After a solid 4 months or so here I’ll be moving on, changing WordPress adresses if you will. Erik Manning has restarted Play a Hard Nine and has graciously allowed me to ride his coat tails over there. Starting now I’ll be posting over there full time (or at least as close to full time as I get). I’ll be doing exactly the same kind of stuff over there as I have been doing here, so update your RSS feeds accordingly (all 2 of you).
Thanks for visiting, and say hi over at PAH9 when you get the chance.
In the last post cautioning the position change for Mike Cameron, I neglected to mention that he’ll lose 1 WAR for the position change, but likely recapture some of back in runs saved for his defense as he’d be baselined against other LFers and not CFers. I believe with the way the position adjustments are constructed it would mathematically be a wash (i.e. he’d be a +20 LFer instead of a +10 CFer). Do I completely believe he’ll be a +20 LFer (ranking at or near the top of the league)? Eh probably not, but the 1 WAR decrease I talked about is probably a little overstated.
For those that have been beating the Mike Cameron drum as a left field option, remember that when you quote his WAR value, it’ll decrease by one when you consider the postion adjustment from CF to LF (from ~+2.5 to ~-7.5). Clearly some of that would be mitigated if he plays CF vs. LHP, and I’m not saying he wouldn’t be an ok option at the right price, just be wary if you think he can post ~4 WAR moving forward.
I’m still working up the similarity score stuff, I have RHP pretty well done, just have to put it into a coherent post. and I’m also in the process of finishing up the regressed UZR stuff for the rest of the positions. Whenever I get them both done I’ll also make the spreadsheets available in Google docs or something like that.
If you caught my previews of the first two NLDS games you saw that I did a couple of heat graphs (aka heat maps). I had what I thought was a decent idea for another version of that graph. The attempt is to capture a pitches effectiveness by movement. Without further ado, we have Carp’s curveball’s whiff rate.
The vertical axis is vertical movement in inches, and the horizontal axis is horizontal movement. The picture is from the catcher’s perspective. The basic takeaway is the more straight down the pitch broke, the higher percentage of whiffs he got. The chart doesn’t break out for batter handedness, and I also removed some periphery data points where the sample size skewed the chart. Anyway, what do ya’ll think? is there some value here, or is it simply a pretty picture for the sake of pretty pictures.
The guys over at Cardinals GM asked whether the Cards should look into resigning Joel Piniero. After some initial research into the topic I think I’ve come up with some parameters for what would make a profitable deal for the Cardinals. This quick look analysis will be fairly simplified to only include the next year and not years 2 and 3 of a potential deal (in other words not exactly looking at reality). First some projections for two of the key players in the discussion, Joel and John Smoltz. For these “projections I just used a 5/4/3 weighted average of the last 3 years of xFIP.
While these projections are not as robust as those that will come out in the near future from various sites, they are a place to start the conversation. We could easily take Piniero’s projection and conclude that he’d be worth ~11M next year on the open market; however, why should we pay market value when we have other needs to address (i.e. left field). With that in mind let’s look at two scenarios that could play out. Option 1 would be to sign Smoltz to be the 4th starter and spend the rest on LF. Option 2 would be to sign Piniero and spend the rest on LF. For the sake of this analysis we’ll assume Smoltz will sign for the same 5.5M he did last year. We’ll also assume the Cards haev 25M to spend between these two positions (any number would suffice here, 25 seemed nice and round). Under those working assumptions, and tinkering a little with the IP on the projections I can generate the following WAR chart
The 200 Projected Piniero line takes the Piniero projection and pushes it to 200 innings. The 3.5 WAR line assumes less regression for Piniero, and performance that is a little closer to this years, only still nowhere as good as this years. The numbers in front of Smoltz are the numbers of innings assumed for Smoltz. Clearly the higher the line the more WAR that combination produces (it assumes paying market value for a LFer with the remaining dollars). So if you take a leap of faith that Smoltz can pitch 130 innings then projected Piniero is only a better value at 6.5M. If we assume less of a regression of Piniero than he is the better value up until ~11M. Anyway, just a little more info for the conversation. I’ll save the charts and maybe update them with ZIPs and CHONE at a later date.
After reading some discussions over at The Book blog about UZR and regression to scouting reports I thought it would be a good idea to use the fans scouting reports as a regressing factor for UZR.
My methodology was as follows: I binned players into groups based on their positional ranking within the scouting reports, and then calculating the weighted average of the UZR/150s of the players within the bins. The following table is the results using the data from 2007, 2008, and 2009. (Quick Edit, the below table is for SS only, sorry for any confusion)
At this point my methodology diverges, as I wasn’t sure which method I like better. Method 1 is to regress each individual season’s data based on the players rank that season to get a new seasonal UZR, and then weight across the 3 years of data. Method 2 is to weight across the three years of data and then regress using the most recent fans scouting report ranking (in this case the interim 2009 results).
Method 1 is clearly sensitive to the ebb and flow of the fans, and is also a little more dependent on those rankings since the UZR’s being regressed have a smaller number of defensive games associated with them. Method 2 does not create “single season” stats as some people would probably like, and it only uses the most recent fan’s ranking. Overall I think I prefer Method 2, but could be swayed either way. The following table lists the top 10 shortstops ranked by Method 2 (I really need a better name).
|Rank||Name||3 year uzr||Method 1||Method 2|
and the bottom 10
|Rank||Name||3 year uzr||Method 1||Method 2|
A couple of quick caveats, if you read the comments on the above linked thread, I noted that defensive games at fangraphs looks a little messed up. Those going back to normal would likely change these results. Also, I didn’t do a great job of searching the blogosphere, so if this has been done before, I apologize for presenting it as a new methodology.
A few days ago in his 10@10 Derrick Goold talked about velocity and the Cardinals bullpen. I thought I’d take a look at velocity vs. effectiveness for relievers, and the following table is what I came up with
|Velocity||value/100||total value||FIP||ERA||K/9||% FB Thrown|
Data is from Fangraphs and compiles all of the totals from the “qualified” relievers.
A couple of quick bulletized points
- Velocity is the reliever’s average fastball velocity, not on an individual pitch basis
- Value numbers are in runs above average
- I prefer to use the value/100 as it removes the playing time element. It’s value per 100 pitches thrown.
- It appears that once you get below 96 there is not a whole lot of differentiation other than in the amount of fastballs thrown (not surprising)
- Even the more global metrics (FIP, ERA) don’t have a lot of differentiation between the groups