Category Archives: Season Ranking

Relief Staff Ranking

Let’s discuss relief pitching today.   Relief pitching is very important because starters seldom go a complete game and usually are pulled after 6 or 7 innings leaving 2 or 3 innings for the relief pitching to deal with.  We need a way to measure value of a relief squad because they usually make up 1/3 of the game for PITCH.  If there is a bad pitcher starting the relief staff could end up pitching most of the game.

Since WAA has additive properties we can simply add up the relievers’ WAA to compute the WAA for the entire relief staff.  This is exactly what is done for lineups and any group of players.  Here are the top 5 relief staffs in MLB as of yesterday.

TeamID WAA IP WinPct
CLE 5.65 99.8 0.755
CHA 5.17 102.5 0.727
NYA 4.33 104.3 0.687
BOS 4.16 106 0.677
CHN 3.46 135.3 0.615

Both Cubs and White Sox are in the top 5.  The Cubs have a very high number of relief innings pitched, almost 4 per game.  That could be a choice Joe Maddon makes to keep his relievers sharp — and according to the above table they are.

The WinPrct column is calculating using this property of WAA.  The formula is simply:

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

For pitching number of games played is innings pitched divided by 9.  An average MLB game is not always 9 innings but that’s the constant used for over a century to compute ERA and it’s still good enough.

But what does WinPct represent?  It doesn’t mean that if CLE is ahead and they pull their starter they have a 0.755 chance of winning that game.   That percentage places  WAA in context and shows how far above average much like the WinPct listed with a team’s W-L record.   CLE, at 18-16 has a slightly above average team and the above table shows their relievers are carrying a lot of weight.

Edit: The WAA value measure  exactly describes a relief staff’s contribution to their team’s win loss record.  At 18-16 CLE has a real WAA=+2.   Their relief staff has a WAA=+5.65. If they had an average relief staff that 5.65 would be taken off the books and their real WAA so far this season would be -3 or -4 and CLE would be a sub 0.500 team.  Ranking for any player or any group of players is done through the weighted WAA measure — not WinPct.  It is possible for a relief staff to have a higher WinPct than a team with a higher WAA.    WinPct provides context to WAA,, nothing more.  We know exactly how much CLE relievers are carrying that team and it’s 5.65 wins.  The rest of CLE adds to WAA=-3.65.  

Like win loss records, top batting averages, the long baseball season will average things out and the above high WinPct will converge closer to 0.500.   For example, here are the top three relief teams last season:

TeamID WAA IP WinPct
LAN 11.28 545.2 0.593
BAL 10.46 597.3 0.579
WAS 9.51 487.9 0.588

Top MLB teams hover around 0.600 in their win loss records too. Now let’s look at how the Cubs relief staff breaks down at a player level. Often this is Too Much Information (TMI) but right now it illustrates how we measure relief staff.

WAA Name_TeamID Pos IP
1.49 Mike_Montgomery_CHN PITCH 23.0
1.41 Wade_Davis_CHN PITCH 15.3
0.99 Carl_Edwards_CHN PITCH 15.3
0.42 Koji_Uehara_CHN PITCH 13.7
0.21 Hector_Rondon_CHN PITCH 13.7
0.19 Dylan_Floro_CHN PITCH 4.3
0.08 Miguel_Montero_CHN PITCH 1.0
0.04 Felix_Pena_CHN PITCH 5.0
0.04 Brian_Duensing_CHN PITCH 14.0
0.04 Pedro_Strop_CHN PITCH 11.7
-0.25 Rob_Zastryzny_CHN PITCH 4.0
-1.20 Justin_Grimm_CHN PITCH 14.3
TOTAL WAA=3.46 IP=135.3 WinPct=0.615

The above table shows the value of all relievers who pitched for CHN this season.  As the season progresses the active relief roster changes and the above number is not valid for use as a value indicator for a particular game.  The above is the accumulated value for a season.  When ranking a team on a seasonal basis we want all the numbers.  When evaluating on a daily or game basis, we want the current value.  Lineups and relief staff value can be quite different from seasonal value especially after trade deadline when playoff wannabes stock up on good players and discard their poor performers.  More on that later .  Until then….

Giant Mets Analysis

The Ouija board says!

DATE 10_05 8:05_PM Oct_5_14:02 SFN NYN
LINEAWAY SFN [ 0.512 ] < 0.535 >
STARTAWAY 7.37 Madison_Bumgarner_SFN
LINEHOME NYN [ 0.535 ] < 0.488 >
STARTHOME 6.76 Noah_Syndergaard_NYN

I converted the lines into probabilities they represent which is what they really are.  The number in [] is the starting bid, in <> is current as of this snapshot at 2pm.

It’s moving slightly towards SFN.  Even though both starters are about equal SFN has home field advantage as an away team.  Home field advantage is around 0.540 for the  past 50 or so years.

BAT|PITCH|Rs|Ra|W|L|UR|LR|TeamID
-48.2 94.9 671 617 87 75 12.0 -5.6 NYN
-13.2 71.8 715 631 87 75 16.0 3.4 SFN

Both teams have won through pitching.  SFN’s bat is a bit better.  Both teams have  extraordinary URs, unearned runs above average at +12 and +16.

Bumgarner is a vet and Syndergaard is a relative newbie so the Ouija board tilts towards SFN.  Not sure if it matters who the Cubs play because they had better be able to take either team if they’re a WS champion caliber team.

I don’t have lineups yet but here’s a snapshot of both team’s last game on 10/2.

DATE 10022016
-0.06 Curtis_Granderson_NYN CF
-0.36 Asdrubal_Cabrera_NYN SS
3.74 Jay_Bruce_TOT RF
0.53 Lucas_Duda_NYN 1B
-1.87 Kelly_Johnson_TOT 2B
0.04 Michael_Conforto_NYN LF
-0.25 Eric_Campbell_NYN 3B
-1.91 Kevin_Plawecki_NYN C
-0.04 Gabriel_Ynoa_NYN P
TOTAL -0.19

DATE 10022016
-2.69 Denard_Span_SFN CF
0.82 Brandon_Belt_SFN 1B
2.33 Buster_Posey_SFN C
1.68 Hunter_Pence_SFN RF
1.07 Brandon_Crawford_SFN SS
0.13 Angel_Pagan_SFN LF
1.26 Joe_Panik_SFN 2B
-0.00 Conor_Gillaspie_SFN 3B
-0.65 Matt_Moore_TOT P
TOTAL 3.95

Both lineups better than their status lines but SFN is still a little better hitting wise.
Also note Curtis Granderson, at -0.06, has made a tremendous comeback from a poor start.  Here’s his trajectory since start of August.

08052016 -2.42 Curtis_Granderson_NYN RF
08112016 -2.21 Curtis_Granderson_NYN RF
08152016 -2.50 Curtis_Granderson_NYN RF
08192016 -2.65 Curtis_Granderson_NYN RF
08232016 -2.44 Curtis_Granderson_NYN RF
08282016 -2.62 Curtis_Granderson_NYN RF-CF
09032016 -2.10 Curtis_Granderson_NYN RF-CF
09072016 -1.76 Curtis_Granderson_NYN RF-CF
09112016 -1.53 Curtis_Granderson_NYN RF-CF
09152016 -1.28 Curtis_Granderson_NYN RF-CF
09192016 -1.45 Curtis_Granderson_NYN RF-CF
09252016 -1.07 Curtis_Granderson_NYN RF-CF
09302016 -0.40 Curtis_Granderson_NYN RF-CF
2016-final 0.04 Curtis_Granderson_NYN RF-CF

He’s one the main reasons the Mets are playing this game today.  The Cubs were looking at him a couple years ago IIRC.

We’ll see.

I’ll do Cubbies Friday.

2014 RISP Leaders

Top Ten 2014 RISP Leaders

RISP calculations are described here.  Now that we have event data for 2014 we can calculate RISP runs above average for each player.  RIght now the RISP script does not pull in player position.  Here are the 2013 RISP Leaders for comparison.

Rank RAA RISP PA TOTAL PA Name_Teamid
1 34.4 247 660 Adrian_Gonzalez_LAN
2 33.2 189 622 Jose_Abreu_CHA
3 27.1 215 685 Miguel_Cabrera_DET
4 27.0 171 665 Robinson_Cano_SEA
5 26.2 187 705 Mike_Trout_ANA
6 24.0 208 676 Michael_Brantley_CLE
7 23.5 161 460 Russell_Martin_PIT
8 23.4 215 641 Victor_Martinez_DET
9 23.3 197 637 Giancarlo_Stanton_MIA
10 23.2 212 673 Jose_Bautista_TOR

Bottom Ten 2014 RISP Leaders

Rank RAA RISP PA TOTAL PA Name_Teamid
1 -17.3 179 650 Dee_Gordon_LAN
2 -17.2 181 594 Xander_Bogaerts_BOS
3 -15.8 181 668 Denard_Span_WAS
4 -15.6 160 574 Gerardo_Parra_MIL
5 -15.5 173 583 Leonys_Martin_TEX
6 -15.2 149 582 B.J._Upton_ATL
7 -14.6 134 492 Brock_Holt_BOS
8 -14.6 154 626 Ben_Revere_PHI
9 -14.2 92 248 Jose_Molina_TBA
10 -13.5 176 710 Nick_Markakis_BAL

Darwin Barney

Summary: This will be the last WAR vs WAA post for awhile and I only include it because Darwin Barney’s WAR rating inspired me to devise the WAA rating.  If things slow down later in the year perhaps we can examine historical ratings from season best to career best.  I have done that analysis and in general, over the long term for great careers, WAR and WAA ratings are extremely close.  Where WAR and WAA differ the most is on a season basis where statistical anomalies can pollute the data.

I started this data model last spring when someone pointed out that Darwin Barney had a WAR=4.8 which was the highest on the Chicago Cubs that year. I didn’t think Barney was anywhere near the best Cub that year let alone being considered one of the best non-pitcher in the league.

Like the previous post we’ll show the top either WAR overrated or WAA underrated differences for 2012.  Darwin Barney tops the list.  Below are the rank and pertinent stats table of the top 5 in that category.  Then we’ll list top 5 Cubs for both systems.

WAA vs WAR 2012 batters

Diff WAR WAA Name_tm
462 27 489 Darwin_Barney_CHN
419 22 441 Denard_Span_MIN
387 34 421 Erick_Aybar_ANA
262 12 274 Michael_Bourn_ATL
238 19 257 Martin_Prado_ATL
WAR WAA BA OBP PA RBI R Name_Tm BAT/PITCH
4.8 -2.0 0.254 0.299 588 44 73 Darwin_Barney_CHN BAT
5.1 -1.4 0.283 0.342 568 41 71 Denard_Span_MIN BAT
4.3 -1.3 0.290 0.324 554 45 67 Erick_Aybar_ANA BAT
6.0 -0.4 0.274 0.348 703 57 96 Michael_Bourn_ATL BAT
5.5 -0.3 0.301 0.359 690 70 81 Martin_Prado_ATL BAT

2012 Chicago Cubs WAA and WAR rankings

The following tables will show the top three players on the 2012 Chicago Cubs according to each system.

Ranked according to WAA

Alfonso Soriano should have been listed as the Cubs best player for 2012.  Ryan Dempster would have rated higher but he got traded mid season so isn’t listed here.

WAR WAA BA/IP OBP/ERA PA/G RBI/W R/L Name_Tm BAT/PITCH
2.0 3.9 0.262 0.322 615 108 68 Alfonso_Soriano_CHN BAT
0.7 1.5 69.3 3.25 77 7 7 James_Russell_CHN PITCH
1.8 1.3 174.7 3.81 28 9 9 Jeff_Samardzija_CHN PITCH

Ranked according to WAR

And here is Darwin Barney amongst the other top rated 2012 Cubs according to WAR.  Starlin Castro also seems to have garnered a rather large WAR rating.  Was it the 0.323 OBP that propelled him or his league leading 27 errors?

WAR WAA BA/IP OBP/ERA PA/G RBI/W R/L Name_Tm BAT/PITCH
4.8 -2.0 0.254 0.299 588 44 73 Darwin_Barney_CHN BAT
3.6 -0.3 0.283 0.323 691 78 78 Starlin_Castro_CHN BAT
2.3 0.8 0.285 0.342 368 48 44 Anthony_Rizzo_CHN BAT