@CamdenDepot right. best defense and most HRs in MLB! Probably top 3 in runs scored. Improved SP and more depth. Buck factor. 90+ wins lock.This perspective as well as the bounty of tweets, follows, and unfollows the Depot collected in response to us being a bit nonplussed about the path chosen by the team this off season as well as to what these additions actually meant with regard to meaningful September and October baseball. Sort of related due to the similarity in numbers between what he earned and what Nelson Cruz will earn, this Orioles Hangout poll from 2011 was something I also found interesting. When Andy MacPhail signed an old, broken down Vladimir Guerrero, it was done with an incredible amount of fan fare. That masterstroke, according to that poll of 255 Orioles faithful, resulted in over 67% of them giving him an A- or better (89% gave it a B+ or better).
— Robert Hunt (@rahunt22) February 23, 2014
The Guerrero signing was memorable for me because of two things. One, I had a series that year that followed Vlad's attempt up old DH mountain. He finished with a bWAR of 0.4, good for 20th out of 25 all-time. Two, it resulted in this article railing against my pessimistic view and suggesting that it might well be Vlad's curtain call. In a series of tweets (of which I have no idea how to find), the author stated that me equating Vlad's 2010 offensive output with Matt Wieters' 2010 offensive output given the context of their respective defensive positions made me a "liar". Of course, even if my opinion was faulty, that would make me simply misguided as opposed to being a liar. Really, though, I think what the author was really trying to express was that he was very much emotionally involved with the team and highly invested to see them succeed. That can be difficult to explain or even comprehend about oneself, so strangely calling someone a liar may suffice.
So where this leads me is about emotional expectation and the use of rather unaware projection modeling. Why are projections unaware? They are unable to adequately assume player usage, past (to some extent) or future injuries, weight training, etc. Basically, all of the reasons why many folks claim that there projections are useless. However, their inability to clearly predict the future does not mean that are worthless in terms of projecting the future. In other words, a team projected to win 55 games will not make the playoffs. The models know enough about the histories of player populations to realize that this event is literally almost impossible.At a projected talent bases increases, then those probabilities grow larger and should give some hope to fans (along with a dose of realism).
In order to show this, I took a projection (devised with PECOTA, ZiPS, or MARCEL) and compared that with the actual results from (2003-2011). I did not double count years. From 2003-2009, I used PECOTA projections I had on hand. From 2010-2011, I used MARCEL. From 2012-2013, I used ZiPS. The PECOTA projection model was reported by Baseball Prospectus. The MARCEL and ZiPS projection models were reported by Replacement Level Yankees Blog. It may look messy to take things from so many sources, but the point here was not specifically to test a specific model. It was to casually use models blindly under the assumption they perform rather similar.
So, what does the table above mean? Hopefully, the graphic below helps. Each standard deviation includes a certain amount of the population. If we assume that win deviation is normally distributed, then we would assume that a team will perform within 9.3 games better or worse about 68% of the time. To cover 95% of all events, a range of 18.6 games better or worse would be expected. Using this approach, you would expect a team to perform 27.9 games better or worse would happen about 1 times in about 11 seasons. In our data set of 11 seasons, this has indeed happened only once (2004 Arizona Diamondbacks, 81 projected wins, 51 actual wins).
Year St DEV Model All 9.3 2003 8.8 PECOTA 2004 11.7 PECOTA 2005 7.6 PECOTA 2006 7.5 PECOTA 2007 6.4 PECOTA 2008 9.7 PECOTA 2009 11.7 PECOTA 2010 9.6 MARCEL 2011 10.1 MARCEL 2012 10.9 ZiPS 2013 8.7 ZiPS
The last two years there has been some grumbling from the fan base that ZiPS has been unfair to the Orioles in its projections. Both seasons, the Orioles have, as a team, outperformed the projection using ZiPS. Of course, a sample size of two is not a powerful sample size and it would make more sense to assume that it was a statistical anomaly unless we identify some mechanism that ZiPS and/or the team projection model has issues with. For instance, if Buck Showalter is the difference between a 69 win team and a 93 win team then neither projection system will be able to pick that up. Additionally, Buck needs to talk to his agent because if he was worth 24 wins then he needs to be paid about 144 MM a year.
Below is a sampling of the last two seasons the Orioles enjoyed as well as Clay Davenport's current projection of the team winning 83 games after adding Ubaldo Jimenez and Nelson Cruz.
|exWins||range||n||stdev||low||high||% to 93||% to 96|
|2012||69||66 to 72||42||8.9||-15||24||2.4||0|
|2013||79||76 to 82||100||9.4||-30||19||7||2|
|2014||83||80 to 86||123||9.5||-30||19||16.3||6.5|
For better or worse, I expanded the projected win totals in order to get larger sample sizes to work with. In that first line, the Orioles were projected to win 69 games in 2012. The Orioles outperformed that mark by 24 games. To make the Wild Card (93 wins is a decent number to use for that), a team at 69 wins needs to outperform by exactly 24 games. The Orioles are the only team in that group to perform so well. Historical events suggest a 2.4% possibility. In 2013, the Orioles were projected to win 79 games and outperformed that mark by 6 games. That was not good enough for the playoffs. What they needed was in the neighborhood of outperforming their mark by 14 games. In that data set, only seven out of 100 teams have manage to do that. Of those seven, two did well enough to improve to a point with the divisional crown was a likelihood.
What does the history of teams in the 80 to 86 win bracket look like with respect to under and over performing their projected wins?
The above is a weighted distribution graph. Just based on this one grouping, it appears that teams that crash, crash to varying degrees. Perhaps, this has to do with increased play of prospects, dealing of players, or something along those lines. Still, it holds up pretty well as data that appears normally distributed. The Orioles would be looking to improve by 10 games over this projection, which has happened about 16% of the time in the past. Greedy for a division crown? That number drops to 6.5%. Those odds would be 1 in 6 and 1 in 15, respectively. Keep in mind that in Davenport's projection that the Orioles would need to leap frog several teams. Briefly, it is more likely for the Orioles to over perform and another team to under perform than it is for them to over perform and two teams under perform. That whole concept though will not be addressed in this post.
Going back to the original tweet suggesting that 90 wins are a lock, a team must to projected to win 98 games or more to have not fallen below 90 wins. Six teams have been described as 98 win or better teams. Six out of 330.
For projected 83 win teams, four out of ten won 90 games. In other words, it is possible for the Orioles to be a 90 win team. History suggests that. However, that same history also suggests that it is not likely.
Proj. 90+ wins n 98+ 100% 6 97 50% 2 96 50% 2 95 67% 3 94 50% 4 93 43% 7 92 43% 7 91 50% 10 90 80% 5
Addendum (Model Projections)
FG (STEAMER) 78-84