Tangotiger posts the official results of his 2007-2010 projection tests. Specifically he tests CHONE, PECOTA, Oliver, ZiPS and the Marcel projection systems using wOBA.
It’s very in depth and there’s a lot of really great information here about how different projection systems fared for different “classes” of players.
With several recent discussions (here and here and here and here) on home team advantage (HTA) – which began with Tobias Moskowitz’s and L. Jon Wertheim’s new book Scorecasting – I decided to see if I could find any reasonable causes for the advantage. I decided to look into areas that I thought home teams may have an advantage, namely errors (not much – about 2 wins league wide) and base running (some), but the number that caught my eye was the differences in batting average on balls in play from the home and away team. Here are the differences in BABIP for the home and away teams over the last few years:
After analyzing all of the preceding numbers (here and here), I bucketed various players into different bins according to their age, BMI and if they attended college.
The main problem I’ve run into with my analysis is that, as I divide the data, the sample sizes get smaller. With only 947 samples with which to work, the numbers get scattered quickly. For this chart, I’m only looking at the player’s age and his BMI.
With the general overall numbers available from yesterday’s article, here’s each variable:
Age
I divided the data into several buckets, according to individual pitchers’ ages. Here are the results:
This is the first in what will be a series on the disabled list. Here’s a link to the data.
I recently posted a projection formula (here and here) that estimated the chance of a starting pitcher spending time on the disabled list. To say the least, it generated several questions.
So I’m going to take a step back and show historic DL numbers for starting pitchers. For the purpose of this post, I’m only looking at pitchers with 20-plus starts and more than 120 innings from the previous season.
Teams lose players to the disabled list every year, but which teams had the most money tied up with these injured players in 2010? The following list ranks the teams that had the most dollars spent on players on the disabled list and the percentage of total payroll allocated to these days lost:
Yesterday, I went through the formula used for predicting which starting pitchers have the greatest chances of going on the DL in a given year. Now here are the projections for 2011. Besides revealing the list, a few other points and possible improvements to the process will be discussed.
First, here are the five most and least likely starting pitchers (>20 GS and >120 innings in 2010) to go onto the DL in 2011 (creating these projections is still a work in progress, so no one should take too much stock in them right now):
I’d like to solicit the help of our community in building a useful fantasy player valuations guide. When we have the parameters set, I’ll code it and put it up on FanGraphs.
There are a couple goals here:
1. Building a useful and easy to use fantasy player valuation guide.
2. Full transparency in how all the rankings work.
I’ve dabbled in this a bit, so I will first give a starting point:
In one of the World Series chats I hosted, it was stated that Matt Cain gave up weak fly balls and that is the reason that his xFIPs (2010 = 4.19 and lifetime = 4.43 ) are higher than his FIPs (2010 = 3.65, lifetime = 3.84). After finally getting all the wrinkles worked out, I am able to get the average distance for fly balls given up by a pitcher. So, does the fly ball distance given up by a pitcher help to explain the difference between his xFIPs and FIPs?
I took just the pitchers that threw over 60 innings in 2010 and subtracted their FIPs from their xFIPs. Then I got the average distance of all the fly balls for these pitchers and here are the top five leaders and laggards:
I have been wanting to have this win prediction tool available for a while and finally have what I think is rather simple working model. This spreadsheet can be filled out with the players anyone thinks will be playing, along with their all their stats and then the team’s projected wins will be calculated.
Note: An error was found on the spreadsheet dealing with position adjustment and corrected around 4:30 EST on 11/4. If you downloaded it before then, you will need to re-download it. Sorry for the inconvenience. -Jeff
While it can be used to get an idea of how many wins a team might get in the up coming season, I plan on using it to evaluate changes in a team. Those changes could be a free agent signing, a trade, an injury or a rookie called up to the majors. The team’s expect wins before or after the roster change can be evaluated .
Today, I am not going to do look at any team. I just wanted to make it available and once the Royals sign Cliff Lee, I can see how their expected wins compare before and after the acquisition.
