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Expecting Wins

The Mets beat the Cubs in dramatic fashion Saturday afternoon, with a couple of home run blasts by two rookies. First, Victor Diaz hit a three-run home run in the bottom of the ninth, with two outs and two strikes, to tie the game. Then Craig Brazell won the game with a solo shot in the 11th. Of course, this was a welcome sight for Mets’ fans, who have nothing but the future to look forward to — as in two or three years from now. For Cubs’ fans, this was agony.

How unlikely was this turnaround? Well, from 1979 through 1990, there were 137 games in which the home team was at bat, behind three runs, with two outs and runners on first and third. And the home team only won seven times. In 95% of the cases, the visiting team won. Billy Goat curse, indeed.

Here’s another example: the Dodgers and Giants played a nail-biter Friday night. The Dodgers were winning 3-2 entering the bottom of the ninth in San Francisco. At that stage their chances of a win were pretty good (80%).
After retiring the first two batters, their chances increased to 95%. Incredibly and unfathomably, with Barry Bonds on deck and J.T. Snow in the hole, Gagne proceeded to throw four straight balls to Pedro Feliz, decreasing the Dodgers’ chances of a win to 90%. Of course, Mr. Bonds was walked and, less obviously, Snow eked out a free pass too. Bases loaded, two out, and the Dodgers chances of winning were now 72%.

Luckily for Dodger fans, Torrealba lined out to left and the win was preserved, and the Dodgers’ chances of winning turned to 100%. This sort of thinking is called “Win Expectancy,” and I’ve found it a fascinating way to watch a ballgame. Once you get a feel the tables and the odds of each base/out situation, you start to think about the game differently.

You can take the concept further and assign win expectancy values to players, based on what they do during the play. For instance, Victor Diaz can claim 45% of a win, because he increased the Mets’ chances of winning from 5% to 50%. Craig Brazell gets about 37% of a win for his game-winning shot. Jay Bennett has been doing this sort of analysis for awhile. His system is called Player Game Percentage (PGP), and you might find his analysis of the 2003 World Series interesting.

Pitching Fits

By the way, the Cubs’ pitching has been red-hot in September. Their September ERA is 3.09 through Saturday’s games, and their :FIP: is an even better 2.85. That is the best month of pitching in the majors this year.

Although Jason Schmidt’s late summer decline has been well documented, it’s the Giants’ pitching that has kept them in the playoff race. Here is their FIP by month:

    April     May    June    July   August    Sept
     4.76    4.34    4.53    4.78     3.81    3.75

Over the last twenty games, Schmidt has actually been their worst starter — Tomko, Reuter and Lowry have been carrying the load. Tomko, in particular, has been lights out.

Here’s another team that has seen its pitching improve throughout most of the year.

    April     May    June    July   August    Sept
     4.21    4.35    4.12    3.87     3.77    3.55

That would be the Minnesota Twins, with even Kyle Lohse starting to contribute. As I’ve already mentioned, Johan Santana deserves serious consideration for the MVP; it will be interesting to see if the voters give it.

Is the Yankees’ Pythagorean Variance historic? Huh?

This has been pointed out before, but the Yankees do not have the best run differential (Runs Scored minus Runs Allowed) in the league. The Red Sox have them roundly trounced in Run Differential. Unfortunately for Sox fans, real victories are all that matter.

But one little statistical quirk is coming out of this. The Yankees are beating their predicted victory total (based on Run Differential, through a calculation called the Pythagorean formula) by ten games. That’s actually quite a lot, and it’s been the source of some interest throughout the year.

Over at Baseball Think Factory, someone (I forget who — sorry) asked if the Yankees have the largest Pythagorean Variance in history among division winners. The answer is no, but it’s close. Here’s a list of the top Pythagorean Variances among all teams who qualified for postseason play since 1900:

Minor Tales: The Seventh
A tale inspired by true events from the 2019 MiLB season.
Team        Year     Wins   Pyth Wins  Diff
Reds        1970      102      91       11
Reds        1961       93      83       10
Giants      1997       90      80       10
Athletics   1931      107      98        9
Athletics   1930      102      94        8
Twins       2002       94      86        8

Now, the Yankees could still finish eleven, even twelve games, over their Pythagorean projection, but it doesn’t seem likely. They’d probably have to lose another 22-0 game to do so.

The real key to the Yankees Pythagorean variance is their 46-23 record in games won by two runs or less. They’ve won more close games than any major league team this year. Now, many fans believe that bullpens are the key to positive Pythagorean variances, and the Yankees obviously have an excellent pen.

The historic oddity on this list is the two-year reign of the Philadelphia Athletics. The 1930-1931 A’s featured Lefty Grove out of the bullpen (in addition to starting, of course). On the other hand, over half their games were complete games, including 49 by Grove himself.

Career Home Runs and Win Shares — never seen together at the same time…

Barry Bonds is now in third place in both career Win Shares and career home runs. You know what’s eerie? The all-time scales for the two stats are eerily similar. Here is a list of the top twenty career Win Share leaders, along with the top twenty career home run leaders.

Win Share Leaders         Home Run Leaders
Babe Ruth         756     Aaron        755
Ty Cobb           722     Ruth         714
Barry Bonds       662     Bonds        702
Honus Wagner      655     Mays         660
Hank Aaron        643     Robinson     586
Willie Mays       642     McGwire      583
Cy Young          634     Killebrew    573
Tris Speaker      630     Jackson      563
Stan Musial       604     Schmidt      548
Eddie Collins     574     Sosa         539
Mickey Mantle     565     Mantle       536
Walter Johnson    560     Foxx         534
Ted Williams      555     Palmeiro     528
Pete Rose         547     McCovey      521
Rickey Henderson  535     Williams     521
Mel Ott           528     Banks        512
Frank Robinson    519     Mathews      512
Joe Morgan        512     Ott          511
Roger Hornsby     502     Murray       504
Nap Lajoie        496     Gehrig       493

See what I mean? The names are different, but the milestones (500, 600 and 700) are the same. The only difference is that Win Shares follow a smoother, more normal distribution.

That wacky Bill James. Think he did it on purpose????

References & Resources
The Win Expectancy data comes from Phil Birnbaum’s site (and many thanks to Jon Daly and Tangotiger for the guidance). The records of historical pennant winners courtesy of Sean Lahman’s database. All of our great in-season data comes from our friends at Baseball Info Solutions.

Dave Studeman was called a "national treasure" by Rob Neyer. Seriously. Follow his sporadic tweets @dastudes.

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