With opening day of the 2013 regular season just around the corner, I thought it would be fun to look at a couple of projections for the AL East and some Orioles players to see how fancy schmancy computer programs thought this year would turn out. And if you’re a fan of the Orioles, you probably won’t like what you see.
Zips Projection System
First up is the Zips projection system, which was created by Dan Szymborski (used at Fangraphs). On Tuesday, Szymborski posted an article on ESPN projecting the standings for each division (Insider subscription required). How does Zips work? According to Szymborski:
“Seasons are simulated a million times using a Monte Carlo method, the percentile performance of player projections and estimates of roster construction.”
The Fangraphs Library also has a definition if that wasn’t enough. In addition to projected wins and losses, the table includes (in order) the percentage each team has to win the division, finish in last place, earn a wildcard, make the playoffs, and win the world series.
| Team |
W
|
L
|
GB
|
PCT
|
DIV%
|
LAST%
|
WC%
|
PLAYOFF%
|
WS WIN%
|
| Toronto |
94
|
68
|
—
|
0.580
|
42.90%
|
5.80%
|
25.20%
|
68.10%
|
6.90%
|
| Tampa Bay |
88
|
74
|
6
|
0.543
|
23.10%
|
13.40%
|
25.80%
|
48.90%
|
4.50%
|
| Boston |
84
|
78
|
10
|
0.519
|
13.20%
|
23.30%
|
20.90%
|
34.10%
|
3.00%
|
| New York |
83
|
79
|
11
|
0.512
|
11.30%
|
26.70%
|
18.40%
|
29.70%
|
2.60%
|
| Baltimore |
82
|
80
|
12
|
0.506
|
9.50%
|
30.70%
|
16.70%
|
26.20%
|
2.20%
|
If you’re wondering how the projected lineup and starting rotation are expected to perform, you’re in luck…
Projected Opening Day Lineup
| Name |
POS
|
AVG
|
OBP
|
SLG
|
WAR
|
| Brian Roberts |
2B
|
0.244
|
0.309
|
0.363
|
0.2
|
| Nick Markakis |
RF
|
0.281
|
0.350
|
0.428
|
2.0
|
| Adam Jones |
CF
|
0.277
|
0.326
|
0.474
|
3.6
|
| Matt Wieters |
C
|
0.256
|
0.330
|
0.433
|
4.5
|
| Chris Davis |
1B
|
0.252
|
0.308
|
0.450
|
0.9
|
| J.J. Hardy |
SS
|
0.258
|
0.304
|
0.424
|
3.5
|
| Nate McLouth |
LF
|
0.231
|
0.318
|
0.379
|
1.2
|
| Nolan Reimold |
DH
|
0.246
|
0.322
|
0.420
|
1.0
|
| Manny Machado |
3B
|
0.252
|
0.311
|
0.418
|
2.7
|
Projected Starting Rotation
| Name |
W
|
L
|
ERA
|
WHIP
|
K/9
|
BB/9
|
FIP
|
WAR
|
| Jason Hammel |
9
|
6
|
3.72
|
1.27
|
7.98
|
2.98
|
3.59
|
2.5
|
| Wei-Yin Chen |
10
|
8
|
4.10
|
1.23
|
4.86
|
1.85
|
4.44
|
1.3
|
| Miguel Gonzalez |
6
|
7
|
4.57
|
1.43
|
6.60
|
3.61
|
4.59
|
0.3
|
| Chris Tillman |
14
|
12
|
4.29
|
1.31
|
6.90
|
3.04
|
4.44
|
1.3
|
| Jake Arrieta |
8
|
11
|
5.16
|
1.51
|
7.09
|
4.23
|
4.81
|
0.5
|
PECOTA
If you didn’t find the projection provided by the Zips system to your liking, you should probably stop reading, because the PECOTA projection system developed by Nate Silver and Baseball Prospectus looks worse. To make its projections, PECOTA relies heavily on statistics in a player’s previous seasons and statistics of historically similar players.
| Team |
W
|
L
|
GB
|
PCT
|
DIV%
|
WC%
|
PLAYOFF%
|
WS WIN%
|
| New York |
88
|
74
|
—
|
0.541
|
45.8%
|
21.2%
|
67.0%
|
9.0%
|
| Tampa Bay |
85
|
77
|
2
|
0.525
|
25.4%
|
23.5%
|
48.9%
|
4.7%
|
| Boston |
83
|
79
|
5
|
0.511
|
15.0%
|
19.0%
|
34.1%
|
2.6%
|
| Toronto |
82
|
80
|
6
|
0.506
|
12.0%
|
16.2%
|
28.2%
|
2.0%
|
| Baltimore |
75
|
87
|
13
|
0.462
|
1.7%
|
3.4%
|
5.0%
|
0.2%
|
As you can imagine, the individual player projections in PECOTA aren’t as high on the Orioles players as Zips, and I don’t want to add to the negativity of this column, so we’ll leave those out.
So what is the take away of this? Well there’s good news and bad news. The bad news is the projections don’t think the Orioles have much of a chance to return to the playoffs. The good news is that last year, the Orioles made the playoffs when the projection systems didn’t give them much of a chance. There is a reason why the games are actually played, and it’s because no matter how sophisticated a computer model you have, it can’t predict the future, and it sure can’t predict baseball.
On that note, happy Opening Night and enjoy the season!
Orioles Proving Ground 