Over the weekend, Rich Lederer of The Baseball Analysts pointed me to an ESPN story by David Srinivasan (Insider $) about a statistic I developed a few years ago, PrOPS. This led to few comments on the site that I wanted to address. I’ve had numerous conversations about PrOPS since its invention, so I wanted to write a post to bring people up to speed on its development.
First, let me offer a brief introduction. PrOPS (which stands for predicted OPS) is a measure that generates an OPS—on-base percentage(OBP) plus slugging percentage(SLG):mdash;for a player based one a few things that players do. Rather than focus on outcomes on balls in play (hits, outs, etc.) that generate OBP and SLG, PrOPS uses batted-ball types (line drive rate, groundball-to-flyball ratio) and a few other things to generate the typical outcome for a player who hits the ball in this manner.
Now, PrOPS has its origins in my wanting to use batted ball types recently made available by The Hardball Times. In the introductory article on the subject, I used PrOPS to predict which slumping and hot hitters were due for a rise and fall in the 2005 season. The initial numbers were based on one season of data. A few people responded that many of the under-performers were speedy while the over-performers tended to be big and slow. So, I made a minor adjustment to the formula to account for speed. However, the adjustment did very little.
At the end of the season, I wrote a chapter for The Hardball Times Baseball Annual 2006, refining PrOPS using several seasons of data. When including several seasons, I found no relationship between any existing measure of speed and over/under-performance. This doesn’t mean that speed has no impact, but it doesn’t seem to be very important. A few months ago, I posted a summary of the findings.
There is a highly statistically significant relationship…between a player’s over/under performance and his decline/improvement. And the greater the the deviation between PrOPS and OPS, the larger the reversion is the following season. For every 0.01 increase/decrease in a player’s over/under performance, his OPS is likely to fall/rise by 0.008 the following season. For example, a player with an OPS 10 “points” above his PrOPS, can expect his OPS to fall by eight points in the following season. That is quite a reversion.
I also generated lists of the top-25 over and under performing season from 2002-2004. And what happened to them?
Of the top 25 over performers, 20 players had lower OPS in the following season.
Of the top 25 under performers, 21 improved their OPS in the following season.
The article also lists the top-25 over and under performers for 2005. What happened to those players in 2006?
Of the over performers, 12 players declined, 7 improved, and 6 did not deviate more than 20 OPS-points from the previous season. Of the under performers, 11 players improved, 7 declined, 3 had no change, and 5 didn’t garner serious playing time. It’s not an air-tight projection system, but there seems to be some information there.
OPS explained approximately 43% of the variance in OPS in the following year, while PrOPS explained about 46%.
PrOPS is not a stand-alone projection tool. You should not look only at a player’s PrOPS and assume it’s exactly what the player should be doing. When I look at it, I also consider the player’s recent hitting history, injuries, aging, and all that other stuff we sometimes use to evaluate hitters. But when I see a player have a career year, and his PrOPS don’t show it, I start to get suspicious.
If you’re curious about the over/under performers of 2006, see The Hardball Times.