2009 NL West WAR: Comprehensive Review

In October, I set out on a project to report the strength of each of the NL West teams position by position utilizing WAR.  

If you missed any of the prior articles, this is where you can catch up.

This project proved more difficult than I expected, given that Fangraphs does not have offensive or value splits by position and Baseball Reference does not carry WAR  in its tables.  Thus, I had to do all the work myself.  The overall method was highlighted in the first base and second base articles, but here is a quick rundown:  1) Extract positonal splits for every player in the division from Baseball Reference.  2) Apply component park factors from Statcorner.com for every offensive event.  3) Calculate wOBA from this data using Fangraphs' formula and convert it to Batting RAR (Runs Above Replacement).  4) Utilize UZR for the defensive component, and calculate positional and replacement RAR, just like Fangraphs.  5) Sort, organize and sum.

The prior installments can be found below.

C | 1B | 2B | 3B | SS | LF | CF | RF | P

For your convenience, I have tabulated the rankings of each team at every position below.  If you disagree or can't fathom why one ranking is as such (say...the top two teams in CF for example), I encourage you to check out that edition - it's all explained there.

  Position  AZ COL LAD SDP SFG
        C 1st  2nd  3rd  5th  4th
       1B 5th  2nd  3rd  1st  4th
       2B 1st  3rd  2nd  5th  4th
       3B 3rd  5th  2nd  4th  1st
       SS 3rd  1st  2nd  4th  5th
       LF 4th  3rd  2nd  5th  1st
       CF 5th  4th  2nd  1st  3rd
       RF 1st  4th  2nd  5th  3rd
P (batting) 1st  3rd  2nd  4th  5th
       SP 4th  1st  3rd  5th  2nd
       RP 4th  1st  2nd  5th  3rd
   P Staff 4th  1st  3rd  5th  2nd

The very first thing I notice in the above table, other than the Rockies pitchers kicking butt, is the Dodgers.  Every team had at least two positions that they were tops in the division, but not Los Angeles.  Actually, they took "consistently good" to a ridiculous level, having the 3rd best catching, first base, rotation and overall staff and placing second at every other position.

 This is the perfect place to evaluate where each team's strengths and weaknesses were in 2009.  That's essentially what I was going for with this series, but there are some very interesting developments after the jump.  If you're skeptical of WAR or intrigued by it, I urge you to continue reading.

WAR

Rankings alone don't tell the entire picture.  Naturally, the distances between 1st and 2nd and 4th and 5th aren't congruent.  To get a more illustrative and mathematical tabulation, the following table details the Wins Above Replacement (WAR) for each position/team.

  Position   AZ  COL  LAD  SDP  SFG
        C  4.09  3.28  2.46  1.56  1.72
       1B -0.90  4.65  1.92  6.45  1.81
       2B  5.97  0.80  3.19 -1.04 -0.26
       3B  3.70  1.56  4.95  3.19  6.04
       SS  2.49  6.85  3.64  1.78  0.74
       LF  2.10  3.94  4.57  0.24  4.66
       CF -1.18  2.14  4.97  5.37  2.76
       RF  4.85  1.68  2.56 -0.39  1.76
 P (hitting) -3.39 -3.65 -3.56 -3.75 -3.92
    P staff  17.9  23.6  19.4   5.9  21.7
Total WAR 35.63 44.85  44.1 19.31 37.01

Note:  the Total WAR summed in bold at the bottom.  I did not include contributions from pinch-hitting, baserunning or the designated hitter.  Though I did not calculate these, I believe the Rockies were above average at all three.  

 

Conclusion

Now we are getting somewhere.  A "replacement level team" would win about 48 games, by definition.  That's why Wins Above Replacement actually means something to us.  If you add 48 wins to the team's total WAR, you get an estimated number of wins the team theoretically should win.  

A very common yet far more simple method of estimation is Pythagorean W/L.  Below, I have compared the estimated number of wins from my calculated team WAR, compared to Pythagorean wins.

Wins  AZ COL LAD SDP SFG
PF's Wins 83.6 92.9 92.1 67.3 85.0
Pyth Wins  75   90  99  67  86
Actual Wins  70   92  95  75  88

See? The table in the WAR section may seem to be filled with random non-intuitive numbers.  But it means something.  My system seemed to overvalue Arizona and undervalue Los Angeles, but the other three teams were either in line with the Pythagorean expectation or actual win total.

There is bound to be random noise in stats when literally thousands of calculations are involved.  Just like Pyth W/L, the WAR fits sometimes and misses other times, but the small errors on both sides even each other out:

NL West Division Total Wins % off
Actual Wins       420     -
PF's Total Wins      420.9   0.2
Pythagorean Wins       417   0.7

So there you have it.  Take it or leave it, but either way, thanks for reading.

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