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Winexp 6: Pitching 2005 & Comparing Win Shares

As part of this on-going series on win expectancy (search for AN diaries on winexp), I compiled in an automated way the win expectancy from play-by-play data for all of 2005. I think (as do many other folks, notably the folks at Hardball Times) win expectancy is fascinating and thought provoking, but there is a question of what it really indicates. This is too big a topic to tackle at once, so I'm going to just address a little bit here on pitchers.

Star-divide

In the automated way I computed win expectancy, pitchers got credit for every play that happened when they were pitching, except for one involving errors. Those got credited to the fielders and tracked separately. It's a weakness of the methodology that positive fielding plays (e.g. incredible dives) are not credited to fielders. Win Shares estimates that fielding is about 20% of the battle, so this is not a trivial amount to overlook. But keeping that in mind, one can still get interesting insights into pitching and hitting contributions. This is a limitation of the automation, not the win expectancy framework. Places like Lookout Landing have painstakingly assigned fielding credit to great plays by hand... there is no automated way to do this. The calculations were done using software dubbed Baby Winexp, the 'baby' in recognition that there are probably relevant bugs to work out but that she does pretty cool and interesting things already.

In any case, here are the win expectancy contributed (WXC) for the A's pitchers this year. It corresponds to the number of games over .500 that a player contributes. The team's WXC sums to within .5 of the number of games above .500. The number is probably only meaningful rounded to the nearest .5 (i.e. we probably shouldn't make too much of differences of less than .5), but for convenience, I'm reporting them to 3 decimal places.


Win Expectancy Contributed

Huston  Street       3.656
Rich    Harden       2.829
J       Duchscherer  1.59
Joe     Blanton      1.448
Kiko    Calero       0.514
Barry   Zito         0.397
Ron     Flores       0.064
Britt   Reames       0
Jai P.  Garcia      -0.003
Keiich  Yabu        -0.072
Tim     Harikkala   -0.203
Ricardo Rincon      -0.205
Jay     Witasick    -0.217
Seth    Etherton    -0.222
Dan     Haren       -0.246
Kirk    Saarloos    -0.397
Octavio Dotel       -0.657
Joe     Kennedy     -0.822
Juan    Cruz        -1.068
Ryan    Glynn       -1.307

Now for comparison's sake, let's look at Pitching Win Shares for A's pitchers. In principle, Win Shares tries to assign credit for actual team wins, so in principle if Winexp is meaningful, then there should be some relationship, right?


Pitching Win Shares

B   Zito         14.5
J   Blanton      14.4
D   Haren        14.3
H   Street       13.3
R   Harden       12.9
J   Duchschere   10.6
K   Saarloos     9.5
K   Calero       5.5
J   Kennedy      2.9
K   Yabu         2.8
R   Rincon       2
O   Dotel        1.9
J   Witasick     1.8
R   Flores       1.1
J   Garcia       0.2
T   Harikkala   -0.1
B   Reames      -0.5
R   Glynn       -0.9
J   Cruz        -1.9

I found this initially surprising. I don't know about you, but I see no relationship between the rankings of Winexp and Win Shares. In particular, Winexp declares Huston and Harden to be clear #1 and #2 followed by Duke and Blanton, followed at a distance by Zito and Calero and a bit further back Haren and Saarloos. This is NOT AT ALL what Win Shares says, which says Zito, Blanton, and Haren are basically equivalent and Street and Harden are close behind.

My surprise went away when I recalled that Win Shares is biased towards players who play more. In principle, because Zito pitched a ton more than Harden, he could catch up in Win Shares even if the rate of his performance was poor.

Hardball Times also provides an adjustment called Expected Win Shares which is the Win Shares an average player would get in the same playing time and one can compute "Win Shares Above Average" by subtracting WS - EWS. Here's the list. This is more comparable to Winexp, which is a relative measure to an "average" player in the sense that a Team that scores 0 WXC total would have a .500 record.  Let's see what we get...


Win Shares Above Average        Win Expectancy Contributed

H   Street       6.3            Huston  Street       3.656
R   Harden       5.9            Rich    Harden       2.829
J   Duchschere   4.6            J       Duchscherer  1.59
J   Blanton      3.4            Joe     Blanton      1.448
B   Zito         2.5            Kiko    Calero       0.514
D   Haren        2.3            Barry   Zito         0.397
K   Saarloos     1.5            Ron     Flores       0.064
K   Calero       1.5            Britt   Reames       0
J   Garcia       0.2            Jai P.  Garcia      -0.003
R   Flores       0.1            Keiich  Yabu        -0.072
B   Reames       0              Tim     Harikkala   -0.203
J   Kennedy     -0.1            Ricardo Rincon      -0.205
O   Dotel       -0.1            Jay     Witasick    -0.217
J   Witasick    -0.2            Seth    Etherton    -0.222
R   Rincon      -1              Dan     Haren       -0.246
T   Harikkala   -1              Kirk    Saarloos    -0.397
R   Glynn       -1              Octavio Dotel       -0.657
K   Yabu        -1.2            Joe     Kennedy     -0.822
J   Cruz        -2              Juan    Cruz        -1.068
                                Ryan    Glynn       -1.307

It's so beautiful when you get similar results from two completely different computations. Winexp and Win Shares use none of the same numbers to compute a pitcher's contribution, and yet they give nearly the same pitcher rankings. (Keep in mind that differences of less than one Win Share and .5 WXC are probably not meaningful.) At this point, I am convinced Win Expectancy Added measures something interesting.  

Now the next step would be to investigate the differences between Winexp and Win Shares Above Average rankings. Recall, we investigated two wacky results from winexp before: (1) Baby Winexp thought Crosby was a huge albatross on the team, despite him being our lucky charm; a close look at the statistics convinced me that Winexp was absolutely correct; (2) Baby Winexp spat on Miggy. Again, a close look at the stats in meaningful situations (runners on, game within three runs) convinced me she was right again.

The case study that might be worth doing would be on Haren (who impressed my eyeballs... it has annoyed me that Baby W thinks he's noticeably worse than the top three starters) and Calero. Baby W thinks Calero is worth a swing of one and half games more than Haren and WSAA tells us Haren was about a third of a win better.  WSAA and Winexp are so indifferent about the other players that there aren't any huge discrepancies as in the Crosby numbers. The only other notable exception is Dotel, and I think we remember why Baby W think he was a huge loser for us. I vote with Baby W on Dotel.

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Nice job.
Some things to think about:
  1. If WSAA and Baby Winexp give you the "same" answer, does Baby Winexp add anything do the discussion?  
  2. The main advantage (and it's a big one) that Baby Winexp has is that it uses PxP data.  But if the PxP method and the non-PxP method yield the same result, what does that tell us about context?  Could it be that context is not all that important?
  3. Is Baby Winexp data easier to compute or is WSAA easier?
  4. Can you modify the Baby Winexp parser to take Retrosheet data?  Because that would be flat-out awesome.
Copernicus felt the same way about the geocentric crew.

by salb918 on Oct 16, 2005 2:54 PM PDT reply actions   0 recs

good points
  1. They tell different stories, though they do correspond in important ways. For instance, in Offensive Win Shares, Bobby Crosby is right up there, but in Winexp, he's the pits. I believe in cases where there is discrepency, there is usually an interesting story to be uncovered. In the Crosby case, he really really sucked with runners on base, something Win Shares doesn't (and perhaps was not meant to) pick up.
  2. I believe there are some technical objections to using Win Shares on partial season results. I don't know enough about it to know how serious that is.
  3. WS is sort of convoluted to compute; there are spreadsheets that will help you do it. WS is definitely better understood in the sense that it's been subject to more scrutiny. WS computations vary depending on who's doing it. I went by Hardball Times simlpy because their data was the easiest to reach.   On the other hand Winexp is a pain in the rear to compute, unless you do it automatically. The lack of fielding data is an issue as I mentioned. They are both basically hard. I think winexp is much more transparent in what it's measuring and how (in principle) one calculates it.
  4. I started doing a Retrosheet parser, but I got tired. It's on my to do list... I've downloaded all their existing data...  I wanted to see how well WXC predicted the following year's results and compare that to WS, VORP, etc.  WXC is kind of cool as a credit-thing, but it would be even better if it did project something forward. I have no idea whether it does or not, as I only have pxp data for 04 and 05.

by Apricot on Oct 16, 2005 3:51 PM PDT up reply actions   0 recs

Good answers
4. BPro plays an interesting game to see if a player has a "demonstrated ability" to do something from season to season.  They plot the "odd year" data on one axis and "even year" data on the other axis.  If there is a correlation, one can infer that the ability is one that the player exhibits consistently.  (Example: plot home runs hit in even years versus home runs hit in odd years, using individual players as data points.  You will find a strong positive correlation, indicating that the ability to hit home runs is something that a player exhibits from year to year.)

You could do this with historical WinExp data to identify "clutch" players.  Run a regression of WinExp Data to OBP/ISO.  You will get an equation of the form WinExp (predicted) = a*OBP + b*ISO.  I imagine the r^2 will be something like 0.90.

Then, you can define Residual WinExp = WinExp (actual) - WinExp (predicted).  This RWE is the win expectancy contributed beyond what one would expect given only his OBP and ISO. (You may have to adjust for playing time, or you could just use VORP instead of OBP/ISO.)  You could then use the BPro year-to-year methodology to see if some players consistently show the ability to add WinExp above and beyond what their basic stats show.

Copernicus felt the same way about the geocentric crew.

by salb918 on Oct 16, 2005 4:11 PM PDT up reply actions   0 recs

hardcore
Nice ideas. Maybe you can do the analysis once the numbers are up. My gut tells me that winexp is much more volatile than most summed number, but it will be good to see if it's true in practice.

by Apricot on Oct 16, 2005 4:23 PM PDT up reply actions   0 recs

I'd be
happy to do the analysis once your numbers are up.  You know me, I'm your homey and I'll keep the numbers real (old school style).  Peace out.
Copernicus felt the same way about the geocentric crew.

by salb918 on Oct 16, 2005 5:16 PM PDT up reply actions   0 recs

WinExp
I have a tough time looking at this as a stat to use to value a player without first seeing what predicive value it has.  So much of a players WinExp value is determined by others, that I worry there won't be any year-to-year correlation.  

IMO it makes perfect sense that it matches up well with a stat like WSAA, the better players will tend to perform better in "clutch" situations.  

Good work on getting this together!  I'm always interested in seeing new ways to look at the game.

by chri5 on Oct 16, 2005 5:56 PM PDT reply actions   0 recs

excellent notes
I also wonder about the year-to-year. On the one hand, winexp is clearly a "looking backwards" stat, trying to assess credit in retrospect, and most sabermetrics focus on year-over-year predictibility.

For me, winexp is a complementary measure that does reward 'clutch' plays even though there probably are not 'clutch' hitters. So in some sense, it's not surprising if it fluctuates from year to year.
Sort of like BABIP... it fluctuates a lot (Voros argued it moved randomly in his original work), but it does measure something interesting.

by Apricot on Oct 16, 2005 8:27 PM PDT up reply actions   0 recs

Random thought
From the times I looked at the WinExp graphs during the season it seemed like there was always a massive uptick for the last out of the game.  That is, before the 27th out you maybe had a 75% chance of winning the game because you were in the lead, but it's the 27th out that kicks you up to 100%.

Street's role means that when he's playing he's almost always going to be throwing the last out of the game.  So is this WinExp ranking artificially biased in his favor?  I'd be interested to see the data with the "last out" values removed, just to see how much of an impact it has on Street's cumulative total.

Which is not to say that I think Street wasn't massively valuable to the club last year.  I just wonder if this statistic is overstating his value somewhat.

by LoveDemAs on Oct 16, 2005 6:36 PM PDT reply actions   0 recs

also excellent point
I have wondered this myself. However, I have come around to accepting the judgment of Winexp:

Consider bottom of the ninth, two outs, down one, the last out costs:

bases clear: -.040
runner on 1st: -.090
runners on 1st and 2nd: -.142
loaded: -.268
2nd & 3rd: -.270

So to get really dinged for the last play, you have to blow a pretty promising situation on the bases...

As far as Street goes, often he comes in at the top of the inning, so he comes in in the bottom of the ninth to protect a 1-run lead, he gets +0.127 if he gets out of it. Which is a reasonable amount (that's typically not the highest WXC of a game). The games where he racks up more are usually because he came in with runners on and the game very much in the balance. Winexp definitely respects the relief staff more than conventional wisdom, but most of the credit comes from getting out of tough situations before game's end, not finishing off the game.

I'm genuinely not sure whether Winexp overrates relief pitching. My initial reaction was Yes, but I realized I don't have any rational reason for thinking that.

by Apricot on Oct 16, 2005 8:55 PM PDT up reply actions   0 recs

why doesn't winexp like haren and saarloos?
the a's always seem to win when they're pitching...
A's v Giants "is kind of like the difference between going to see the Ramones and going to see the Bee Gees. A's fans will go see the Ramones." -BB 07/27/05

by xbhaskarx on Oct 16, 2005 10:25 PM PDT reply actions   0 recs

you'd need to look more carefully
at how they pitched with leads they were given, etc. Just like Winexp HATED Crosby even though the team won like gangbusters with him in the lineup... and I think Baby W had a legitimate complaint in the end.

My bet is that they blew a number of leads they were given, even if they ended up holding on for the W...  but I haven't done a careful analysis.

by Apricot on Oct 16, 2005 10:56 PM PDT up reply actions   0 recs

forgive my ignorance
But what do winexp try to measure?  simply how effective a player is, and not their overall worth?
is the winexp similar to VORP?
"If people don't know who he is, they'd better turn on the television and check him out."

by jacobo2u on Oct 17, 2005 3:23 PM PDT reply actions   0 recs

a good start is
http://www.hardballtimes.com/main/article/the-one-about-win-probability/

Also, you could look back through the first 5 diaries on this, starting with http://athleticsnation.com/story/2005/9/17/43715/2203

In a nutshell, it's the amount of probability of winning that a player's play contributes to the team.

It's vaguely like VORP, but people's contributions are weighted depending on the game situation.

p.s your ignorance is forgiven and your questions are welcome!

by Apricot on Oct 17, 2005 3:35 PM PDT up reply actions   0 recs

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