I’ve been asked to give an explanation of PrOPS. Since it is a stat that I expect will be used frequently, at least by me, at this site, I’ll oblige with a little crash course in PrOPS. PrOPS is available at the stats section of The Hardball Times.
First, lets think way back to a time when we thought ERA was the best measure of a pitcher’s talent, ouch! Finally, as I assume most people know, Voros McCracken came up with DIPS, defensive independent pitching. His most basic conclusion is that you can better predict ERA by taking away hits then including them. As time progressed it was David Gassko who finally gathered coefficients for all the batted ball types and came up with a new dips formula that relied on the type of batted balls. The difference between DIPS 3.0 and what McCracken came up with was the same as the difference between McCracken’s formula and just using ERA. Basically, the improvement was huge. This is one of the most important concepts in the evolution of Sabermetrics, the luck of the single. By establishing that pitchers did not have control of balls in play, things changed. Could this work for hitters. Enter PrOPS.
First, let me post this table once more, from fangraphs.com and Dave Studeman.
Type AVG SLG OPS
FB .265 .720 .978
GB .236 .259 .495
LD .719 .948 1.667
This is what PrOPS is based on. There is clearly a relationship between the type and outcome.
PrOPS was invented by JC Bradbury, using batted ball data from BIS. He developed a formula that includes the following information: LD rate, GB: FB ratio, walk rate, HBP rate, K Rate, HR Rate, and home ball park. It was discovered that PrOPS was a very good indicator of what a players OPS would be minus luck. Most importantly, PrOPS explained OPS the next year better than actual OPS did. The basic result is that players who got abnormally lucky on batted balls got their numbers normalized. If you hit more line drives than another player, you should do better. Since most of the value of a flyball is dependent on homeruns, player’s averages on those should differ more than any of the others. Clearly Sexson is going to have more productivity from his flyballs than J-Lo, and PrOPS factors that in.
One of the reasons I love this stat is that, obviously, it works. If there were 1million game seasons, PrOPS would be unnecessary, but for stats like BABIP to stabilize, they need much more than the 600 pa’s in a season. If you asked me to predict Jose Guillen’s stats for next year, I could better do it with his HR rate, LD rate, etc than I could by actually looking at his OPS. That’s pretty important. Almost all players that severely over/underperform their PrOPS will return towards the total by PrOPS. BABIP (a factor in OPS) is a horrible stat to predict future performance, and this does better. There is a huge relationship between over/underperformance and rise/decline in the following year. JC hints that the formula is something close to [PrOPS-OPS)*.80]+OPS. Basically take 80% of the difference between PrOPS and OPS and add it to OPS. I believe this only works after a season, not during.
There are criticisms. Shouldn’t Ichiro! have a higher BA on GBs than Jose Vidro (there is no adjustment for speed). JC insists that there is no long-term correlation between speed and over/under performance. I would speculate that players that are faster tend to be weaker, and perhaps have lower averages on their FB’s and LD’s and it might tend to even out. Another criticism is that players do in fact have some control over their batting averages on batted ball types. This control is small, however, and except in a few severe cases (Ichiro! perhaps) I wouldn’t guess it would be a huge deal. It could also be a subject of improvement in the future.
PrOPS is one of the best, if not the best at spotting luck in a player. Most good sabermetricians are not going to be happy simply saying that Richie Sexson got unlucky, they want a number. I assume most people can look at a site like fangraphs and decipher that if Yu-Bet is hitting .254 on BIP, he was getting unlucky, but how unlucky, what should his BABIP be, given how he has hit the ball? That is where you use PrOPS. It is not good, however, for what has already happened. If you are trying to evaluate the past, things like linear weights and base runs are what you should use in those cases. What should have happened is irrelevant after it does happen, and PrOPS tells you what should have happened. So naturally it would be a good projection tool, right? Well, it kind of was. Tom Tango ran regressions with it and found it scored up with the PECOTAs and ZIPs of the world, but JC was not very compliant with using it that way. A real system might be slightly limited by a lack of batted ball knowledge. PECOTA is using 100 years of data, PrOPS at the most would use like 8. Also as far as I know it never included injury or age adjustments, which would be important. That it still scored as high as other projections with these limitations tells me something good was going on there. I believe there will be a stat that comes out sometime soon that uses batted ball data even better than PrOPS, and hopefully whomever does this will make a projection system out of this. For now PrOPS is what it is though. It is a great way to both identify and quantify luck, so we can say that Richie Sexson was one of the unluckiest guy in baseball last year, and he was this unlucky. I am very sure I will be using it a lot at the beginning of the year when Beltre is hitting .150 or William F. Bloomquist is up around .450. After this year I’ve already used it to say Sexson is better than Vidro, regardless of last year. PrOPS is a limited stat, but certainly a good one to add to your repertoire, when used correctly it can give good information as to what is likely to go on in the future.