Can Billy Hamilton's Legs Make Up for his Bat?

Photo Credit: Keith Allison

Billy Hamilton is having one of the strangest seasons I've ever seen.  He has been absolutely terrible at the plate.  Really, really bad.  Thus far, heading out of the All-Star break, he is hitting .220/.269/.287 with a 52 wRC+.  Compared to his 2014 season, when he was merely not good, he is making more contact (19% K rate in 2014, 15% in 2015).  However, this contact has been more often soft contact (22% Soft, 21% Hard in 2014, versus 23% Soft, 17% Hard contact in 2015) and he has been hitting slightly fewer ground balls (41.5% in 2014, 40% in 2015).  There have been times when I have been more confident in the pitcher's offense than in his, which doesn't happen very often with starting position players.

Remarkably, there are currently four qualified batters who have a lower wRC+ than Hamilton this year.

Alexei Ramirez, 43 wRC+, -1.0 WAR
Mike Zunino, 45 wRC+, -0.3 WAR
Chris Owings, 48 wRC+, -0.7 WAR
Omar Infante, 48 wRC+, -0.3 WAR
Billy Hamilton, 52 wRC+, +1.6 WAR

Ramirez and Infante, at least, have a history of being at least decent, if not plus, hitters for their positions.  Zunino is a catcher with a good defensive reputation.  Owings is a sophomore and former good prospect who has fallen on hard times.  If any of those guys don't pick up their offense in the second half, however, there's a good chance that they'll lose their starting position.  As a result, they won't achieve "qualified batters" status by the end of the season.

Going back 10 years (2005-2014), there are only five players who have managed a full season of playing time with a wRC+ lower than Hamilton's:

2006 Clint Barmes, 38 wRC+, -0.7 WAR
2010 Cesar Izturis, 46 wRC+, -0.4 WAR
2006 Ronny Cedeno, 48 wRC+, -1.8 WAR
2006 Angel Berroa, 48 wRC+, -1.5 WAR
2013 Alicedes Escobar, 49 wRC+, +1.1 WAR

(of note: Zack Cozart in 2014 had a 56 wRC+, +1.2 WAR)

Therefore, while Billy has been unquestionably bad at the plate, it hasn't quite risen to historical status.  The interesting thing to me about these two lists, beyond the magnitude of these hitters' offensive struggles, is that only two of them show up as having positive WAR: Alicedes Escobar and Billy Hamilton.  In both cases, the players' WAR totals are buoyed by strong baserunning numbers and strong fielding numbers.  There are those who will scoff at fielding data, but I can swallow that an elite defensive player like Escobar could still be worth 1 win above replacement.  That's still well below average, and not good.

Hamilton, though, currently has 1.6 WAR.  That puts him on pace for almost exactly 3 WAR, which would put him as a slightly above-average baseball player.  This is despite owning (if it doesn't improve) a batting line that would rank him 6th-worst since 2005.  Is that really possible, or is WAR broken when it comes to Hamilton?  Let's go component by component.

Hamilton's Baserunning

Perhaps more than any other ballplayer, Billy Hamilton's value comes on the basepaths.  While last year he was caught regularly, this year he has become far better at determining the best time to go.  Heading into last night's game, he had stolen 44 bases and been caught only 6 times, good for an 88% success rate.  That's Barry Larkin-level success rates, except that Larkin only topped 44 bases in one season.  Hamilton is on pace for 83 thefts.

And it's not just his basestealing.  Hamilton sails from first the third with ease, and scores from first on anything even remotely resembling an extra base (assuming he hasn't already stolen second).  He's been brilliant.

The two best baserunning totals since 2005 are Mike Trout in 2012 and Willy Taveras in 2008 (+14 runs each).  Hamilton is already 17th on that list at +10.6 runs, with 76 more games to play.  That puts him in really good position to be at the top of this list by the season's end.  He might even do that by the end of August.

Given everything we know about his actual baserunning abilities, I believe these numbers.  He's really special out there.  He might not reach +20 runs by the season's end (which he's currently on pace to do), but would anyone really be surprised if he posted the best baserunning season of the past decade at +15 runs or so?

Hamilton's Fielding

Thanks in large part to MLB's condensed games, I've been able to watch at least the meaningful plays of almost every Reds game this season.  The thing that has stuck out to me, even more so than Hamilton's baserunning, is his defense.  I can't count how many times I've seen a ball hit hard into the gap, cursed in anticipation of a double, only to see Billy somehow close on the ball and catch it running.  And then there are all those times when he's made those brilliant diving plays in shallow left-center.  He's wicked fast, and to my eye it seems like he takes good routes.  He's been amazing to watch.

Hamilton currently has +11 UZR, which is the fielding metric that FanGraphs uses in WAR.  That ranks him 37th among center fielders over the past decade+,  and 2nd this season behind Kevin Kiermaier.  He is one of only 6 center fielders to have made a play that Inside Edge classified as a "Remote Chance" play, ranks 3rd among CF's among plays ranked as "Unlikely," and 9th among CF's on balls judged as having an "About Even" chance to catch them.

Last year, Hamilton was a +22 UZR center fielder, which is what he's pacing right now.  The best center field seasons of the past decade have been in the 20-30 range, topped by Franklin Gutierrez's 2009 season at +34 runs.  I think most of those are probably overestimates high due to the volatility of fielding stats, but they were all very good fielding players (a young Andruw Jones, Coco Crisp, Carlos Gomez, Michael Bourn, Willy Taveras, etc).

Therefore, I think evaluating Hamilton as a +15-20 run fielder in center field is pretty fair.

Offense + Baserunning + Fielding + Position = WAR

Let's put it all together.

Offense: Hamilton is on pace for -32 batting runs above average.  Let's assume, for now, that he won't improve and go with that.

Baserunning: Let's assume +15 runs as a baserunner

Fielding: I'm going to be slightly conservative and put him as a +15 fielder.

Position: I'll give him the standard +2.5 runs/season bump for CF's, prorated down to +2.1 runs for 86% playing time (he misses games here and there with small injuries due to his playing style).

Replacement: I'll peg Replacement Level as -2.25 wins below average, which is about -20 runs vs. average on the season, prorated down to -17 runs due to playing time (using 9 runs per win).

So, if this is correct, it would be Billy's valuation at:

-32 (offense) + 15 (BsR) + 15 (fielding) + 2 (pos) + 17 (field) = +17 runs above average, or 1.9 WAR.

In other words, Hamilton rates out as almost exactly a league average player.  I'm not arguing he's a plus player, and he's definitely not an all-star.  Just that he's a unique, extreme, league-average ballplayer.  You can even argue that I'm being conservative.  Maybe he's a +20 run fielder.  And his rest of season projections have him about equal to his 2014 offensive performance than his miserable 2015 performance (ZiPS has him at .251/.301/.346 the rest of the way).  But league average feels about right to me.  I really don't think Hamilton is killing the Reds this year, especially not hitting 9th as Price is smartly deploying him.

I don't know how long this will last.  I worry that Hamilton's all-out play in the field and on the bases will slow him down over the coming couple of years.  And if he slows, I don't think his game is amenable to almost any kind of aging.  But for now, I think he's an acceptable starting outfielder.

And who knows?  Maybe he'll still learn to hit a little bit.  After all, even just taking a few more pitches, given the generally poor results when he actually swings, could really help his value.

A Comparison of BIS Quality of Contact to StatCast Batted Ball Velocity

Yesterday, I showed that StatCast's Batted Ball Velocity data is correlated, at least, to performance statistics like ISO and HR/FB rate.  We've received another, similar, data source this year: BIS's Quality of Contact ball classifications.  For each batted ball, BIS classifies the balls as either "Hard," "Medium," or "Soft."  They're not recording actual velocity, but these aren't purely subjective either.  They apparently use a combination of hang time and landing spot information to determine the quality of contact category.

So, the question is, how well do these correlate to the (hopefully) more accurate StatCast data?  Pretty well!

If you look across the top row of the matrix, you see Batted Ball Velocity (on y-axis) plotted against Hard %, Med %, and Soft %.  You can see that it tracks very well with Hard Hit Ball %, in particular, and shows a pretty strong negative correlation with Soft %.  This is just as expected, but it's nice to see the data looking pretty solid.

Interestingly, Batted Ball Velocity also tracks negatively with Med %, though it's a weaker relationship; the harder you hit the ball, the fewer medium-hit balls you will make.  Similarly, Hard Hit % is negatively correlated with Med % and Soft %.

Here's a correlation matrix of those data:

	BBVelo	Hard %	Med %	Soft %
BBVelo	---	0.660	-0.359	-0.502
Hard %	0.660	---	-0.713	-0.554
Med %	-0.359	-0.713	---	-0.185
Soft %	-0.502	-0.554	-0.185	---

Using Quality of Contact as a Surrogate for Batted Ball Velocity

For seasons prior to this one, where we don't have StatCast data available, it would be really nice to be able to estimate batted ball velocity based on these data.  Unfortunately, given how correlated each BIS variable is with the others, it's hard to use more than one of them in a regression because they introduce multicollinearity and, potentially, don't provide much additional information.  To check, however, I did an all possible subsets regression analysis, using combinations of Hard %, Med %, and Soft % variables to predicted batted ball velocity.  Here's the output:

This is kind of a weird figure, but what it shows is the quality of fit (as measured by adjusted R2 on the y-axis) versus the different possible models (shown on the x-axis).  What we see is that a simple regression predicting Batted Ball Velocity with Hard Hit Ball % alone gives an adjusted R2 of 0.43.  It is, by far, the best of the single-variable models.  Furthermore, adding additional variables provides almost no additional explanatory power (it maxes out at 0.46).  Therefore, our best option is to simply predict Batted Ball velocity using Hard %.

If you'd like to do this at home, this is the regression equation: Velocity = 80.69 + 26.6842 * Hard %

This will give you a pretty solid fit:

R2 = 0.43.  It looks like, most of the time, you'll be within about + 5% of the actual batted ball velocity using Hard %.  Maybe it gets better with larger samples; well see later in the season.

But hey, something is better than nothing! Right now, this gives us a basic format that will permit us to look at quality of contact in seasons prior to 2015.  And, like Batted Ball Velocity, Hard % tracks pretty well with variables like ISO and HR/FB...and, like BB Velocity, it does NOT track well with BABIP:

So, in short, while I love using actual velocity data, the Quality of Contact Data--and particularly the Hard % data--provided by BIS looks to be high quality and very usable.

Next up (probably): more of this stuff, but applied to Reds hitters

Batted Ball Velocity Data Predicts Performance

Jay Bruce leads the Reds with 92 mph Batted Ball Velocity,
but his ISO and HR/FB rates a just middle of the pack.
Photo credit: Trev Stair

We've long known that random events can influence hitters' batting lines.  We'll see Jay Bruce crush a ball to deep center field only to see it caught.  And then, the next batter, we'll see Brandon Phillips bloop a "dying quail" over the second baseman's head for a single.  Probably, we'd expect that a hitter's future results will relate better to how hard he hits the ball, rather than his past "luck" in "hitting it where they ain't."

The advent of StatCast batted ball velocity data in the gameday feed is an exciting development in that, for the first time, we have a direct measure of how hard hitters are striking the ball.  The data are still a bit hard to come by; our best source is at Baseball Savant, who scrapes the data together from gameday.  I know others have worked with these data already, but what follows is my first foray into analysis using these data.

Describing batted ball velocity data

I pulled average batted ball velocity data for all players in Savant's database using the link above.  One can do more specific comparisons using his pitchf/x search tool, but I was happy with overall average velocity for a first look (that said, I have no doubt that variation in his number is extremely important).  I linked this up, by name, to hitters from FanGraphs' database.  All data were through May 17, 2015.  Many thanks to these two sites for the data.

After culling out anyone who did not have velocity data, I then stripped down the sample to those with 60 PA or more.  It seemed like a good mid-range number.  That left 291 players in my dataset.  Here's a histogram of their velocity data:

The average batted ball velocity in this set of players was 88.5 mph, and you can see that the distribution is almost perfectly normal.  Very few major league hitters average over 95 mph, and very few average under 82 mph.  The latter is probably a selection process; if you don't hit the ball harder than that, you're not likely to be in the big leagues for long.

How well does batted ball velocity predict offensive numbers?

It seemed to me that there were three primary variables that should be most directly affected by batted ball velocity:

• BABIP: the harder one hits the ball, the more balls in play should fall in as hits.  
• ISO: the harder one hits the ball, the more extra bases you should gain.
• HR/FB: the harder one hits the ball, the more often fly balls should become home runs.

I didn't look at overall performance statistics, like wOBA or OPS, because those numbers will be affected by non-contact events (strikeouts & walks).  Any affect on those summary numbers will occur specifically due to changes in the above statistics.

Let's go through those one by one.

Batting Average on Balls in Play (BABIP)

As it turns out, there was no significant effect of batted ball velocity on BABIP (P = 0.06, R2 = 0.012).  This really surprised me.  But maybe there is some signal that is lost in the overall comparison?  For example, maybe batted ball velocity is better for fly ball hitters, but is worse for ground ball hitters who are trying to beat out infield hits.  

Therefore, I decided to split up my hitters into three groups:

• Ground Ball Hitters: - hitters who had a ground ball % in the upper quartile of the data (a GB% greater than 50%)
• Fly Ball Hitters: hitters who had a ground ball % in the lowest quartile of the data (GB% less than 39.3%).
• Non-GB Non-FB Hitters: hitters who were in the two middle quartiles of the data.

Here's what happened:

Neat, right?  When you look at fly ball hitters, average batted ball velocity does predict BABIP, at least a little bit (p = 0.008, R2 = 0.07).  But there's no relationship for other hitters.  

I guess the lesson here is that BABIP is still a pretty volatile statistic, with other factors (luck/fielding/pitchers/parks/weather) playing a large enough role that it masks any potential effect.  Or, perhaps we need to be even more nuanced; maybe ground ball-speed guys, like Dee Gordon, might have a different relationship with batted ball velocity than ground-ball slow guys?  It's a topic for future study.

Also, another fun thing: there's not really much difference in BABIP between the different hitter types.  FB Hitter = .289,  GB Hitter = .305, Middle 50% Hitters = 0.302.  Compared to the spread in the data, that's not much of a difference.  ....  although it probably matters more in larger samples.

Isolated Power (ISO)

Isolated power (which is SLG - AVG) is a measure of how many of one's hits result in extra bases.  Here's the overall trend:

Batted Ball Velocity does predict isolated power (p < 0.0001, R2 = 0.22).  Here's the breakdown by hitter type, as I did for BABIP:

It looks like the relationship is pretty consistent across hitter types.  The one caveat is that the more fly balls you hit, the higher your ISO and the higher your slope.  In other words, by hitting fly balls, you are going to get more extra bases.  And increasing batted ball velocity results in a more extra bases if you're hitting fly balls than if you're hitting ground balls.  That all makes sense, I think.

Home Run per Fly Ball Ratio (HR/FB)

One more: does hitting the ball harder result in more home runs per fly ball?  For this one, I removed anyone from the dataset who hadn't yet hit a homer...because I don't like 0's when running regressions.

While HR/FB is a notoriously volatile number, even for hitters, there is a significant relationship once again (p < 0.0001, R2 = 0.19).  And if we break down by hitter type:

...much the same story.  Interesting thing with the ground ball hitters, though: despite mostly-similar batted ball velocity, their fly balls turn into home runs at a lower rate than the dedicated fly ball hitters.  This must be a swing angle effect; fly ball hitters probably use an upper-cut swing, and therefore will hit the ball hard and in the air, which converts into home runs.  In contrast, if ground ball hitters have more of a level swing, when they hit it in the air it is likely to be a mistake, and not among their harder-hit balls.  As a result, they turn into outs rather than home runs more often.

Can we predict future regression based on batted ball velocity?

So, we have two variables that are predicted well by batted ball velocity: ISO and HR/FB.  Can we predict players who will regress (positively or negatively) in these statistics based on how hard they've hit the ball thus far?

Let's look at isolated power first.  Here is a graph showing residual ISO (the difference between actual and expected ISO values, based on our regression line) of a bunch of players:

It's messy, but at least you can make out the guys on the extremes.  Players near the top of the graph have higher isolated power than their average batted ball velocity would predict.  In contrast, players with a residual below 0.0 show improvement in their ISO.

Here's a list of the largest residual players:

So, yes, of course the guys who are expected to decline have high ISO's, and the guys expected to improve have low ISO's.  But we've got more precision than just a sort of ISO now.  Giancarlo Stanton, for example, has an ISO of 0.293 currently, and yet he hits the ball so hard that his residual is only slightly positive.  Similar things can be said about Joc Pederson.  On the other side of the coin, Jordan Schafer, Cesar Hernandez, and Ichiro Suzuki all hit the ball very lightly, and so their low ISO's (0.04-0.06) all seem very appropriate.

I don't want to overstate the effect, but this should help us anticipate player who will regress.

Also: Grady Sizemore is playing this year?  I had no idea.

Now, let's do HR/FB:

Again, higher residuals = better HR/FB than expected based on batted ball velocity.  Here's the players who stand out:

Again, we have more information here than just picking the highest and lowest HR/FB guys.  Giancarlo Stanton hits the ball really hard and has a high HR/FB, and this is not disputed by the regression.  Ichiro and Billy Hamilton are at the low end, and that doesn't seem strange.  But when there's a mis-match between HR/FB and BB Velocity, they show up on this chart.

So, maybe we can make better predictions now.  That said, I think a lot more would need to be done before this is ready for any kind of "real" use (in fantasy baseball, or otherwise).  A lot of the guys on the "probably will regress" list are fly ball hitters, and therefore we'd expect a higher HR/FB as a result.  A lot of guys on the "probably will improve" list are ground ball hitters.  At the least, if we're trying to project, we need to take that into consideration.  I'm just not there yet.

Nevertheless, I think this is promising enough that I just put a reminder in my planner to go back and check on this at the start of July and see how we did, compared to players who had similar HR/FB or SLG but had corresponding batted ball velocity.

Next up: how does StatCast batted ball velocity data compare to the BIS Hard-Hit ball data?

Reds Payroll, Now and in the Future

With the start of the season, and in light of the Reds' latest long term salary commitment, I thought it would be interesting to take a look at the Reds' payroll for this and the coming few seasons.  First, based on this article from Deadspin, here are opening day payrolls for each team:

The Reds have pushed themselves up above the median, ranking 12th overall.  They are in a cluster of teams from the Arizona Diamondbacks (11th) through the Milwaukee Brewers (16th) that have pretty much average payroll.  This is a far cry from where the Reds used to be a few years ago, often in the lower third of payroll.

Why do we worry so much about payroll?  We hear a lot of talk about parity.  But nevertheless, there still is a clear relationship between team payroll and team quality:

There's scatter, but the regression line explains 32% of the variance between payroll and projected wins.  All but one team (the Phillies) with a payroll over $125 million is projected to win 83 or more games.  The two teams spending the least are projected to have terrible records.

Now, of course, there's a lot of scatter.  Teams spending roughly the same amount on player salaries may differ by 15 or more projected wins.  And these are just projected wins: inevitably, the relationship between actual 2014 wins and payroll will likely show even more scatter.

But the fact remains that teams that spend more, and especially those that spend more wisely, are projected to win more games.  Teams with higher payrolls can afford more talent, whether that's signing free agents or keeping their existing players.  Therefore, it makes sense to keep an eye on the Reds' payroll.

Here are the Reds' current payroll commitments for 2014, as well as the subsequent two seasons.  All data are from Cot's Contracts.

Reds 2014-2016 Payroll Commitments

Name	POS	ML Srv	Length / Total Value	2014	2015	2016
Votto, Joey	1b	6.027	10 yr/$225M (14-23)	$12,000,000	$14.00	$20.00
Phillips, Brandon	2b	9.022	6 yr/$72.5M (12-17)	$11,000,000	$12.00	$13.00
Bruce, Jay	rf	5.125	6 yr/$51M (11-16)	$10,041,667	$12.04	$12.54
Cueto, Johnny	rhp-s	6	4 yr/$27M (11-14)	$10,000,000	$0.80
Bailey, Homer	rhp-s	5.017	6 yr/$105M (14-19)+20 opt	$9,000,000	$10.00	$18.00
Ludwick, Ryan	lf	8.109	2 yr/$15M (13-14)+15 opt	$8,500,000	$4.50
Chapman, Aroldis	lhp-c	3.034	1 yr/$5M (14)+converted bonus	$7,835,772	$11.75	$15.67
Latos, Mat	rhp-s	4.079	2 yr/$11.5M (13-14)	$7,250,000	$9.67	FA
Broxton, Jonathan	rhp	8.02	3 yr/$21M (13-15)	$7,000,000	$9.00	$1.00
Leake, Mike	rhp-s	4	1 yr/$5.925M (14)	$5,925,000	$7.90	FA
Marshall, Sean	lhp	7.088	3 yr/$16.5M (13-15)	$5,500,000	$6.50	FA
Parra, Manny	lhp	6.063	2 yr/$5.5M (14-15)	$2,000,000	$3.50	FA
Schumaker, Skip	2b-of	7.051	2 yr/$5M (14-15)+16 cl opt	$2,000,000	$2.50	$0.50
Heisey, Chris	of	3.157	1 yr/$1.76M (14)	$1,760,000	$2.35	$2.93
Simon, Alfredo	rhp	4.142	1 yr/$1.5M (14)	$1,500,000	$2.00	FA
Ondrusek, Logan	rhp	3.125	2 yr/$2.3M (13-14)	$1,425,000	$1.90	$2.38
LeCure, Sam	rhp	3.072	2 yr/$3.05M (14-15)	$1,200,000	$1.85	$2.47
Santiago, Ramon	ss-2b	9.095	1 yr/$1.1M (14)	$1,100,000
Hannahan, Jack	3b	5.065	2 yr/$4M (13-14)+15 c opt	$1,000,000	$2.00
Pena, Brayan	c	6.081	2 yr/$2.275M (14-15)	$875,000	$1.40	FA
Cozart, Zack	ss	2.084	1 yr/$0.6M (14)	$600,000	$5.12	$7.68
Frazier, Todd	3b	2.071	1 yr/$0.6M (14)	$600,000	$6.48	$9.72
Mesoraco, Devin	c	2.028	1 yr/$0.525M (14)	$525,000	$4.64	$6.96
Hoover, J.J.	rhp	1.102	1 yr/$0.52M (14)	$520,000	$0.57	$1.20
Cingrani, Tony	lhp-s	0.163	1 yr/$0.5125M (14)	$512,500	$0.56	$0.62
Partch, Curtis	rhp	0.086	1 yr (14)	$500,000	$0.55	$0.61
Christani, Nick	rhp	0.039	1 yr/$0.5M (14)	$500,000	$0.55	$0.61
Hamilton, Billy	cf	0.028	1 yr/$0.5M (14)	$500,000	$0.55	$0.61
Marshall, Brett	rhp	0.033	1 yr/$0.5M (14)	$500,000	$0.55	$0.61
Soto, Neftali	3b	0.036	1 yr/$0.5M (14)	$500,000	$0.55	$0.61
Barnhart, Tucker	c	0	1 yr (14)	$500,000	$0.55	$0.61
Totals				$112,669,939	$136.33	$118.30

The data above assume that the Reds will not pick up any of their options, and thus just pay the buyouts.  More on that in a sec.  Also, the values in red are estimates of what these players might get in salary arbitration, based on either their current salary or a WAR-based salary estimate, and a 40/60/80% estimate for what players will get each of their three years in their arbitration years.

I think these numbers are a little concerning.  Right now, the Reds have three players set to be free agents following this season (assuming the Reds don't pick up the options they have): Johnny Cueto, Ryan Ludwick, and Ramon Santiago.  Roger Bernadina should probably also be on this list, but I accidentally omitted him.  In any case, collectively, those three are combining to make $19.6 M this season, compared to $5.3 M next season in contract buyouts.  That would seem to free up $14.3 Million.

Unfortunately, at the same time, the Reds have three important players who will be arbitration eligible for the first time next year:  Zack Cozart, Todd Frazier, and Devin Mesoraco.  The latter three are all somewhere in the vicinity of league-average players.  Therefore, even though they'll only make 40% of their free agent value, they will probably earn an extra $15 million by themselves.  There goes the savings.  And on top of that, just about all players with existing contracts get an incremental raise, along with players who are in their arbitration years (Aroldis Chapman, Mat Latos, and Mike Leake).

All told, the minimum salary commitments the Reds currently are dealing with have them at about $136 million in 2015, which is about $24 million more than the 2014 payroll.  And that's assuming they don't pick up Johnny Cueto's option!  That scenario seems unfathomable right now.  But picking up his $10 M option would bring payroll to $145.5 Million.  If they try to keep Ludwick, they're over $150 million.  All of this would appear to make the Reds pretty strapped for cash in the coming year.  And it doesn't get a lot better after that: while the payroll commitments drop in 2016, that would require that the Reds lose all of Cueto, Latos, Leake, Marshall, and Parra to free agency.  Between escalating salaries for the young guys, plus Votto and Bailey getting big jumps in their salaries, the Reds are going to need that TV deal to pan out for them to avoid having to gut the team.

We don't know what the Reds' financial situation really is, of course.  It could be that they're running (or anticipating) the kinds of profits that would permit this kind of payroll explosion.  But if this season goes poorly, and the Reds aren't as competitive as we all hope them to be, I would think some effort to trim payroll at the trade deadline might be in order.

Thoughts?  Will the Reds' TV contract let them keep this team together?  Or are we looking at the onset of a retooling period in the next few years?

Was Homer Bailey's Contract An Overpay?

The Reds locked up Homer Bailey this offseason,
but was the cost too great?
Photo credit: David Slaughter

The Reds' biggest offseason player move was unquestionably handing Homer Bailey a $105 million/6 year contract extension this offseason.  While this was exciting for most fans (myself included), reaction around the internets tended to lean toward this being a pretty substantial overpay.

I'm sure that people have done nice analyses of Homer's contract.  I didn't really look around at the time, though.  So, I decided to finally take a look myself.  Well, a few looks.  

Approach #1

The first approach I used is close to what I've been doing for years to understand contracts, and is inspired by people like tangotiger.  It works as follows.

1. Make a projection for a player during the contract.  I used 2014 Steamer and ZIPS projections for that (courtesy of FanGraphs), and then did a "standard" 0.5 WAR/year decline.  That might be generous aging for a pitcher given pitchers' inherent tendencies to break, but we'll run with it.
2. Come up with a cost per win translator for each year of the contract.  This is trickier than it used to be, because some of Dave Cameron's recent work has made it clear that the cost per win is not constant anymore; above-average players are getting more dollars per win than below-average players.  Fortunately, in that article he presented a regression line for this relationship, and it showed a really simple relationship: 2 WAR players (i.e. league-average) are getting $6M/win, 3 WAR players are getting $7M/win, 4 WAR players get $8M/win, etc.  I'm assuming that this "bonus" is set in the first year, such that players do not see their $/win decline as their performance declines.
3. Estimate salary inflation.  I'm guessing wildly here, but based on past increases, as well as the amazing amount of money coming into the game right now with all of the TV contracts, I'm estimating a fairly aggressive 10% inflation on the $/win of an average player.  I'm just going to assume that the extra million bonus a 3-WAR player gets per WAR is fixed and not subject to inflation.
4. Multiply the estimated WAR each year by the player-specific $/win numbers.  This gives salary value each year.  Then, you just sum up all of the years to get total contract value.

Here's what I got when I did this for Homer:

On the far left are years, my estimated average $/win with inflation, and Homer's actual salary.  I'm assuming the Reds will not exercise their part of his mutual 2020 option, so they pay the $5 million buyout.  Then you have the Steamer projected WAR, his $/win, and estimated salary.  Similarly, I report ZIPS estimated salaries.  Finally, on the right, I'm presenting a projection that would be required for the contract to make sense using my salary model.  In other words, this is apparently how the Reds are valuing Bailey.  Also, please note that I'm ignoring the fact that 2014 is Bailey's last arbitration year; we should really subtract $3 million from his total 2014 salary in each case to account for the fact that players make about 80% of their free agent value in their 3rd arbitration year.

It doesn't look like a particularly good contract, does it?  Steamer and ZIPS have his contract valued at between $50 and $67 million over six years.  Furthermore, Steamer's projection for 2014 is low enough that it doesn't even make sense to give him a 6th year.  The difference between the Steamer and ZIPS projections is entirely playing time: Steamer projects a 3.62 FIP in 173 innings, while ZIPS projects at 3.62 FIP in 192 innings.  Homer has thrown 200+ innings for two consecutive years, and has been very healthy in those seasons.  But just one trip to the DL would drop him into the 170-territory, and the floor in any given season is 0 innings.

To get the contract to make sense, you have to set his 2014 projection to 3.3 WAR.  That's not outrageous; Bailey was worth 3.7 fWAR last season (and 3.2 bWAR), after all.  But that was easily his best season thus far.  It's pretty hard to project that he'll do that again this season, at least based on standard player behavior.

On the other hand, what we're really dealing with here is a projected difference of 0.6 to 0.9 wins.  Given how large the error bars are on projections, this really isn't that bad.  If the Reds have special scouting information that indicates that Bailey really did take a significant step forward last year, and one that he's very likely to continue in future seasons, you could at least make an argument that this is a reasonable projection...

Approach #2

Dave Cameron recently posted a pair of new models that look at free agent salaries.

The first is a model that just takes total projected WAR in a contract and uses that to estimate a player's salary.  It's a simple regression equation, but it explains 95% of the variation in free agent salaries from the offseason.  Not too shabby!  Let's run it for Homer and our various projections:

This is a bit more encouraging.  Based on the regression equation, and our projection systems, Homer Bailey's contract estimate comes in at between $70 million and $85 million over 5 years (or six years, for that matter; he's projected to be replacement level in 2019).

I adjusted the Apparent Reds projection a bit here, because this regression model tends to result in higher estimated salaries than my first approach. Here, a projection of 3.1 WAR gets him where he should be for the contract to be an even value.  That's an 0.4 to 0.7 WAR difference from projections.

Approach #3

In that same article, Cameron also put together a salary estimation "toy."  It's very simple, not horrifically rigorous, but it works pretty well.  You can go to the article to read about it.  I applied it to Homer:

Cameron's toy suggests that, based on the Steamer and Zips projections, the market length of a salary like Homer's would be about 4 years.  But if we extend it to 6 total years, we get total estimates between $72 million and $81 million.  That's pretty close to the regression equation above.

The closest I could get his contract to the actual value was 3.2 WAR.  Push it to 3.3 WAR, and Cameron's toy extends him another year to 7 WAR, and the total contract value shoots to $115 WAR.  But again, the estimates are indicating that the +Cincinnati Reds are valuing Homer by about a half-win higher than the projection systems.

Conclusions

By the numbers, I think it's pretty easy to see why so many see this as an overpay.  Steamer, which has been the champion of pitcher projections the last few years, estimates his monetary value between 50% and 70% of the actual contract value, depending on which approach one takes.  That's a tough pill to swallow, especially when you consider that the salary models I'm using aren't making any allowances for the fact that pitchers are inherently more risky than hitters, aside from the projections.

That said, the other thing that this exercise impressed upon me was that the systems that I, at least, am using are highly volatile when examining long-term salaries.  For the most part, we're dealing with differences of just a half a win.  That's easily within our margin of error.  Any small difference in projection gets compounded with each year of an extension.  This is further enhanced by the fact that the cost per win changes with player quality.  As a result, a difference of less than a win in a projection can result in a $50 million difference in a contract valuation.  In Homer's case, that's half of his salary!

What do you think?  Is it reasonable to project Homer to have a 3+ WAR season in 2014?  Are these salary approaches so sensitive to small changes in player projection that they are almost useless?  Or was this a big overpay by the Reds?