 21 silver, 18 bronze) and most golds. The USA is second in both categories (71: 30 gold, 19 silver, 22 bronze). In 2008 there was some controversy about whether the ranking should be done by largest total number of medals or largest golds; here’s the table from Wikipedia. If I recall correctly (though it’s surprisingly hard to search for this) non-Americans were saying that the “right” way to do it is by golds, but Americans insisted on doing it by total medals. Not surprisingly the USA had the most total medalists in ’08 (110, to China’s 100) but not the most golds (36, to China’s 51).

But it hardly seems fair to expect, say, France to get as many medals as the USA, simply because they have about one-fifth the population…

View original post 516 more words

Olympic Medal Counts – August 8, 2012

 

Canada loves winning bronze medals….

MLB Rankings – 8/6/2012

StatsInTheWild MLB rankings as of August 6, 2012 at 8:17pm.  SOS=strength of schedule

Team Rank Change Record ESPN TeamRankings.com SOS Run Diff
NYY 1 63-44 3 1 5 +92
Texas 2 63-44 4 2 13 +83
LA Angels 3 58-51 9 6 7 +49
Washington 4 ↑2 65-43 2 4 23 +82
ChiSox 5 ↑4 59-48 7 5 14 +64
Cincinnati 6 ↑5 66-42 1 3 29 +72
Oakland 7 ↑1 58-50 8 7 8 +28
Detroit 8 ↓1 58-50 13 9 12 +24
TampaBay 9 ↑1 56-52 14 12 3 +19
Atlanta 10 ↑5 62-46 5 8 21 +62
Boston 11 ↓6 54-55 17 13 6 +29
Toronto 12 ↓8 53-55 18 14 2 +9
St. Louis 13 ↑1 59-49 10 15 30 +110
Baltimore 14 ↓2 57-51 16 11 1 -57
Pittsburgh 15 ↓2
61-46 6 10 28 +36
Seattle 16 ↑1 51-59 20 17 4 -3
Arizona 17 ↑4 55-53 15 19 26 +42
SF 18 ↓2 59-49 11 16 27 +19
LA Dodgers 19 ↓1
59-50 12 18 25 +15
NY Mets 20 53-56 19 20 17 -5
Minnesota 21 ↑3 47-61 25 22 11 -79
Kansas City 22 45-62 26 23 10 -60
Cleveland 23 ↓4 50-58 21 21 9 -90
Milwaukee 24 ↓1 48-59 22 26 19 -13
Philadelphia 25 49-59 24 24 19 -29
Miami 26 49-60 23 25 15 -100
Chic Cubs 27 ↑1 43-63 28 27 18 -79
San Diego 28 ↓1 46-64 27 28 22 -61
Colorado 29 38-68 29 29 20 -117
Houston 30 36-73 30 30 16 -142

Past Rankings:

7/23/2012

7/9/2012

7/2/2012

6/25/2012

6/19/2012

6/9/2012

5/28/2012

5/23/2012

5/14/2012

5/7/2012

4/30/2012

4/23/2012

4/16/2012

4/13/2012

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What Car Brands Tell Us About Our Political Participation

What Car Brands Tell Us About Our Political Participation

 

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Olympic Medals – August 6, 2012

 

 

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Bad Badminton

I read this story about some badminton players that were thrown out of the Olympics for intentionally losing a match.  In the words of Deadspin:

The Chinese team of Wang Xiaoli and Yu Yang and South Koreans Jung Kyung-eun and Kim Ha-na played a farce of a match in which players served into the net on purpose or lazily launched shots out of bounds. The Chinese players’ incentive to lose was a bracket placement that would keep them on the opposite side from the other top-ranked Chinese team, meaning they’d avoid facing them until the finals. The Koreans, having sensed the plot from Wang and Yu, attempted to lose themselves in response.

Wait.  What?  They were trying to lose the match on purpose to avoid playing a team that they didn’t want to play until the finals?  That seems like a totally rational thing to do.  The goal at the Olympics is to win a medal.  The goal is not to get as high a seed as possible coming out of pool play.  If you don’t want situations like this then, don’t play this format.  Sure, they were throwing the game, but they weren’t involved in a betting scandal or anything.  They were losing on purpose to give themselves, in their minds, the largest probability to win a medal.  Isn’t that the rational thing to do if your goal is to win a medal? And the goal is to win medals, right?  Right?

Now, you could look at this as China colluding to try to maximize the number of medals they can win.  By avoiding each other in the elimination rounds until the finals, they avoid eliminating each other from medal contention.  This might be frowned upon, but again, isn’t it the rational thing for the Chinese team to do?   I guess “always trying your hardest” is more of an Olympic ideal than “doing the rational thing”.

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Olympic Medals: Top ten teams – August 1, 2012

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