Why isn’t baseball’s free agent market clearing?February 21, 2019

Posted by tomflesher in Baseball, Economics.
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There’s been some discussion of the free agent market in baseball and its alleged inefficiency – that players like Manny Machado don’t sign until February and Bryce Harper is still unsigned, for example. Adam Wainwright, for example, has threatened a strike over free agency.

Certainly, there are many factors in play. However, the fact that there are stars who aren’t being picked up doesn’t mean that there’s anything nefarious afoot. Brad Brach, who signed with the Cubs on February 11, has complained about the teams’ use of algorithms to value players:

Let’s take that at face value and build a model of algorithms and noise. (It seems that Brach is implying collusion by teams, but in a future post I’ll discuss why I don’t think that’s likely.)

First, the simplifying assumptions:

1. Players have an accurate valuation of their own talent levels (This is difficult to justify because players have an incentive to overvalue themselves, but the conclusions would not change qualitatively by relaxing this assumption)
2. Teams have a noisy valuation of players based on the players’ talent levels (This is essentially the face value Brach’s claim: that teams use ‘algorithms’ based on player talent.)
3. There are two teams with similar noise levels. (Modeling different forms of bias, or different preferences by teams, would probably not change the outcome very much, but would affect the distribution of players. Meanwhile, the market for some players is fairly large, but for many it’s very small, especially as prices rise.)
4. All contracts are for one year. (This avoids the trouble of modeling players’ intertemporal rates of substitution, but a future version of this model may include preferences about both pay and number of years.)
5. If a player is offered a contract that he thinks accurately reflects or overpays him, he signs with the team that offers him the bigger contract.

Poorly-constructed R code for a simulated free agent season:

data<-matrix(1:5000,nrow=1000,ncol=5) for (i in c(1:1000)){data[i,1] <- runif(1) data[i,2] <- data[i,1]+rnorm(1,mean=0,sd=.05) data[i,3] <- data[i,1]+rnorm(1,mean=0,sd=.1) data[i,4] <- max(data[i,2],data[i,3]) data[i,5] <- if(data[i,4]>=data[i,1]) data[i,5]=1 else data[i,5]=0}

Basically, generate a vector of random player talent levels; team 1 accurately values players with a standard deviation of .05, while team 2 accurately values them with a standard deviation of .1. 1000 players go on the market. Outcome:

 V1 V2 V3 V4 V5 Min.     :0.0008885 Min.   : -0.1324 Min.   : -0.2024 Min.   : -0.1324 Min.   :0.000 1st Qu.:0.2613380 1st Qu.: 0.2621 1st Qu.: 0.2608 1st Qu.: 0.3012 1st Qu.:1.000 Median :0.4984726 Median : 0.4968 Median : 0.5133 Median : 0.5511 Median :1.000 Mean     :0.4997539 Mean   : 0.4987 Mean   : 0.5087 Mean   : 0.548 Mean   :0.754 3rd Qu.:0.7425434 3rd Qu.: 0.743 3rd Qu.: 0.7566 3rd Qu.: 0.7912 3rd Qu.:1.000 Max.     :0.9995596 Max.   : 1.1115 Max.   : 1.2508 Max.   : 1.2508 Max.   :1.000

That’s right – only 754 of the 1000 players signed. (In multiple simulations, the signing rate hovers around 75%. This makes sense theoretically, since valuations are independent: half the players will be undervalued by each team so 1/4 will be undervalued by both teams.)

Interestingly, player 973 is unsigned:

[973,] 0.9683805341  0.9472948838  0.874961530  0.9472948838    0

He evaluated himself at below the 97th percentile, but got unlucky in that both teams evaluated him below that: team 1 would offer him a 95th percentile contract and team 2 would rank him even further down.

Meanwhile, player 25 gets lucky:

[25,] 0.0109281745  0.0236191242  0.089982324  0.0899823237    1

Despite being in the 1st percentile, both teams accidentally overvalue him, and his contract ends up being suited to a player with nearly 9 times his value. (For the phenomenon where competition leads reliably to overpayment, see “winner’s curse.”)

We’re going to see both of these types of errors in any market where there’s a subjective evaluation of players. Particularly if the teams are using algorithmic valuations, much of the information they’re based on is going to be publicly available; even if teams weight it differently, efficient algorithms are likely to produce similar results.

Modeling Run ProductionJune 19, 2010

Posted by tomflesher in Baseball, Economics.
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A baseball team can be thought of as a factory which uses a single crew to operate two machines. The first machine produces runs while the team bats, and the second machine produces outs while the team is on fields. This is a somewhat abstract way to look at the process of winning games, because ordinarily machines have a fixed input and a fixed output. In a box factory, the input comprises man-hours and corrugated board, and the output is a finished box. Here, the input isn’t as well-defined.

Runs are a function of total bases, certainly, but total bases are functions of things like hits, home runs, and walks. Basically, runs are a function of getting on base and of advancing people who are already on base. Obviously, the best measure of getting on base is On-Base Percentage, and Slugging Average (expected number of bases per at-bat) is a good measure of advancement.

OBP wraps up a lot of things – walks, hits, and hit-by-pitch appearances – and SLG corrects for the greater effects of doubles, triples, and home runs. That doesn’t account for a few other things, though, like stolen bases, sacrifice flies, and sacrifice hits. It also doesn’t reflect batter ability directly, but that’s okay – the stats we have should represent batter ability since the defensive side is trying to prevent run production. The model might look something like this, then:

$\hat{Runs} = \hat{\beta_0} + \hat{\beta_1} OBP + \hat{\beta_2} SLG + \hat{\beta_3} SB + \hat{\beta_4} SF + \hat{\beta_5} SH$

This is the simplest model we can start with – each factor contributes a discrete number of runs. If we need to (and we probably will), we can add terms to capture concavity of the marginal effect of different stats, or (more likely) an interaction term for SLG and, say, SB, so that a stolen base is worth more on a team where you’re more likely to be brought home by a batter because he’s more likely to give you extra bases. As it is, however, we can test this model with linear regression. The details of it are behind the cut. (more…)

Trends in DH useJune 11, 2010

Posted by tomflesher in Baseball, Economics.
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Last night, Keith Hernandez was talking about how the Mets are scheduled to play in American League parks starting, well, today. He pointed out that the Mets will be in a bit of a pickle because they aren’t built, as AL teams are, to carry one big hitter to be the full-time DH. Instead, an NL team will be forced to spread the wealth among lighter hitters who are carried for their defensive acumen as well as their offensive prowess. Keith then corrected himself and said that AL managers are using the DH differently – to rest individual players instead of having an everyday DH.

That pinged my “Stuff Keith Hernandez says” meter, and so I decided to crunch some numbers and see if that’s true. I interpreted Keith’s statement as implying that the number of designated hitters should be increasing, since managers are moving away from an everyday DH and toward spreading the DH assignments around a bit more. The crunching also needs to account for interleague play, which should obviously increase the number of DHes. So, after controlling for interleague play, does DH use show an increasing trend with time?

How much should Manny’s stats have dropped?June 8, 2010

Posted by tomflesher in Baseball, Economics.
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In an earlier post, I used Manny Ramirez‘s differential line to make the case that discontinuing use of performance-enhancing drugs was largely responsible for his drop in production. That’s vulnerable to the criticism that Manny is 38, and that even the best 38-year-old player’s stats drop from his 37-year-old stats.

With that in mind, I queried Baseball-Reference.com’s Play Index to find the stats of players from 1961 to 2009 who, like Manny, played 50% or more of their games in the outfield or as a designated hitter (where Manny might be if he played for an AL team). On average, the 37- and 38-year old players played about 105 games, so I scaled Manny’s drop in stats over the first 27 games to 105 games in order to make the comparison clear. The differential line is behind the cut.

Manny’s First 27 Games (or, the Marginal Product of Drug Use)June 4, 2010

Posted by tomflesher in Baseball, Economics.
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Last year, Manny Ramirez was suspended for 50 games on May 6. The suspension came after his 27th game of the season. On May 25th of this year, Manny played his 27th game of 2010. That means we can take a look at the first 27 games of each season, when he was using performance-enhancing drugs (in 2009) and when he wasn’t (presumably, this year). The differential line is behind the cut.

Does the DH Rule Cause Batters to be Hit?June 2, 2010

Posted by tomflesher in Baseball, Economics.
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