Class of 2018 HS Baseball Player National Rankings | Perfect Game USA

So this is the early list of the top HS prospects for the Giants to consider with the 2nd pick overall.

from Perfect Game USA:
Class of 2018 HS Baseball Player National Rankings | Perfect Game USA:

The buzz from the west coast is the Giants are paying attention to Matthew Liberatore, which should scare the daylights out of the entire Liberatore family the way that Giants pitchers are flocking to the DL. And why is a kid from AZ playing for a Central Florida travel team. Come on people!! But a 6-5, 200 LHP who throws 95MPH will get a lot of national attention.

• Ethan Hankins, the 6-6, 200 RHP from Georgia still sits at the top of the list. Throws 97MPH.
• Kumar Rocker, also from Georgia and 6-5, 250 RHP to boot is still lurking around the top. Throws 98MPH.

Does anyone pitch at < 90 MPH anymore?

The Florida prospects,

• Carter Stewart 6-6, 200 RHP from Melbourne, FL 
• Mason Denaburg 6-4, 195 RHP from Merritt Island 

round out the pitchers in the Top Ten. Denaburg throws 97MPH and Stewart throws a low-90's FB      ( OMG!! ) but has the best spin rate TrackMan has ever recorded. So TrackMan loves him, but what does TrackMan know? If this Stewart kid hits, owners will wonder (a la Marge Schott) why we need all these scouts when we have TrackMan?

It seems like a deep pitching class this year, which could entice the Giants to go HS pitcher/College pitcher or vice-versa with their first two selections. I can't see them going for an everyday hitter since they have to hit and the Giants don't seem to have anyone that can develop hitters on staff. If they did, OF Travis Swaggerty is drawing comparisons to Andrew Benintendi, who was a darling of the prospect-paparazzi in Gigante-land.

So, put me down for Swaggerty as a long shot, if Beede and Suarez step-up and deliver on their potential. Otherwise, all other things being equal among the HS pitchers, it's Liberatore based on his left-handedness.

'via Blog this'

NHL commissioner Gary Bettman speaks on a panel during the 2018 MIT Sloan Sports Analytics Conference. (Photo by Patrick Daly / ESPN Images)

It's going to be tough to walk away from this completely. Teams will present it as a net positive re: performance enhancement, while the players will be somewhat wary of the negative feedback and it's use against them. Double-edged sword. But it seems inevitable that we will see FitBit for athletes, coming soon. 

from sporttechie.com
https://www.sporttechie.com/player-tracking-data-contract-negotiations/

Player Tracking Data Presents Touchy Subject For Contract Negotiations

Joe Lemire March 1, 2018

The Denver Broncos have been using Catapult's wearable technology since 2014, but running back C.J. Anderson said he only gained access to his data this past fall — and had a career-high 1,007 rushing yards, saying, "I believe I had a better year on the football field because of the data that I have."

While speaking on a technology panel at last week's Sloan Sports Analytics Conference, Anderson said that, as helpful as the tracking data was to him on the field, it might not be so beneficial when shared with team executives.

"Now if I walk into [general manager] John Elway's office and say, 'Hey Elway, A-B-C,' but he says, 'X-Y-Z,' I might not get paid as much as I would like or as much as I think I should be getting paid because he can use that data of the issues and the problems that I have. 'This is why I don't want to pay you,' or 'this is why I'm getting rid of you, this is why you're getting waived, this is why you're getting traded.'

"I think that will be the fear in our business will be the negotiation side of things. It's already cutthroat."

Even seemingly straightforward measurements, like a hockey player's skating speed, may have layers of complexity. As Don Fehr, the executive director of the National Hockey League Players Association, explained on a player advocacy panel, context matters.

"There's this big push to quantify everything without, as of yet, knowing what those quantities mean," he said. "So we can say you skated a little bit slower than you did three years ago, does that mean your play is better or worse? Are you taking time to figure out where you're going or can you not keep up any more?

"So there's a real danger in negotiations of having a lot of statistics that merely provide excuses for people to do what they want to do."

Therein remains the thorny nature of the proliferation of wearable data and how that information will be applied in professional sports. Some leagues have begun addressing the issue in their respective collective bargaining agreements, with voluntary participation a cornerstone of each policy, except the NFL that mandates players wearing the Zebra tracking chip in their shoulder pads during games. The NBA has banned the data from contract negotiations or player transactions. Major League Baseball prohibits the data from use in salary arbitration discussions. The NHL's CBA, finalized in 2013, does not explicitly address the issue, which will become more pressing given the league's stated desire to have league-wide tracking within two years. "Anything that can help grow the game," NHL commissioner Gary Bettman said.

The NBA is forming a committee to further explore the issue, but National Basketball Players Association executive director Michele Roberts was clear that player privacy was paramount in their conversations.

"We caution the players about how, if at all, they should be disclosing this information," she said. "Efficacy, validation — those things are important to us. We don't know if some of this stuff is, frankly, junk. And we certainly don't want it to have it be the source of or be used in contract negotiations. Maintaining control of the data has been our primary focus."

That echoes the refrain of NFL Players Inc. president Ahmad Nassar who, when speaking at SportTechie's State of the Industry event last month, said player ownership of data was the "central bedrock starting point" of his group's partnership conversation with Whoop.

Recently retired MLB pitcher Chris Capuano shared the prevailing opinion of his peers, when he explained, "I guess their fear is, 'How is this data going to be used against me?'" Baseball already has an external radar- and camera-based tracking system, Statcast, that measures players' movements on the field.

Generally, such external tracking information and other movement data is seen as less circumspect as the biometric wearables that glean heart rate and other such metrics.

"The way I look at it is two different buckets of wearable technology," said longtime NBA agent Jim Tanner, whose client list has included Tim Duncan, Grant Hill and Ray Allen. "The first is the movement tracking — acceleration, deceleration, load, things like that. That doesn't concern me as much as the biometric stuff where they're analyzing the oxygen in your blood or the recovery rates or things like that where, to me, that's very personal to the player."

Indeed, that is about the only consensus on the issue: players, agents and unions all believe the athletes should own and control their own data. What shape tracking technology takes in the years to come — whether it's personally worn or externally collected — will steer the direction of the conversation, along with the implications that can be definitively drawn.

"Obviously this is the new hot fad, and it's going to be out there," Fehr said. "Whether it'll be around in any meaningful fashion five or 10 years from now, I think, is anybody's guess."

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What Do You Need to Know as a Sport Scientist Besides Sport Science | Complementary Training

This should keep me busy for the next year or two. Mladen Jovanovic is a genius.

from complemntarytraining.com

http://complementarytraining.net/what-do-you-need-to-know-as-a-sport-scientist-besides-sport-science/

What Do You Need to Know as a Sport Scientist Besides Sport Science

There are new roles emerging in contemporary sports field: sport scientist and performance manager. To be honest I have no clue how to define them – sometimes they are just a way to break out of the normal terminology (see Semantic Stretch and Power of Association in Made to Stick book, which is by the way excellent) and appear fancy and important. But sometimes they represent genuine additional roles and obligations on top of strength and conditioning / physical preparation positions. I will focus on those.

Please note that these are only my opinions, so take them with grain of salt. I will cover some of those extra skills needed and provide good resources where to find quality knowledge.

1. Basic and Advanced Statistics

As a sport scientist you will mostly deal with descriptive statistics, although if you plan publishing you will need to make generalizations or inferences to a bigger population from a sample you have. In a club settings, coaches will not give a f#ck about p values and confidence intervals – they generally have no interest in making generalizations beyond the group of athletes they are working with. Also, they are not interested in 'averages', but rather in single individuals and outliers.

This makes most of the statistical methods useless (unless you are publishing in journals). This is also a positive thing, because you will use lot simpler approaches;  approaches that are understandable by both coaches and athletes.

Here are some of the topics you will need to know

Basic

1. Effect size statistics (e.g. Cohen's D)
2. Linear regression and model fitting and model diagnostics (e.g. Cook's distance etc)
3. Concept of Reliability (Typical Error) and Smallest Worthwhile Change (SWC) when assessing an individual and performance changes

Advanced*

1. Non-Linear regression and generalized linear models (e.g. logistic regression)
2. Time-series analysis
3. Clustering, Factor Analysis and Principal Components Analysis (PCA)
4. Machine Learning, Data Mining, Predictive Modeling

* You do not need to know these in detail – only be kinda familiar with them (especially the last one) so you can hire someone to implement those with the data you have. Just make sure to know what they are and what they give to you. Besides these are definitely not going to be understandable to athletes and coaches 🙂 

I am by no means expert on those methods and I am learning them myself as well.

The good sources of information are Statistics in Kinesiology by Vincent and Weir

A New View of Statistics by Will Hopkins

Coursera courses on Statistics, Data Analysis & Machine Learning
Excellent paper on Mathematical Modeling by David Clarke and Philip Skiba

2. Data Visualization

It is very important to be able to convey data and insight to decision makers. Effective graph and table design is then a must have skill. I really love the books by Stephen Few

3. Excel

Excel is lingua franca of sport scientists. Even the richest clubs still use Excel and Dropbox combo. Here are some of the great resources for you to consider if you want to learn Excel

And excellent Excel tricks for Sport by John Lythe

4. PowerPivot

Some say this is the best addition to Excel since, well, Excel. It is very powerful add-on to Excel. I made Annual Planner for Sports v2.0 using PowerPivot.

5. R/MATLAB/Python

It is important to know at least one of those programming languages. R is getting bigger and bigger in data analysis and it is completely FREE, with a LOT of 'extensions' and libraries (see the list below). Python is also completely free and is catching up with data analysis, visualization and statistics. MATLAB is awesome engineering tool (SIMULINK is great tool), but more and more scientists are switching to R/Python.

If you want to learn R here are the couple of great books to start with

As a R user you will definitely need to learn great packages developed Hadley Wickham, like ggplot2, reshape2, plyr and stringr.

Some other 'extensions' of R are:

Shiny (to make web apps)
Slidify (to make awesome interactive presentations/slides)
Knitr (for reproducible research and dynamics documents)
rCharts and googleVis (for interactive charts)

To see excellent presentation that uses all the above features click HERE and HERE.

6. LabView

I have very little experience with LabView, but it is a important to know how it works if you plan working with lab equipment or programming your own (e.g. force mats, pressure mats). It is visual programming environment similar to SIMULINK.

7. Knowledge of some Athlete Management Software (AMS) Platforms

It is important to have at least basic experience working with some of the following AMS platforms: SMARTABASE, EDGE10, Apollo, TrainingPeaks.

8. Managing People

This is pet-peeve of mine. As Carl Valle used to say: "It's not what you know it's what you can get your athletes to do".

I have put this last on the list, but it is actually the most important.

Here are some of the titles I found helpful and some that are recommended by smarter people than me. This is by no means extensive list – there are a lot more great resources out there.

Also make sure the check Dan Ariely free course on Coursera on irrational human behavior and two books by Ray McLean from Leading Teams.

 

Quotation of the day on how the success of others is now a grievance, rather than an example..... - AEI

from AEI.org
Quotation of the day on how the success of others is now a grievance, rather than an example..... - AEI:

"…. is from Thomas Sowell, writing in his 2002 book The Quest for Cosmic Justice:

There has now been created a world in which the success of others is a grievance, rather than an example. Irrational as such ideological indulgences may be, they are virtually inevitable when equality becomes the social touchstone, for equality can be achieved only by either divorcing performance from reward or by producing equal performances. Since the latter is all but impossible, if only because everyone is not equally interested in the same kinds of performances, the passion for equality leads toward a divorce of performance and reward – which is to say a divorce of incentive and behavior, and even a divorce of cause and effect in our minds."

'via Blog this'

Minor League Players’ Wage Suit against Major League Baseball suffers a huge setback – HardballTalk

Like the Curt Flood example, you will not get redress from the courts. They will point the finger at Congress. You will not get redress from Congress, they are bought and paid for by entities more powerful, better connected and better funded than you and your union.

You might get redress from your union when they negotiate a new CBA with management. You might want to contact your player reps and see how much this means to the brethren..

History is a great teacher if only folks would listen and learn from it.

MLBPA where are you? MIA?

from nbcsports.com
http://mlb.nbcsports.com/2016/07/22/minor-league-players-wage-suit-against-major-league-baseball-suffers-a-huge-setback/

Minor League Players' Wage Suit against Major League Baseball suffers a huge setback

A judge handed minor leaguers looking to hold Major League Baseball liable for underpaying and exploiting them a huge setback today, ruling that the case cannot go forward as a class action. Minor leaguers who want to sue over their pay and treatment still can, but they'll have to do it individually. The ruling saps the minor leaguers of their leverage, as Major League Baseball would likely be able to fend off individual cases which, by themselves, might only amount to several thousand dollars per claim.

The background: in 2014, former Miami Marlins player Aaron Senne sued Major League Baseball, Bud Selig, and three major league clubs claiming that minor leaguers are underpaid and exploited in violation of the Fair Labor Standards Act. He was later joined by former Royals minor leaguer Michael Liberto and Giants farmhand Oliver Odle. Eventually others joined and the suit had been expanded to 22 teams as defendants.

The upshot of the case is that, while the minor league season lasts only part of the year, players are required to do all sorts of things outside of merely playing games for which they are not compensated. Training, meetings, appearances and the like. When all of that time is added up, the players claim, their already low salaries are effectively far below minimum wage in violation of the law. Major League Baseball has countered this by claiming that minor leaguers are basically part time seasonal workers — like landscapers and pool boys — who are not subject to federal labor laws.

Last year the judge gave the case conditional certification, allowing the players to try to establish that it should go forward as a class action. This would streamline the case from the plaintiffs' perspective and give them the power of collective action by asserting hundreds or more similar cases into one proceeding. The judge's ruling today, however, was that the cases really weren't factually similar and thus collective action was not appropriate because figuring out how many hours each player worked and what was required of him varied too greatly among the players.

From his order:

"The difficulties associated with determining what activities constitute 'work' in the context of winter training are compounded by the fact that there appear to be no official records documenting these activities. Because it may be impossible to determine from official records the types of conditioning activities in which the players engaged, membership in the state classes based on winter training would depend largely upon the players' ability to remember, with a reasonable amount of detail, what they did during the off-season (often for multiple years and for many, several years in the past) to stay fit."

The judge said that, in light of this, each case would be unique and would require "individualized inquiries" to find damages and liability. That phrase –"individualized inquiries" — constitutes magic words which sink would-be class actions. If a company overcharges all of its customers by $8 due to an error repeated a million times, it's easy to look at one set of facts and judge them together. If you had to look at a million different wrongs, that's no class action. And so it is not a class action for the players.

As many courts who have dealt with these sorts of cases have noted, for many plaintiffs, a class action is the only practical method of adjudicating Fair Labor Standards Act cases because individual plaintiffs are frequently unable to bear the costs of separate trials. They are, by definition, (allegedly) exploited workers. They're not going to be able to pay legal costs and fight off a multi-billion dollar business in order to collect the few thousand dollars they were underpaid. At the same time, however, the defendants have rights too and, if the facts of each players' treatment truly differ (e.g. the Yankees make their minor leaguers do more than the Brewers do) it's not fair to bind one defendant's defense to the acts of another.

So, where does this leave the players? Not dead. Not yet, at least. Their claims have not been dismissed on the merits. They have only been denied the right to act collectively. The individual plaintiffs can now file separate lawsuits against their former employers and Major League Baseball under the same theories. It would be harder to land a big blow in such a scenario, but if enough do, it could end up being death by a thousand cuts for the clubs and the league. Their legal fees might go up and, eventually, if they lose enough of these cases, more might be filed. There are a lot of former minor leaguers, after all, and once there's some blood in the water, more of them — and their lawyers — may enter the frenzy. Decertification is certainly a win for the league right now, but it's not necessarily a permanent win.

There are likewise some other quasi-collective forms this case could take such as multi-district litigation in which the cases, while individual, are coordinated in a loose fashion. That could lead to some efficiencies for suing players even if it's not as robust as a class action.

We've written quite a bit about minor league pay and treatment in this space by now, so you probably know where we stand on it. We believe that minor leaguers are exploited and underpaid and we believe that Major League Baseball has been happy to exploit and underpay them for some time. Ultimately we believe that this state of affairs cannot and will not persist and that eventually, somehow, baseball will either see fit to pay its workers fairly or, more likely, will be forced to do so by a court or by collective bargaining of some fashion.

Today, however, was a big setback for the minor leaguers. Today's ruling will give Major League Baseball and its clubs more time and more comfort in which to underpay them. There's no doubt about it.

Follow @craigcalcaterra

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Minor League Baseball Players are About To Get Screwed - McCovey Chronicles

Of course they are....they are in out of their league when it comes to playing the game called politics. There is nobody fighting for them including the MLBPA. If you want to rail at somebody in all this, rail at them.

Minor League salaries are abysmally low in order to allow guys like Bryce Harper a shot at ringing the register for $400 million dollars. The NFL players union caters to the stars, the NBA players union caters to the stars. Nobody advocates for the lesser lights.

Break off a piece of that action and sprinkle it across the minor leagues and then tell me more about fairness and equality. Why do I feel like some these guys who advocate this position drive around with Bernie Sanders bumper stickers? Clueless....

MLBPA where are you? MIA?

from mccoveychronicles.com
https://www.mccoveychronicles.com/2018/3/21/17146268/the-prospect-round-up-3-21-18-minor-league-baseball-players-are-about-to-get-screwed?ref=digest

The Prospect Round-Up 3/21/18 - Minor League Baseball Players are About To Get Screwed

Kevin J. Cunningham

Design by Kevin J. Cunningham

TRIGGER WARNING: I'm about to talk Politics.
I know I'm going to get some comments or replies on this saying to "Stick to Sports". Oh well. This is about sports.

I hate it when the sport I love does something that makes me want to stop supporting it, writing about it, and move on to something else. But it's doing it right now.

The organization of Baseball (comprised of both the Major and Minor Leagues) is attempting an end run around legislation subcommittees and lawsuits, by including a rider on a massive government spending bill that exempts minor league players from federal labor law protections.

Omnibus subplot: Effort afoot to write labor-law exemption for minor league baseball into spending bill, quashing players' wage claims.https://www.washingtonpost.com/powerpost/spending-bill-could-quash-minor-league-baseball-players-wage-claims/2018/03/18/d31cd76e-2b0a-11e8-8ad6-fbc50284fce8_story.html …— Mike DeBonis (@mikedebonis) 10:48 PM - Mar 18, 2018 The ultimate in inside baseball. MLB billionaires working to secretly get an exemption from minimum wage laws so they can continue to pay minor leaguers nothing during spring training and $4 per hour during the season. https://twitter.com/mikedebonis/status/975564643157463040 …— Garrett Broshuis (@broshuis) 11:30 PM - Mar 18, 2018

By doing this, they will skip various subcommittees that are there to discuss labor matters in full. It will just happen. And the travesty of how minor league players are paid and kept in poverty will remain.

Garrett Broshius, former Giants prospect and the lawyer helping to lead the lawsuits against Minor League Baseball for their payment of players, has been vocal about this latest version of the bill. He shared this letter written by a prospect who left baseball, and explaining why.

Here's a legit MLB prospect walking away from the game because of financial woes. He made under $8,000 last year at the upper levels of the minors. But yes, let's give MLB an exemption from minimum wage laws.

http://www.stlsportspage.com/CARDSBASEBALL/tabid/91/entryid/12071/economics-of-playing-in-minors-prompts-cardinals-prospect-to-retire.aspx#sthash.clydCl4S.4AQExARk.dpbs …— Garrett Broshuis (@broshuis) 11:26 AM - Mar 19, 2018

It takes a while for him to get to the financial side of it, but it's worth reading. The fact that teams pay players as "Seasonal Employees" when they are expected to maintain rigid offseason workout programs and report their progress in, but not be paid to do so nor have those facilities to do it in, is insulting. Minor League players are also not paid for Spring Training.

Baseball insists that minor leaguers should view their employment as a "Stepping stone" rather than a career, and compares them to fast food employees in that regard, according to an interview published by Baseball America.

They continue to push the argument that raising the salaries of minor leaguers will essentially destroy minor league baseball, throwing those clubs into debt and closing them, ending the jobs of thousands of other workers at those stadiums who are protected by minimum wage law. This is complete BS, since minor league players are paid by their major league team, not the minor league ownership. I have yet to see anyone try to get the organizations of baseball to explain this argument further.

O'Conner says "We're not saying that it shouldn't go up," in the Post article…and yet, they spend millions on these lobbyists rather than making minor league salaries go up.

I feel strongly about this. I feel disgusted by the people who run this sport that I love and that I write about (for free). This is my opinion. But if it is yours as well, now is the time and try and get those in Congress to do something. This isn't a party issue; both Democratic Leaders and Republican Leaders seem to support this bill.

If you want to, you can use this website to help find your constituents. I would also suggest contacting the four Congress leaders: Senate Majority leader Mitch McConnell, Senate Minority Leader Charles Schumer, House Speaker Paul Ryan, and House Minority Leader Nancy Pelosi.

Tell them you believe Minor League Baseball Players are more than interns. Ask them how they can allow workers to be asked to work year-round and not get paid. Ask them why it's minor league organizations that are being threatened when the pay of the responsibility of Major League clubs. Ask them why they think this deserves to skip the methods other labor rules have to take. Ask them why baseball gets to be the exception.
Ask them why, just because it's a game played by kids, does it not protect its players like adults.

Okay, that's my political rant. I won't do that often, but I feel very strongly about this issue. If players don't get paid in the minors, they can not be in top shape. They can not stay healthy or well-fed. They can not support families that they are kept away from for months at a time. And, ultimately, it causes players to leave the game, or young athletes to choose other sports.

This will hurt baseball. Not tomorrow. But in the long run. And it will hurt real people tomorrow.
This is a baseball issue. I'm not a fan of the Player's Union, but this is cheap baseball owners trying to save a buck rather than invest in the players who will (in their near-future) compose the teams they make their bucks on. It's frustrating.

The Steroid Strength Advantage: A Theoretical Approach • Strengtheory

Interesting stuff. Very wonky, but interesting. I like wonky.

from strengtheory.com
http://strengtheory.com/steroids-and-strength-differences/

The Steroid Strength Advantage: A Theoretical Approach

Nov 22 2016

Over the past year, I wrote a series of loosely related articles discussing the relationship of strength and muscle mass.I'm realizing now that I wrote them completely out of order.  This is the order they should have come out in, to introduce the concepts in a logical sequence.

1. Who's the Most Impressive Powerlifter to introduce the concept of allometric scaling and the difference between absolute strength and relative strength

1. Strength vs. Size: How Important is Muscle Growth for Strength Gains to discuss the relationship between strength and muscle size in more depth,

1. The Drug-Free Muscle and Strength Potential articles (one, two) to introduce the equations (based on muscle mass relative to height) that can be used to predict, quite accurately, strength potential for people who focus on powerlifting and are relatively gifted for the sport.

1. How Much More Muscle Can You Build With Steroids to rigorously assess the advantage steroids give you for building muscle.

1. This current article, to show the relative strength advantage you should expect to get from steroids.

1. Steroids for Strength Sports:  The Disappointing Truth to assess whether the predictions in this article, based on the equations presented and developed earlier in the series, actually play out in the real world.

However, I wrote them in almost the exact opposite order, so the first installment, which looked at the relative strength advantage provided by steroids, caused a bit of an uproar.  This was partially my fault since I hadn't provided enough context.  To many people, the estimate that steroids provided a relative strength advantage of roughly 10% seemed like it simply had to be too low (though it was based on the best available data).Couched in the context of the rest of this series, however, it's easy enough to demonstrate mathematically that the advantage you'd expect, based on the current research, is somewhere in the neighborhood of 10%.  That's what I aim to do in this article to tie this series together and put a nice little bow on it.If you've already read this series and you more-or-less understand all of the info and equations, you can just play around with the spreadsheet I made that does all the calculations for you.  Otherwise, keep reading for a quick recap of this series and an explainer of the equations used to arrive at the prediction.

---

Absolute vs. Relative StrengthThis is a key distinction and one that often gets lost in these discussions:Absolute strength is simply the amount of weight you can lift.  Relative strength is the amount of weight you can lift, relative to how large you are.For example, if your squat goes from 500 to 550lbs, your absolute strength has increased without doubt.  If it goes from 500 to 550lbs without a change in bodyweight, your relative strength has also increased.  However, if your squat goes from 500 to 550lbs while your weight goes from 200 to 250lbs, your relative strength has decreased.Relative strength is a thorny subject, because there's no single agreed-upon way to assess it.Strength to bodyweight ratios (i.e. squatting 2x bodyweight) are popular because they're simple, but they're not a great way to judge relative strength because they almost always favor lighter lifters.  There are a lot more 150lb lifters who deadlift 450lbs than there are 250lb lifters who deadlift 750lbs.Most powerlifting organizations use formulas like the Wilks formula or Glossbrenner formula to normalize relative strength performances and select the "best lifter" in a powerlifting meet.  The Sinclair formula in weightlifting serves the same purpose.  These formulas certainly do a better job than strength to bodyweight ratios, but I have some methodological quibbles with them.  They're not worth rehashing here, but you can read more about them in this article.The scaling method I prefer for assessing relative strength is 2/3 power allometric scaling.  This method of allometric scaling is based on the assumption that body mass scales linearly with body volume which is a third-order characteristic (which is a mathematically true relationship, assuming density – body composition in this case – remains constant), and strength scales linearly with muscle cross-sectional area which is a second-order characteristic (which may not always be true for people in the general population, but which seems to be true in the highly trained lifters for whom normalizing relative strength is important).Regardless of the method you use to assess relative strength, the distinction between absolute and relative strength is an important one.  Steroids certainly help you get stronger, but they primarily help you get stronger by helping you build more muscle.  That muscle isn't weightless, however, so the boost they provide for relative strength will be smaller than the boost they provide for absolute strength.Predicting Strength Based on Jacked-nessAs discussed in these two articles (one, two), there is a very strong relationship between strength and fat-free mass per unit of height in elite powerlifters.  Variation in FFM/cm can explain roughly 75% of the variability in bench and deadlift strength and almost 90% of the variability in squat strength.Based on these relationships, we can use a simple regression equation to predict someone's maximal strength capabilities based on how jacked they are:  Powerlifting total (in kg) = 1563.9(FFM/cm)+77.32So, for example, if someone's 180cm tall and has 80kg of lean body mass, you'd expect them to total around 760kg (1676lbs) if they were a very skilled powerlifter.Now, this formula doesn't hit the nail on the head every time.  Some people exceed the predictions if they're exceptionally skilled lifters and very gifted for strength, and many people fall short of the predictions if they don't train in a way that's optimized for strength development or if they're less gifted for strength development (and since the equation is based on high-level powerlifters, it is probably a bit too optimistic for a lot of people).  However, it certainly does a good enough job to put us in the right ballpark for predicting maximal strength capabilities for a larger group of people when we're dealing with averages.How Much More Jacked Can You Get With Steroids?For the long answer to this question, you can check out this article.  But here's the short version:Fat-free mass index (a formula to normalize the amount of lean body mass you have relative to your height) is often used to assess human muscularity.  The higher your FFMI, the more jacked you are.The average untrained male has an FFMI around 18.9.  With training, it seems the typical drug-free male ends up with an FFMI around 22.3, with a standard deviation of 1.9 FFMI points.  On the other hand, the typical steroid user ends up with an FFMI around 25.5, with a standard deviation of 2.6 FFMI points, based on data from Kouri and Brennan.In other words, the average steroid user gains roughly twice as much lean body mass over the course of a training career:  6.6 vs. 3.4 FFMI points.  For an average-height male, that means the typical steroid user ends up with 10.4kg (23lbs) more lean body mass than a non-user.Furthermore, the typical range of FFMIs for steroid users is larger than the typical FFMI range for non-users:  5.2FFMI points vs. 3.8 FFMI points (±1 standard deviation), meaning that as you get further from "average," the gap between users and non-users grows.  When you're dealing with averages, the typical steroid user may be 3.2 FFMI points bigger and have 10.4kg more lean body mass, but by the time you get 3 standard deviations above the mean (i.e. where elite athletes would end up), steroid users have a 5.3 FFMI point advantage, corresponding with about 17kg (38lbs) more lean body mass for an average-height male.In short, this should not come as a surprise to anyone, but steroids work really, really well for helping you build more muscle.The Effects of Steroids on Relative StrengthNow that the stage has been set, we can predict the relative advantage afforded by steroids with some simple arithmetic using the data and formulae above.  Click on the footnote to check my work. I'm just including all of the algebra so you can see I'm not using any mathematical sleights of hand.  I'll illustrate using allometric scaling to calculate relative strength.Assuming you don't want to do all the math by hand, you can play around with this spreadsheet instead.Let me walk you through it.Page 1:  Initial AssumptionsThe numbers you enter on this page will substantially influence every other calculation.The data entered in cells C6 through C10 are editable.  The first four come filled-in based on the FFMI data provided by Kouri and Brennan, but you can play around with different assumptions to see how they affect the calculations.  Cell C10 (odds a random person in a population is drug-free) is entirely up to you, and it affects the odds of someone's natty-ness with a given FFMI on the third page.Page 2:  Steroid Strength AdvantageThis page does all of the really ugly algebraic calculations for you and shows you the differences in users and non-users every step of the way.All you need to do is fill in cells C4 through C7, and it'll do the rest.This is fun to play with, I think.  It seems taller people are afforded a larger advantage than shorter people.  The assumptions you make about the differences in body fat percentage has a pretty large impact.  If you assume steroids let people stay 10% leaner, the relative strength advantage is roughly 60% larger than if you assume steroids let people stay 5% leaner, for example.  Finally, you can see how the advantage afforded by steroids gets progressively larger the further you get from the mean.  The relative strength advantage is roughly 50% larger at 3-4 SDs from the mean versus 0 standard deviations from the mean.Also note that the relative strength advantage is always smaller than the absolute strength advantage.  This page also lets you see who's advantaged and disadvantaged by each relative strength formula at various body weights.  More muscle provides a larger allometric scaling benefit than Wilks benefit for lighter lifters, but the trend is reversed for taller/heavier lifters.Page 3:  Odds Someone Is Drug-FreeThis page is an upgraded version of the tables near the end of this article.It's pretty simple.  You fill in the blue squares, and the sheet calculates the FFMI based on the data you input.  Then, based on some more simple math (probability density functions for FFMIs of users and non-users, based on the means and standard deviations of each population, weighted by your assumptions about the proportion of a population you think is actually drug-free), it tells you the probability that someone with such an FFMI is drug-free.The blue curve is the odds of attaining a specific FFMI for a drug-free person, and the red curve is the odds of attaining a specific FFMI for someone on steroids.  Both are weighted based on the percentage of the specific population you think is drug-free.  For example, if you think 80% of the people in a given population are drug-free, it'll make the blue curve bigger and the red curve smaller.  The peak of the red curve is further to the right, denoting a higher average degree of muscularity for steroid-users, and there's also more spread, denoting the larger standard deviation (potentially arising from differences in the compounds and dosages people use) for steroid-users' FFMIs.The yellow curve is the probability that someone with a given FFMI is drug-free (the likelihood is on the right y-axis).  You see that where the blue curve is higher than the red curve, the probability is higher (indicating more drug-free people with a given FFMI), and where the red curve is higher than the blue curve, the probability is lower (indicating more steroid-users with a given FFMI).  At the FFMI where the two curves intersect, the probability of someone being drug-free is 50/50.The assumptions you start with on the first tab will affect this graph substantially – if you change the mean FFMIs for each group, that will shift the red and blue curves left or right.  If you change the FFMI standard deviations, that will affect how spread-out the red and blue curves are.  If you change the proportion of a population you think is drug-free, that'll impact the overall size of each curve.  All of these changes will affect the probability that someone with a given FFMI is drug-free (the yellow curve).

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Perception = Reality.  The Power of Confirmation BiasThis is where the rubber meets the road for this whole series.Depending on the assumptions you start with, you can get any outcome you want from this spreadsheet.  The first page (initial assumptions) determines how the rest of this sheet will behave.If you go with the FFMI data from Brennan and Kouri (FFMIs of 22.3 ± 1.9 for non-users, and 25.5 ± 2.6 for users) and assume a steroid user can stay about 5% leaner than a non-user, you'd expect steroids to provide a relative strength advantage of roughly 7% for an average person, and around 11% for people 4SDs from the mean (averaging allometric scaling and Wilks).If you start with the assumption that an FFMI of 25 is a hard limit for non-users (a common myth), then you'd expect a relative strength advantage of 16-17% 4 SDs from the mean.  If you combine that assumption with the assumption that users can stay 10% leaner instead of 5%, and the relative strength advantage jumps to almost 20%.Similarly, if you start with the assumption that 50% of drug-tested powerlifters or bodybuilders are lying about drug use, then based on Brennan and Kouri's FFMI data, someone who's 180cm tall, 90kg, and 10% bodyfat with an FFMI of exactly 25 would have a 33.7% chance of being drug-free.  If you assume 95% of drug-tested athletes are actually drug-free, then this person would have a 90% chance of being drug-free.  If you assume 80% of them are lying, however, his odds of being drug-free would be only 11%. Top image: Same total number of users are non-users. Middle image: 19x more non-users than users. Bottom image: 4x more users than non-users. Notice how much earlier the yellow probability line drops in the bottom image vs. the middle image.I think this is the fundamental reason why this is such a contentious subject.  People come to this discussion with different sets of assumptions, and those assumptions alter their expectations.  Those expectations affect how they interpret what they see (and even what data they'll accept and what data they'll reject), which further ingrains their biases.  People who start with charitable assumptions about what drug-free athletes can accomplish and charitable assumptions about the proportion of drug-tested athletes who are actually drug-free are automatically labeled as naïve. Conversely, people who start with low assumptions about what drug-free athletes can accomplish and who assume most tested athletes are just cheaters who are beating the tests are automatically labeled as overly cynical.This spreadsheet should show you how both "sides" can feel comfortable with their conclusions, based on differences in starting assumptions.

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Bringing this series full-circle, the roughly 10% relative strength advantage from steroids proposed in this article seems to be a figure with experimental, observational, and (now) theoretical support.  If you use Kouri and Brennan's FFMI data, for most reasonable heights, body composition differences (0-10%), and distances from the mean (i.e. unless you project things out to 6+ standard deviations from the mean), the predicted relative strength advantage afforded by steroids tends to hover between 6-13% for both Wilks and Allometric Scaling.If you disagree with the figure, there are a few ways you could dispute it:

1. Provide better data showing average FFMIs for users and non-users.

1. Show that the relationship between FFM/cm and strength is substantially different from the one found in Brechue and Abe's work.  Crucially, the strength increase for each increase in FFM would need to be larger than the one they found (a smaller increase per kg of FFM would decrease the predicted relative advantage of gaining FFM via steroids).

1. Provide solid data showing that steroids increase strength independent of gains in muscle mass in elite athletes (i.e. that they raise the limit of attainable normalized muscle force, and don't just potentially increase the rate of increase in untrained people).  This is an idea I've seen floated before, but haven't come across any solid data to support it.

Otherwise, I think it's time to put a bow on this series for now.Featured Image Credit:  hookgrip

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Basic formula:(Allometric scaling score drug-free – allometric scaling score with steroids)/(allometric scaling score with steroids)Expand the allometric scaling formula; Allometric scaling score = weight lifting × (body mass)^-2/3(Drug-free powerlifting total × (drug-free body mass)^-2/3 – Powerlifting total with steroids × (body mass with steroids)^-2/3)/(Powerlifting total with steroids × (body mass with steroids)^-2/3)Expand the formula used to predict strength; Powerlifting total (in kg) = 1563.9(FFM/cm)+77.32((1563.9 × (Drug-free FFM/cm) + 77.32) × (drug-free body mass)^-2/3 – (1563.9 × (FFM with drugs/cm) + 77.32) × (body mass with steroids)^-2/3)/((1563.9 × (FFM with drugs/cm) + 77.32) × (body mass with steroids)^-2/3)Expand the body mass formulae; Normalized FFMI = FFM/(height in m)² + 6.1 × (1.8 – height in m), so FFM = (FFMI – 6.1 × (1.8 – height in m)) × (height in m)².  Body mass = Lean body mass/(1 – body fat percentage)((1563.9 × (((Drug-free FFMI – 6.1 × (1.8 – (cm/100)))*(cm/100)²)/cm) + 77.32) × (((Drug-free FFMI – 6.1 × (1.8 – (cm/100)))*(cm/100)²)/(1 – drug-free bodyfat percentage))^-2/3 – (1563.9 × (((FFMI with steroids – 6.1 × (1.8 – (cm/100)))*(cm/100)²)/cm) + 77.32) × (((FFMI with steroids – 6.1 × (1.8 – (cm/100)))*(cm/100)²)/(1 – bodyfat percentage with steroids))^-2/3)/((1563.9 × (((FFMI with steroids – 6.1 × (1.8 – (cm/100)))*(cm/100)²)/cm) + 77.32) × (((FFMI with steroids – 6.1 × (1.8 – (cm/100)))*(cm/100)²)/(1 – bodyfat percentage with steroids))^-2/3))Now allow for FFMI variability to account for standard deviations; FFMI = Mean FFMI ± (number of standard deviations × size of standard deviation)((1563.9 × ((((mean drug-free FFMI ± (drug-free FFMI standard deviation × standard deviations from the mean)) – 6.1 × (1.8 – (cm/100)))*(cm/100)²)/cm) + 77.32) × (((((mean drug-free FFMI ± (drug-free FFMI standard deviation × standard deviations from the mean)) – 6.1 × (1.8 – (cm/100)))*(cm/100)²)/(1 – drug-free bodyfat percentage))^-2/3 – (1563.9 × (((((mean FFMI with steroids ± (FFMI standard deviation with steroids × standard deviations from the mean)) – 6.1 × (1.8 – (cm/100)))*(cm/100)²)/cm) + 77.32) × (((((mean FFMI with steroids ± (FFMI standard deviation with steroids × standard deviations from the mean)) – 6.1 × (1.8 – (cm/100)))*(cm/100)²)/(1 – bodyfat percentage with steroids))^-2/3)/((1563.9 × (((((mean FFMI with steroids ± (FFMI standard deviation with steroids × standard deviations from the mean)) – 6.1 × (1.8 – (cm/100)))*(cm/100)²)/cm) + 77.32) × (((((mean FFMI with steroids ± (FFMI standard deviation with steroids × standard deviations from the mean)) – 6.1 × (1.8 – (cm/100)))*(cm/100)²)/(1 – bodyfat percentage with steroids))^-2/3))The number it spits out will be negative, denoting the relative disadvantage of a drug-free athlete.P.S. I'm almost certain there are too many parentheses above.We can plug in height in cm, means and standard deviations for FFMI with and without steroids (based on Kouri and Brennan's data, or your own assumptions), how many standard deviations from the mean you're interested in (i.e. are you interested in the average advantage of steroids, or the advantage they give elite competitors), and the bodyfat percentages where you think someone would perform best on steroids and drug-free (since most people assume that steroids allow you to get leaner before performance is compromised), and this formula will predict the relative advantage steroids will give.

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