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Using Team Context To Better Understand The Trade Market: The San Francisco Giants

NOTE: This was written before the Giants acquired Ryan Garko. Still, the main point of this article still holds, and it is largely theoretical in nature.  While considering the already hypothetical situations, ignore the acquisition of Garko.  

Here at Beyond The Boxscore, we've found some interesting ways to evaluate trades, such as Sky's Trade Value Calculator.  In many cases, context-neutral analyses like these accurately represent, at least in terms of wins, the value being returned to the team.  However, some teams are simply too abnormal for the value calculator to truly show us how many wins a team can expect to add from adding a certain asset.  

The best example of this kind of team in the trade market of 2009 is the San Francisco Giants. Most teams show at least a little bit of balance between their run production and run prevention.  The Giants show no balance whatsoever.  Looking at pRAA (based on tRA) and bRAA (based on wOBA) from StatCorner, and UZR from FanGraphs, we can see how the Giants have performed relative to average.   Here are the 2009 numbers for San Francisco as of July 26.

pRAA 43.5
bRAA -68.0
UZR 35.9
Total +11.4

Now, we can separate these into run prevention and run production pretty simply.  Run prevention is the combination of pitching and defense, so the Giants run prevention is +79.4 runs above average.  Obviously, run production is hitting, so the Giants run production is -68.0 runs above average.  Using the PythagenPat formula, we can find a projected winning percentage based on their run production and prevention. The PythagenPat formula

In this case, the Giants are expected to have scored 371 runs and have allowed 360, for an expected winning percentage of .514.  This percentage is no different than a team that is worth +5.7 runs both offensively and defensively, for a similar total of +11.4, despite the difference in runs scored and runs allowed.  However, the application of PythagenPat comes in when considering the impact of players added.  Let's take a look at 3 hypothetical trades for the Giants.  

The Giants were reportedly in the running for Roy Halladay, until recently.  The impact of replacing Barry Zito (currently -8.4 pRAA) with Roy Halladay (+30.0 pRAA) is 38 runs over 98 the 98 games so far, and so it would be 25 runs over the 64 games remaining, a 25 run boost to run prevention.  Let's take a look at some players that would be similar in their impact, despite doing it in different ways.

So far, Giants shortstops have combined to be 24 runs below average, thanks to a -2 UZR and -22 bRAA out of mostly Edgar Renteria, but with contributions as well from Kevin Frandsen and Juan Uribe.  Marco Scutaro so far has been worth +6 runs with the glove and +9 with the bat.  The magnitude of Scutaro's impact is equal to Halladay's - +25 runs over 64 games.  Scutaro provides a 5 run boost to run prevention and a 20 run boost to run production

While this example is completely unrealistic in the trade market, it will serve as a good exercise in the use of PythagenPat.  The Giants, at first base this year, have been ten runs above average, thanks to a +11 UZR and a -1 bRAA.  Albert Pujols, so far this year, has been worth 48 runs with with the bat and -2 with the glove.  That works out to a 36 run increase.  To make the calculations easier, let's call Pujols +49 with the bat and -1 with the glove.  That puts him at +38 over the Giants first basemen so far this year.  Albert's total contribution to run prevention, then, would be -10 runs over the rest of the season, and to run production it would be +35 runs.

Now, let's take a look at what we could expect in the 4 hypothetical continuations of the season:  no change, Giants replace Zito with Halladay, Giants replace Renteria et. al. with Scutaro and Giants replace Ishikawa et. al. with Pujols.

RSAA = Runs Scored Above Average, RAAA = Runs Allowed Above Average.  All per 64 games, which is how many games were remaining when these stats were pulled.

Scenario RSAA RAAA RS RA RS-RA WPCT Wins Added
No Change -44.41 51.85 242 234 8 0.514 0
+Halladay -44.41 76.67 242 209 33 0.562 3.07
+Scutaro -24.82 51.85 262 229 33 0.559 2.88
+Pujols -11.76 43.85 275 242 33 0.557 2.75


What we can learn from this exercise:

  • Run Prevention heavy teams like the Giants get slightly bigger impact out of their moves than neutral or Run Production heavy teams.  They only add 25 runs of value, which roughly translates to 2.5 wins.  However, in the run environment the Giants have created, 25 runs are worth between 2.75 and 3.1 wins.
  • If you are a run prevention heavy team, it helps to play to your strengths and boost your run prevention even more.  This is the way dynamic run and win estimators like PythagenPat work.  Because of the low run environment, the average wOBA in Giants games is lower, and as such, the hitters with low wOBAs already employed by the Giants have their value increased by the low run environment.  This wouldn't really apply to, for example, a team like the Brewers who have combined to be 61 runs below average with their pitchers.

Within the next couple of days, I will explore how these same types of deals impact teams on the other end of the run spectrum: above average teams with great run production.