Tangotiger Blog

Thursday, December 07, 2023

Bill James Walkoff, Part 2: Live-blogging

My thoughts on the rest of the book is here in part 1.

Below I will live-blog Bill's chapter on Win Shares (and WAR).  I have not read it, so as I read it, I will update this thread.  I only have an hour, so I may have to pick this up tomorrow, we'll see.  For those new to the topic, Bill has generally been as tough on WAR as I have been on Win Shares.  Obviously, both of us feel we are on firm ground.  

To prepare yourself, you may want to read this back-and-forth we had (though because Bill did not give explicit permission to post his words, I only showed my words).

Anyway, time to open the book.  Jump the line when ready...

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Bill James Walkoff, Part 1: Cold-blogging

Bill released his latest (and last) handbook, called Bill James Handbook Walk-off Edition.

I'm going to do a Part 2, Live-blogging Bill's Win Shares / WAR article. I have not yet read it, so I will reserve commentary on that.

In this one, I have read the rest of his book, and I'll comment on a few things that jumped out on me.

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Monday, November 30, 2020

How Earl Wilson was Bob Gibson for one season

It’s 21:34. I’m doing this as I type, nothing was pre-written, pre-researched or pre-anything.

***

In 1966, Earl Wilson had a 3.07 ERA, compared to the nominal AL average of 3.44. He allowed (earned) runs at 89% of the AL average. That’s a good number. Gibson had a 2.44 ERA in a league average of 3.61 (or a great 68% of NL average). According to WAR at Baseball Reference, Earl Wilson had the highest WAR among all AL pitchers (5.9), and sixth in MLB just behind Bob Gibson at 6.1.

Something looks terribly, terribly wrong… right?

Let’s work through Bob Gibson to understand how he gets to 6.1 WAR, and fifth place among all pitchers.

1. Runs Allowed

The first step is that we discard ERA in favor of RA/9. Gibson has 14 unearned runs, so his RA/9 is 2.89, compared to the NL average of 4.10. That’s 70% of the NL average. So, he loses a tiny bit of lustre here, but still great.

2. Opponent Adjustment

He faced slightly tougher opponents, a collection of teams that scored a park-adjusted 4.19 runs per game, compared to the league average of 4.11. So he gains a tiny bit of lustre back.

3. Fielding Adjustment

The Cardinals had the best fielding team in the NL, an impact of 0.27 runs per game. So Gibson loses a bit of lustre now (part of his low runs allowed was because of the fielding team). So the opponents, who would normally score 4.19 runs per game, would therefore against a Cardinals defense score 3.92 runs.

4. SP/RP Adjustment

Gibson is a SP and it’s tougher to pitch as a starter, which is an impact of 0.07 runs. So, now his opponents, with Cardinals defense against a SP, would score 3.99 runs per game.

5. Park Adjustment

Finally, Gibson played at overall park-neutral sites, so there’s almost no impact there. It ends up that his opponents, against his fielders, against a SP, at Gibson’s park, would score 3.96 runs per game.

To recap: 2.89 runs allowed per game by Gibson against a context of 3.96 runs (or 73%). Still great, but a smidge less than the initial check using only ERA with zero adjustments.

3.96 runs in 280.1 IP is 123 runs allowed. That’s the “average” baseline. Gibson allowed 90 runs. That difference is 33 runs. That’s his RAA.

(Note: Baseball Reference shows 30. Not sure why, but we’re not going to let 3 runs stop us for this illustration.)

6. Runs to wins

To get it on a more familiar scale, we can convert to wins. We don’t HAVE to. It just makes it easier. These are not “actual” wins, but more like runs-derived wins. The runs-to-win conversion looks to be close to 8.5. So 30 RAA translates to 3.6 WAA (wins above average).

7. Bring in replacement

To account for playing time, we don’t want to compare to “average” but to the bubble player. There is value to being able to eat up innings. There are 13.67 wins assigned to pitchers per team. In a 10 team NL, that’s 136.7 WAR. With 14551 IP, that means we give out 0.0094 WAR per IP (or if you like 0.0845 WAR per 9IP).

Since Gibson has 280.1 IP, he gets 2.6 WAR for his playing time.

8. Now you get WAR

That’s 3.6 + 2.6 = 6.2 WAR. (Baseball Reference shows 6.1. Let’s chalk the 0.1 difference to rounding error.)

As you can see, a good amount of effort to handle all the little things.

***

Now that we’ve figured it out for Bob Gibson, let’s see how Earl Wilson could have possibly gotten so close to Gibson.

1. Runs Allowed

Wilson had only 4 unearned runs. That’s a 3.20 RA/9 compared to the league average of 3.90 (or 82% of AL average). As you can see, that’s a big step forward, 10 fewer unearned runs than Gibson.

2. Opponent Adjustment

He faced slightly tougher opponents, a collection of teams that scored a park-adjusted 3.99 runs per game, compared to the league average of 3.90. So he gains a bit here too.

3. Fielding Adjustment

Both his teams were above average MLB(*) in fielding, and so he benefited by 0.11 runs here. So the opponents, who would normally score 3.99 runs per game, would therefore against his defense score 3.88 runs. (This context is pretty close to Gibson at this point.)

(*) This will be important in the addendum.

4. SP/RP Adjustment

Wilson is a mostly SP, which is an impact of 0.06 runs. So, now his opponents, with his defense against a SP, would score 3.94 runs per game. (This context is still pretty close to Gibson at this point.)

5. Park Adjustment

Finally, Wilson played at heavy hitters park at Fenway with league-average at Tiger Stadium. Overall, the effect is 1.03X. So the 3.94 runs we have goes up to 4.06. (Baseball Reference shows 4.08, so some rounding errors on my side.) It ends up that his opponents, against his fielders, against a SP, at Wilson’s parks, would score 4.06 runs per game.

To recap: 3.20 runs allowed per game by Wilson against a context of 4.06 runs (or 79%). So this is pretty good, and a big step up from the initial check using only ERA with zero adjustments.

4.06 runs in 264 IP is 119 runs allowed. That’s the “average” baseline. Wilson allowed 94 runs. That difference is 25 runs. That’s his RAA. That’s 8 runs worse than Gibson.

(Note: Baseball Reference shows 30. Not sure why, so now I am concerned that I can’t get closer. My calculations shows an 8 run gap against Gibson, 33 to 25, but Reference shows a RAA of 30 to 30. Why? If I spent more time on this, I’m going to assume this is a league adjustment. I can probably figure this out if I took another 30 minutes, but it’s 22:08 as I write this, and I’m pretty spent. So, let’s assume I’m missing the AL/NL League Adjustment step.)

6. Runs to wins

With a similar runs-to-win conversion, Wilson’s RAA translates to 3.7 WAA.

7. Bring in replacement

Since Wilson has 264 IP, he gets 2.5 WAR for his playing time.

8. Now you get WAR

That’s 3.7 + 2.5 = 6.2 WAR. (Baseball Reference shows 5.9. Let’s chalk the 0.2 difference to rounding error.)

So there you have it. A 68% ERA compared to the NL, and a 89% ERA compared to the AL comes out to a similar WAR.

***

Addendum:

(*) This is where the issue comes with the fielding adjustment. For most of MLB history, including 1966, the two leagues were in fact separate. So the “league average” truly meant AL and NL were two distinct leagues. But the fielding average is not set to 0 in each of AL and NL. This (I think) probably leads to a construction issue. Again, not sure, but maybe. It’s 22:14. That’s all I’ve got in me tonight.

Wednesday, June 17, 2020

When Heroes Collide

This is a good article by Patriot on the issues Bill James had with Pete Palmer and Linear Weights, back in 1985.  

Linear Weights is the by-product of the foundation of how I tackle sabermetrics: the run expectancy matrix.  The RE matrix was first described to me in The Hidden Game of Baseball, by Pete Palmer and John Thorn.  It was an instant classic, and time continues to be kind to that marvel of a book.  The RE matrix was an epiphany to my young mind, and set me on the course of my life.

Saturday, April 04, 2020

Summaries and Excerpts from ALL Bill James Abstracts

Rich Lederer ran a wonderful series 15 years ago, recapping all the Bill James Abstracts. I’d highly recommend all those out there to check them all out:

1977 Baseball Abstract
1978 Baseball Abstract
1979 Baseball Abstract
1980 Baseball Abstract
1981 Baseball Abstract
1982 Baseball Abstract
1983 Baseball Abstract
1984 Baseball Abstract
1985 Baseball Abstract
1986 Baseball Abstract
1987 Baseball Abstract
1988 Baseball Abstract

Thursday, February 20, 2020

WAR backgrounds and foregrounds

At the end of an article comparing MVP votes to WAR and Win Shares rankings, Bill James gives us his view on some shortcomings of WAR.  Bill invited response, so here's mine!  (Bonus article for people who won't see me and Bill on stage at Sloan.)

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Saturday, April 15, 2017

Exit Velocity and Bill James

?Bill made a terse comment on Twitter, as that is its raison d'etre, but his comment belies a more specific valid point that Bill has previously made.  Below is a "hey bill" I had with him back in December.  Everything in quotes is Bill, the rest is me.

...the speed of the ball off the bat is presumed to be a consequence of three things:  the speed with which the ball was thrown, the bat speed, and the degree to which the bat was centered on the ball.   Hopefully after we have a few years of data people will figure out that exit velocity doesn't correlate very highly with the quality of offensive production, and then we can stop speaking about bat speed as if it was actually important.  

With regards to this, "(a) the speed with which the ball was thrown, (b) the bat speed, and (c) the degree to which the bat was centered on the ball": that's actually a great summary. ...    

For (a), Alan Nathan has shown that 18% of the pitcher's speed is added to the exit velocity.  So, 100mph incoming and 90mph outgoing means that 18 of that 90 is due to the speed of the pitch, and the other 72 is the batter himself.  Meaning that had he done the exact same thing (with b and c above), it would have exited at 72. ...  

For (c), Alan has also formulated in terms of trading speed to get loft, something that anyone hitting knows, but not necessarily can express precisely.  I show an example from the HR Derby with Stanton, where 2 of his 3 highest exit velocity were among his shortest, because he hit it too dead-on (no loft), along with Alan's technical explanation. 

http://tangotiger.com/index.php/site/comments/statcast-lab-are-players-getting-better-launch-angles-this-year

In terms of comparing exit speed (and a series of other metrics) to future production, the excellent saberist Craig Edwards at Fangraphs has a study here:

  http://www.fangraphs.com/blogs/exit-velocity-part-iii-applying-meaning-to-the-data/  

And he shows that the first-half components of ISO, Exit Speed, wOBA, SLG, OBP each forecast the second-half wOBA around the same.  Fascinatingly, the one metric that does best is BB/PA.  Which really means that a high BB is an indicator or proxy for alot of other things, which of course you can say that about any of them, including exit speed.  The real test is that if you already have everything else that I mentioned, how much more (if any) does Exit Speed add.

Well. . .my concern would be the loss in bat control.   Bat speed is competing with bat control.  We've reached the point NOW where I'm ready to say that we've gone too far; we'd be better off (in building a team) to focus on guys who make solid contact, rather than those who hit the ball hard when they do hit it.    I'm not CERTAIN whether this is true; it is just kind of what I think.    Not sure how exactly the research relates to this point.   

"rather than those who hit the ball hard when they do hit it": I agree with you.  It's the Nuke Laloosh issue, but for batters.  Carlos Peguero is probably a good example.  Basically, by not making contact, you get a "pass" in terms of it not counting against you in exit speed.  So, there is an extra layer needed there to handle the swing-and-miss.  Another example would be Ortiz v Votto: for all we know, if Votto tried to swing harder, he could hit as hard as Ortiz, but may strike out even more than [he already does].  So, you can't blindly go on exit speed.  Therefore, I think we're on the same page here.

Friday, December 02, 2016

Do we want to account for all the wins when analyzing the contribution of players?

?One of the things that Bill James hammers home (though since he writes so well,  it doesn't feel like he's hammering, more like a light push) is that since we know certain factual things, like how many runs scored, or how many games were won,  we have a natural goal.  If you want to know how many runs a player created, and you know the team scored 700 runs, you can't have the individual runs of the players on a team to sum up to say 950 runs or 473 runs.  If you do that, it probably points to a problem with your calculation. 

If you do runs at the seasonal level, then why not runs at the game level?  Or at the inning level.  That in fact is what RE24 is: it makes sure all the runs are accounted for at the inning level (indeed at the play level).  It is actually the closest bridge we have between sabermetrics and the mainstream.

But what about wins?  Matt Cain gives up no runs in 9 innings, while in the same game Cliff Lee gives up no runs in TEN innings, a game that the Phillies lose.  If the intent is to use wins and losses as a natural end point to make sure things add up, a checksum so to speak, then we want things to add up at the game level.  We shouldn't come up with something that says that the Phillies had 0.45 wins and 0.55 losses in a game they lost, and similarly, the Giants shouldn't add up to 0.55 wins and 0.45 losses in a game they won.

Well, maybe YOU do.  Maybe you actually don't care about who actually won and lost.  You just care about what the players did, and a margin of victory of 1 run and 10 runs should lead to different answers at the team level.  Suddenly, to YOU, it's not just about wins and losses but also about margin of victory.  And if you lose two games 1-0 and you win one game 10-0, you don't have a won-loss record of 1-2, but a won-loss record of 1.9-1.1.

So, this preamble is to setup the article that Bill James wrote here, and you can see his responses in the comments area, as well as mine.  For those who aren't members on his site, I'll copy/paste all of my comments below, plus a tiny snippet from Bill that directly relates to my comments:

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Friday, October 07, 2016

Bill James on the virtue of W/L records

?Bill makes his case.  This is my response:

First, I agree with the 2D representation using W/L.  I do it for baseball, and I've done it for hockey and I've dabbled it for basketball.  It's clear, it's concise, it keeps the system "in check" because of a verifiable point: the sum of the individuals should add up to the whole, the team W/L record.

***

I think it's fair enough to say that if Drew Hutchison was on the mound when the Jays scored 7 (!) runs per start in 2015, while his mate RA Dickey was on the mound 4 or 4.5 runs per start, that we shouldn't assume that they should both have received the Jays average of 5.6 (!) that year.  Maybe the conditions Hutch pitched in was more like 5.8 and Dickey was more like 5.4 or something.  

http://www.baseball-reference.com/teams/TOR/2015-pitching.shtml#players_starter_pitching::none

I'm totally on board with that possibility.  But, if we totally ignore run support, this is akin to totally ignoring the W/L record.  Information is information.  And Hutch got 2.5 to 3 more runs than Dickey.  Maybe it should be 2 to 2.5 because Hutch played in tougher conditions.  That should still knock out some .200 to .250 win% from Hutch's record.

Hutch had a 13-5 record with a 5.57 ERA, while Dickey was 11-11, 3.91.  If the net effect is to suggest that Dickey's 11-11, 3.91 can be represented as 11-11, and that Hutch's 13-5, 5.57 can be represented as 9-9, then I think that is still too much deference paid to W/L records, and still not enough to run support.

Therefore, I would like to see what kind of impact the use of W/L records are having.  I can accept "some" and "small", but I'd like to see its impact specifically on the Jays pitchers in 2015.

Wednesday, September 21, 2016

Bill James new Season Score

?Much like Game Score, Bill has a Season Score.  He tinkers with it, as he should.  After all, we learn new things, as new data comes in.  We have to revisit what we do.  And when he ran the latest version, he compared it to Cy Young voting, and it seemed to do pretty well.  Over the weekend I'll do a comparison of his new Season Score to his old Cy Young predictor.  I have no doubt that it'll do better, at least for the present era.  To that end, here is the Season Score for the 2016 pitchers:

• 293 Lester
• 279 Scherzer
• 276 Cueto
• 271 (RP) Jansen
• 245 Bumgarner
• 242 Hendricks

I should point out that had I run this yesterday, Scherzer would have been #1.  Much like my Cy Young predictor, there's a daily game of leap frog.  We have the same top 5 starting pitchers, just not in the same order.  Cueto v Hendricks is really the difference in the system.  That plus he has relievers.  I doubt Jansen finishes anything close to that, but has Britton in the mix:

• 304 (RP) Britton
• 295 Porcello
• 258 Happ
• 246 Sale
• 235 Kluber

He's got Happ in there, while I have him below Kluber.  But he does get Britton in the mix, possibly too high?  Britton and Jansen are pretty closely linked, so whatever changes you make to the system on one you do on the other.

Anyway, it's possible that the weight for relievers is too high, and this weekend I'll take a look at that.  What's interesting is that Season Score wasn't intended to be used for Cy Young predictions, but it certainly looks better than his old predictor.  And we'll know more at the end of the season how it all shakes out.

Bill also generously pointed out that one of the changes he made was a reflection of work that I've adopted with respect to the weighting of K and BB.  This is what sabermetrics is about, as everyone learns from everyone else, and we move forward together,

Saturday, August 01, 2015

Bill James on FIP

?Well-said:

What we're always trying to do is see through the illusions created by the numbers and see what is underneath and real and the fielding independent pitching numbers are quite helpful in that respect because it's a systemized, organized effort to filter out the things that are in the pitcher's record which aren't real. They're not related to his skill, it's just something that happened. That's tremendously helpful and tremendously significant.

If you go back to the oldest way of looking at a pitcher — 1975 — pitchers were evaluated by win-loss records. You'd have a pitcher sometimes who might have an ERA of 4.80, but they scored a ton of runs for him and he finished 17-9. People actually thought that he was a great pitcher because he had this ability to pitch well enough and win.

In the modern world, we know that it's nonsense and they just scored a lot of runs for him. Even the dumbest guy in baseball knows that win-loss records aren't that reliable because the offense doesn't even out for people. That's a circumstance-dependent record. ERA is a circumstance-dependent record. But even if you filter out the illusions in ERA and the illusions in run support, some guys are just lucky. Fielding independent pitching stats are an effort to filter that out and to the extent that they're successful, it's tremendously useful to do that.

Thursday, January 29, 2015

Bill James Site and my comments on his fielding system

Bill is churning out daily articles on his update to the fielding portion of Win Shares and Loss Shares.  There's an enormous amount to read, and I would even suggest that a trial subscription would be worth it, just to absorb it all.  You get quality and quantity. 

Anyway, I have tons of comments in his articles, but I figured, maybe I should capture them for my blog.  So, below you will find virtually the entirety of my comments, with just a couple of blurbs from Bill.

I've also tagged each comment, so if you want to reference it, you can.?

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Saturday, March 09, 2013

Bill James SABR keynote speech

?Highlights, and full audio.