A few weeks ago I posted a set of defensive projections for SS based on regressing a 3 year average UZR to a population based on the Fan’s Scouting Report created by tangotiger. After some discussion over at The Book Blog, I altered my methodology a little and have come up with a set of projections for all positions.

First a quick discussion about the methodology. The projected values are a weighted average of the

- Players 3 year weighted UZR (5/4/3 style)
- The UZR mean of the “scouting population” to which the player belongs (more on this in a minute)
- The league average (i.e. 0).

The weights are

- Effective defensive games over the three year sample (also weighted, so not just the sum)
- 125 games
- 125 games

which basically means the larger the 3 year sample, the less impact the “regressions” have, which falls under the basic premise of the more data you have the less you need to regress.

The scouting population is determined by where the player ranks in Tango’s Fans Scouting Report (FSR). I took the last three years of FSR data and found the average UZR/150s for various bins of players (currently done by ordinal ranking, but will likely transition to binning by overall score once 2009 numbers are computed by Tango ). I then crossed that data with were the specific player ranked in the 2009 voting, with that number becoming the scouting regressing factor.

For those that read my previous post on it, Method 2 was the methodology adopted (as MGL pointed out that it was the correct method). Anyway on to the results. First the leaders (with a minimum of 60 effective DGs)

Name | Pos | UZR/150 |
---|---|---|

Travis Ishikawa | 1B | 5.6 |

Chase Utley | 2B | 10.8 |

Omar Vizquel | SS | 9.3 |

Evan Longoria | 3B | 11.9 |

Carl Crawford | LF | 10.9 |

Franklin Gutierrez | CF | 12.2 |

Jayson Werth | RF | 11.2 |

You’ll note that the projections are for UZR/150 so you’d need to utilize an expected playing time to convert these to runs. For example, I find it highly unlikely that Omar Vizquel will get enough playing time to save ~9 runs, but clearly if he played 75 DGs then he’d save ~4-5 runs.

Now for the laggards

Name | Pos | UZR/150 |
---|---|---|

Jason Giambi | 1B | -5.6 |

Alberto Callaspo | 2B | -5.7 |

Yuniesky Betancourt | SS | -10.1 |

Edwin Encarnacion | 3B | -8.9 |

Adam Dunn | LF | -14.9 |

Vernon Wells | CF | -10.1 |

Brad Hawpe | RF | -19.1 |

For those that want to make the argument that Dunn won’t be playing left field again, second to last went to Delmon Young. For those making the same argument about Giambi, second to last there was Billy Butler. I’m posting the results spreadsheet on google docs with the link over on the sidebar, so feel free to download it and use it for whatever you want. The sheet contains the position the projection is for, the projection itself, 3 year UZR/150, and the effective DGs.

Finally, since this is a Cardinals blog, I wouldn’t leave you without giving you the key returning Cardinal players

Name | Pos | UZR/150 |
---|---|---|

Brendan Ryan | SS | 7.2 |

Colby Rasmus | CF | 5.5 |

Albert Pujols | 1B | 5.0 |

Ryan Ludwick | RF | 1.0 |

Skip Schumaker | 2B | -5.1 |

Julio Lugo | SS | -5.9 |

A couple of final caveats about the projections. I know there are players missing, and there are definitely player/position combos missing. As a first pass I only projected the position that they had been identified with in the FSR. I plan to remedy that, but it’ll have to wait until the next iteration. Also, I didn’t apply an aging factor, which is clearly not a good way to go about projecting. In his BtB piece Jeff mentioned a -0.7 UZR, but I want to give some thought about how to apply that to UZR/150. Hopefully the next iteration will have some aging factor applied, up until then, apply whatever you see fit. Anyway, download away, and let me know if you have questions/problems.