Trading for Stanton and Other Half-Baked Notions

Let’s get this out of the way right now and say that the Orioles are not going to trade for Giancarlo Stanton. The reasons for this are as obvious as the no doubters Stanton blasts with regularity: His massive 10 year, $295 million contract, his opt out provisions, his cost in prospects, his extensive injury history, and his no trade clause. Any one of these would be enough to tank a deal, but all of them, combined with the the fact that the Orioles front office regularly loses sprints to snails, makes it an utter and complete impossibility.

Having said that, let’s make a deal!

First, let’s consider what Stanton is. An MVP winner? You bet. An elite power hitter? Sure. An injury prone, not super young guy with a lot of mileage and whose team is seemingly desperate to move him? Well, yeah, that too. You know, there was a guy the Orioles traded for once. An “old 30” kinda guy. The kind of guy that, if he played now, would probably be in Stanton’s position; an older, highly paid player on a team going nowhere that has the ability to take a new team to the next level. I’m not saying Stanton is Frank Robinson, but let’s take a look at their numbers through their age 27 seasons, just for fun.

Stanton: .268/.360/.554, 146 OPS+, 267 homers, 672 RBI, 34.1 WAR
Robinson: .303/.389/.557, 148 OPS+, 262 homers, 800 RBI, 47.4 WAR

The biggest difference between the two players is Robinson’s durability. Through his first 8 seasons he played in 1190 games, while Stanton has played in a relatively paltry 986. Robinson was also a more complete hitter and player, but Stanton gets the edge as a power guy. The point here is not to say that Stanton is as good as Frank Robinson, because he’s not, but that adding a player like him would be in the same realm of magnitude as when the Orioles acquired Frank before the 1966 season. MVP’s don’t get traded in their prime very often, yet that’s exactly what is likely to happen this offseason. Stanton is a great player when healthy, and none of his injuries are of the chronic variety, so there’s at least a chance he has gotten over the health hump and can consistently play 150+ games. If that happens, you’re talking about a top 5 or so player in baseball.

So Stanton is great. We know that much. Why would the Orioles make this deal? Well, other than the fact that they wouldn’t, they’d do it because they have decided to throw caution to the wind and try to win a World Series in the next few years. They’d also do it because (bear with me, here) they’ve just signed Manny Machado to a ten year, $300 million extension, and Alex Cobb, and Tyler Chatwood, and Mike Minor, and, you know, a couple more pitchers! What an offseason they’ve had in my fevered brain.

Realistically, this would be far out of the realm of possibility for the Orioles at their current payroll level, but let’s say they’ve gone as crazy as I have and will commit to running a $180 million payroll for the foreseeable future. Suddenly, there’s room in the budget not only to sign Machado but to bring in another huge piece. And, since Stanton is the craziest one of all, he’s decided that he wants to play in Baltimore and be the modern day Frank Robinson. It’s all coming back around, folks!

Under a $180 million budget we can make this happen. In our offseason blueprint series, we set a budget of $155 million, and within that, the team would be able to pay guys like Cobb and Chatwood. Stanton will make $25 million this season, and assuming Cobb and Chatwood sign for their BORAS projections, they're already at the $180 million cap. But! Since the Orioles are going to deal Austin Hays, Hunter Harvey, and Ryan Mountcastle to the Marlins for Stanton, Miami is also going to take Mark Trumbo and be grateful for it. Oh, yeah, I forgot to mention, we’ll be needing to trade those guys to get Stanton. We subtract Trumbo’s $12.5 million salary and add it onto Machado’s projected $17 million arbitration deal and, bingo! $180 mil on the dot.

Easy enough? Ok, obviously not. Trading the team’s best two position prospects and arguably the most talented pitcher in the organization is a very steep price, though shedding the Trumbo deal makes it sting a bit less. But, if we assume that Machado bounces back to his pre-2017 level and the pitching improves with the additions of Cobb, Chatwood, and whoever else, adding a +7 WAR bat suddenly makes this team look like a legit contender. Even if the Orioles only sign one of the better arms on the market (which, let’s be honest, they’d basically have to do if they went this route), the addition of Stanton itself might be enough to get this team back into the playoffs.

Also, come on. Are you telling me you’d rather have Austin Hays than this:

I mean, really. Adding Stanton to a team with Machado, Schoop, Jones, Davis, and Mancini would, if nothing else, be incredibly fun. While I know that it's not going to happen, and that there are pretty good arguments for why it shouldn't happen even if it could, Stanton could hit 70 in Camden Yards. For late November, in an off season that hasn't exactly been a rip roaring joyride, that's not the worst dream in the world.

Dan Duquette, I've laid it all out. The rest is up to you. Godspeed, sir.

How The Orioles Can Be Projected To Win 118 Games

The Orioles received no respect from the projections this offseason. Fangraphs projected the Orioles to win only 80 games and PECOTA had them at 72. Despite the Orioles spending hundreds of millions of dollars in the offseason, the general public was not expecting much from the Orioles. Then the Orioles had a nice seven game win streak and all of a sudden the calculations shifted.

As one can imagine, some fans got excited. Jonah Keri wrote an article asking whether we might have underestimated the O’s again.  Matt Kremnitzer wrote that we should enjoy the Orioles’s suprising win streak because they are playing well and its fun. Steve Melewski joked that “Orioles are MLB's only remaining unbeaten team. Pecota is not available for comment.” It’s questionable whether Melewski will retract his statement in the unlikely event that the Orioles suffer a losing streak this season. ESPN Stats and Info reported that only 28 teams since 1903 have started 7-0 and better and 5 of those won the World Series. Eutaw Street Report and Camden Chat both decided to write articles talking about how Orioles' fans should enjoy this but that the team wasn't going to win 162 games.

Alas, CBS maliciously jinxed the Orioles by noting that they were on pace to win 162 games before Wednesday’s game. Unsurprisingly, the Orioles lost a game in which they stranded ten baserunners, had a runner thrown out at third, and scored only two runs. The dreams of a perfect season were over. As Roch said (), “the ’72 Dolphins can pop the champagne corks.” Thanks CBS.

But while the win streak may be over, its impacts on the standings remain intact. No one can deny that the Orioles are leading the AL East. Jon wrote a number of statements about teams that have gone 7-1. It turns out that the team with the worst overall result to start the season 7-1 was the 1977 Oakland Athletics. They ended the season with a 63-98 record. The most wins that a team that has started a season 7-1 is 103 shared by the 1968 Tigers, 1990 Athletics and 2002 Yankees.

Most importantly, it has also resulted in improved rankings in the projections. As Jeff Sullivan wrote, the Orioles chances of making the playoffs increased by nearly 15% from the start of the season to 4/11 and that they doubled their playoff chances. As of 4/14, Fangraphs now projects the Orioles to win 82 or 83 games (Fangraphs Playoff Odds project the Orioles to win 81.8 games while Fangraphs Projected Standings have the Os winning 83) with a 30% of reaching the playoffs while PECOTA has the Orioles as an 80 win team with a 21.9% of reaching the playoffs.  That’s a pretty large bump in the standings for only eight games. Perhaps it has something to do with the interesting tidbit that 43.3% of teams that have a seven game winning streak make it to the playoffs. Others have been more pessimistic, including this largely incoherent article from the Washington Post.

Sure, this is good news. But what if we want to be even more optimistic? Suggesting the Orioles will win ONLY 103 games is definitely harshing my buzz. It’s possible to be even more optimistic as long as we forget just about everything we know about MLB.  Forget all knowledge about current players and farm systems and historic distributions of wins in MLB.  That knowledge simply isn’t going to tell us what we want to hear. Dilbert's boss explains everything via this comic strip.

In such a case, all we’d know is that the Orioles have gone 7-1 in 2016. The chances of that happening are really low; there’s only a 3.1% chance of a random binary sequence resulting in the same outcome 7 out of 8 times and only a .7% chance of it resulting in the same outcome seven times straight. Obviously, these are highly unlikely and therefore would strongly suggest that the Orioles have a better than even chance of winning a ball game.

In order to determine the Orioles actual chances of winning a game, we’d have to resort to using basic probability. The chances of the Orioles winning a game if they go 7-1 could be derived using the following formula:

(8 choose 1) * (x ^ 8) = .5
Solving this equation leads to a result of x ~ .707

This suggests that the Orioles should have a 70.7% chance of winning any of their remaining games provided that the assumptions above are true. If so, they should be expected to win another 111 games. Add that to the 7 games that they’ve already won, and they should be expected to win 118 games. Even before yesterday’s loss, this method suggests that the Orioles were on pace to win 147 games.

Of course, this method requires us to ignore everything we know about MLB. If we take player quality into account, then it’s highly implausible that the Orioles’ players are that much better than average. If we take historical win distributions into account, then it’s easy to understand that there is a low likelihood of even excellent teams winning seven games in a row and that few teams win 100 games. If we even just used runs scored and runs allowed to determine Pythagorean wins, then the Orioles would still be expected to win ONLY 115 games. I suppose that wouldn’t be a bad result.

So, if anyone feels discouraged about yesterday’s loss, then they can take heart. This method suggests that they should never have been expected to win 162 games, despite articles suggesting that they were on that pace, but rather only 147. And even now, they should still be expected to win 118 games.

But if someone offers you a bet that the Orioles will win 118 games, myself and the other Camden Depot writers strongly urge that you take the under. 

The Challenge Of Quantifying Outfield Defense

A few weeks ago, a commentator asked whether the Orioles should consider having Wieters play in the outfield. Jon asked for predictions of Wieters defensive ability as an outfielder via twitter and 54% of respondents thought he’d have a -50 UZR/150. While the Orioles' aren't going to use Wieters in the outfield, this discussion piqued my interest because that is a large amount of runs for a corner outfielder to concede.

UZR seems to suggest that such an outcome is reasonable. Hanley Ramirez was terrible defensively last year in LF and had a -30 UZR/150. Manny Ramirez had a few years where he had a -30 UZR/150 in LF. Unlike these players, Wieters has no experience playing in a corner outfield position and could very possibly struggle to make even simple defensive plays. If the worst corner outfielders are worth -30 runs, then Wieters could be worth -50 runs.

On the other hand, there are a limited number of balls hit to corner outfielders in a game and most of them are either easy or impossible plays. According to Inside Edge Data, only 40 to 50 balls hit at corner outfielders per season are even in question. A defensive outfielder would need to consistently botch the easiest plays that require minimal range to be worth 50 runs below average. Even a slow professional baseball player would be challenged to perform that poorly.

For context, Chris Davis’ offense was ranked at only 40 runs above average and Machado’s offense was ranked at 30 runs above average meaning that having Wieters in the outfield would have the same impact as replacing Machado's offense with a league average third baseman.

Furthermore, Machado is an excellent fielder and is only about 10 runs above average defensively at third base.  Simmons is an excellent shortstop and is usually 20 runs above average defensively. If UZR values the worst corner outfielders as worth -30 runs defensively, does this mean that there's a problem with UZR? It seems difficult to see how an experienced corner outfielder could cost his team that many runs.

Fangraphs argues that WAR works because there is a strong correlation between a team’s total WAR and their actual record. Glenn Dupaul from the Hardball Times argues that if WAR does a correct job at explaining where wins come from, the linear regression equation should have an intercept equal to roughly replacement level (47.7 wins) while each WAR should be worth roughly one win.

When I did this test with data from 2002-2015, the resulting formula returned had an intercept of 47.6 wins with each WAR being worth 1.00093 wins. When I split WAR into pitching and position WAR, the resulting formula was 46.84+.89*Position WAR+1.20*Pitching WAR which has interesting implications when thinking about this article. In any event, there can no debate about whether WAR works even if it could potentially be improved.

This is relevant because UZR is part of the WAR formula. One way to determine whether UZR works is by breaking WAR up into its components and seeing whether UZR is a significant factor for predicting teams’ total wins. I did such a test using a stepwise regression and found that it is significant. It was the third most important factor behind pitching and hitting although considerably less important than either of the other two. This implies that UZRs’ efficacy is limited, but still better than nothing.

The next question is whether UZR works for each position and specifically LF or RF. For this test, I input each teams’ defense score at each position (in order to replicate this it’s necessary to grab the data from the Fielding Tab rather than the Batting Tab at Fangraphs) for each team and season.  For most positions, UZR is helpful. Surprisingly, first base defense seems to have the highest correlation with wins (which possibly implies something about how defense is measured at other positions), while third base and center field data are also helpful. Data from shortstops, catchers, left fielders and right fielders are less helpful but still add some certainty. Surprisingly, second base defense as measured by UZR doesn’t add predictive power to the model.

The last question is which aspects of UZR are relevant for each position. Just because one aspect of UZR is relevant for a given position doesn’t mean that all of the aspects are relevant. In order to test this, I used each teams’ range, error, arm and double play range for each position to see which ones can be used to model wins. None of these factors apply to catchers and therefore this method can’t measure their performance.

My results suggested that range is relevant for first basemen, third basemen, center fielders, shortstops, extremely minimally for second basemen and has no relevance for corner outfielders. This is problematic because range is the largest component to UZR and therefore suggests that defense values for three positions are valued improperly and in a format that doesn't help us predict team wins.

Likewise, errors are only relevant for third basemen and shortstops. The average team’s left fielders have 5.2 errors over a season. The 75th percentile is 7 errors while the 25th percentile is 3 errors. Right fielders average 5.75 errors over an entire season with the 75th percentile having 7 errors and the 25th percentile having 4 errors. Even if an outfield error costs a team .8 runs, that still means errors from left fielders cost most teams about three runs a season and about two runs for right fielders.  Similar stories can be told for center fielders (average=4.8, 75th percentile = 6, 25th percentile = 3), first base (average = 10, 75th percentile = 13, 25th percentile = 7) and second base (average = 13, 75th percentile = 16, 25th percentile = 10).

The only statistic that is relevant for corner outfielders is arm strength. This would work for a player like Wieters as he’d likely struggle with range and errors but has a strong arm. It also means that teams are right if they discount UZR range values for corner outfielders and players like Heyward could be overvalued due to possibly overvalued defensive ratings.

The bottom line is that it is hard for us to accurately measure corner outfield defense and therefore can't determine how much damage a bad defensive outfielder inflicts on his team.

Matt's Free Agent Predictions

Jon recently introduced the Ballplayer Observation-based Remuneration Assumption System otherwise known as BORAS to determine future free agent salaries. I believe that the best way to predict future free agent salaries is by determining the most similar former free agents and using them as predictors. This method takes into account performance metrics and age, but also strongly depends on position. It is problematic because it takes considerably longer than just using a formula and is less structured.  As it takes longer and requires actual comparable players (that may or may not exist), I only looked at a few position players. Given the limited amount of free agents, it seems reasonable that an analyst for a team could spend the time necessary to implement this method for all relevant free agents even if it is difficult for me to do so.

The first player I considered was Alex Avila (29) is Jarrod Saltalamacchia (29). Salty is slightly better both offensively and defensively but not by much. Salty received a deal for 3 and 21 is part due to a strong year before entering free agency. I expect Avila will receive slightly less and therefore will receive a 2 year deal for about $12 million.

The next players I looked at were Howie Kendrick (32) and Daniel Murphy (31) and their comps are Omar Infante (32), Marco Scutaro (37) and Chase Headley (30). I’d take Kendrick and Murphy over both Infante and Scutaro. Headley is similar offensively but plays third instead of second base. If Infante and Headley can both get 4 years, then Kendrick and Murphy should get at least as many. I’m a bigger fan of Kendrick than Murphy and therefore expect Kendrick to receive 4 years and $56M while Murphy ends up with 4 years and $50M although a fifth year for both is possible.

Ian Desmond (30) has three comparable players, Peralta (32), Reyes (29) and Rollins (33). It’s hard to tell how highly Desmond should be valued due to his poor 2015. Before 2015, he compared favorably with Peralta and Rollins while he wasn’t as good as Reyes. After a poor 2015, he probably is in the same ballpark as Peralta and Rollins. All three of these players received deals until 34/35 so I’d expect Desmond to do the same. I project him to receive a deal akin to 5 years and $65M.

The comparables for Austin Jackson (29), Denard Span (32) and Dexter Fowler (30) are Bourn (30), Pagan (31) and B.J Upton (28).  I consider Fowler to be better than all three of these comparables even if he’s likely to need to move to a corner in the near future. As a result, I see him receiving five years like Upton did rather than four like the other two comparables. I think he’ll get the same contract (5/75M) that Upton did because he may be better than Upton but teams pay for home runs.

I also like Denard Span more than all three of those listed but he’s recovering from an injury. The fact that he didn't receive a QO could indicate that there are questions about his health. I think he ends up with either 3 and $42M or 4 and $56M as his health questions force him to take a shorter contract than he’d like.

I think Jackson is worse than all three comparables and therefore see him getting 3 years and $27M.

The comparables I use for Alex Gordon (32) are Jayson Werth (32) and Torii Hunter (33 – 2009 deal with the Angels).  I find it unlikely that Gordon will end up receiving less than Hunter outright or less than Werth given the time difference between when each signed their contract. I have Gordon at 6 years and $132M.

Justin Upton (28) appears to be much better than I thought he was. His comparables are Matt Holliday (30) and Shin-Soo Choo (31), and I believe that he’s better than Choo but worse than Holliday. This suggests he’s going to be paid and I see him receiving an 8 year deal for $150 million.

Yoenis Cespedes’s comparables are Choo and Grandy. Cespedes is better than Grandy although not as good as Choo. I see him earning 5 and $90M.

The best comparable for Rasmus is Granderson and therefore I see Rasmus earning 3 and $36M. This suggests that Rasmus would have been better off rejecting the QO but that it was close either way and wasn’t a bad decision.

Finally, I think that the best comparable for Chris Davis (30) is Josh Hamilton (32). Josh Hamilton had a longer track record when he signed his deal. However, both had their best year three years before free agency, struggled the next year and bounced back with a solid year. Both are huge power hitters and are adequate as corner outfielders. Both come with concerns, Hamilton is well-known for his personal issues while Chris Davis was suspended last year for taking Adderall. I think that Davis’s issues are less problematic than Hamilton’s and Davis is younger before entering free agency. Hamilton was able to get a contract for 5 and $125M and so I predict that Davis will receive a 7 year and $175 million deal with an outside chance of receiving 8 years and $200M.

The following chart shows a list of predicted contracts for each of the free agents discussed. After free agency, this will it possible to see whether any of the methods were accurate.

It indicates that BORAS is far more conservative than everyone else. Fangraphs had the second lowest predictions for these free agents and have claimed it should be expected because they use averages while the team willing to pay the most is the one that ultimately signs free agents. My method predicts that free agents will receive slightly less than MLBTRs predictions over two more years.