OBR – The Ballad of Rio T and Fat Elvis

Truth time in the trust tree: I just realized these trades happened this past week.  Why?  Because I am heavily disinterested in the baseball off-season until the Cardinals decide whether or not they’re signing Albert Pujols.  In any event, the team acquired two players of note, who likewise have histories with the team, albeit playing for rivals in the National League Central.

During the Astros-Cardinals rivalry of a few years ago, I personally never disliked Lance Berkman as much as say, Jeff Kent, Mike Lamb, Morgan HGH Endsberg and of course the diabolical Carlos Beltran.  Additionally in his favor, the nickname is “Fat Elvis”.  Writers seem to be going with “Big Puma” instead;  that’s bullshit.  You don’t kick a sobriquet like that to the curb anymore that you get rid of the best Halloween costume ever (NSFW language and implication in the clip below).

Vodpod videos no longer available.

But as for the other trade.  It’s solid to get something of value for the Blake Hawksworth experience, as Viva El Birdos pointed out (see below for articles), but I mean, come on…it’s Cubs douchehelmet Ryan Theriot!  Guhhhhh…

Enervating but not world-ending.  I’m less than happy that it displaces Brendan Ryan, who, while an atrocious hitter, provided some quality entertainment in the field.  It’s entirely possible he’s bipolar, making for dramatic television at times.  Better than Dan McLaughlin and Al Hrabowsky’s passive-aggression theater.

Whether it helps the team remains to be seen, but I find the El Birdos analysis sound on the deal, inasmuch as it probably doesn’t hurt the team.  I’m also finding it difficult to get behind this from an emotional standpoint, as I am a fragile teacup of anti-Cubs bitterness.  Because when the Cards were playing the Cubs, Theriot’s just the type who causes your eyes to narrow while muttering “fucking Theriot”.

Noted: it’s a very long way from Zambrano.

Viva El Birdos on the trades:

Also, should the Cubs by some act of Satan sign Albert Pujols, there’s a 99% chance I never watch baseball again.  That will be the day we know that our God has failed us.

I’m hedging here because I once stated I would burn my Rams’ jersey if they drafted Sam Bradford instead Ndamakong Suh.  That appears to be turning out well (although Bradford’s glass shoulder terrifies me) and I clearly did not make good on that threat (see teacup description earlier).  So pussywillow escape clauses it is!

As a coda to the Cardinals previous season, I have some stats.  DO NOT DISPUTE MY INFALLIBLE NUMBERS!  CAPITAL LETTERS!

Cardinals record against the four National League playoff teams:

  • Atlanta Braves: 6-2 (0.750)
  • Cincinatti Reds: 12-6 (0.667)
  • Philadelphia Phillies: 4-4 (0.500)
  • San Francisco Giants: 3-3 (0.500)
  • Combined: 25-15 (0.625)

Record against the five worst National League teams (yes, adding in the Astros skews this lower, as Houston was not among the worst four teams):

  • Pittsburgh Pirates: 9-6 (0.600)
  • Arizona Diamondbacks: 5-4 (0.556)
  • Washington Nationals: 3-3 (0.500)
  • Chicago Cubs: 6-9 (0.400)
  • Houston Astros: 5-10 (0.333)
  • Combined: 28-32 (0.467)

Even removing the Astros only raises the winning percentage to a game over 0.500–against the worst teams in the NL.  Yeesh.

I’ve played around with running even more statistics on the Roman orgy of statistics that is Major League baseball.  For hitters and pitchers, I did a relative comparison based on all qualifying players to make ESPN’s stat charts.  For any given stat, the comparison was the cumulative normal probability–basically all data points are plotted for a particular stat (e.g., batting average) as if the data sample conforms to a normal curve, with the probability of point A representing the chance a random point X would be below it.  This allows not only a ranking, but a magnitude comparison; that is, if a player’s stat is much better than his peers, it will show up as a higher number.  Since this is a probability, numbers will range between 0 and 1 for every stat.  Then the probabilities from every stat would be averaged to come up with a final score, which I multiplied by 100 to make easier to read.  Given higher probabilities mean that the player is above more players (and by a wider margin) in any given stat, a higher number is better.

I compared NL players only to NL players (likewise with the AL), since the comparison would be based on batters facing roughly the same pitching and fielding.  The result stat would also suffer from the assumption that  the data conforms to a normal distribution.  Moreover, it is a relative comparison, meaning a player would look better in any given year if his peers happened to perform poorly–that is, it’s not an absolute measure.  Finally, I also tried to take into account only those stats that the player had control over; for example, I didn’t use Wins for pitchers but rather adjusted them with sabermetric quality starts and tough losses.  In any event, the results are curious because of the number of Cardinals (highlighted in red) involved in some of the higher rankings.

Statistics Used to Compile Batting Score:


Statistics Used to Compile Pitching Score:

Adjusted Win/Loss Percentage: (Wins – Cheap Wins)/[(Wins – Cheap Wins) + (Losses – Tough Losses)]

TLOSS %: Tough Losses/Total Losses


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