Mariano's Walk-Off Beanball September 12, 2010
Posted by tomflesher in Baseball.Tags: As, David Robertson, Derek Jeter, hit batsman, hit by pitch, Jeff Francoeur, Jose Molina, Lenny DiNardo, Mariano Rivera, Nelson Cruz, odds, probability, Rangers, Yankees
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Mariano Rivera did something strange tonight: He plunked in the winning run. He hit Jeff Francoeur of the Texas Rangers to force in Nelson Cruz for the winning run in extra innings. It was his fourth hit batsman of the year and only his third loss.
A walk-off beaning requires an extraordinary set of circumstances. First of all, like all walk-off plays, it requires the home team to be at bat in the bottom of the inning. In this case, it was in extra innings rather than the bottom of the 9th. It additionally requires a tied game in the bottom of said inning. Finally, it requires the bases to be loaded when the plunking occurs.
This is all magnified by the face that Rivera does not ordinarily load the bases. Assuming his 2010 OBP against (.214) held, the probability the bases being loaded with two outs or fewer is:
Then, if that situation occurs, we still have to deal with the unlikely event of Mariano hitting a player with a pitch. Before this evening, Mo had hit three batters in 196 plate appearances, for a rate of about .0153. Thus, the probability of Mariano Rivera hitting a batter with a pitch after having loaded the bases is
That means that in 10,000 innings, we would expect that to occur about 4 times, assuming that Mariano wasn’t removed after having walked the bases (which would obviously introduce some bias).
Oddly, the last walk-off hit by pitch also involved the Yankees, albeit on the other side, way back on July 19 of 2008. That night, the A’s’ Lenny DiNardo hit Jose Molina with a pitch to force in Derek Jeter, again in extra innings. David Robertson grabbed the win that night.
Teixeira and Cano: Picking up slack? August 5, 2010
Posted by tomflesher in Baseball, Economics.Tags: A-Rod, Alex Rodriguez, binomial distribution, Mark Teixeira, probability, Robinson Cano, statistics, Yankees
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Michael Kaye, the YES broadcaster for the Yankees, often pointed out between July 22 and August 4 that the Yankees were turning up their offense to make up for Alex Rodriguez‘s lack of home run production. That seems like it might be subject to significant confirmation bias – seeing a few guys hit home runs when you wouldn’t expect them to might lead you to believe that the team in general has increased its production. So, did the Yankees produce more home runs during A-Rod’s drought?
During the first 93 games of the season, the Yankees hit 109 home runs in 3660 plate appearances for rates of 1.17 home runs per game and .0298 home runs per plate appearance. From July 23 to August 3, they hit 17 home runs in 451 plate appearances over 12 games for rates of 1.42 home runs per game and .0377 home runs per plate appearances. Obviously those numbers are quite a bit higher than expected, but can it be due simply to chance?
Assume for the moment that the first 93 games represent the team’s true production capabilities. Then, using the binomial distribution, the likelihood of hitting at least 17 home runs in 451 plate appearances is
The cumulative probability is about .868, meaning the probability of hitting 17 or fewer home runs is .868 and the probability of hitting more than that is about .132. The probability of hitting 16 or fewer is .805, which means out of 100 strings of 451 plate appearances about 81 of them should end with 16 or fewer plate appearances. This is a perfectly reasonable number and not inherently indicative of a special performance by A-Rod’s teammates.
Kaye frequently cited Mark Teixeira and Robinson Cano as upping their games. Teixeira hit 18 home runs over the first 93 games and made 423 plate appearances for rates of .194 home runs per game and .0426 home runs per plate appearance. From July 23 to August 3, he had 5 home runs in 12 games and 54 plate appearances for rates of .417 per game and .0926. That rate of home runs per plate appearance is about 8% likely, meaning that either Teixeira did up his game considerably or he was exceptionally lucky.
Cano played 92 games up to July 21, hitting 18 home runs in 400 plate appearances for rates of .196 home runs per game and .045 per plate appearance. During A-Rod’s drought, he hit 3 home runs in 50 plate appearances over 12 games for rates of .25 and .06. That per-plate-appearance rate is about 39% likely, which means we don’t have enough evidence to reject the idea that Cano’s performance (though better than usual) is just a random fluctuation.
It will be interesting to see if Teixeira slows down as a home-run hitter now that Rodriguez’s drought is over.
Is A-Rod's Performance Different? August 3, 2010
Posted by tomflesher in Baseball, Economics.Tags: A-Rod, Alex Rodriguez, Choke Index, OBP, p-value, probability, SLG, statistics, t-value, Yankees
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In games between milestone home runs, is Alex Rodriguez’ hitting similar to other times? (This is all a very polite way of asking, “Does A-Rod choke?”) It’s difficult to answer, because there’s so little data about those milestone home runs. A-Rod, though, has some statistically improbable results and it would be interesting to look at it a bit more closely.
Over 2008-2009, Alex played in 262 games and had 1129 plate appearances with 281 hits, 65 home runs, a triple:double ratio of 1:50, an OBP of .397, and a SLG of .553. His OBP has a margin of error of .0146, so we can be 95% confident that over those years his baseline production would be somewhere between .368 and .426 and absent any time or age effect that is the range in which A-Rod should produce for any given period.
Two recent milestone home runs come to mind as examples of Rodriguez’s reputed choking. First, the stretch between home run #499 and #500 was 8 games and 36 plate appearances. (I’m intentionally ignoring extra plate appearances on the days he hit #499 and #500.) During that time, Alex had an OBP of only .306. That’s a difference of .091 over 36 plate appearances and that performance has a standard error of about .078 when compared with his regular performance, implying a t-value of about 1.16. With 35 degrees of freedom, Texas A&M’s t Calculator gives a p-value of about .127, so this difference is marginally within the realm of chance. (The usual cutoff for significance would be .05.)
A-Rod hit his last home run on July 22. Discounting the plate appearances after his last home run, he’s played in 11 games with a paltry .255 OBP and .238 SLG over 47 plate appearances. His .255 OBP has a difference of about .142 and a standard error of about .064. That implies a t-value of about 2.21, with a p-value of about .016. That is, the probability of this difference occurring by chance is less than 2%. That gives us one result as close to significant and one as probably significant.
As a side note, A-Rod’s Choke Index continues to rise. He’s gone 48 plate appearances without a home run, and at a rate of .055 home runs per plate appearance the probability of that occurring by chance is about .066. That leaves his Choke Index at .934.
The Best Game Ever July 30, 2010
Posted by tomflesher in Baseball.Tags: 600 home runs, Alex Rodriguez, Andy Marte, Chan Ho Park, Colin Curtis, designated hitter, Frank Hermann, Gabe Kapler, Indians, Jess Todd, Joe Girardi, Joe Smith, losing DH, Marcus Thames, Mitch Talbot, Nick Swisher, position players pitching, probability, Rafael Perez, statistics, Tony Sipp, Yankees
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Two of my favorite things about baseball happened during tonight’s game between the Yankees and the Indians.
First of all, in the top of the ninth inning, corner infielder Andy Marte pitched for the Indians. Marte pitched a perfect ninth and coincidentally struck out Nick Swisher, who was brought in to pitch for the Yankees in a similar situation last year and struck out Gabe Kapler of the Tampa Bay Rays. I can’t promise it’s true, but I think that puts Swisher at the top of the list for involvement in position player pitcher strikeouts.
Marte’s presence was necessary because the Indians used seven other pitchers. Starter Mitch Talbot went only two innings, and the Indians got another two out of Rafael Perez. Frank Hermann took the loss for the Indians during his 1 1/3 innings. Tony Sipp pitched another 1 1/3, and Joe Smith managed to give up four earned runs in 1/3 of an inning before being removed for Jess Todd for an inning. In the bottom of the 9th, Marte was all the Indians had left.
Not to be outdone, Joe Girardi gave up his designated hitter by moving his DH – funnily enough, it was Swisher – into right field as part of a triple switch. Swisher moved to right field; Colin Curtis moved from right field to left field; Marcus Thames moved from left field to third base; finally, pitcher Chan Ho Park was put into the batting order in place of Alex Rodriguez, who came out of the game.
Finally, A-Rod is up to 33 plate appearances without a home run. Assuming his standard rate of .064 home runs per plate appearance, the likelihood of this happening by chance is . I stand by my belief that there’s something other than chance (i.e. distraction or other mental factors) causing Rodriguez’s hitting to suffer.
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.
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.)
Quickie: Dallas Braden's Perfect Game May 11, 2010
Posted by tomflesher in Baseball.Tags: Baseball, Braden's perfect game, Buehrle's perfect game, Dallas Braden, Oakland As, probability, sabermetrics, Tampa Bay Rays
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Dallas Braden of the Oakland As pitched a perfect game Sunday, on Mother’s Day. Under the methods discussed last year after Buehrle’s perfect game, Braden – who’s been active for four seasons – has an OBP-against of .328. That means he has a probability for any given plate appearance of .672 of the batter not reaching base.
Since he sat down 27 batters consecutively, the probability of that event happening is (.672)27, or .0000218; equivalently, given his current stats, a bit over 2 in every 100,000 games that Braden pitches should be perfect games.
Over the same period (2007-2010), the American League OBP has hovered between .331 (this year) and .338 (2007). .336 was the mode (2008, 2009), so I’ll use it to estimate that the chance for a perfect game facing the league average team would be (.664)27, or .0000157, or equivalently about 1.5 out of every 100,000 games should be a perfect game.As you can see, it’s more likely for Braden than the average pitcher, but not by much.
Nice job, Dallas!
As a side note, the Tampa Bay Rays were the victim of BOTH perfect games. Their team OBP was .343 in 2009, with a probability not to get on base of .657, meaning that the probability of getting 27 batters seated consecutively is about 1.2 in 100,000. Since many other teams have lower team OBPs, it’s very surprising that the Rays were the victims of both games.