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Primate Studies — Where BTF's Members Investigate the Grand Old Game Thursday, July 04, 2002Earned Wins Percentage, A New Pitcher Rating ToolThis young man asked for some feedback. Introduction 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. ProcessThe 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%).
EvaluationThe 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. |
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1. tangotiger Posted: July 04, 2002 at 12:33 AM (#605411)You may be interested in my win expectancy chart which can be used for many different things, including establishing win% rates based on runs and innings. I'm sure Wolverton uses something similar for some of his work. Good luck!
http://www.geocities.com/tmasc/we.htm
One thing that I think you'll have to do is adjust each start for the ballpark at a minimum. You probably also want to adjust for the quality of opponent's offense somehow. Leiter keeping up with Schilling is good, but he did it with 1/2 his starts at pitcher friendly Shea, while Schilling was doing the same thing in what's probably the 3rd best hitter's park in the NL.
You are definitely on to something here. ERA and WHIP are unduly influenced by one really bad start. Look at John Smoltz this year, that early 9-run 1-inning outing. He'll never recover from that. David Wells had a start in 1992 where he gave up 13 runs in 4 1/3 IP, and his ERA for the year was almost a run higher, despite the fact that it only cost the team 1 game.
Thanks for the park adjustment suggestion. I looked at the SNWL park adjustment numbers, and I'm experimenting with them now. I'm hoping to calculate every start in 2003, hopefully with park adjustments. Thanks!
The List I've Used
ARIZONA DIAMONDBACKS: -2.3%
ATLANTA BRAVES: -6.2%
CHICAGO CUBS: -6.9%
CINCINNATI REDS: +5.2%
COLORADO ROCKIES: +60.4%
FLORIDA MARLINS: -16.3%
HOUSTON ASTROS: +12.0%
LOS ANGELES DODGERS: -13.8%
MILWAUKEE BREWERS: -7.7%
MONTREAL EXPOS: -0.2%
NEW YORK METS: -13.1%
PHILADELPHIA PHILLIES: -0.1%
PITTSBURGH PIRATES: -3.6%
ST. LOUIS CARDINALS: -3.6%
SAN DIEGO PADRES: -17.3%
SAN FRANCISCO GIANTS: -16.8%
ANAHEIM ANGELS: +3.8%
BALTIMORE ORIOLES: -3.2%
BOSTON RED SOX: +10.8%
CHICAGO WHITE SOX: +5.7%
CLEVELAND INDIANS: +7.1%
DETROIT TIGERS: -3.8%
KANSAS CITY ROYALS: +6.1%
MINNESOTA TWINS: +13.2%
NEW YORK YANKEES: -2.7%
OAKLAND A'S: -2.5%
SEATTLE MARINERS: -15.6%
TAMPA BAY DEVIL RAYS: +10.9%
TEXAS RANGERS: +10.9%
TORONTO BLUE JAYS: +6.0%
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