MLB Relief staff ranking

Note:  This site experienced a 72 hour outage recently.  Currently there is no fail over site when the Internet goes out.

Below are the top 5 relief staffs in MLB.  This was computed by adding up all relievers listed for  each team and adding their WAAs and innings pitched.  WAAs are additive amongst any set of players.  The sum of all WAAs for an entire team must and always does (because the formulae have proofs) add up to the W-L delta for a team according to the Pythagorean Expectation derived formula that converts runs into winning percentage.

In this case we chose as our set of players all relievers for each team.

TeamID WAA IP
SDN 8.7 339.6
SEA 7.3 351.4
OAK 7.1 375.9
SFN 6.6 352.3
WAS 6.0 307.9

Although San Diego doesn’t have a very good team this year, their relief squad stands out as best in MLB.  First let’s do a Winning Percentage calculation for SDN as shown in the previous two posts:

Win% =  0.5*WAA/(number of games played) + 0.5

number of games played = 339.6/9 = 37.7

Winning % = 0.5*(8.7)/(37.7) + 0.5 = 0.615

Here are all relievers registered to having played for SDN this season:

Rank WAA IP ERA G W L Name_Tm Pos
1 2.2 33.0 1.09 33 1 0 Huston_Street_SDN PITCH
2 2.0 43.0 1.88 42 4 2 Joaquin_Benoit_SDN PITCH
3 2.0 45.0 2.00 49 3 3 Dale_Thayer_SDN PITCH
4 1.5 40.0 2.25 48 1 0 Alex_Torres_SDN PITCH
5 1.0 32.0 2.53 34 1 2 Kevin_Quackenbush_SDN PITCH
6 0.9 21.0 2.14 17 0 0 Blaine_Boyer_SDN PITCH
7 0.7 10.3 0.87 3 0 1 Jason_Lane_SDN PITCH
8 0.2 7.3 2.45 8 0 0 Troy_Patton_SDN PITCH
9 -0.0 2.0 4.50 1 0 0 Hector_Ambriz_SDN PITCH
10 -0.2 43.0 3.98 28 2 2 Tim_Stauffer_SDN PITCH
11 -0.7 32.7 4.68 36 0 2 Nick_Vincent_SDN PITCH
12 -0.7 30.3 4.75 16 1 0 Donn_Roach_SDN PITCH

Perhaps the biggest advantage the WAA weighting has over any other measure in baseball Sabermetrics is its ability to accurately compare not only individual players but sets of players; in this case each team’s relief staff.

In the last few posts we showed how to calculate winning % for a starting pitcher, an entire relief staff backing him up, and a batting lineup. We can take a harmonic mean of 2/3 starting pitcher to 1/3 relief staff winning percentages to get a pitching component percentage. Then we can take a harmonic mean of pitching and batting (lineup) components to get an overall winning percentage for a particular day. Compare this with the winning percentage derived for an opposing team and it’s possible to estimate a winning probability for each team where:

P(home team) + P(away team) = 1

But this is fodder for a future post.   The WAA  weighting value derived from this data model  makes it possible to make these kinds of calculations.