What does a long game do to teams? April 13, 2015
Posted by tomflesher in Baseball.Tags: extra innings, file drawer, linear model, linear regression, Red Sox, Yankees
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Friday, the Red Sox took a 19-inning contest from the Yankees. Both teams have the unfortunate circumstance of finishing a game around 2:15 A.M. and having to be back on the field at 1:05 PM. Everyone, including the announcers, discussed how tired the teams would be; in particular, first baseman Mark Teixeira spent a long night on the bag to keep backup first baseman and apparent emergency pitcher Garrett Jones fresh, leading Alex Rodriguez to make his first career appearance at first base on Saturday.
Teixeira wasn’t the only player to sit out the next day – center fielders Jacoby Ellsbury and Mookie Betts, catchers Brian McCann and Sandy Leon, and most of the bullpen all sat out, among others. The Yankees called up pitcher Matt Tracy for a cup of coffee and sent Chasen Shreve down, then swapped Tracy back down to Scranton for Kyle Davies. Boston activated starter Joe Kelly from the disabled list, sending winning pitcher Steven Wright down to make room. Shreve and Wright each had solid outings, with Wright pitching five innings with 2 runs and Shreve pitching 3 1/3 scoreless.
All those moves provide some explanation for a surprising result. Interested in what the effect of these long games are, I dug up all of the games from 2014 that lasted 14 innings or more. In a quick and dirty data set, I traced the scores for each team in their next games along with the number of outs pitched and the length in minutes of the game.
I fitted two linear models and two log models: two each with the next game’s runs as the dependent variable and two each with the difference in runs (next game’s runs – long game’s runs) as the dependent variable. Each used the length of the game in minutes, the number of outs, the average runs scored by the team during 2014, and an indicator variable for the presence of a designated hitter in each game. For each dependent variable, I modeled all variables in a linear form once and the natural log of outs and the natural log of the length of the game once.
With runs scored as the dependent variable, nothing was significant. That is, no variable correlated strongly with an increase or decrease in the number of runs scored.
With a run difference model, the length of the game in minutes became marginally significant. For the linear model, extending the length of the game by one minute lowers the difference in runs by about .043 runs – that is, normalizing for the number of runs scored the previous day, extending the game by one minute lowered the runs the next day by about .043. In the semilog model, extending the game by about 1% lowered the run difference by about 14; this was offset by an extremely high intercept term. This is a very high semielasticity, and both coefficients had p-values between .01 and .015. Nothing else was even close.
With all of the usual caveats about statistical analysis, this shows that teams are actually pretty good at bouncing back from long games, either due to the fact that most of the time they’re playing the same team (so teams are equally fatigued) or due to smart roster moves. Either way, it’s a surprise.
A fifteen-inning offensive drought July 18, 2011
Posted by tomflesher in Baseball.Tags: Jacoby Ellsbury, Rays, Red Sox, weird lines
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Last night’s ESPN game, between the Red Sox and the Rays, was a pitchers’ duel of the highest magnitude. John at Baseball Reference already looked for other games where both starters had game scores of 85 or higher, and neither team had to call on a position player to pitch, but I thought one of the most interesting things to happen was offensive in nature.
Neither team scored until the sixteenth inning, at which point Dustin Pedroia followed up a John Reddick walk, a Jason Varitek sacrifice, and a Marco Scutaro infield single (to move Reddick to third) with a single to right field. Every batter up to that point was productive and helped manufacture that run… except Jacoby Ellsbury, who flied out to left between Scutaro and Pedroia. In fact, every lineup spot had either a hit, a walk, or a productive out except for Ellsbury, who led off. (Granted, Varitek’s only productivity was his sacrifice, but that’s enough.) Ellsbury had 8 plate appearances, all of them at-bats, and didn’t reach base at all.
Even getting 8 plate appearances is rare. Since 2002 (and through July 7), only 403 batters have had 8 plate appearances, including a handful with 10 and quite a few with 9. All five of the 10-plate-appearance games took place on April 17, but some of them took place in 2008 and some in 2010. (Just an odd coincidence.) Of those 403, only 12 failed to reach base at all. Corey Patterson and Trot Nixon share the record for most plate appearances without reaching base, with 10.
Ellsbury’s streak of 8 plate appearances without reaching base is especially weird because he’s so talented. Ellsbury has a .370 OBP, meaning that on average he reaches base 37% of the time (or, he only gets sent back to the dugout 63% of the time). If we assume last night’s plate appearances were random draws, the probability of 8 times without reaching base would be
or, in English, vanishingly rare.
Weird Pitching Decisions Almanac in 2010 December 24, 2010
Posted by tomflesher in Baseball.Tags: baseball-reference.com, Carl Pavano, Cheap Wins, Clayton Kershaw, Colby Lewis, Cubs, Felix Hernandez, Francisco Rodriguez, Hiroki Kuroda, Jeremy Affeldt, John Lackey, Justin Verlander, Mariners, Phil Hughes, Red Sox, Rodrigo Lopez, Roy Oswalt, Royals, Tommy Hanson, Tough Losses, Tyler Clippard, vulture wins
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I’m a big fan of weird pitching decisions. A pitcher with a lot of tough losses pitches effectively but stands behind a team with crappy run support. A pitcher with a high proportion of cheap wins gets lucky more often than not. A reliever with a lot of vulture wins might as well be taking the loss.
In an earlier post, I defined a tough loss two ways. The official definition is a loss in which the starting pitcher made a quality start – that is, six or more innings with three or fewer runs. The Bill James definition is the same, except that James defines a quality start as having a game score of 50 or higher. In either case, tough losses result from solid pitching combined with anemic run support.
This year’s Tough Loss leaderboard had 457 games spread around 183 pitchers across both leagues. The Dodgers’ Hiroki Kuroda led the league with a whopping eight starts with game scores of 50 or more. He was followed by eight players with six tough losses, including Justin Verlander, Carl Pavano, Roy Oswalt, Rodrigo Lopez, Colby Lewis, Clayton Kershaw, Felix Hernandez, and Tommy Hanson. Kuroda’s Dodgers led the league with 23 tough losses, followed by the Mariners and the Cubs with 22 each.
There were fewer cheap wins, in which a pitcher does not make a quality start but does earn the win. The Cheap Win leaderboard had 248 games and 136 pitchers, led by John Lackey with six and Phil Hughes with 5. Hughes pitched to 18 wins, but Lackey’s six cheap wins were almost half of his 14-win total this year. That really shows what kind of run support he had. The Royals and the Red Sox were tied for first place with 15 team cheap wins each.
Finally, a vulture win is one for the relievers. I define a vulture win as a blown save and a win in the same game, so I searched Baseball Reference for players with blown saves and then looked for the largest number of wins. Tyler Clippard was the clear winner here. In six blown saves, he got 5 vulture wins. Francisco Rodriguez and Jeremy Affeldt each deserve credit, though – each had three blown saves and converted all three for vulture wins. (When I say “converted,” I mean “waited it out for their team to score more runs.”)
Grand Slam, First Career At-Bat June 15, 2010
Posted by tomflesher in Baseball.Tags: batting order position, Daniel Nava, first career at-bat, grand slam, Jeremy Hermida, Kevin Kouzmanoff, probability, Red Sox
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On Saturday, Daniel Nava hit a grand slam in his first at-bat (hitting ninth for Boston). Needless to say, the odds against this are exceedingly long.
So far in 2010, there have been 1786 home runs hit in 73122 Major League Baseball plate appearances, for a rate of about .024 home runs per plate appearance. The American League has a league on-base percentage of .331 and the National League’s OBP is .329. That means that the prospect of any plate appearance ending in an out is (using .330 as the average OBP) .670. The likelihood of the bases being loaded at any point in an inning is the sum of three probabilities – three on base with 0, 1, or 2 outs.
![p[bases loaded, 0 out] = .33*.33*33 = .036](https://s0.wp.com/latex.php?latex=p%5Bbases+loaded%2C+0+out%5D+%3D+.33%2A.33%2A33+%3D+.036&bg=ffffff&fg=666666&s=0&c=20201002)
![p[bases loaded, 1 out] = .33*.33*.33*.67 = .024](https://s0.wp.com/latex.php?latex=p%5Bbases+loaded%2C+1+out%5D+%3D+.33%2A.33%2A.33%2A.67+%3D+.024&bg=ffffff&fg=666666&s=0&c=20201002)
![p[bases loaded, 2 out] = .33*.33*.33*.67*.67 = .016](https://s0.wp.com/latex.php?latex=p%5Bbases+loaded%2C+2+out%5D+%3D+.33%2A.33%2A.33%2A.67%2A.67+%3D+.016&bg=ffffff&fg=666666&s=0&c=20201002)
![p[bases loaded] = .036 + .024 + .016 = .076](https://s0.wp.com/latex.php?latex=p%5Bbases+loaded%5D+%3D+.036+%2B+.024+%2B+.016+%3D+.076&bg=ffffff&fg=666666&s=0&c=20201002)
Note that this slightly overestimates the probability, since it ignores the likelihood of an extra-base hit. Obviously an extra-base hit would increase the chance that three people made it to base but one or more scored, leaving the bases unloaded.
Now, with a home run probability of .024, and a bases loaded probability of .076, the (again, slightly overestimated) probability of a grand slam is about .002, or .2%. That is, about one in every 500 at-bats should be a grand slam.
Since 1920, there have been only 10 people who have hit a home run and had 4 or more RBIs in their first game. The list is here. Of those games, six (including Nava’s) involved any player hitting a grand slam (including three hit by the rookie in his first game – Nava, Kevin Kouzmanoff on September 2, 2006, and Jeremy Hermida on August 31, 2005). Incredibly, both of them hit grand slams in their first career at-bats, with Kouzmanoff in the lineup as the DH in the #8 slot and Hermida pinch-hitting in the #9 spot.
Also interesting is that Hector Luna played with both Kouzmanoff and Hermida when they hit their grand slams, and that in 2009, the Red Sox had no home runs with runners in scoring position by the #9 hitter. Quite a turnaround.
(I should point out that Bill Duggleby also hit a grand slam in his first career at-bat in 1898, but that the searchable data doesn’t go back that far.)
Poor Kazmir. October 17, 2008
Posted by tomflesher in Baseball.Tags: ALCS, Baseball, Cy Young, John Smoltz, Mike Mussina, Rays, Red Sox, Scott Kazmir, weird lines
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Last night, Scott Kazmir pitched 6 scoreless innings in ALCS game 5, giving up 2 hits and 3 walks but striking out 7 batters. He totalled up to a game score of 72 points. His bullpen then proceeded to give up 8 runs, allowing the Red Sox to come back and win the game (thus extending the series to game 5).
Has Scotty suffered the greatest postseason indignity ever? Nope. Not even close. That honor belongs to Mike Mussina of the 1997 Orioles.
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