At first glance, it looks like Pedro Alvarez is doing exactly what we thought he would this year. He’s hit 8 long balls and is sporting a Mendozian .210 average, which somehow falls below his pre-season expectations of being in the .230 range. Most fantasy baseball outlets are sounding alarms and wondering aloud how much longer owners can live with his team killing average. In reality though, Pedro is producing a familiar stat line but is getting there in a very different way and mostly through bad luck.
It still feels early in the season but there are already some stats that have stabilized and are now significant for evaluating how players are performing. For when statistics stabilize I’m using this terrific post from 2011 (http://www.fangraphs.com/blogs/525600-minutes-how-do-you-measure-a-player-in-a-year/) which is definitely worth a read on its own. The cliff notes needed for this article are that when statistics stabilize, they start to tell us more about a hitter’s current season than league averages do. Statistics regress to the mean but when a statistic stabilizes we equally weight an individual’s performance with the league average when creating future projections.
Pedro is at the 150 plate appearances plateau and three key statistics have already stabilized and are now telling us more about his performance this year than league averages or his career stats, they are swing percentage (swing%), contact rate (contact%), and strikeout rate (K%).
2014 Pedro is swinging at 44.4% of pitches. This is down from swinging at 50% of pitches last year and is close to being a career low (in his call up season his swing% was 43.7%). This doesn’t tell us all that much by itself. You can get yourself in bad counts by watching strikes go by just as much as you can by swinging at balls. But, Pedro is making contact with 73% of pitches, four points above his career 69.3% average and almost a full 7% higher than last season. A hitter’s swing% first stabilizes at 50 PAs and contact% stabilizes at about 70-75 PAs, and this shows Pedro turning into a more patient and more selective hitter. He’s actually swinging less often than the league average and while the league average contact rate is 79% this year, Pedro has much more power than your average hitter.
The third stat I want to look at is the big one, strikeout rate. In each of Pedro’s first four seasons in the majors he has posted a K% north of 30%. The league average K% ranged from 18.5% to 19.9% during that time, so Pedro really excelled at striking out. Pedro’s K% through 150 PAs this season is only 21% though. Now it just stabilized, so there will probably be some regression towards his career norm but this is 9% lower than his career rate and is only .4% higher than the league average this year. I’m expecting this to regress, at least somewhat, because a 9% drop in K% is too good to be true but Pedro has definitely improved in this area and even regressing to a 25% or 26% strikeout rate would be a significant improvement.
All of these stats might look good but Pedro is still batting an abysmal .210, what’s up with that. The biggest culprit is his .209 BABIP. BABIP doesn’t every stabilize, if Pedro’s reverted to his career average (.292) he’d have a batting average of .267, if it reverted to last year’s rate (.276) he’d be batting .256. Home runs are BABIP-proof, there’s nobody to field them, and Pedro’s is off to a great start with 8 already. More of the non-homers are going to start falling for hits and barring injury, Pedro looks to be good for 35+ HRs and closer to a .250 average. Every fantasy team can use that guy. So before his bad luck starts to end, run out and buy low on Pedro.
I’m entering my fourth season of fantasy baseball this year and in my quest for my first championship I stepped up my preseason work to include making my own projections for players and creating my own dollar value system for my league’s custom scoring (6×6, standard with OPS and K/9 added). When making projections for players this year, I looked at their last three seasons in the Majors and used their Steamer and ZiPS projections to make sure I was in the same universe or had solid reasons for my different projection. I made projections for about 300 hitters and 200 pitchers, which I feel are grounded in reality and will give me an edge in my fantasy endeavors this year.
However, while I’m pleased with my projections and it’s definitely better than when I first started playing and just knew Yankees and other AL East players, my projections are still very limiting. One of the main problems is that I’m producing a single stat line for each player. It’s based on what they’ve done previously, how they’re trending, and how I and other systems think they’re mostly likely to produce in 2014, but it’s still just a single projection. More advanced projection systems, like PECOTA, compare a given player to thousands of other Major Leaguers to find comparable careers and produce various projections and each projections probability of occurring.
Projection systems like this recognize the inherent uncertainty of projecting future baseball performance and instead of giving one stat line, give us a range of outcomes with their likelihood and produce more accurate results. Now, I am just dipping my toe in the water of finding comparable players and making projections based on that but I wanted to see how this type of system would change my valuation two outfielders who will turn 27 this season, Justin Upton and Jay Bruce. Bruce will turn 27 in April and Upton turns 27 in August. They’ve both been big fantasy contributors in the past, Bruce is more consistent in his production while Upton has been streakier, with hot and cool months and peaks and valleys of home run and stolen base totals. I’ve put my projections for them below with a dollar value based on a 12 team league with 22 roster spots and a 70-30 hitters-pitchers split.
I’m projecting them to produce similar value, but Bruce definitely has an edge. To find comparable players to Bruce and Upton, I looked at all MLB season from 1961 through 2013 (61 being an arbitrary start date based on how much data my laptop could sort through and organize with John Henrying it’s CPU). I narrowed down to players with similar home run and stolen base totals in their age 23 to 26 seasons, along with average, OPS, strikeout and walk percentages, and playing time in an attempt to find a list of similar hitters.
For Jay Bruce I found 19 comps and I found 26 for Upton, there’s a link to the google doc with the full list below which I recommend checking out, it’s not included here so I can save some space. Now that I have the comparable players, I want to see how the performed in their age 27 season to give me a range of outcomes for both Bruce and Upton. I’ve included some bullet points here, again with the full spreadsheet linked at the end.
Mean and Median Value of Comparable Players’ Age 27 Season
Best Case Scenario
More Realistic Good Scenarios
Outside of Injury, Worst Case Scenario
The Merciful Conclusion
I know this took up a lot of room and we’re all happy this is almost over, but what does this mean. First, this is pretty rudimentary with no set formula for finding comparable players, I did my best but they’re definitely not one to one matches and should be taken with a grain of salt. However, I think this helps articulate a fundamental difference between Jay Bruce and Justin Upton. Bruce is a high floor, more limited ceiling guy and I’ve got more confidence that his 2014 will fall close to my projections. I know I’m buying about a .260 average, with a couple of stolen bases, mid 30s home runs with a little wiggle room, in a good lineup.
Justin Upton is a lotto ticket guy. I’m sticking with my projection for his season which falls between the extremes, but if he repeats his 2011 or puts together his tools that he has demonstrated at different points of his career, he could finish right behind Mike Trout among fantasy outfielders. At the same time, I could see him producing a line like his big brother BJ did last year, okay maybe not that bad, but definitely not worth his draft price. Who you take depends on what path you want to believe and who you already have on your team, but I think laying out these options and using player comparables definitely adds to fantasy projections and will be a staple I’ll use next year.
As promised, here’s the link to the full list of comparable players used for this article: https://docs.google.com/spreadsheet/ccc?key=0AmP-CH5MqzENdFZSZ0xhQVZiYWxNSVQxYzBsOFh3YkE#gid=0