Page rendered in 0.4924 seconds
59 querie(s) executed
— Where BTF's Members Investigate the Grand Old Game
Thursday, July 04, 2002
Earned Wins Percentage, A New Pitcher Rating Tool
This young man asked for some feedback.
Although there are many different ways to evaluate a pitcher, the following
My name is Daniel Scotto. I am a 15 year old high school student who was interested in creating a stat for pitcher evaluation. Last summer, I tried to work out a formula to see which hitter is most deserving of the Most Valuable Player award, by including a combination of OPS and winning percentage. Instead, I was led to my current hitter evaluation tool, XR, or extrapolated runs, by Jim Furtado. My system was a failure, but I still wanted to try my hand at a new evaluation of pitchers.
The first thing that occurred to me while reading Mr. Wolverton?s article was the fact that the wins that a pitcher gets, although important to a team, are not exactly a very good evaluation of a pitcher?s personal performance. This is quite obvious; it is impossible to get wins without run support. The simple earned run average is useful as well, but it is not a good evaluation of a pitcher?s game-to-game performance. Other stats, like walks + hits per innings pitched (WHIP) and a K/BB ratio are extremely helpful, but they do not tell the whole story of a pitcher.
The main thing that I gained from Mr. Wolverton?s article was the importance of the individual start. So the thought occurred to me that the way to determine if a pitcher has earned himself a win would be to consider how many different games could surmount his own. Simply put, what is the percentage of games that a pitcher has earned a win in based on his personal innings and runs totals?
Innings and runs are basically the only two stats which show the pitcher?s importance to a team. In an offseason, if I was a general manager for a baseball team, and I take a look at 3 free agent pitchers, I am interested in many different stats: innings, ERA, W/L, K/BB, WHIP, HRs allowed, the list goes on. But instead of examining the season as a whole, I would prefer to examine it at a game-to-game level. I would look at every one of the starts from each of the pitchers, and I would try to find the trend to attempt to predict his next year?s performance.
Examining starts on a game-to-game level as described in the preceding paragraph is truly a qualitative study. There would be no true mathematical calculation to see which is better. If there were numbers to assign to each of these starts, then a more accurate evaluation could be made.
And that was the thought process that led me to my system.
The first part of the experiment was to see the percentages of occurrence of a particular start. This involved looking at countless old box scores and placing only two elements of data into my Excel spreadsheet: innings, and runs. I did this for 200 games, courtesy of the box score archives from ESPN and CBS Sportsline. This simply converts into 400 pieces of data.
The next part of the evaluation was to group the various starts. I decided to form a chart like the one below:
The numbers in the left column are the innings; the numbers in the top row are the runs allowed.
I decided that if a pitcher gave up more than seven runs, he did not give himself any chance of victory.
The hardest part of the experiment was ordering the quality of start. Obviously, the best chance of winning is 9 innings, 0 runs. It was not as concrete for the other ones. Which was better: 6 innings, 0 runs, or 9 innings, 1 run? The thought kept occurring to me that it truly depends. I asked for various people?s opinions. The two that stood out were my sister?s opinion and my friend?s opinion: my sister said that she?d rather have 9 innings, 1 run, because there is less chance for the bullpen to blow it, but my friend said that he would rather have 6 innings, 0 runs, because he is a strong supporter of a good bullpen. So the epiphany came to me: two charts, one for innings, and one for runs. The average would be the final number, although one could look at either chart based on team need.
Below are the orders for each preference of start.
Below are the final percentages for each start based on the calculations from the first chart. (eg: to get the innings chart percentage for 6 innings, 3 runs, you would subtract the percentage of every start better than it from 100%).
The following percentages are of the seasons of pitchers in 2001. The percentages are means and medians. The first debate will be of Johnson v. Schilling, 2001.
Let?s examine 4 pitchers in the New York Mets rotation last year (I leave out the 5th, because it was a combination of Rick Reed and Bruce Chen). I personally am a Mets fan, and they had rather good pitching last year.
A third one that came to mind immediately was the AL Cy Young Award of 2001. Although I had no one stat or point, I felt that Freddy Garcia, Mike Mussina, and Tim Hudson all were more deserving than Clemens. Let’s take a look.
What these numbers mean to me is what I have felt all along. All three are better candidates than Clemens. However, this is only 1 stat; it would not be the lone determiner for me. My top 3 for Cy Young would have been Mussina, Garcia, and Hudson, respectively.
These are just three of the many comparisons that can be done with the new stat, earned wins percentage (EWP). It is quite easy to use, just go to CBS Sportsline and use their game logs. Record every one of a pitcher?s starts, and match the corresponding percentage with a start. Hopefully, this information will be able to help someone compare pitchers.
Questions, comments? Email Me.
You must be logged in to view your Bookmarks.
Loser Scores 2015
(12 - 2:28pm, Nov 17)
Loser Scores 2014
(8 - 2:36pm, Nov 15)
Winning Pitcher: Bumgarner....er, Affeldt
(43 - 8:29am, Nov 05)
Last: ERROR---Jolly Old St. Nick
What do you do with Deacon White?
(17 - 12:12pm, Dec 23)
Last: Alex King
(15 - 12:05am, Oct 18)
Nine (Year) Men Out: Free El Duque!
(67 - 10:46am, May 09)
Who is Shyam Das?
(4 - 7:52pm, Feb 23)
Last: RoyalsRetro (AG#1F)
Greg Spira, RIP
(45 - 9:22pm, Jan 09)
Last: Jonathan Spira
Northern California Symposium on Statistics and Operations Research in Sports, October 16, 2010
(5 - 12:50am, Sep 18)
Mike Morgan, the Nexus of the Baseball Universe?
(37 - 12:33pm, Jun 23)
Last: The Keith Law Blog Blah Blah (battlekow)
Sabermetrics, Scouting, and the Science of Baseball – May 21 and 22, 2011
(2 - 8:03pm, May 16)
Last: Diamond Research
Retrosheet Semi-Annual Site Update!
(4 - 3:07pm, Nov 18)
What Might Work in the World Series, 2010 Edition
(5 - 2:27pm, Nov 12)
Last: fra paolo
Predicting the 2010 Playoffs
(11 - 5:21pm, Oct 20)
SABR 40: Impressions of a First-Time Attendee
(5 - 11:12pm, Aug 19)
Last: Joe Bivens, Floundering Pumpkin