## Spitballing: Position Players Moving AroundJanuary 24, 2014

Posted by tomflesher in Baseball, Economics.
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Earlier this week, I posted about Lucas Duda and how he’s being forced out of his natural position. This came up a few years ago for the Mets as well, when Angel Pagan was being forced out of the outfield – many fans suggested pushing him to second base (in the hole now filled by Daniel Murphy). The sense seems to be that players can move freely around the field, going wherever the team needs them. There are a couple of theories on this, and a couple of good examples, but it doesn’t always work out.

Typically, the best moves take someone from a more defensively-demanding position and move him to one that’s less so. Victor Martinez still catches occasionally, but he’s made a move almost entirely to the DH role and played more games at first base last year. Alex Rodriguez has also made some moves in that direction, moving from the very demanding shortstop position to the slightly less difficult third base (perversely, to allow the much lousier Derek Jeter to stay in his position), and mostly toward DH these days. Johnny Damon and Jorge Posada were among the revolving door of older Yankees to do time out of position at first base over the past few years, with varying degrees of success. On the other hand, even moves down the defensive spectrum don’t always work. Gary Sheffield was famously described as “painful to watch” at first by Michael Kaye. Kevin Youkilis was solid in his move from third to first, but the extra speed required for left field left him looking like he couldn’t hack it, and even though the corner positions are great places to stick a team’s best sluggers, it rarely makes sense to move a broken-down catcher there instead of to first or (rarely) third. Even a solid third baseman wouldn’t necessarily have the ability to cover ground needed by an outfielder, even if he had the requisite ability to predict the ball’s flight – a skill that probably needs time in the field to develop.

Pitching is kind of a weird exception. The best example in recent memory has to be Rick Ankiel, whose meltdown on the mound during the World Series led to his second career as an outfielder. On the opposite side, Juan Salas went from being a cannon-armed third baseman to pitching reasonably well, and the Dodgers’ Kenley Jansen has saved 53 games for the Dodgers since being converted from light-hitting catcher to closer. (He also has a lifetime .500/.667/.500 batting line, in the “Utterly Meaningless Statistics” category.) Similarly, Ike Davis went from being his college team’s Friday-night starter to the least defensively-demanding position (first base) in the majors.

Defensive position moves tend to be difficult to make. In Duda’s case, he’d technically be moving up the defensive spectrum, but it’s hard to even consider the speed required to be a competent outfielder on the same scale as the abilities of an infielder. It’s unremarkable to me that Johnny Damon was able to move to first, but putting Youkilis in left field a few years ago was a true head-scratcher. In order to move a player freely between the infield and the outfield, you’ll need a special kind of player unless you’re willing to give up a lot defensively. As an economist, I’m all about specialization given constraints; Duda’s constraints are just too tight to make this move work.

## Is Bobby Abreu a good investment for the Phillies?January 23, 2014

Posted by tomflesher in Baseball, Economics.
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## Ike Davis and his 12% raiseJanuary 21, 2014

Posted by tomflesher in Baseball, Economics.
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So, Ike Davis was pretty lousy last year. He batted .205/.326/.334 in an injury-shortened season with 106 total bases on 377 plate appearances, meaning he expected to make it to first a bit over a quarter of the time. Throw in his paltry home run figures and a handful of doubles, and you’re not looking at a major-league first baseman; his 0.2 wins above replacement put him in the company of Lyle Overbay and Garrett Jones.

Now that that’s out of the way, I’d like to point out that Overbay played 142 games and Jones played 144; Davis definitely presented more bang for your buck than those two, especially since he was earning $3.125 million. He’ll be getting a 12% raise this year, having re-signed for$3.5 million. Again, his numbers were pretty lousy.

But if you add up all of Davis’s appearances as a starter, you’ll see that the Mets scored 354 runs in those games, and allowed 376, meaning that the Pythagorean expectation for those games is 0.46989 – that corresponds to an expectation of about 76 wins over a 162-game season (or 41 wins over Davis’ tenure). The Mets’ overall winning percentage was .457 (74 wins), and their Pythagorean expectation was about .45, corresponding to around 73 wins; but without Davis, the team scored 265 runs and allowed 308, leading to an expectation of .425 and around 69 wins on the season. Additionally, the team actually won only 39 of the 87 games Davis started, for about a .45 winning percentage – right on with their season-long expectation, and two wins below expectation.

Now, there are some caveats. When Davis was active, the team was still doing its best to win, and players like John Buck and Marlon Byrd were still active. Toward the end of the season, the Mets moved more toward development and away from trying to win every game. It’s therefore entirely possible that the effect of having Davis start the game are wrapped up in the team’s changing fortunes. Still, the team would have been expected to perform better with Davis in the lineup, at least according to the Pythagorean expectation formula, and actually underperformed.

## Comparing Contracts: Parnell and GeeJanuary 20, 2014

Posted by tomflesher in Baseball, Economics.
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A few days ago, Bobby Parnell and Dillon Gee both re-signed with the Mets; though there are some incentives in Parnell’s deal, he’ll be making $3.7 million to Gee’s$3.625 million. Those numbers were oddly close (and the contracts similar despite the difference in position), so I decided to check out the players’ recent statistics. Since the players are each negotiating one-year deals, and these players are neither very old or very young, it seems reasonable to treat the best predictor of future performance as the players’ most recent performance.

Gee started 32 games (almost exactly every fifth game) in 2013 to a 3.62 ERA and a .301 opposing BABIP. The median numbers for starters with 162 or more innings pitched were about 3.51 and .295, so Gee is performing almost exactly like a full-time starter (and thus presumably a bit better than your average pitcher). Gee’s performance corresponds to 2.2 wins above replacement, a shade below the median of 3.0 for full-time starters.

I’m not Parnell’s biggest fan, and his season was shortened by an injury (causing him to miss all of August), so I expected the numbers not to operate in his favor. However, his 2.16 ERA is well below the median of relievers with 40 appearances or more, and his 0.7 WAR is right on the median. Oddly, his BABIP at .268 is much lower than the median of .290, indicating that he’s benefiting, to some degree, from good fielding behind him. If we restrict the numbers to only pitchers with 15 saves or more (all 32 of them), those medians adjust to 2.645, 1.4, and .277, respectively, keeping him on the good side of ERA and BABIP but cutting his WAR performance considerably. Let’s see if we can extrapolate – in 104 team games, Parnell played 49, meaning that he played in about 47% of the team’s games. At that pace, he probably would have been put into about 27 more games, meaning his current stats are about 65% of what his season stats might have been. In that case, let’s hold his BABIP and ERA constant and extend his WAR to 1.08 (by dividing by .65). That would have ranked him with Huston Street and Addison Reed – much better company than his current competition. It also, interestingly, would have put him much closer to Gee’s WAR, at a higher-leverage position.

Again, I’m not Parnell’s biggest fan, and I was skeptical about this deal. Assuming that the injury hasn’t harmed him, though, Parnell’s contract really does make sense compared to Gee’s.

## Quick thoughts on the MetsAugust 11, 2012

Posted by tomflesher in Baseball.
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• So, I’m a little late to the party on this one, but way back on August 4, Mike Baxter tied the National League record for most walks in a 9-inning game with 5. 5 was, incidentally, his total number of plate appearances. That was unusual in part because prior to August 4, Baxter had made 82 plate appearances (mostly as a pinch hitter) and walked in 8 of them, for a rate of .0972 walks per plate appearance. That makes the probability of having five consecutive plate appearances all end in walks about .09755, or a little under 9 in every million five-PA strings. In total this year he’s walked 52 times in 342 plate appearances, for a rate of about .15 walks every appearance. The Pride of Whitestone seems to be normalizing upward.
• R.A. Dickey pitched a complete game gem Thursday afternoon. Batters facing Dickey have a .277 batting average on balls in play, compared with a league average of .299. Dickey may be benefiting from a slightly lower-than-expected BABIP, but he’s helping himself avoid the unpredictability of balls in play with a league-leading 166 strikeouts (tied with Stephen Strasburg). He’s leading the league in WHIP with just 1.004 walks plus hits per inning pitched. It’s a shame he’s on this year’s squad, or he’d be receiving serious consideration for the Cy Young. As it stands, Strasburg has a much better case on player value grounds.
• Just as a side note, A.J. Ellis of the Dodgers has had two games where  he walked in every plate appearance – both of them were 4-plate-appearance games. His stats are otherwise pretty similar to Baxter’s. He just likes to bunch them up a bit more.

## Pujols is happy to be in the AL West. That’s where the Rangers are.August 2, 2012

Posted by tomflesher in Baseball.
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Albert Pujols hit two home runs in the Angels’ 11-10 extra-innings loss to the Rangers yesterday night. It was his first multi-homer game since, well, the Angels 6-2 win over the Rangers the previous night. Albert’s last multi-homer game was an October 22 win over… yep, the Rangers.

Although he’s dragging a bit in comparison to previous years (currently hitting .049 home runs per plate appearance, as opposed to last year’s .057 and 2010′s .06), there’s an argument to be made that he’s the victim of bad luck. For example, the league’s batting average on balls in play (BABIP) is .292, and Pujols’ is a full .016 below that at .276. In his 401 at-bats, that’s about 6 hits that average defense wouldn’t have fielded. Mike Trout, on the other hand, is up at a BABIP of .400. That’s about 36 hits on his 333 at-bats that are above his expectation if he had the league’s average BABIP. This is emphatically not to say that Trout’s season is a fluke, or that Pujols’ is, but sometimes the human element of the game has odd results.

## Game-Ending Outs: Maybe A-Rod isn’t as bad at this as we thoughtJuly 19, 2012

Posted by tomflesher in Baseball.
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So, a friend of mine made the following comment on Facebook the other day:

###### Someone has to look this up for me. Has any player, ever, made the last out for his team more often than A-Rod? He’s like the bizarro Mo.

At the time, Alex Rodriguez and Curtis Granderson were tied with 5 game-ending outs apiece for the Yankees. Since then, I thought it would be interesting to see what the average “game-ending out” looks like, at least according to Baseball Reference.

As of July 18, there were 1264 game-ending outs in the majors this year.  Aaron Hill, Jesus Guzman, and Kyle Seager are ties for the lead with 9 apiece, with a spate of other batters tied for second at 8. Unsurprisingly, the 8th batting-order position makes the game-ending out most often; managers (of course) tend to arrange their strongest batters earlier in the lineup. By and large, game-ending outs tend to be short at-bats, with 850 coming with 4 or fewer pitches.

450 were strikeouts, with the league-leading total of 5 shared by Edwin Encarnacion, Giancarlo Stanton, and Ryan Ludwick. Craig Kimbrel of Atlanta leads the league in game-ending strikeouts, having thrown 15 of them. Kimbrel also led last year, with 31, which surprised me. Mariano Rivera had less than 2/3 as many, with only 20.

## The Spectrum Club, 2011 EditionJanuary 19, 2012

Posted by tomflesher in Baseball.
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2011 yielded 5 new members to the prestigious* Spectrum Club; the Spectrum Club is a collection of baseball players who have played at each end of the defensive spectrum, playing at least one game each as a pitcher and designated hitter. Those players were Michael Cuddyer, Don Kelly, Mitch Maier, Mike McCoy, and Darnell McDonald.

Of these five, Kelly was the most versatile, playing at every position except second base and shortstop this year. Maier and McDonald were the least: each played three outfield positions in addition to pitching and hitting, while Cuddyer played first base, second base, and right field. McCoy, a typical utilityman, played second, third, short, center, and right. Kelly’s tenure on the mound was the shortest (one batter, one out), with everyone else pitching a full inning. McDonald gave up two runs on a hit and two walks in six batters faced; Maier faced four and gave up one hit, but no runs; Cuddyer allowed one hit and walked one for six batters faced and no runs; and McCoy pitched a perfect inning.

There’s no telling who will join these fellows next year – Skip Schumaker and Wilson Valdez each pitched an inning this year, but  while Valdez is a journeyman, he’s unlikely to DH, and Schumaker is locked in with the Cardinals for the next two years.

*not a guarantee

## Home Runs Per Game: A bit more in-depthDecember 23, 2011

Posted by tomflesher in Baseball, Economics.
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I know I’ve done this one before, but in my defense, it was a really bad model.

I made some odd choices in modeling run production in that post. The first big questionable choice was to detrend according to raw time. That might make sense starting with a brand-new league, where we’d expect players to be of low quality and asymptotically approach a true level of production – a quadratic trend would be an acceptable model of dynamics in that case. That’s not a sensible way to model the major leagues, though; even though there’s a case to be made that players being in better physical condition will lead to better production, there’s no theoretical reason to believe that home run production will grow year over year.

So, let’s cut to the chase: I’m trying to capture a few different effects, and so I want to start by running a linear regression of home runs on a couple of controlling factors. Things I want to capture in the model:

• The DH. This should have a positive effect on home runs per game.
• Talent pool dilution. There are competing effects – more batters should mean that the best batters are getting fewer plate appearances, as a percentage of the total, but at the same time, more pitchers should mean that the best pitchers are facing fewer batters as a percentage of the total. I’m including three variables: one for the number of batters and one for the number of pitchers, to capture those effects individually, and one for the number of teams in the league. (All those variables are in natural logarithm form, so the interpretation will be that a 1% change in the number of batters, pitchers, or teams will have an effect on home runs.) The batting effect should be negative (more batters lead to fewer home runs); the pitching effect should be positive (more pitchers mean worse pitchers, leading to more home runs); the team effect could go either way, depending on the relative strengths of the effects.
• Trends in strategy and technology. I can’t theoretically justify a pure time trend, but I also can’t leave out trends entirely. Training has improved. Different training regimens become popular or fade away, and some strategies are much different than in previous years. I’ll use an autoregressive process to model these.

My dependent variable is going to be home runs per plate appearance. I chose HR/PA for two reasons:

1. I’m using Baseball Reference’s AL and NL Batting Encyclopedias, which give per-game averages; HR per game/PA per game will wash out the per-game adjustments.
2. League HR/PA should show talent pool dilution as noted above – the best hitters get the same plate appearances but their plate appearances will make up a smaller proportion of the total. I’m using the period from 1955 to 2010.

After dividing home runs per game by plate appearances per game, I used R to estimate an autoregressive model of home runs per plate appearance. That measures whether a year with lots of home runs is followed by a year with lots of home runs, whether it’s the reverse, or whether there’s no real connection between two consecutive years. My model took the last three years into account:

$\hat{HR}_t = .0234 + .5452HR_{t-1} + .1383HR_{t-2} + .1620HR_{t-3} + \varepsilon_t$

Since the model doesn’t fit perfectly, there will be an “error” term, $\varepsilon$ , that’s usually thought of as representing a shock or an innovation. My hypothesis is that the shocks will be a function of the DH and talent pool dilution, as mentioned above. To test that, I’ll run a regression:

$\varepsilon_t = DH_t + logbat_t + logpitch_t + logtm_t$

The results:

$\begin{tabular}{c|ccc} Variable & Sign Predicted & Estimate & P \\ Intercept&0&-0.011070&0.1152 \\ DH&+&-0.000063&0.9564 \\ logbat&-&-0.000245&0.9335 \\ logpitch&+&\bf{0.005550}&0.0489 \\ logtm&?&\bf{-0.006854}&0.0237 \\ \end{tabular}$

The DH and batter effects aren’t statistically different from zero, surprisingly; the pitching effect and the team effect are both significant at the 95% level. Interestingly, the team effect and the pitching effect  have opposite signs, meaning that there’s some factor in increasing the number of teams that doesn’t relate purely to pitching or batting talent pool dilution.

For the record, fitted values of innovations correlate fairly highly with HR/PA: the correlation is about .70, despite a pretty pathetic R-squared of .08.