Archive for projections

Mitchel Lichtman’s projection pieces on hitters and pitchers for the rest of the season were discussed quite a lot last month starting with this.  It is hard when you are rooting for a team, and subsequently its players, not to buy in when someone is doing well or poorly.  So let’s look at the heartless projecting system ZiPS to see if it is actually buying into some of the performances of 2014 so far.

To do this I pulled the 2014 pre-season wOBA projections and compared them to the ZiPS (RoS), rest of season, projections.  If you take the RoS wOBA minus what ZiPS was expecting prior to 2014 you should be able to see which players are now expected to hit significantly better or worse the rest of the way.  Here are the top/bottom-five players:

The bottom five, with the exception of Colvin, have been very disappointing and their respective teams would love even the RoS numbers at this point.  The projection still believes Brown can be an above average offensive player despite his putrid play to this point of 2014, but it is starting to look like Raburn’s age might be catching up to him and Gyorko’s rookie year might have been a mirage.  Schierholtz makes less sense, but he has been so bad that ZIPS can’t ignore it, and he was never a great player to begin with.

Others names of note that are projected to finish the year worse may not be surprising.  Raul Ibanez looks done with eyes and statistics, Jean Segura’s lack of plate discipline has really caught up to him, and Brian McCann may not be aging particularly well despite being a lefty with power in the Yankees’ home park.

There are a lot of players on the positive side, and you can see that the nominal and percent wOBA changes are larger for the improvement group too.  There are 31 players with RoS wOBA at least 5% above their pre-season projection while only 17 projected to be 5% or more worse than expected.  Does this mean that ZiPS is actually an optimist?

The Padres believe in Seth Smith as well, having recently signed him to extension.  He is a righty masher, though they only rarely let him face same-handed pitching.  Victor Martinez is 35 years old and decided to have a renaissance, and may end up with his best hitting season ever.  Baseball is weird.  I’m not sure what to make of Steve Pearce.  He has been around since 2007 without ever accumulating more than 200 PAs, but this season he finally has and the Orioles are making out like bandits.  The other two are what you expect on such a list, young players taking a step forward.  JD Martinez was who I was thinking about when I started this.  I have seen him play several times recently, and he seems to put together a quality plate appearance every time up. Mesoraco, like Martinez, is 26 and has had a huge power spike along with a lot more strike outs to the point where he seems like a different player altogether.

Two Cleveland Indians just missed the top five improvers: Michael Brantley and Lonnie Chisenhall seem to have finally taken a step forward too.  There were two notable Brewers as well.  ZiPS seems to have finally decided to believe in Carlos Gomez and Jonathan Lucroy.

Yes, believing in projections sometimes means we need to temper our enthusiasm when a player we like breaks out or be patient with someone slumping.  It can also be a good way to see when players are truly locking into higher levels of play.  For the older players here it is likely that they will come back to the pre-season projections again next year because Victor Martinez is probably not going to turn into a much better hitter year after year at this age, but for the younger guys we may be starting to see who is taking a step forward.

In early May I introduced Ottoneu players to the Advanced Standings Dashboard, a tool that allows team owners to decipher the early season standings in an effort to better gauge where their team might be headed as the 2014 season comes together. You can download that tool here (http://goo.gl/pbXI5), but now that we’ve just entered July, the traditional halfway point of the baseball season, it’s time to take a deeper look at a few ways this tool can be used to effectively to manage your team into contention in the second half.

Since the tool can be updated easily with just a couple of copy/paste actions, I use this tool almost daily in my own FGPoints Ottoneu league.  But for fun, let’s walk through a few features as they apply to the FanGraphs Staff League, with a special focus on Eno Sarris’ team, “It’s A Perm“.

Eno enters July as a 3rd place team, nearly 400 points out of 1st place, and 150 out of 2nd.  In general, with at least seven teams over the 8,000 point mark, this league looks competitive at a glance.  But with the recent pickup of Ryan Braun, Eno clearly has his sights set on a title (https://twitter.com/enosarris/status/483016142831644672), so let’s break down the standings using the tool to see if Eno has the momentum to win it all in the 2nd half.

The first tab of the tool is simply the statistical breakdown of the Ottoneu standings into some common sabermetric calculations.  While we can easily see Eno leads the league offensively at 5.44 P/G, the underlying statistics also support it, showing he maintains an (slight) advantage in OPS, OPS+, wOBA, Runs Created, and Total Bases.  What may be more interesting is that Eno has more points scored from his offense than any other team in the league.  In fact, just over 58% of his points have come from his hitters (tab 3, ‘Projected Finish”). With roughly 55% of league scoring in Ottoneu coming from offense, Eno is clearly banking on this approach of shoring up the side of the ledger that carries the most weight.  The acquisition of Braun will only help.

So It’s A Perm is built on bats, but what about the pitching? Unfortunately, this is a weak spot, as Eno’s FIP, WHIP, and BB/9 are all higher than the two teams he’s chasing.  I’m sure he knows this instinctively as his 5.03 P/IP is below the league average of 5.13 P/IP (and further below the top 7 teams of 5.19 P/IP), but the dashboard makes it quicker and easier to point out these pitching deficiencies.  One possible area of improvement: the bullpen.  Without looking at his roster, I can tell you pretty quickly he’s probably pretty frustrated with his bullpen, which has been almost 42% less effective (“PEN” = Saves + Holds/IP) than the league leader, A Little Out of Context.  Shoring up a bullpen is often easier and cheaper than finding an ace SP mid season, so does Eno speculate on the eventual Sergio Romo replacement? Does he approach John Heyman’s Last Sirloin about shedding some of his bullpen pieces in a plea to “deal from strength”?

Once you’ve taken the time to digest some of the traditional sabermetric outputs in the Dashboard, your eyes will naturally gravitate toward the end of the first tab into the “League Projections” section, which is where the real power of the tool comes alive.  The key takeaways here are the “Otto” score and the “Pace” columns.  The Otto score can be better explained here by Chad Young (http://goo.gl/KK4Xy), while the “Pace” attempts to project the season-ending point totals for each team based up a range of factors, including current P/G and P/IP values, remaining IP and GP, and league averages in these areas.  In many leagues these are the columns that can better identify contenders from pretenders, but for the FanGraphs Staff league we see more evidence that the actual standings are, for the most part, very accurate, as Eno is also projected to end the season with the 3rd most points (18,042, or about 400 points out of 1st place).

There are a few interesting things to note here, however. First, John Heyman’s Last Sirloin actually has the third highest Otto score (13.66), but is still projected for 4th place, most likely due to his slower pace in IP (1,416 projected).  If this team can pick up the IP pace in the 2nd half with similar quality IP (5.54 P/IP), this team could make up ground quickly.  This team is clearly riding a league-best bullpen and trying to maximize its RP innings as much as possible.

Second, Ground Rule Double Helmet, despite sitting in 4th place with a strong 9,000 points, has had to overtax a very week pitching staff (4.74 P/IP) just to get there (1,577 IP projected).  The tool sees as much and projects this team to end the season in 5th place, but unless the pitching staff sees a significant improvement in the 2nd half, I’d expect this team to possibly fall even further as the season shakes out.

And that’s just the first tab…Once you get familiar with the tool, you’ll actually find the third tab, “Projected Finish” to be the most useful summary of some of these features described above, as it will give you a daily update of the projected champion for the league.  With Eno just 400 points out of both the actual and projected season-ending standings, this league is just too close to call on July 1st, but there are at least four clear contenders here, and It’s A Perm is one of them.  Will Ryan Braun help the cause? Just for fun, let’s say Braun increases Eno’s offense by just 2.00% (from 5.44 to 5.55).  Well, that could be all it takes, as that small increase moves the needle for It’s A Perm enough to overtake Johan Santa Claus by 100 points in the projected season-ending standings, and less than 200 points out from 1st place.  Of course, that’s if everything else stays the same, and, as in life, the only thing constant in baseball is change.  This will be a fun league to watch as the summer heats up, so enjoy the tool and use it where possible to get that 2% edge.

Draft prep: Framing the problem

So you’re preparing for your fantasy draft. You’re caught up on FanGraphs, checked for recent injuries at Rotoworld, maybe skimmed a few headlines from your other top 11 baseball news sites. Maybe you’ve even downloaded the FanGraphs positional rankings, and are planning to keep the file open during the draft as a reality check against the pre-set rankings of the site your league uses.

But really, what do the guys at FanGraphs know? Sure, they know a lot about baseball, and statistics, and this year’s projections, and a handful of underlying stats that tend to predict future performance. But what they don’t know is whether your league uses OBP instead of AVG, or OPS, SLG, or batters’ strikeouts, or maybe holds and FIP and pitcher fielding percentage. If this is your situation, then I feel your pain. My fantasy league uses eight statistics for batters and pitchers, three each beyond the usual five. (In case you’re curious, the mysterious six are: Batter hits, K’s, & OPS; Pitcher holds, losses & complete games).

These differences matter. If your league uses OBP, Joey Votto turns from a fantasy player who’s solid in four categories (including average, where his impact is limited because he walks all the time) to a guy with a truly elite skill. Maybe it’s easy for you to account for the relative value of a Joey Votto, but how well can you project the 25th through 35th outfielders? Some might be much better or worse in your league. If you have batter strikeouts, as in my league, how do you value Mark Trumbo and his home run power against the elite contact skills of Norichika Aoki?

Generating your own rankings

One answer, and the one I opted for, is to generate rankings based on your own league’s stats. Now, this may sound a bit too work-intensive and time-consuming for most of you (especially those of you with relatively normal priorities), but in reality it wasn’t as time-consuming as I expected.*

First of all, there’s no need to reinvent the wheel. There are lots of projection systems out there that are available to the public, and some of them are quite good. I decided I would simply download all the projections listed on FanGraphs, and average them out. And then, after thinking for a little while about the costs and benefits of that approach, I decided I wouldn’t do that at all, and instead would use the results of just one projection system. But which one should I use? Luckily, that’s yet another bit of analysis we don’t need to bother with, because the Interwebs are full of crazy mathematicians who love baseball and have nothing better to do. After searching for a few articles that evaluate projection systems, like this one and this meta-one, I decided that the forecasts I trusted most (and were easiest to obtain) were Steamer for batters and FanGraphs fans for pitchers. (The high accuracy of the latter shocked me at first, but then I realized that fans assimilate the results of all the projection systems into their own player projections, departing from them only as dictated by common sense, inside scoop, and hope.)

Operationalizing the Solution

Here’s where it gets tricky. What advanced data manipulation packages and techniques are best for downloading reams of data from the FanGraphs site into your spreadsheet? Certainly there was no need for me to copy and paste the data 50 players at a time like someone living the dark ages, was there? No, of course not. And I probably never really did that.

Instead – bear with me if you’re not technically inclined – I hit the gray “Export Data” button to the upper right of my chosen projection page. This involved a lot of loading the correct page, hovering my mouse over the text, and clicking, but in the end it was worth all the work, because 5 minutes of sweat, plus a beer, had finally paid off in spreadsheets full of data.

*If you’re not interested in these details, the fun stuff is posted in a couple of tables towards the end. (I like writing, so this is likely to go on for a while.)

Z-scoring your data points

Z-scoring batter projections is easy. The problem lies in determining what set of players to use in order to calculate means and standard deviations.

This is an important question, at least to the extent that any question in fantasy baseball is important. For example, if you must use every hitter in the league, including the guys projected for 8 at-bats, you create the illusion that lots of players bat .220 or score only 4 runs, as opposed to your league’s reality in which .270 with 70 runs is pretty ordinary. For a little math fun, I compared the results generated using means and deviations 500 players deep (the equivalent of a 25-team league that rosters 20 position players) versus one with more reasonable assumptions. It caused huge increases in variance in runs and rbi’s, so a guy who drove in and scored 100 compared no better to the mean either way (~2+ standard deviations), but smaller increases in the variance in SB’s, HR’s, and OPS, which, together with the lower means, meanings this system overvalues guys who produce in these categories. Martin Prado and Torii Hunter were made sad, whereas Billy Hamilton was elevated to a demigod (or at least a top-40 hitter).

So how do you generate values that represent your player pool?

One method – and a very reasonable one – is to use the final statistics compiled by your league the previous year. With this data, it’s easy to generate per-slot averages based on last year’s performance, and to compare projected performance against it. But I did not choose this method. A more savvy number-cruncher might say that projection systems, while designed to be as accurate as possible for each player, may be systematically biased on the whole, and therefore determining the value of this year’s projections based on last year’s actual statistics is tantamount to comparing apples and oranges.

I was more worried about lazy owners. Any league can have a couple of careless owners who are in it just for fun (the gall!), or who keep BJ Upton when he can’t even see the Mendoza line, because of that one time his cousin shook BJ’s hand at a Jay-Z concert. I know of what I speak. If your goal is to win your league, you want to base your evaluation on the best players available, rather than the happenstance of which Atlanta outfielders spent the whole year on someone’s roster.

I generated means using very precise data, plus a random stab in the dark. First, I looked up the exact number of players at each position in my league from the previous year. Then I mostly ignored this data. Although it’s true that player values vary greatly between leagues depending on how many players start, and how many are rostered, this is the sort of thing you can keep track of during the draft. Don’t draft another first baseman if you already have three of them and no shortstop, and don’t draft a first baseman just because he’s ranked ahead of a shortstop if there are another seven first basemen ranked close behind.

My league rostered only 123 regulars last year. Not a deep league. I used a lot more than 123 in my calculations in an effort to lower the means a bit, to account for the existence of catchers and second basemen. I then haphazardly created sort variables so I could bring the best 150 to 180 players to the fore, with the goal of getting a fair representation of the quality of players in my league. I tried various formulas like [(HR+1) * R * RBI * (SB +1) * AVG * OPS] (adding 1’s so as not to exclude players projected for 0 HR’s or SB’s ) and PA * wOBA. Virtually every one of them produced a good representation of the best hitters projected for regular playing time. In the end, the best way to evaluate the sort is to look at the list and see if the guys near the cutoff are fringe players who are familiar from last year’s waiver wire.

Calculating projected player values

Once you determine which players you want to include, Excel is happy to instantaneously calculate averages and standard deviations for each stat. Once you have these values, you can re-include the entire player pool, or as much of it as you wish, and the formula for each player in each category is simply (his projected value – the average projected value)/standard deviation.

The next challenge is to generate ranks from the Z-scores. The simplest way is simply to add them together (being sure to subtract ones where lower scores are better, such as pitcher walks or batter strikeouts). But here, I discovered another issue. A potential superstar who might not have a full-time job could end up ranked about the same or below a mediocre player who was guaranteed to start. If I wanted my draft rankings to make sense at a glance when I have just 90 seconds to pick a player while eating a sandwich, I needed to distinguish accumulators from guys with potential.

Ranking performance and potential

It matters whether a player is an okay guaranteed performer or a unpredictable potential star. If I find myself with no second basemen in the 22nd round, I might want to take the best guy who’s pretty much guaranteed 140 days in the starting lineup, like an Anthony Rendon or a Howie Kendrick. If my roster’s pretty much set, I might prefer a hitter who has a better chance to bust out and hit 45 home runs, like Chris Carter (unless I’m in my league, in which his 80% strikeout rate falls 37 standard deviations below the mean).

What I decided to do was generate two rankings for each batter, one based on projected totals, and one based on projections per plate appearance. Luckily, Steamer has already done the work for us by projecting everyone in both ways. For instance, Everth Cabrera is projected as the 479th-best player by wOBA, with 74 runs and 45 stolen bases. At the other extreme, Colorado’s Kris Parker is projected to be the 50th-best hitter in the league, just ahead of Dustin Pedroia, with a .279 batting average and .465 slugging percentage, despite getting only one plate appearance, and not getting a hit.

At this point, there are 2 sets of columns for each batter: 1 set of columns for his Steamer projections for each relevant stat, and 1 for the associated Z-scores. To this, I added 2 more sets of columns: 1 for per plate-appearance projections for each stat, and 1 for those associated Z-scores. (Dividing hits into plate appearances rather than at-bats feels unnatural, but that’s what you need to do if your league counts total hits.) Calculating per-PA quality is then easy, as you can just add the Z-scores (or subtract for negative statistics). But once you have projected rate statistics in your per-PA rankings, it becomes apparent that it doesn’t make sense to include the exact same values in your projected accumulated totals.

To handle this, I weighted the Z-scores for the rate stats. I multiplied the Z-score for AVG by projected AB’s/average projected AB’s, and you can do the same for OBP, using PA’s. My league uses OPS, a value generated by adding two fractions with different denominators (aka OBP & SLG), so to weight those Z-scores I multiplied them by projected (AB’s + PA’s)/average projected (AB’s + PA’s). I then added these weighted Z-scores to the other Z-scores for projected totals. The result of adding these weights is that a player who is one standard deviation above average in both AVG and OPS, and who has an average number of AB’s and PA’s, would get +2 from these categories in the variable used to rank projected totals. By the same lights, the aforementioned Kyle Parker’s AVG and OPS would essentially get no weighting at all, and have no effect at all on his projected totals, just as in real life his performance is not expected to have any effect at all on the rate stats of your team.

The Fun Stuff

And that’s about it. Once you have Z-scores, it’s very easy to rank players, to change the formulas to rank them by different systems, or to sort players by certain categories to see who stands out the most.

Two common variations on the traditional 5 stats are to include OBP instead of AVG, or to play in a points league. (For a points league, just change the Z-score weighting to reflect the point system). Here are the top players in these alternate systems using this evaluation method (I threw my own league in too, just for kicks):

(Note: I evaluated points leagues the same way as the other leagues, generating both a points total and a points/PA score for each player. I scaled the two values to give them approximately equal weight, and ranked players by the mean of the two.)

I expected Joey Votto to be a stud in OBP leagues, but in reality Joey Bats benefits more. Jason Heyward too. Meanwhile, CarGo is top 3 in every other system, but falls to the bottom half of the first round in a points league. In my own crazy league, Norichika Aoki projects as a contact-hitting top-40 stud, while Mark Trumbo’s contact deficiencies show up in strikeouts and hits, as well as AVG, and he drops to 82nd.

I also thought it would be cool to see which players project to be affected most under different scoring systems. Here are the players with the largest variation in ranks between systems (weighted to prefer higher-ranked and therefore more interesting players):

Billy Hamilton projects to be a one-category stud in any system that ranks stolen bases, but many people doubt whether he’ll be an especially good ballplayer in 2014, and the points system shares their skepticism. Carlos Santana will benefit enormously from any league using deeper measures than AVG, while Adam Dunn jumps from irrelevance to potential rosterability in OBP leagues only. A couple more notable players: Alex Rios is vastly more valuable in leagues with the standard five categories, and least valuable in points league, and Adam Jones follows a very similar, if somewhat less drastic, pattern.

And there you have it – the results of one approach to generating player values for leagues with alternative categories.

Twenty-two players have hit 150 home runs or more by the age of 25 (Per baseball reference, the last season included is when a player is no older than 25 on June 30^th of that season).  The list below is a who’s who of players that hit for power at a young age. You’ll notice a large number of active players have accomplished this feat, and that 10 of the 17 retired players are in the Hall of Fame.

A few more active players look like they are about to join the club

Three favorites are Giancarlo Stanton, Mike Trout and Bryce Harper. According to the Oliver five year projection system, each of these players will reach over 150 home runs by the end of his age 25 season.

This chart shows each player’s current home run totals, the seasons played through so far, the number of additional home runs Oliver projects through the 25 season, the projected total runs by the age of 25 (Career HR + Oliver projected), and finally what each player would need to average to hit 150 runs by the age of 25. This last measure is interesting because it gives you an idea of what level each player would need to fall below to miss the mark.

Minor league players might knock out A-Rod for #1

Miguel Sano, Joey Gallo and Javier Baez make up a trio of minor leaguers who Oliver believes could also make the list.  Not only does Oliver project these three players will to fly past 150 home runs, he predicts Sano and Gallo could pass A-Rod for the most home runs by age 25.

*the Gallo projections are only through his age 24 season so if he kept up the home run pace he would be in the 270s at the age of 25

While Sano, Gallo and Baez have a high number of projected home runs, they also have a high number of projected strikeouts. Adam Dunn shows that you can be very successful as a player who strikes out & hits home runs frequently. But the three minor league players could be even more extreme. Dunn struck out 26% of the time and homered 5.7% of the time through his age 25 season.  The minor league trio are predicted to strike out between 32 and 43% of the time and homer between 7 and 8% of the time. Could these three players redefine the all or nothing hitter, or are they somehow breaking projection systems?

Reasons to be skeptical

The Oliver model is complex and would take a long time to completely dissect, but from what I can tell it has the following limitations (these limitations are intentional because they add other value to the projections system):

#1 The Assumption of Games – Oliver projections assume a player gets 600 major-league plate appearances every year. This is not necessarily a given because top minor league players will likely spend part of a season in the minors before moving up to the majors, or in Sano’s case miss games rehabbing an injury.

#2 Inherent Uncertainty –  First, projections based off minor league numbers have more uncertainty than those based off major league numbers. Second, each additional year projected in the future adds more uncertainty because each year you go out you are guessing what happened the previous year – vs. knowing what happened the previous year. Compounded, these two stated effects create a good deal of inherent uncertainty.

So, what does this all mean?

If the projections are anywhere close to correct, it looks like we are going to see a new breed of power hitter in the major league soon. Although the projections are far from foregone conclusions, it’s another great reason why we watch the game of baseball.

Often I would like to have an estimate of a player’s true talent in a past year. Projection systems are always only focused on predicting future performance based on past results, but what I wanted was the best estimate of the expected performance for a player in a given year, based on his results in that year and the surrounding years.

I wanted to find suitable weights to assign to performance in the given year, plus the years immediately before and after, and have the right amount of regression to the mean. But I kept running into the same mental block; how to assign a weight to the given year’s performance, since that is exactly what I am trying to “predict”?

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