Archive for Baseball

Dynasty leagues are a little bit like the stock market.  What makes a good owner is finding things that may go up in value; this can be players, draft picks, or even money (not all leagues allow trading money, but ours does).  When you find a player that you think will go up in value you try to trade for him, pick him up, or draft him.  Anyone can sign good players in the auction for a lot of money, but what sets the good teams apart is their ability to find the players that are going to go up in value, or as we say “break out.”  I’m a fan of David Peralta.  He has already made quite an impact for teams in 2015.  Hitting .312/.371/.522 will do that.  The good thing for us is that he is not being properly valued right now in fantasy baseball leagues.

Now is the time of the year for rankings.  Every single site out there is coming out with their rankings getting everyone all set for their leagues.  Thankfully, we have sites like FantasyPros to get us consensus rankings and average draft positions.  Right now experts rank David Peralta an average of 40th among outfielders.  He is being drafted on average as the 38th outfielder off the boards.

David Peralta came up as a starting pitcher with the St. Louis Cardinals.  After multiple shoulder injuries he decided to bow out and head back home to Venezuela where he remade himself into an offensive player.  After an impressive year in an independent league he was signed by the Diamondbacks.  He quickly shot up through the system, learning fast for a player already in his mid 20s.  He’s only had a year and a half in the big leagues now, but he’s still been improving.  From what I understand, he is a very hard-working and upbeat player.

Numbers?  How about an improved hard-hit rate, going from 30% in 2014 to 35% in 2015?  A wRC+ jump from 110 to 138?  A HR/FB jump from 9.6% to 17.7%?  Even within 2015 he improved all three of those stats, getting up to a 38% hard-hit rate and a 162 wRC+ in the second half.  That’s destroying the baseball.  He’s spend most of his time batting fourth behind Goldschmidt and Pollock, so the RBI opportunities will continue.

You want to know what the most shocking thing is?  He only started 116 games.  The logjam in the Arizona outfield was to blame.  Well guess what, Ender Inciarte is gone and Yasmany Tomas sucks.  David Peralta is going to have no problem being the permanent cleanup hitter.  If we just took his 2015 stats and ignored any improvement whatsoever and prorated them for a reasonable 150 games we would be looking at 79 runs, 22 home runs, 101 runs batted in, and 12 stolen bases.  That’s even giving him two whole weeks off.  If you bake in some improvement due to his second-half numbers it’s not very hard to see 25-30 home runs with 200 combined runs and RBI.  Those numbers look a lot like what we’d expect from someone like Ryan Braun, Adam Jones, or Matt Kemp, all of whom are going in the 15-25 range.

The only website I’ve seen give Peralta his due was ESPN when Tristen Cockroft put him 25th among outfielders.  So at the very least that means I am not the only one thinking this is a huge value opportunity.  For dynasty leaguers, you need to go out and get him now.  He’s more than likely got a nice cheap contract or he might even be available in an auction because someone didn’t think he’s worth keeping around.  Listen to me, get him now and lock him up.  It’s a done deal.  Guess what, I’ve already done that in my league.  I traded Ken Giles ($1/3) for Peralta ($4/1) and a second-round draft pick back in November, so I put my money where my mouth is.  That was before Giles was in Houston and in our league contracts can be doubled up each additional year so I traded away about seven years of a top-10 closer for three or four years of Peralta and a second-round pick (for the minor-league draft).  But enough about me, don’t worry about my deal.  Go and get him.  Rarely are breakouts this easy to predict.

The game is on the line. It’s the bottom of the 9th inning, runners on first and second with two outs for the Mets. Justin Turner drives a fly ball off the bat at a speed of 88.3 mph. All hope for the Braves looks to be lost. In a blink of an eye or just .02 seconds Jason Heyward reacts and races out of center field traveling 18.5 mph to make an incredible diving catch to save the game.

This data set was one of the earlier Statcast recordings released to the public. It shows how important such information could potentially be to clubs in the future. Statcast can record data such as Acceleration, Route Efficiency, Reaction Time, Max Speed, Distance Covered and more. Although not all of their data is available to the public, I wanted to further explore how a baseball club would benefit by using this technology to research defensive analysis on improving a player’s abilities and a club’s defensive positioning.

First off, a team could compile this data and separate each player’s metrics by direction. Players move differently when heading in different areas of the field. It’s obviously easier to move forward than running backward, so having this data would allow teams to identify key information and make comparisons down the road. This can be done so by separating a fielder’s range into eight different quadrants (see graphic below). Once that is done, averages are created based for each quadrant. For instance, on average, what is Brett Gardner’s route efficiency when moving right? When moving in quadrant 6, what is Charlie Blackmon’s average reaction time?

Quadrants

#1: Forward

#2: Right Forward

#3: Right

#4: Back Right

#5: Backwards

#6: Back Left

#7: Left

#8: Left Forward

All this information, separated into different quadrants, will help in visualizing and breaking down defensive ability. When we have averages of acceleration, max speed and reaction time it can create a visual graphic or “Statcast Range” to witness how much distance a player could potentially cover in a certain amount of time. For example, lets say Jason Heyward’s average reaction time, acceleration and max speed when going left was .02 sec, 15.1 ft/s^2 and 18.5mph respectively. We know using this information Heyward could cover approximately 81 feet in 4 seconds. Time can help us represent a player’s estimated “Statcast range.” Each player’s range will look differently as they may show in which directions they are better at fielding. We can then use this analysis to compare fielders and also adjust defensive positioning.

This information will help guide a team in improving its players’ abilities. Teams can compare players much easier and understand what flaws coaches must look into fixing. For example, if a fielder has below-average route efficiency or reaction time to a certain part of the field, this information can be relayed to the coaching staff to further improve a player’s ability over time. In order to put this in perspective, Eugene Coleman of the University of Houston found that the average major-league ballplayer ran 24 feet per second. Using this number, having 0.04 more seconds means the average major leaguer can cover 11.5 more inches of ground. That’s almost a foot more and within only .04 seconds. If a ballplayer cuts down his reaction time, improves his route efficiency, and more, he would be able save time in covering several more feet of ground and thus improving his defensive ability.

To adjust a player’s defensive positioning, a team would have to combine its knowledge from this analysis with the understanding of a hitter’s batted balls. If they know a certain player is a pull hitter and hits to certain parts of the field, they can track his batted-ball locations, hang time and exit velocities to project areas in the field to which he may hit. Using what we know about a fielder’s Statcast metrics and “Statcast Range “ a player’s positioning could be adjusted. Doing so would lead to more accuracy. Improving the range of a team’s fielders will help save distance and time. The ability to increase production of more outs will provide a club with a better advantage for winning the game.

The timeless struggle between pitcher and batter is one of dominance — who holds it and how. Both players use a repertoire of techniques to adapt to each other’s strategies in order to gain advantage, thereby winning the at-bat and, ultimately, the game.

These strategies can rely on everything from experience to data. In fact, baseball players rely heavily on data analytics in order to tell them how they’re swinging their bats, how well they’ll do in college, how they’ll perform at Wrigley versus Miller.

Big data has been used in baseball for decades — as early as the 60s. Bill James, however, was the first prominent sabermetrician, writing about the field in his Bill James Baseball Abstracts during the 80s. Sabermetrics are used to measure in-game performance and are often used by teams to prospect players.

Baseball fans familiar with sabermetrics, the A’s, and Brad Pitt have likely seen Moneyball, the Hollywood adaptation of Michael Lewis’ book. The book told the story of As manager Billy Beane’s use of sabermetrics to amass a winning team.

Sabermetrics is one way baseball teams use big data to leverage game theory in baseball — on a team-wide scale. However, by leveraging their data through the concepts of game theory on a smaller scale, baseball teams can help their men on mound out-duel those at the plate.

Game theory studies strategic decision making, not just in sports or games, but in any situation in which a decision must be made against another decision maker. In other words, it is the study of conflict.

Game theory uses mathematical models to analyze decisions. Most sports are zero-sum games, in which the decisions of one player (or team) will have a direct effect on the opposing player (or team). This creates an equilibrium which is known as the Nash equilibrium, named for the mathematician John Forbes Nash. What this means is that if a team scores a run, it is usually at the expense of the opposing team — likely based on an error by a fielder or a hit off a pitcher.

In the case of pitching, game theory — especially the use of the Nash equilibrium — can be used to predict pitch optimization for strategic purposes. Neil Paine of FiveThirtyEight advocates using big data and sabermetrics to analyze each pitch in a hurler’s armory, then cultivating the pitcher’s equilibrium — the perfect blend of pitches that will result in the highest number of strikeouts, etc.

Paine has gone so far as to create his own formula, the Nash Score, to predict which pitcher should throw which pitches in order to outwit batters.

In perfect game theory, the Nash equilibrium states that each game player uses a mix of strategies that is so effective, neither has incentive to change strategies. For pitchers, Paine’s Nash Score uses their data to find the optimal combination of pitches to combat batters, including frequency.

Paine does point out that creating this kind of equilibrium in baseball can be detrimental to a pitcher. He is, after all, playing against another human being who is just as capable of using game theory to adapt strategies to upset the equilibrium.

If a pitcher’s fastball is his best, and his Nash Score shows that he should be using it more often, savvy hitters are going to notice. “ . . . In time, the fastball will lose its effectiveness if it’s not balanced against, say, a change-up — even if the fastball is a far better pitch on paper,” writes Paine.

In this case, a mixed strategy is the best — in game theory, mixed strategies are best used when a player intends to keep his opponent guessing. Though pitch optimization using Paine’s Nash Score could lead to efficiency, allowing pitchers to throw fewer pitches for more innings, it could also lead to batters adapting much quicker to patterns, thus negating all the work.

Take a look at Chris Davis’s stat line in August: 11 games, 45 PA, 14 Ks, 7 BBs, 6 HRs. Nothing really jumps out; it’s pretty typical for Chris Davis. Looking deeper though, this selection of plate appearances is actually quite remarkable. 27 out of the 45, or 60% of them, ended with a strikeout, walk, or home run, known as the “three true outcomes” where the ball does not end up in play.

As Baseball Prospectus explains in its definition of TTO, the statistic actually gained relevance with the introduction of DIPS, FIP, and other pitching estimators that ignored the outcomes of balls in play. While still not commonly used, it’s certainly interesting to take a look at once in a while to see what players are taking luck into their own hands.

Chris Davis is actually not the most extreme three true outcome player. Despite his 60 TTO% August, his season-long percentage through August 13 stands at 48.9%, good for 5th in baseball of those who have at least 300 plate appearances. The rest of the top-10 leaderboard features both good names and bad. On the good side, we have Giancarlo Stanton, the only player to feature a HR% over 8% (his is 8.5% , and he actually leads second-place Nelson Cruz by 1.4%). Other names you might associate with quality players are Bryce Harper, Joc Pederson, and George Springer, all of whom have a K% under 30% and a HR% of over 4%. The players who might not be as happy to be on this list include the aforementioned Chris Davis, Chris Carter, Steven Souza, Kris Bryant, and Colby Rasmus, who all feature a K% of 31% or higher. Mike Zunino, who comes in at 10th, sports a walk rate and home run rate of just 5.6% and 2.8%, respectively, but more than makes up for it with a 34.2% strikeout rate, second only to Souza.

Now that we’re done with the fun facts, let’s get into what it really means. TTO players are swing-for-the-fence players, those who aim to hit the ball over the wall every time they make contact. This is the cause behind their multitude of strikeouts. It also accounts for their walks, with the reasoning that pitchers are simply afraid to throw them hittable pitches.

The real question becomes “Are these TTO players valuable?” Looking at a graph comparing TTO% to wRC+ over the past 15 years, there is little correlation. It seems as though it is slightly more productive to be a TTO player, mainly because of the home runs and walks. This is far from a correlation though, as many bad players have a high TTO% and vice versa.

If we split it up into its parts, we might get a better view. League average TTO% has risen over the last decade, from 27.3% in 2005 to 30.3% this year (with a high of 30.5% in 2012).

We know the overall percentage has risen, but what’s driving it? If you’ve been following baseball, you know that the quality of pitchers has improved in recent years. Predictably, this has led to a decrease in walk rate and home run rate.

If 2/3 of the TTO% has decreased, but TTO% has still increased, that must mean the change in the third category must be drastic. This happens to be exactly the case. While BB% and HR% have fallen approximately a combined 1% over the past 10 years, league wide K% has risen by 4%.

What this means is that nowadays, if you are a TTO player, it’s likely much of that is coming from your strikeouts. In fact, out of the top-25 TTO% players with at least 200 PAs, only Paul Goldschmidt has a K% under 20%. Does this make high TTO% players bad? As I said before, there really isn’t a correlation, You’ll see players like Bryce Harper and Mike Trout with a high TTO%, while Buster Posey has one of the lowest because of his low K%.

The reality is, there are many different kinds of players. Some have adopted this TTO mentality, but others have stayed with a more conservative contact-focused approach. Without further information, it’s difficult to say which strategy is better. As a fan of statistics, I prefer the TTO players because it’s much easier to predict their performance. I don’t think they care much about that though.

Also, if you were curious, here’s a list of the top TTO% players with 200 PAs, created using FanGraphs data through August 13.

A.J. Pollock is, at the moment, one of the best fantasy outfielders in major league baseball.  He’s 4th according to the ESPN player rater but since most of you and I don’t REALLY know what that means, let’s say it a different way.  He is one of only four players with at least a .290 AVG, 10 HR, and 15 SB.  Still, whenever I talk with anyone about Pollock’s performance, the consensus opinion on him is more of a resonating question: “Is A.J. Pollock really this good?”  Let’s attempt to answer that.  Dating back to the beginning of 2014,  Pollock has played in 161 games.  We could round that up to 162 games, especially since players rarely play every single game of a season, and call it a full season, but I’m going to go the extra mile here and pull the last game from his 2013 campaign to have a constant 162 games for this exercise.  The stat line he has produced is impressive.

Let’s lower the bar a little bit so that we can find more players in THIS search: how many other players over their last 162 games have hit at the very minimum: .290, 90 R, 15 HR, 60 RBI, 25 SB?  The answer is 1, and that man is Starling Marte.

I’m not sure if that makes Marte a fair comparison.  We can compare them, but Marte delivers more line drives and raw power than Pollock does.  Marte, despite having a paltry 19.3% FB rate, has averaged 312 feet on his fly balls this year, 4th best in the majors, allowing him him to post an absurd 29.5% HR/FB rate – and we’re not even ready to get into park factors yet.  Pollock is just a bit more refined than Marte, posting a better BB rate and K rate than Marte has, by, obviously, swinging at better pitches to hit.

Pollock, too, has a fine average fly ball distance.  It’s 295 (a number he’s increased each year), which is good for 39th overall, smack dab in between Adam LaRoche to the north and Nolan Arenado to the south.  But Pollock has also been incrementally improving his BB/K ratio over the last three years, bringing it from 0.40 to 0.47 this year.  It could be as simple as that – a good player that has made strides in his approach at the plate, but I can’t just leave it at that.  Despite these improvements, albeit, very small ones, his batted ball profile looks right around league average.

Pollock is fast, so hitting a lot of ground balls works in his favor.  He’s been able to have higher than average IFH and BUH percentages in each of the last three years because of his speed.  However, despite being a below average line drive hitter this year, and throughout his career, he is less susceptible to BABIP fluctuations than other high frequency GB hitters like Alcides Escobar, Elvis Andrus, Jean Segura, because Pollock produces a hard hit rate higher than the league average – he is an authoritative hitter.  Curious though, that with his below average LD rate, this is the case.  So his hard hit% is driven by either hard contact on fly balls or ground balls relative to league average.  Since he has an IFFB% above league average I’m going to predict that he’s a high authority GB hitter.  There’s logic in that, right?

He’s right at about league average for hard hit fly balls, but he does seem to have a hard hit ground ball percentage markedly higher than league average.  In fact, his 23.1% hard hit GB rate is 18th best in the league.  The 17 players in front of him have combined for an average line of:

The list also includes names like Tulo, Miguel Cabrera, Posey, Pederson, Upton, Donaldson, Trout, Jose Abreu, and Yoenis Cespedes.  It’s guys that we generally perceive to be hard contact hitters, or I guess, more specifically, power hitters.  But he’s 18th on the list and produced a quality hard hit ground ball rate last year, too.

But, he still has a league average hard hit fly ball rate and a below average line drive rate.  These are reflected in his numbers compared to the league.

Lastly, he plays in Chase Field, which, throughout its history, has been a hitters park.  From 2008-2014 it had an adjusted park factor of +111.  For right handed hitters (and left handed hitters) like A.J. Pollock, it has had only positive affects, but this table is solely for righties:

Put Pollock in a neutral park and his numbers for the last 162 games would theoretically look like this:

I was kind of hoping to see more signs that Pollock is experiencing more luck.  Not because I don’t like Pollock, I love him as a baseball player and I’m sure he’s a fine person, but because of the questions regarding the sustainability of his play in the first half of the 2015 season by many of my peers.  The answer to, “is he really this good”, is that he is pretty darn close and I can see him performing to any of the projection systems’ expectations the rest of the way (ZiPS, Steamer, or Depth Charts).  He should experience some fluctuation in BABIP because of his GB rate, but so far he really hasn’t – and again that’s partially due to the authority with which he hits them.

In terms of finding a player closest in comparison to Pollock, Marte might be a pretty decent choice.  If I can just brainstorm using the cloud technique, I would probably have, with A.J. Pollock’s name in the middle: Starling Marte, Jason Heyward, Christian Yelich, Charlie Blackmon, Brett Gardner, and Lorenzo Cain as smaller clouds extending off the big, middle cloud.  Here are stats based on the last 162 games played.

This group kind of works as a spectrum.  I see the players on the extreme north and south columns least like Pollock and the players in the middle most like Pollock.  There is no one player to compare A.J. Pollock with that is playing currently, although Mitch Webster would be a pretty good historical comparison using his ages 26 – 28 seasons.

Mitch Webster ages 26-28 162 G AVG:

Probably too high of a walk rate, but that looks pretty good.

The average season of the group above would look like this:

A little too high of a K rate, but that also looks pretty good.

And finally A.J. Pollock ages 25-27 162 G AVG

In conclusion, A.J. Pollock is very close to this good if he’s not actually THIS GOOD and I think these players are pretty good comparisons.  And hopefully Pollock has more long lasting success than Mitch Webster.  In a time when speed/power combo players are in decline, what Pollock is doing is clearly elite in that sense.  What I really would like to see would be the history of authoritative ground ball hitters with good speed who have played in parks that have buoyed their power numbers.  Unfortunately, I don’t have access to batted ball profiles for hitters throughout history – how many Pollocks does it take to gather that information?

It’s baseball season. Which means it’s fantasy baseball season. Which means I have to keep reminding myself that, even though it’s already been a month and a half, that’s still a pretty short time in the long rhythm of the season and every performance has to be viewed with skepticism. Ryan Zimmerman sporting a 0.293 On Base Percentage (OBP)? He’s not likely to end up there. On the other hand, Jake Odorizzi with an Earned Run Average (ERA) less than 2.10? He’s good, but not that good. I try to avoid making trades in the first few months (although with several players on my team on the Disabled List, I may have to break my own rule) because I know that in small samples, big fluctuations in statistical performance in the end  are not really telling us much about actual player talent.

One of the big lessons I’ve learned from following baseball and the revolution in sports analytics is that one of the most powerful forces in player performance is regression to the mean. This is the tendency for most outliers, over the course of repeated measurements, to move toward the mean of both individual and population-wide performance levels. There’s nothing magical, just simple statistical truth.

And as I lift my head up from ESPN sports and look around, I’ve started to wonder if regression to the mean might be affecting another interest of mine, and not for the better. I wonder if a lack of understanding of regression to the mean might be a problem in our search for ways to reach better health.
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I have every reason to hate Dee Gordon.

Prior to the 2012 season, I found myself struggling to figure out who would get the final keeper slot in a longtime, highly competitive fantasy league I played in. It came down to two players: Mike Trout and Dee Gordon. They both would have cost me the same, but Gordon was coming off a rookie campaign where he batted .304 with 24 steals in a miniscule 224 at-bats. Trout, on the other hand, was heading into 2012 with what seemed to me like a more clouded future. He had just posted a pedestrian .671 OPS with a 22.2 K%–albeit as a 19-year old–the year prior. He was also blocked in LF at the time by the great Bobby Abreu, and was looking at possibly another year of seasoning in the minors. In the end I chose Gordon, and the rest is terrible, nightmare-inducing history.

So how strange that I find myself here now, defending Dee Gordon, the very man who hoodwinked me into choosing him over Mike mother-flippin’ Trout.

Ironically, I think the hate for Gordon has gone a bit too far this year. It’s odd to think that there’s any hate for a guy coming off a season where he led all of baseball in steals while also posting a top-25 batting average of .289. But some people seem awfully down on the guy coming into 2015. Perhaps they too were burned by his 2011 breakout, and refuse to make the same mistake twice. Though I can’t fault them if that is the case, there is reason to believe that Dee Gordon’s days of breaking our hearts are over.

The first thing to point out are his batted-ball rates. As the graph illustrates, there weren’t any earth-shattering changes occurring here. It is worth noting, however, that Gordon set a career high in groundball percentage and a career low in fly-ball percentage. And if you’re willing to consider 2013 an aberration like I am (he only managed 106 plate appearances that year), he has actually been gradually trending in the right direction with both his fly-ball and groundball percentages while maintaining a fairly steady line-drive rate. Spikes in groundball percentages are rarely considered ideal, but when a player has the elite speed Gordon does, the odds of turning a weak dribbler or a grounder towards the hole into a hit get a very favorable bump.

Which brings me to perhaps the most eyebrow-raising aspect of Gordon’s 2014 season: his bunt-hit percentage (BUH%). After averaging a 28.5 BUH% over the prior three seasons, Gordon posted a ridiculous 42.6 BUH% in 2014. To put that number into perspective, here’s how it stacked up against the league’s other elite speedsters:

Bunting for hits is a skill. The fact that his success rate rose by nearly 15% last year tells me that he worked on and dramatically improved this skill. Perhaps more importantly, though, it tells me that he’s keenly aware of how dangerous a weapon this skill can be for him when used effectively. When paired with his declining fly-ball rates–and especially his new career low IFFB% of 8%, down from 13.2%–the numbers start to paint the picture of a player who may have finally begun to consciously tailor his plate approach to his strengths.

While I will never forgive Dee Gordon for what he did to me, I do see reasons to be optimistic about his 2015 season. Should his elite ability to bunt for hits carry over into this season, his .346 BABIP shouldn’t see as much regression as people seem to think, and another year of plus average and a stolen-base crown seems well within his reach.

For every Dontrelle Willis–who continues to get looks from Major League teams despite over eight years of complete ineptitude–there exists a handful of other players who fade into relative obscurity only a year or two removed from a dominant season. All it generally takes is a down year resulting from–or paired with–an injury to send a guy spiraling below the radar. These are often the players that can return the most value during fantasy drafts if you can make the distinction between a year that’s an aberration, and one that is a bellwether for a significant, irreversible decline in skills.

While I can’t say with complete confidence that Curtis Granderson’s 2014 doesn’t fall into the latter category, there were a couple of encouraging things going on below the subpar surface stats that make me think he can return some solid value this year, especially considering where he’s going in most drafts.

Granderson was 33 last year and coming off an injury-shortened season. He was also trading a left-handed pull hitter’s haven in Yankee Stadium for the cavernous confines of Citi Field. All things considered, it was natural to expect some significant regression. And when he hit .136 through his first 100 at-bats of the season, it seemed like the Mets might have had a disaster of Jason Bay-like proportions on their hands.

Fortunately for them, Granderson managed to right the ship to an extent, putting together a couple of excellent months. His final line of .227/.326/.388–dragged further down by a nightmarish .037 ISO, 16-for-109 August–wasn’t spectacular by any stretch. But there were some nice takeaways buried in there.

For one, his bat speed doesn’t seem to have slowed enough to justify the statistical hits he took across the board. Despite seeing 56.3% fastballs–the most he’s seen since 2010 by a wide margin–his Z-Contact % of 85% was in line with his 85.8% career average, and not far removed from the league average of 87%. I suspect the uptick in fastballs resulted from opposing teams banking on an age-slowed swing, but Granderson’s contact rates on high velocity pitches in the zone didn’t suffer for it.

Granderson also set a career high in O-Contact % with a 62.7% rate. This could usually indicate a lack of plate discipline as much as it could a sustained bat speed, except that Granderson’s O-Swing % of 26.2% is roughly the average of what he did in the four years prior. He also managed to post the second-highest walk rate of his career (12.1%) and his lowest strikeout percentage since 2009 (21.6%). These are not particularly impressive rates in their own right, but in the context of Granderson’s career they do help to dispel the notion that last year was the beginning of the end for his hitting ability.

That is not to say, of course, that I foresee a return to the 40 home run, .260+ ISO form that he flashed in his early Yankee years–there’s no way he ever touches the absurd 22 HR/FB% that sustained that run. But with the right field fences at Citi Field moving in–a change that apparently would have resulted in 9 more home runs for Granderson had it been done last season–and some improvement on last year’s uncharacteristically bad .265 BABIP, I would not be at all surprised to see a home run total between 25 and 30 to go along with double-digit steals and a batting average that won’t kill you. And that has value when it is being drafted as low as Granderson currently is.

The 15-team LABR Mixed Draft is the most exciting of the expert fantasy drafts each year. Amateur fantasy owners from all over the globe tune into the live spreadsheet broadcast and debate each one furiously on social media.

Most of these amateurs are looking for expert guidance to help them in their own draft. They see a player getting drafted well above their ADP and they often move the player up on their own personal big board.

I do not think this is the best way to approach and absorb the most information out of LABR. When one expert reaches on a pick, we have no idea if there is a consensus. It could have been just one expert making a stand on a player he himself feels strongly about, or there could have been several owners who felt the same way about that player. We just don’t know.

What we do know is that when certain players drop well below their public rankings, there is an agreement of pessimism. That is the information that could be significant for the rest us. Every owner in the league letting a player fall well below their ADP is the expert consensus we should be looking for.

Here’s a quick look at nine players who the experts are cool on.

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I got caught up in an interesting Twitter debate Friday afternoon regarding Astros 2B Jose Altuve with FantasyAlarm.Com’s Ray Flowers that prompted a detailed response from Flowers about our Altuve dispute where he doubled down on his assertion that Altuve’s ADP of 10th overall is huge mistake.

The main crux of his argument is that Altuve is not an across-the-board contributor. He claims Altuve’s lack of power in this current environment makes him a terrible choice at the end of the 1st round.  In this article I’m going to demonstrate why this shouldn’t be a major concern for you.

Hitting Your Marks

In 5×5 rotisserie leagues, the goal is to construct a lineup that gives you a chance to accumulate as many points as possible in the various categories. In NFBC 15-team leagues, I’ve come up with these target numbers for each category.

Hitting each of these five offensive targets should put you in the Top 3 of each category, accumulating at least 65 of the maximum possible 75 points. There are 14 hitting positions to fill, so you are looking for these averages per active roster spot:

Value Is Value

The key to winning fantasy baseball leagues is to constantly find the best value in each of your picks no matter what round you are in. Getting power-happy in the early portion of the draft has been a trendy tactic over the past couple years as power has declined in baseball. Let’s look at a couple of the players Flowers suggested he’d rather pick over Jose Altuve in the 1st round and their Steamer projections:

NFBC has a player rating system that compares a player’s statistics to league average and creates a score to show what their true 5×5 Roto value is. Based on the above 2015 Steamer projections, here is where each of these players would have finished last season:

Altuve is the more valuable player based on 2015 Steamer projections (and most likely more valuable based on any credible projection system).

And now we get to Flowers’ main point. He says that “Power is harder to find than ever before.”  He is absolutely right but that does not mean there isn’t an island of misfit power bats available in the middle rounds. You should not be worried about missing out on power in the early rounds because THERE IS home run pop that you can add later in the draft.

In a recent NFBC draft of my own – where I took Altuve 12th overall – I had the powerful but flawed Chris Carter land right in my lap in the 10th round, 139th overall. Let’s look at his projection:

Carter, a source of tremendous power, has been scaring the daylights out of fantasy owners for the past couple of years. Nobody wants to take on his treacherous batting average as it will surely drag their team average into oblivion. Well because we took the proper value in the first round (Altuve), we are now in a position where Chris Carter is worth significantly more to us than to the guy who took Anthony Rendon or Adam Jones. We get extra value from Carter because we can absorb his batting average better than they can!

Here is what our first round pick, combined with Carter would look like as a composite player. Remember, we need 18 HRs, 66 Runs, 66 RBIs, 11 SBs, and .270 Avg to crack the Top 3 of those categories.

If we were to have chosen Rendon or Jones in the first round, Carter would be a terrible fit for us in the 10th round. We’d be in solid shape in three categories, but face crippling deficits in stolen bases and batting average. But because we chose Altuve (the most valuable of the 3 players), it allowed us to spend some of our excess batting average and stolen bases to acquire a middle-round power bat that nobody else wants to touch. With Altuve+Carter, we exceed our minimum requirements in FOUR categories and are not very far behind in a 5th.

A NFBC Draft Champions league that I won in 2013 stands out in my memory. The early rounds of the draft provided me a surplus of batting average and stolen bases, and I continued to take the best player available each round after that. The brutish Adam Dunn, who was coming off a terrible .159, 11 HR season, was getting drafted around 185th overall that year as people feared the damage his average would do. Because of the excess wealth I accumulated in other categories, Dunn was worth more to me than everybody else. I determined that if Dunn were to bounce back to the .220 range, I could absorb his average and bet that his home run power would return. After all, he did average 40 HRs a year for seven straight years prior to his 2012 abomination. I ended up being able to reach above his ADP and take him in the 11th round, 165th overall. He provided me with 41 HRs, 96 RBIs, and 87 runs in 2014 and was a key cog in winning the league.

Finding Speed

I suppose the counter argument to this approach would be, “Well we don’t need batting average lagging Chris Carter or Adam Dunn in the 10th round. Since we accumulated the extra power with Rendon or Jones, we can go after a speed merchant in these rounds. Perfectly reasonable case to state. You should be trying to balance your roster out. But does it work better than Altuve+Carter? Let’s look at the speedy Ben Revere, who went late in the 8th round of my draft, 118th overall. Under this scenario, since we took more power early, let’s grab this high average/stolen base machine from the Phillies and make up the ground we lost, right?

And our new composite player:

Revere is a light hitting lead off man with virtually zero pop. You have now elevated your composite player into the upper echelon in stolen bases and batting average at the expense of HRs, runs, and RBIs. Despite Revere getting drafted a round or two earlier than Carter, the combinations with Rendon or Jones are worse in those three categories compared to Altuve+Carter.

There’s a myth going around that cheap steals are always available late in the draft. While it’s true you can occasionally hit the jackpot on a Dee Gordon from time to time, it is a very risky play to ignoring steals early in hopes of finding one of these guys late. These players are also dangerous to the health of your power categories as you can see from the Revere example. It just seems like an unnecessary strategic risk to plan on these guys delivering for you. Other owners plot this same strategy and often they reach above ADP to grab one of the speedsters you were also planning on supplementing your power with. Roster construction? Out the window.

Also, Chris Carter is not your only option to complement your team in these middle rounds. There are several very good targets to keep an eye for if you’re lucky enough for Altuve to land in your lap at the end of the 1st round. Lucas Duda (.234, 24 HR) and Marcell Ozuna (.255, 22 HR) were both available in the 9th round. I personally drafted Brandon Moss (.248, 28 HR) in the 12th round. Pedro Alvarez (.242, 26 HR), I got in the 14th round. Again, I could absorb these averages because I repeatedly took the best player available earlier in the draft, often players with overlooked batting averages. I constantly kept an eye on my roster construction to ensure I could absorb these lower batting averages and lack of stolen bases.

In 2014, there were 56 hitters drafted between selections 201-to-300. 16 of these hitters would hit at least 18 home runs. Meanwhile, 15 of the 56 managed 11 steals.

Back to my particular draft this year, after choosing Altuve 12th, I took Jacoby Ellsbury with my 2nd round pick, 19th overall. Between these two players, Steamer projects only 24 home runs between them. Even though I happened to not grab any huge raw power bats in the first two rounds, I still managed to construct a 14-man lineup that is projected to hit the magical 250 HR mark without falling behind in the other categories.

Altuve and .300

A repeated argument was also made that Jose Altuve “is not lock to hit .300 this year”. I believe this is a very pessimistic position to take and I haven’t heard a sensible reason for it. This is a player who hit .286 over his first 1300 PAs as a 22-23 year old youngster. Despite increasing his Swing% rate to over 50% last year, he made more contact than ever (4.4% SwStr) with an uptick of power on his way to a ridiculous .343 average.  This is an elite hit tool.

Not even the most bullish Altuve supporter would think he’s going to hit .343 again. That would be a very unfair expectation. However, not a single person who is bearish on Altuve has made a compelling argument why this 24-year-old can’t hit .300 again. Of course Altuve is “not a lock to hit .300”. By that argument there is no player who is a lock to hit any of their projections, including Mike Trout.

Yes, HR power has declined over the years. But so has batting average. Over the last six years the league average has fallen from .264 to .251. You are not going to find too many players past the 10th round who are going to give you 600+ PAs of near .300 average to complement your sluggers, and if they do hit those numbers they are tremendously weak in other categories.

To wrap this up, I’m telling you not to buy into the hysterics that there is no power available after the early rounds. Do not buy into the major regression talk. You should have no fear in drafting Jose Altuve with your first selection if he’s the best value on the board.